ZACK KASS

THE NEXT RENAISSANCE

AI AND THE EXPANSION OF HUMAN POTENTIAL

AR

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To my mom, who taught me to imagine a better future.

To my wife, who is building it.

To my daughter, may she flourish in it.

Introduction

In October 1930, as the world plunged into the Great Depression, economist John Maynard Keynes published an essay titled Economic Possibilities for Our Grandchildren. Only months after the catastrophic stock market crash, Keynes dared to offer a counterpoint to despair. Where most saw devastation, he saw possibility for long‐term transformation.

He argued that beneath the turbulence of collapse, humanity was moving, however haltingly, toward a future defined by abundance rather than scarcity—a future in which “the economic problem may be solved,” and our challenge would move beyond survival to living wisely and well.

Keynes did not say exactly how such a future would unfold, nor could he have with any credibility. Instead, he pointed toward his vision on the horizon, the possibility of a second Renaissance, when technological innovation would end our struggle to meet all basic needs.

Unsurprisingly, most contemporaries dismissed his vision as hopelessly naive, absurdly optimistic, and, amid widespread suffering, tone‐deaf.

Ninety years later, though, much of what Keynes foretold has taken shape. Mortality rates have fallen, and almost half of the world's population has escaped extreme poverty. Vaccination, sanitation, education, and technology have all altered the arc of human existence. Today, we find ourselves closer than ever to the vision he described, where humans move beyond survival and are able to thrive.

His prophecy has already played out in ways his contemporaries would never have believed, and yet its total fulfillment lies still ahead. That is the most thrilling realization of all; even a century later, Keynes may ultimately be right about more than we can imagine.

There is, I believe, a vast opportunity still before us—what I'll call, perhaps too cleverly, the RenAIssance—driven by a technology Keynes could hardly have fathomed, artificial intelligence.

The first Renaissance was not merely a burst of art or science; it was cultural rebirth. A fusion of breakthroughs across disciplines. Gutenberg's printing press catalyzed literacy. Da Vinci’s anatomical studies advanced medicine. Copernicus redefined our place in the cosmos. Each discovery reinforced the others, building momentum that lifted humanity into an era of awakening.

Today, it feels like we're standing at the edge of a similar inflection point. AI is an intelligence multiplier. Properly harnessed, it could democratize education, revolutionize healthcare, and accelerate innovation. It could help us tear down barriers that have long constrained human potential and reshape the fabric of society itself.

But progress is earned, not granted. Every great transformation brings both opportunity and risk. For AI to truly serve humanity, we will be forced to solve radical new ethical dilemmas, unprecedented economic disruptions, daunting technical challenges, environmental collapse, dehumanization, the loss of identity, and above all, terrifying uncertainty.

I wrote this book to offer a clear‐eyed vision for a future that could be more humane, equitable, and beautiful. This future is within reach, provided we choose to shape it.

In the words of Keynes, “My purpose in this essay, however, is not to examine the present or the near future, but to disembarrass myself of short views and take wings into the future.”We can move beyond the challenges we face, and build something far greater.

I wrote for those who feel both awe and unease about the world racing toward us. For leaders guiding companies, classrooms, and communities. For parents trying to imagine the world their children will inherit. For anyone who senses that optimism is harder to find than it should be, but still worth searching for.

I also wrote this book for everyone who has ever asked me questions like: What should my child study in college? What skills will matter most? What keeps you up at night? What jobs will disappear? Is this a race? Who will win? Who will lose? How do we preserve our humanity? What about the environment? Can AI be trusted? What does a good society look like once AI is everywhere?

Beneath those questions lie deeper ones still: Is humanity nearing its limits, or just beginning its journey? Does life, on balance, get better over time? Can technology expand what it means to be human, or only diminish it?

These questions are too big for prescriptions. They require new frameworks for thinking clearly, acting wisely, and imagining boldly in the face of extraordinary challenges.

This book is for those who know that imagining a better world is not a naive indulgence, but a moral obligation. For those who understand that the work of hope is the work of creation.

Not everyone will be convinced—some cynicism cannot be disabused. But for those willing to imagine the possibilities, this is an invitation to begin.

Ninety years after Keynes's essay, this book is my attempt to ask the question he surely would today: How much better could the future be?

How I Got Here

My optimism is rooted in my temperament and upbringing. I came into the world looking forward to the next day, and I had a childhood that reinforced that disposition. It wasn't until I arrived at Berkeley in 2005 as a history student that I found evidence for what I had long intuited, that the arc of human progress bends steadily upward.

Alongside history, I took a few computer science courses that introduced me to a technical curiosity that has been indispensable throughout my career. History taught me that the world gets better; computer science taught me how. The combination of a historian's perspective and a technologist's tools has shaped how I approach every wave of innovation: informed by the past, and equipped by technology. It's the perspective that still guides me today.

I believe deeply that the world gets better all the time, that today is the best day ever to be born, and that tomorrow will be too. That conviction became the lens through which I watched AI unfold. I've had a front‐row seat, at times a hand on the wheel, as AI moved from backroom demos to boardroom agendas, as the work changed from measuring the sentiment of a tweet to solving the human genome. As the power has grown, so has our responsibility to guide it wisely.

My first job was at a company called CrowdFlower (later Figure Eight), which built the first human data‐labeling infrastructure, the scaffolding that now fuels modern machine learning. I met the CEO, Lukas Biewald, a true pioneer of the industry and my first mentor, at a poker game. He told me if I won, he'd hire me. A month later I was offered $28 an hour to help his company move from a studio apartment into its first office in San Francisco's Mission District.

I sat on the floor that first night on the job, assembling IKEA desks while my new colleagues debated AI's eventual scientific limit. How smart could machines eventually get? I didn't appreciate it then, but there were legendary minds in that room, and most of them agreed: someday AI would transform the world. The sense of possibility was palpable. The pathway was a bit trickier.

At the time, AI's impact was mostly confined to leading tech companies like Google, Facebook, and Amazon, where it primarily powered search, advertising, and product recommendations.

But by 2013, improvements in machine learning algorithms and increased investment from traditional sectors pushed AI into applications people could finally see. Facebook was tagging friends in photos; JPMorgan was detecting fraudulent activity; Spotify was curating personalized playlists; and Apple's Siri was encouraging us to speak to a computer. AI had emerged from behind the curtain and into our hands.

Thanks to the trust of CrowdFlower's CEO, I went from assembling furniture to building one of the first go‐to‐market organizations in applied AI. We helped the world solve daunting problems no one had even attempted before. One of our earliest efforts was Project 4636, launched in the wake of the 2010 Haiti earthquake. In the chaos, thousands of text messages with desperate calls for food, water, and medical support poured in from survivors in both Creole and French. Our platform converted more than 100,000 of these messages into usable maps, allowing relief workers to direct the right aid to where it was needed most. For the first time, AI‐driven human–machine collaboration saved lives in real time.

Later, we partnered with the early Trust & Safety teams at companies like Facebook and Instagram to pioneer solutions to another daunting challenge, detecting hate speech and harmful content in images at internet scale. There was no playbook for moderating billions of posts, and no datasets labeled and ready for training. We invented novel processes and technologies that laid the groundwork for content moderation systems still used today.

In 2017, I joined Lilt, an ambitious Stanford spinout with a bold mission to democratize information by solving AI‐powered language translation. At the time, translation was essentially the domain of Google alone. But as I knew from my time at CrowdFlower, small, focused teams could challenge industries long dominated by giants. At Lilt, we built early versions of large language models years before they became widely recognized. By the time I left, we had redefined the standard for machine translation and proved that the future of AI innovation was being shaped by insurgents as much as by incumbents.

Then, in 2021, I joined OpenAI as one of its first hundred employees. As Head of Go‐to‐Market, I helped the company grow from $1M to $2B in annual revenue and supported the launches of each new product. Even after all of these experiences, nothing could have prepared me for November 30, 2022.

On that day, from the OpenAI offices, we released ChatGPT. Its rapid global adoption, more than 1 million users in a week and nearly 1 billion today, was more than a professional milestone. It was a rupture in time. After 15 years of building these systems, I realized the technology no longer needed me to prove its value. That moment expanded what AI could do, and how people could imagine using it. Now it was people who needed help. Leaders, workers, parents, and communities had to make sense of this groundbreaking technology. They needed to understand its risks, grasp its potential, and decide how to shape it. The most telling moment came when I saw my mom try ChatGPT for the first time. As she typed her first prompt, I felt something I hadn't since the early days of the web—that brief gasp when the future stops being an argument and becomes a tool in your hand. Her face didn't show fear or reverence; it showed possibility. That moment has stayed with me, because it reminds me what this is ultimately about: an ordinary person meeting extraordinary capability, and deciding what to do with the reach suddenly available.

Since the launch of ChatGPT, I've built a global advisory business, taken up multiple academic positions, and spoken to hundreds of thousands of people around the world about how AI will affect their jobs, families, and futures. As part of this journey, I've had the privilege of working alongside the leadership teams at more than 350 of the Fortune 1,000 companies, helping them wrestle with what AI means for their strategies and industries. Those conversations, from Wall Street to Main Street, have convinced me that while the implications of our questions may differ, the hopes and anxieties beneath them are the same.

This realization has deepened in my personal life as well. My wife, Adlee, is a first‐grade Waldorf teacher. Watching her in the classroom and on the playground, I've seen how she creates spaces where learning grows out of play, imagination, and trust—qualities no algorithm can replicate. Her work sharpened my sense of what must be protected as technology advances: human values like hard work, collaboration, and compassion, cultivated in sensory‐rich, hands‐on environments.

Deconstructing Dystopia

As I've spoken to leaders and audiences across the globe, over and over, I'm deeply struck by a common belief. So many people I meet think the human experience is in decline.

In much of the developed world, including the United States, United Kingdom, France, Germany, and Australia, up to 70% of people believe their children will be worse off than their parents.¹ There is a crack between the measurable improvements we can witness, and the gloom and fear so many carry. That crack is starting to look like a chasm.

Keynes saw this distortion as well. Even amid the Great Depression, he reminded us that beneath the surface of crisis, hope and long‐term progress are waiting to deliver us a better future. We must ask ourselves then, why do we tilt so easily toward dystopia? The answer, in part, lies more in our biology than in our reality.

For most of human history, survival depended on caution over curiosity. When the stakes were life or death, it was far safer to assume the worst, rather than to gamble on the possibility of the best. A rustle in the grass might be wind. Or, it might be a predator. Over thousands of generations, this asymmetry trained the human brain to prioritize potential threats above all else. Psychologists now call this negativity bias. We have an overpowering tendency to give far more weight to risks than to opportunities.

We don't like danger, but we really don't like change. Status quo bias gives us a deep preference for familiarity over change, even when the change is demonstrably better. We see this resistance over and over again. Each innovation that now defines modern life was initially met with skepticism and fear because it disrupted what people already knew.

The United States, for example, has resisted adopting the metric system for more than a century, despite it being simpler, universal, and demonstrably better for science, trade, and education. There is no danger in switching, only the discomfort of abandoning what's familiar. Status quo bias explains why Americans cling to feet and inches long after the world moved on.

We are also seduced by rosy retrospection, the bias that paints the past in golden hues and convinces us things were simpler and better “back then,” even if “back then” meant famine, disease, and horrors beyond our comprehension. Together, these biases create a psychological trap where fear feels rational, progress seems suspect, and optimism looks naïve.

Culture amplifies and preys on this neurological wiring. Our books and films imagine collapse with far more intensity than they imagine progress. Orwell and Huxley gave us dystopian visions of control and decay; Hollywood has doubled down ever since. From The Terminator to The Matrix to The Hunger Games, most blockbuster franchises are, at their core, stories about civilization on the brink of collapse, often driven by technology itself. Star Trek remains one of the few enduring utopian visions, precisely because almost no others exist. We have so saturated ourselves with narratives of decline that it has become difficult to picture progress at all. These stories, intended as entertainment, are colonizing our imagination and narrowing what we believe is possible.

The news cycle reinforces the imbalance. Fear sells, so catastrophe dominates. Media coverage metrics consistently show that disasters receive orders of magnitude more attention than breakthroughs. A single plane crash can generate weeks of revenue‐generating headlines and millions of clicks, while a scientific achievement that saves millions of lives, like a new malaria vaccine or even a breakthrough in fusion energy, may barely register in public awareness. The availability heuristic, which makes us judge how likely something is by how easily we can think of examples, internalizes the imbalance with vivid stories of disaster that dominate our sense of what is possible. We mistake the headlines for the world itself.

But when we step back from instinct and culture, and invite perspective and data to examine the evidence, the story is unmistakably brighter than the one we tell ourselves. By nearly every measure, human well‐being has improved dramatically in the last 100 years.

Just two decades ago, one in four people lived in extreme poverty. Today it is fewer than one in ten.² Global literacy has risen from 70% to nearly 90% in just 40 years.³ In 1900, more than 17 people per 1,000 died each year. Today it is fewer than eight.⁴ Life expectancy worldwide has doubled in the past century. Violent deaths are lower, proportionally, than in almost any previous era. Vaccines have eradicated smallpox and nearly eradicated polio, saving millions of children's lives. Global GDP per capita has soared, multiplying opportunities and prosperity across continents. More than five billion people now carry a supercomputer in their pocket, with access to the sum of the world's knowledge. And a greater percentage of the global population enjoy access to education, freedom of religion, and legal rights and protections than at any other point in history.

Put plainly: a child born today is more likely to survive, more likely to read, less likely to live in poverty, less likely to die from violence, and more likely to live a long and healthy life than at any other point in human history. That is not conjecture; it is measurable fact.

Despite these realities, many people still tell me they see the world getting worse. Perception often moves differently than reality. Our old instincts tilt us toward the negative, and turbulence feels stronger than steady lift. Imagine riding a gondola up a mountain. The car is rising, but when the wind sways it back and forth, it can feel as if you're moving backwards, down the mountain. Inside, the motion is unsettling. From a distance, the climb is clear. Progress works the same way—uneven, sometimes jarring, but always trending upward.

But historical progress doesn't excuse extraordinary challenges. Climate change threatens ecosystems and economies. Inequality corrodes trust in institutions and limits opportunity. Civil rights are under siege in places where progress once seemed secure. Wars continue to devastate entire populations and destabilize regions. None of this can be wished away, and optimism should never be confused with apathy. I am not asking you to look away from the awfulness. I am asking you to look toward the solutions.

Perspective matters. Across centuries, humanity has faced crisis after crisis that once looked insurmountable and found ways not to just endure, but to advance. Time and again, we have proven our capacity to adapt, to innovate, to build. The pattern is clear. We are better at creating than destroying, better at renewal than decline.

In the 14th century, as plague, famine, and war ravaged Europe, many people believed they were living through the end of days. Critical institutions that had stood for centuries were crumbling. Fear saturated the culture. Most mothers buried at least one child. Most husbands buried their wives. But from that crucible of despair emerged one of humanity's greatest leaps forward, the Renaissance. What felt like collapse was, in fact, the threshold of renewal.

That is where we find ourselves today. The anxieties of our age—geo‐political turmoil, technology, inequality, and uncertainty—are real, but they may be the prelude to another great chapter, if we are brave enough to believe in it and lead it. To see the path forward, we turn to an era that laid the groundwork for the Renaissance, the Late Middle Ages.

Welcome to the Modern Late Middle Ages

In school, the past is often reduced to a sequence of neatly packaged eras. Textbooks lean heavily on “pre” and “post,” as if history pivots in clean breaks. It reinforces the misconception that humanity stumbled in darkness until, suddenly, a light switch was thrown and everything was illuminated. But history has never worked that way. Real change is gradual, less like flipping a switch and more like turning up a dimmer, with the world coming into focus detail by detail.

The Renaissance unfolded slowly, through a mosaic of discoveries whose true significance would only be realized in hindsight. Gutenberg's press wasn't produced en masse until 100 years after his death. The resulting rise in literacy took generations to spread as knowledge slowly slipped beyond monasteries and royal courts. Da Vinci's anatomical sketches nudged medicine forward incrementally, laying foundations for breakthroughs that wouldn't come until centuries later. Copernicus's heliocentric model was long contested before it eventually reoriented humanity's view of itself in the cosmos. Until the internet and industrial manufacturing, ideas and inventions took centuries or millennia to proliferate. “Pre” and “post” Renaissance aren't moments in time, they are periods during which, almost imperceptibly, society slowly inched forward, and the world fundamentally transformed.

The foundations for this slow accumulation rest deeper than most of us realize, though. The Renaissance was preceded by the often‐overlooked Late Middle Ages, a period of both underappreciated innovation and profound suffering that served as a catalyst for the change to come. Advances in navigation, agriculture, and military technology reshaped daily life.

At sea, the magnetic compass and the caravel ship unlocked the possibility of long‐distance exploration. The compass gave sailors a reliable sense of direction even on cloudy nights when the stars were obscured, while the caravel's speed and maneuverability made it possible to travel farther and more safely than before. These tools opened the oceans, laying the foundations for global exploration and trade routes that would eventually link continents.

On land, agriculture transformed. The heavy plow allowed farmers to till the dense, rich soils of northern Europe that had long been underused, dramatically boosting crop yields. Windmills and watermills multiplied productivity by harnessing natural forces to grind grain, pump water, and power rudimentary machinery. These gains freed up labor, supported population growth, and created surpluses that sustained expanding towns.

Military innovations reshaped power itself. Gunpowder weapons, like cannons, firearms, and siege technologies, eroded the dominance of knights and castles, destabilizing feudal hierarchies and accelerating the rise of centralized states. Strongholds that had seemed eternal crumbled before new technologies, redistributing political authority and changing the organization of societies.

Individually, each of these breakthroughs might have seemed like a technical curiosity, but together they began to alter the very structure of European life in the Late Middle Ages. They opened distant horizons, fed growing populations, and reshaped political power.

These human inventions, though, may have had less impact than a far greater power: disease. In the mid‐1300s, Europe faced the devastation of the Black Death, a catastrophic plague that claimed nearly half the continent's population. Though this tragedy is well‐documented, its transformative ripples often go unnoticed. Peasants, long confined by rigid feudal structures to work nobles' land in exchange for minimal rights, suddenly found themselves with unprecedented leverage. With so many lives lost, surviving workers suddenly became highly sought after. They either demanded higher wages and greater freedoms, or they left, migrating to burgeoning towns in search of new opportunities. Urban centers like Florence, Venice, and Bruges rapidly expanded as peasants reinvented themselves as artisans, traders, and skilled laborers, fueling a vibrant commercial economy.

Towns flourished, currency replaced barter, trade networks expanded, and a powerful middle class emerged. Concurrently, institutions previously viewed as unassailable, notably the Church, began to lose their hold on society. Humanism challenged ecclesiastical authority and transformed education, which had been largely religious and limited to clergy. Mass printing amplified this shift, enabling knowledge and ideas to spread rapidly across Europe. Society grew more informed, increasingly literate, and empowered by a free exchange of information.

Together, these intertwined forces created fertile conditions for exploration, commerce, and urban growth on a scale the world had never seen, and likely couldn't have fathomed. Today, we find ourselves in a remarkably similar era. Technological leaps, growing access to knowledge, economic shifts, and a groundswell of civic engagement may once again be laying the groundwork for humanity's next great awakening. Recognizing these parallels helps us better understand the profound opportunity before us—the RenAIssance. Moments of wild uncertainty are often hiding the seeds of transformation. As I write this, Adlee and I are expecting our first child, a daughter. People often ask me how I think AI will shape her life. The honest answer is, I don’t know. What I do know is that I am committed to building her a better future than I can imagine today.

Notes

1. Pew Research Center. (2022, August 11). Large shares in many countries are pessimistic about the next generation's financial future. Pew Research Center. https://www.pewresearch.org/short-reads/2022/08/11/large-shares-in-many-countries-are-pessimistic-about-the-next-generations-financial-future/.

2. Gapminder, “The proportion of the world's population living in extreme poverty has more than halved in the last 20 years… from 29.5 % in 1997 to just 9.1 % in 2017,” Gapminder, 2019, accessed August 23, 2025, https://www.gapminder.org/questions/gms1-3/.

3. World Economic Forum, “This Is How Much the Global Literacy Rate Grew over 200 Years,” World Economic Forum, Sept. 12, 2022, accessed July 16, 2025.

4. Macrotrends, “World Death Rate (1950–2025),” accessed July 16, 2025, reporting a rate of 7.80 per 1,000 in 2025.

PART I
Foundations of the RenAIssance

AI is going to revolutionize industries, sciences, and daily life. It will solve the existential problems that have confounded us for millenia, and those we’ve created for ourselves in just a few generations. It will rewrite social constructs and redefine what it means to be human.

But before we get into where we are going, I am obligated to explain to you how we got here. At the risk of boring you, and losing your interest forever, I urge you to read about the remarkable confluence of forces and events that made the rapid advancements in AI possible. This matters to you for two reasons:

An understanding of AI’s essential ingredients—architectures, compute, data, open collaboration—will help to leverage the technology better in your life and work.

AI may be the last technology that humans invent on our own, but it won’t be the last technology. You should take a keen interest in how we built the bedrock of superintelligence so that you can make better sense of the waves of innovations to come.

I’m also obligated to tell you the ways in which AI could (and likely will) compromise our lives before I tell you how it will reduce suffering and increase joy.

Only with this history and the appropriate disclaimers, do I consider it honest and reasonable to tell you about my vision for a much brighter future.

CHAPTER 1
The Past, Present, and Future of Artificial Intelligence

I know you're here to think about the future, but we can't chart where we're going without knowing where we've been. The history of AI is inspiring. It is a timeline of thinkers bold enough to wonder and build beyond what machines could do toward what they might do. Artificial intelligence is proof of what science, imagination, and optimism can create.

The roots of artificial intelligence are nearly as old as modern computing itself. In the summer of 1956, a young Dartmouth professor, John McCarthy, convened a workshop to explore the notion of “thinking machines.” The conversations built on the groundbreaking ideas of Alan Turing, the British mathematician who had laid the foundation for digital computing and proposed a means for testing machine intelligence.¹ The gathering gave the field both its name, artificial intelligence, and its ambition: to create machines that could equal human intelligence, and one day surpass it.

McCarthy's summit inspired scientists from around the world to start exploring the subject. Soon, experts were standing in front of computers, closer in size to industrial kitchens than office desks, boldly declaring that in the near future machines would be able to beat the best human chess players in the world. The 1960s, however, passed without such an upset. Progress slowed, funding diminished, and the dream of AI fell dormant.

The coming decades brought a few “false dawns,” small advances followed by setbacks. The 1980s saw brief enthusiasm for machines that could mimic human decision‐making, but they failed to produce meaningful results. In the 1990s, interest focused on “narrow AI,” but it could only attempt highly specialized tasks, which proved largely unhelpful in commercial contexts. At last, in 1997, IBM's Deep Blue finally defeated chess world champion Garry Kasparov, fulfilling a decades‐old prediction, but even Deep Blue's capabilities were limited to chess. The broader goal of general intelligence remained out of reach.

The problem was architectural. To advance to general intelligence, computers would need to learn from data itself, rather than by following human‐written rules. This is what we call machine learning. Early AI pioneers had theorized it, but there had never been enough data or affordable computing power to make it viable. That started to change in the 1990s and early 2000s, though, as processors became cheaper and faster, and the internet supplied troves of new data. By the 2010s, smartphones, cloud computing, and social media were producing enough digital information and power to push the theory into practice.

AI systems could finally move beyond fixed “if this/then that” rules and instead adapt and improve from experience. This is when we began to see voice assistants (frustrating as these early versions may have been), language translation, and early self‐driving cars. Progress accelerated rapidly, and soon machine learning led to deep learning, a new computing architecture inspired by the human brain.

As we learn, billions of neurons fire throughout our brain, strengthening connections and reinforcing the pathways that prove useful for memory, understanding, and critical thought. In deep learning, information passes through “neural networks” in the same way. Artificial “neurons” pass signals forward, and the system adjusts the strength of their connections until the right patterns emerge. First it picks up simple features—an ear here, and eye there—then it combines those features into abstractions—a face. Much like our own minds, they don't memorize every detail. They build associations and use them to make predictions. Even with this massive leap forward, these networks had limits. They struggled with longer text and making connections that spanned time and meaning. For almost a decade, progress remained incremental.

Then, in 2017, AI had its atom splitting moment. Eight Google researchers published a landmark paper, Attention Is All You Need, introducing the transformer model architecture.² This breakthrough, along with several other trends and developments that we’ll call “key ingredients,” triggered a great leap forward.

The Key Ingredients

You don't need to be an engineer to understand how AI works, but you do need to know its ingredients if you want to understand why it's changing everything, and how to make sense of what's coming next. Like any big shift in history, this one didn't happen all at once. It came together piece by piece.

What follows will feel more technical than the rest of this book. But stay with it. These concepts—transformers, compute, data, collaboration, and efficiency—aren't just buzzwords. They are the five breakthroughs that made modern AI possible. Each one solves a different part of the puzzle: how AI learns, what it learns from, how fast it runs, who gets to use it, and how much it costs.

Let's look under the hood.

Transformers

The thing that was so incredible about Attention Is All You Need is that it posited an entirely new architecture for AI beyond deep learning—the transformer architecture. It immediately captured the AI research community, and within months, it became the blueprint for modern AI.

Earlier models worked sequentially, interpreting one small chunk of text, called a “token,” at a time. Processing text in this step‐by‐step way limited how much context the model could keep in mind, especially in long passages where it easily lost or diluted information.

Transformers, on the other hand, could process all the tokens in a sequence together. The model considers each word in relation to all the other words around it and decides how much importance to give each through a new mechanism called attention. Rather than thinking in straight lines, transformers allowed systems to think multi‐dimensionally, processing contextually aware information in networks quickly and consistently, just like the human brain.

Transformers could be fed more data, learn richer patterns, and respond with greater fluency than anything before. Soon, the architecture's versatility even allowed it to extend beyond text into image and audio systems, accelerating its adoption even further. AI could now understand even the most nuanced meaning.

Almost every leading AI system is built on some form of the transformer. It is the simplest explanation for the wizardry of products like ChatGPT, Gemini, and Claude.

Compute

Compute is the engine of modern AI. Incredibly, the primary constraint on AI progress since transformers hasn't been algorithm quality, but the sheer computational power required to build and run the models. Until very recently, training and deploying large‐scale AI models required the resources of a sovereign nation or trillion dollar company.

Compute (which is the industry's preferred nomenclature) refers to the computer chips required to carry out calculations, run applications, or process data at scale. It serves two distinct purposes. First, it is used to “train” (or build) new large language models (LLMs). Second, it is used to “run” those models, allowing us to use it in the real world.

Until recently, compute was limited to central processing units (CPUs). These are the versatile workhorses of general computing that have powered our computers since the 1970s; they power your smartphone, too. There are billions of them in the world because they're very cheap to produce and great at doing most consumer tasks. The problem is, they were not very good at training or running AI systems efficiently.

Thankfully that changed in 2009, when a small team of Stanford researchers discovered that the graphics processing units (GPUs) originally designed for rendering video games proved ideally suited for the processing at the heart of transformer architecture. GPUs could be purpose‐built for the speed and volume demands of deep learning.

Companies like Nvidia took advantage of this breakthrough and began designing their GPUs specifically for AI workloads, with faster memory, greater bandwidth, and software to make them more accessible to developers.

The effect was catalytic. GPUs collapsed the time required to train complex models from months or years, to weeks or even days. Suddenly, not only were models getting much better, but the investment required to experiment with them was plummeting.

Then the broader AI infrastructure advanced in tandem with “compute‐as‐a‐service” (or, cloud, as most know it) models that allowed individuals to rent compute on demand. This allowed even more rapid experimentation, from anywhere in the world.

This explosion in compute access moved the industry forward at a blistering speed and scale, and demonstrated the power of the “scaling laws” of deep learning, which say that performance improves predictably as models grow in both size and quality of training data.

Data

Data is the fuel that drives AI. In 2010, the total volume of data created, captured, copied, and consumed globally was around two zettabytes (ZB), or two billion terabytes. By 2020, that figure had soared to more than 64 ZB, an increase of more than 3,000% in just ten years.³

These numbers are almost meaningless without context, so here's one way to picture it: in 2010, if the world's 2 ZB of data were printed on paper and stacked, that stack would be as tall as a 10‐story building. By 2020, the 64 ZB stack would reach the moon and back. Analysts expect global data volumes to exceed 180 ZB by 2026.

But the nature of data has changed dramatically in the last few years. A decade ago, much of the internet's content was text based, including web pages, documents, and email. Today, along with all that text, data is a mixture of images, videos, audio recordings, 3D scans, transaction logs, geolocation trails, and telemetry from everything from smartphones to industrial robots.

Some of it is neatly structured, like a spreadsheet of customer orders, but most of it is messy and unstructured, a digital flotsam of selfies, social media posts, and free‐form text. That distinction matters, because unlike previous AI models, transformers excel at finding patterns in enormous unstructured data.

Using their attention mechanisms, they can weigh and connect signals to make meaning from chaos. A signal is any small clue in the data, like a single word in a sentence, a pixel in an image, or a note in a song. The more data they train on, the more complex the AI models become.

Much of this increase in complexity can be assigned to the parameters that neural networks are built on. Parameters are like tiny dials, billions of them, within the model that determine how strongly one “neuron” influences another. Turning the dial adjusts the model's weights—its internal rules or blueprint. When a model is trained, its parameters are adjusted until the system can recognize patterns and generate useful responses. The more parameters a model has, the more nuanced the patterns it recognizes can be.

Let's talk about scale again. It'll help us understand how insane the recent growth of these models has been. OpenAI's GPT‐2, released in 2019, had about 1.5 billion parameters, roughly the number of synapses in a fruit fly's brain.⁴ GPT‐3 jumped to 175 billion, closer to a mouse. GPT‐4 is estimated to be in the hundreds of billions, approaching primate scale.⁵ GPT‐5 now operates in the trillions.

With each successive leap surprising capabilities emerged, including reasoning, coding, and creative writing, not because they were explicitly programmed, but because scale unlocked them.

Yet, we are also beginning to see the limits of this growth. Bigger models still improve, but not as predictably, and only at an immense cost in compute, data, and energy. At the frontier, size alone is yielding diminishing returns. The next breakthroughs won't come from piling on parameters, but from the smarter use of them. Innovation will require higher‐quality data, new training methods, and more efficient architectures. The story of AI is shifting—from one of more to one of better.

Open Source Collaboration

If compute is the engine, and data is the fuel, open source collaboration is the oxygen. In the early 2000s, neural network research was still on the fringes of mainstream computer science. Researchers like Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, later nicknamed the “Godfathers of Deep Learning,” published their findings freely, trained students around the world, and released open‐source software.

In 2009, Fei‐Fei Li, sometimes called the “Godmother of AI,” and her team at Stanford unveiled ImageNet, a dataset of more than 14 million labeled images spanning thousands of categories.⁶ ImageNet gave the field a common benchmark, a shared proving ground where researchers could test theories, measure progress, and share ideas.

Many of AI's most important breakthroughs have come from open, global networks of researchers sharing discoveries, publishing code, and building on one another's work, rather than squirreling it away.

The watershed moment for AI open source collaboration came in 2012. That year, neural network AlexNet won a global competition to evaluate images in the ImageNet database by a wide margin. The win was notable; the fact that they then released the code and methods publicly, allowing teams everywhere to replicate, refine, and extend the work, was extraordinary. That openness sparked a wave of breakthroughs in computer vision, speech recognition, and natural language processing.

This ethos carried into the modern era. In the mid‐2010s, Google and Facebook released open‐source machine learning frameworks, the factory floors of AI, giving developers anywhere in the world access to the same tools used in top labs. Hugging Face, a platform for open‐source AI models, launched in 2016 and soon became a central hub for sharing models and datasets, dramatically lowering the barrier to entry. A graduate student in Cape Town or a small startup in São Paulo could work with the same foundational technology as a research team at Stanford or MIT.

Even as AI became fiercely competitive in the commercial arena, the collaborative spirit persisted. In 2023 Meta released their state‐of‐the‐art model, Llama, which they had spent billions of dollars training. It was a huge, deliberate bet that broad access to frontier science would accelerate innovation and make it harder for Meta's competitors to differentiate.

The decision led to open source's high water mark: the release of DeepSeek's R1 in January 2025. Developed by a 200‐person Chinese hedge fund, R1 matched the performance of Western models at a fraction of the cost. Its open release triggered an international wave of adaptations by researchers who otherwise might never have had such access. For the first time, there was a belief that competitive AI could be built far outside Silicon Valley, by anyone.

But the landscape of collaboration is now beginning to shift. Many research labs have begun to guard their most advanced models as heightened corporate competition and geo‐political tensions cast doubt on the notion of a “shared prize.” OpenAI, which fully released GPT‐2 in 2019, withheld the GPT‐4 set of parameters (its blueprint), citing safety and competitive concerns. Anthropic, Google DeepMind, and others have taken similar steps. Yet even in this more guarded environment, collaboration continues in smaller adjacent models, training datasets, and research on optimization, interpretability, and safety.

Tension between open science and proprietary advantage is not new. Fields like cryptography and pharmaceuticals have long wrestled with the trade‐off between collaboration and control. Even still, we should expect to see open source contributions remain a powerful driver of AI progress.

Efficiency Breakthroughs

Transformers, compute, data, and open collaboration provide the raw ingredients of modern AI, but efficiency breakthroughs have made them usable at unbelievable scale.

To be clear, training next generation models remains unfathomably expensive today. Companies like Meta and OpenAI are wagering tens of billions of dollars in talent, compute, and infrastructure to advance the AI frontier. Critically, though, these models can now be run by almost anyone thanks to a breathtaking collapse in inference costs. Training teaches a model its capabilities. Inference is what happens when you actually use it—feeding it a prompt and watching it produce a response. Inference is like the moment a doctor listens to you and gives you medical advice. Training is the years of medical study that allow them to do it.

Efficiency has not made training cheap, but it has democratized inference. Only a few years ago, generating a million tokens with GPT‐3, currency to use the model, cost around $20. Today newer systems from OpenAI, DeepSeek, and xAI can produce the same output for between $1 and $3, a drop of more than 90%. DeepSeek's R1 model, competitive with GPT‐4.5, lists inference at roughly $0.55⁷ ; a 95% discount from even two years ago.

Startups around the world can now build without venture‐scale infrastructure. Academic researchers can process massive datasets without a supercomputer. Individual creators can integrate advanced models into their work at trivial cost. That shift, from exclusive capability to widely shared utility, moved AI from an impressive laboratory achievement to a force reshaping society at large.

Remarkably, today's models are not only cheaper, they are also vastly more capable. A single prompt to GPT‐5 can deliver nuanced reasoning, produce images, write working code, or generate deep research analysis that GPT‐3 couldn't even approximate. The world is now paying less for far more intelligence.

These gains came from three sources at once. Models became leaner, using new designs that eliminated redundant calculations while maintaining accuracy. Hardware advanced rapidly, with GPUs delivering more throughput at lower energy costs. And deployment systems grew more efficient, using new strategies to maximize every cycle of compute. Each improvement shaved costs at the margin, but together they reshaped the economics of the entire field.

AI is on its way to becoming a commodity, perhaps even a utility. Soon, it will be something we take for granted, like electricity flowing out of a wall socket. When critical resources move from scarcity to abundance, good things happen. Clean water, reliable food, electricity, and the internet. Each took decades, centuries, or even millennia to spread universally. Capable AI has followed the same trajectory in just four years.

Phases of AI Integration

If you were walking through Cleveland, Ohio, in 1879, your evening stroll might have included a confounding sight. That year, Cleveland became the first U.S. city to install electric streetlights. The first place people encountered electricity wasn't in their home, but on their walk home.

Electricity first illuminated city blocks, then powered factories, and eventually rewired entire economies. Every transformative technology the world has seen passes through recognizable stages of adoption. Progress does not leap from primitive to perfected. It is a steady evolution along two dimensions: what a technology can do, and how those capabilities reach us.

AI's arc is now moving through its own phase of development: enhanced applications, autonomous agents, and natural language operating systems (NLOSs). Each phase introduces sharper intelligence, new delivery mechanisms, and new ways of working and living. Together, these phases chart the path from today's early integrations to a future where AI is as ubiquitous, and indispensable, as electricity.

Phase 1: Enhanced Applications

You likely have already encountered the first phase, enhanced applications, even if you didn't realize it. Enhanced applications are familiar software that gain a second life when they add AI integration. Word processors still help us write, but now Grammarly adds adaptive nuance. Spreadsheets still crunch numbers, but Microsoft Office integrates more seamlessly. Customer relationship management systems (CRMS) still track leads, but Salesforce manages them with predictive insight. They become faster, more adaptive, and more anticipatory, without changing their underlying purpose.

To the user, these are just background upgrades, but they produce real results to the tune of 20–30% productivity gains, with little friction to adoption.⁸ The launch of ChatGPT marked the global start of this first phase. While we had long been working on AI models, ChatGPT was the first product we brought broadly to market. It was the first window, for people outside the industry, into what AI could do. Now, these developments are integrating across industries.

We're eager to take advantage of these enhancements because switching is easy. The costs are low, workflows are unchanged, and users reap benefits without changing habits. In behavioral science, this is called a “status quo–preserving” innovation. New value. No new behaviors. It's an easy win. The early electrification of factories had a similar transition. Rather than inventing new electric machines, most factories simply replaced their steam engines with electric motors. The factory had lower costs, fewer breakdowns, and workers didn't have to do anything differently.

In my advisory work, I've discovered that the smoothness of this transition hides a risk. Because the surface experience feels so familiar, it's easy for leaders and workers alike to underestimate how much the competitive baseline is shifting. Productivity gains compound, 20% here, 30% there, until laggards suddenly find themselves years behind. In aggregate, these enhanced applications are already transforming sectors and economies.

Phase 2: Agentic AI

Enhanced applications are reactive, they wait until we supply the inputs to leverage their advantage.

In the second phase, Agentic AI, the technology takes a far more active role, proactive even. Agents work behind the scenes on our behalf, anticipating goals, making decisions, and acting toward our aims without needing constant input and direction.

An AI‐enhanced word processor can improve your writing, but it waits for you to put in your text and then ask for help. Agents can take a vague instruction, “Plan a trip to Rome,” and break the request into steps, evaluate options, and decide what to do next. Agents don’t live inside a single app, they reach outward, checking calendars, interacting with other software, pulling in outside data, and even making purchases on your behalf. They can carry context across steps, learn from experience, and coordinate with other agents toward a shared outcome.

I’m often asked, If agents are so promising, why does Siri still suck? The answer lies in the history of agents. For decades, the vision lingered just out of reach. In the early 2000s, IBM experimented with software meant to manage servers the way our bodies regulate basic functions like breathing—continuous, automatic, and without conscious oversight. It helped in data centers, but was far too brittle to handle the unpredictable messiness of daily life.

A decade later, Apple promised a consumer agent with Siri. Amazon followed with Alexa. But those systems were powered by scripts and narrow intent‐recognition, not reasoning. They could fetch the weather, but they couldn’t break down complex tasks, adapt to change, or carry context across conversations. In practice, they were voice‐controlled interfaces, not true agents. They delivered the illusion of autonomy without the substance. And they were really frustrating.

A few big breakthroughs are allowing us to move past these frustrations. First, modern language models have improved enough to reason in steps, break instructions into smaller tasks, and adjust when things change. Second, new integration frameworks let agents access tools and services like booking engines, databases, and payment systems, so they actually work in the real world. Third, improved memory lets agents carry context forward and coordinate across multiple interactions.

A lot is being made about agentic AI, but let me tell you what most people are still missing, that they shouldn’t be.

When we first built the internet, we constructed it as a library. It was a place to store research papers so that scientists could collaborate across borders in real time. Quickly (almost immediately) though, we realized that the far better use for the world wide web was as a shopping mall. The result was a browsable HTML internet designed primarily for trading goods and services—ecommerce. Every website, as a result, became a massive billboard.

Over time, accelerated by social media and the “attention economy,” the internet has become distracting, inefficient, and often unhelpful or even harmful. Information we seek is obfuscated by the highest bidder. When you stare at it for even a moment, you quickly realize how broken the internet is. My bank, for example, sells me a credit card I have owned for nine years every time I visit its website. Checking my account statement requires navigating dozens of offers for products and services I don’t want or need despite the fact that they already know so much about me. I recently ordered food from a delivery app. While I was deciding what to eat, the app began to show me a commercial for an upcoming movie.

AI agents will free us from this mess by searching, navigating, analyzing, negotiating, and even purchasing on our behalf; requiring minimal intervention, and only when we request to be involved.

When we build a world in which machines execute tasks and goals on our behalf, will see three pronounced changes:

First, we will soon discover that the optimal state of the internet is not one designed for our eyes, but one designed for our agents.

Accordingly, the browsable (HTML) internet will give way to an agentic one, composed of trillions of TXT and XML files. HTML layouts and buttons are easier for humans to digest (and a great way to sell more stuff), but their mix of content—ads, analytics, layout details—and inconsistent structures, make them noisy and difficult for models to parse reliably. By switching to TXT and XML architectures, agents will traverse well‐organized files and provide faster, more precise results delivered in plain language.

Second, we will discover that most people are willing to outsource most of their preferences.

Consumer choice in the developed world long ago passed the point of diminishing returns.

Today, we are forced to have preferences between options for almost everything—including staples like toothpaste, paper towels, or eggs. We are required to make hundreds of purchasing decisions every day, weighing features that are often uninteresting or even useless. Increasingly we view convenience, rather than optionality as a luxury.

For centuries, the wealthy have outsourced preference and decision‐making: kings had stewards, executives had assistants, the wealthy had personal shoppers. Soon, everyone will be able to. Agentic AI will allow us to identify the specific brands, items, and differentiators we actually care about, and allow agents to handle the rest.

The consumer journey collapses into a single logic: Do I need a good or service? Do I view the good or service as a commodity? If not, I specify. If yes, the agent decides. Agents will compare, filter, and negotiate in the background, surfacing only a curated set of optimal choices. When we no longer have to have a preference on everything, we’ll realize how few we actually have.

Finally, while the agentic internet will be exciting from a time‐saving and productivity standpoint, it will be revolutionary from a user interface standpoint.

When the internet stops being designed as a browsable interface for human eyes, our reliance on screens will diminish significantly. Laptops and smartphones, so central to the digital era, will start to feel clumsy and even obsolete.

OpenAI recently acquired IO, the hardware startup co‐founded by Jony Ive, Apple’s former Chief Design Officer. Jony’s mandate is to build a category‐defining, AI‐native device. What is public domain, but few outside the industry realize, is that Jony and Sam plan to build a device without a screen; they hope to deliver a device that is “always on” and aware of a user’s surroundings, offering assistance without needing specific verbal prompts. In other words, the designer of the iPhone now wants to usher us into the era of display‐less personal computers.

Phase 3: Natural Language Operating System

The third and final phase will be the rise of the NLOS. AI will communicate with us in our natural languages. Conversation, gesture, and even thought will become the primary ways we interact with machines.

As Mark Weiser, the father of ubiquitous computing, wrote in 1991, “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.”⁹ Nicholas Negroponte, founder of the MIT Media Lab, put it even more succinctly, “The best technology is invisible and seamless. It gets out of the way and helps us live our lives.”¹⁰

I caught a glimpse of that future on December 2, 2022, just two days after the launch of ChatGPT. My mother, who has never been particularly comfortable with technology, sat down at my laptop to try it. I gave her only one instruction, ask it anything. She began with, “What should I have for dinner tonight?” For the next 30 minutes she carried on a conversation with more confidence than I had ever seen her use with Google. In that moment, I realized something profound. When technology speaks our language, its complexity disappears. An NLOS will be inevitable, not just for early adopters, but for everyone.

Devices are already moving in that direction. Wearables are becoming more powerful and convenient, reaching far beyond fitness tracking and notifications. Ray‐Ban's latest Meta AI Glasses can identify objects in view, answer questions about them in real time, and layer conversational AI into your field of vision.¹¹ Medical devices are following suit, from smart hearing aids that adjust for conversational dynamics, to continuous glucose monitors that integrate with AI health coaches.

At the same time, the software layer is evolving to keep pace. AI systems are being trained to share memory, context, and tools across applications. Google is experimenting with AI assistants that can “see” through cameras, “hear” in real time, and continuously “understand” the user's environment. These steps all point toward a unified AI interface that will be less like opening a dozen apps and more like having a single, ever‐present collaborator.

These technologies could be truly liberating for much of the population. Millions of people, particularly the elderly, face small but insurmountable barriers to participating fully in society, that go unaddressed by current digital technology that isn't designed with them in mind.

I'm reminded of this every time I see an elderly traveler at an airport, fumbling with a mobile boarding pass or struggling to navigate a terminal. If such basic interactions feel overwhelming, what does the rest of their day look like? The promise of this technology is that it can transform those moments of frustration into renewed independence.

AI companions could patiently explain a bus route, refill a prescription, or translate a conversation at the doctor's office while requiring only the use of words users already. NLOS may be the difference between dependence and participation. For someone who has long stopped asking for help, out of pride, or fear of burdening others, these tools can quietly reopen doors that felt permanently closed.

When we go a step further, and consider a future of embeddable technologies, the opportunities are even brighter. It's easy to recoil at the thought, popular even in some circles, but optimism shows us how much it could improve lives. Integrating these technologies directly with the human body could return voice and autonomy to those without these most basic joys of humanity. In 2024, Synchron implanted a minimally invasive brain–computer interface into a patient with ALS. The device, placed via the jugular vein onto the motor cortex, translated neural intent into digital commands, allowing the patient to make calls, operate smart‐home devices, and use streaming services.¹² Universities like UCSF and Stanford have demonstrated brain‐to‐text systems capable of decoding speech at up to 70 words per minute.¹³

Adoption, when the utility is clear, will likely be swift. As utility outweighs perceived risk (or social ostracizing, as is the case with some clunky technology), these technologies will spread as quickly as any we have ever seen.

The arrival of NLOS will mark the end of the screen era and the beginning of something new: a world where intelligence is ambient and woven into every interaction. For the first time, access to the most powerful tools in history will not depend on skill, training, or even literacy. Anyone who can speak, gesture, or signal intent will be able to participate fully.

But the pace of that arrival will not be set by technology alone.

Societal Thresholds: The Invisible Hand of Adoption

Often, the most powerful forces in our life are ones we cannot see. Adam Smith, the 18th‐century economist and philosopher, described economic markets as guided by an “invisible hand.” He meant that the decisions of individuals, aggregated across society, created order and direction greater than any single plan. Technology experiences similar forces, especially when it comes to its adoption.

Every disruptive technology advances along two timelines: the technological threshold, the frontier of what the technology can do, and the societal threshold, the frontier of what people, institutions, and laws will let it do. As we've seen with almost every technology on record, these thresholds rarely move in lockstep. There is a gap, and I call that space between them the adoption gap.

At its best, the adoption gap provides breathing room for testing, regulation, adoption sentiment, and adjustment. At its worst, it becomes a drag on progress, sustained less by healthy caution than by fear, habit, or misinformation.

Societal thresholds are the invisible hand of technological adoption. They decide when and how technologies take hold, more quietly than invention, but no less powerfully.

With AI, the technological threshold has advanced rapidly. Systems can now perform tasks that once demanded deep expertise, like generating software code and assisting in medical diagnostics. The societal threshold, however, is moving at a different pace that is shaped by trust, cultural values, and prior experiences with technology.

The future of the RenAIssance will depend on navigating this distance wisely, preserving the thresholds that safeguard us, overcoming the ones that needlessly hold us back, and crossing them in ways that build trust rather than backlash.

Mind the (Adoption) Gap

There is a natural divergence between technological and societal thresholds. Technology can advance in steady or exponential steps, but it always moves forward. Once the technological threshold is crossed, it is crossed. Societal acceptance is much more dynamic. Often moving in irregular bursts, sometimes leaping forward, sometimes sliding back, societal thresholds form through a chain reaction: human biases shape public perception, which then influence regulation and policy.

Electricity took more than a century to move from Franklin's 1752 kite experiment to everyday use, slowed not only by infrastructure but by widespread distrust and fear. Even after Edison introduced his light bulb in 1879, most American homes remained lit by gas or candles for several decades.

People associated electricity with lightning and danger, and even assigned mythical properties to it, imagining it as anything from divine fire to a mysterious force intent to kill without warning. Early accidents like electrical fires and fatal electrocutions directly reinforced those fears. Physicians warned that ambient exposure might be harmful to human health. Gas companies stoked these fears with anti‐electricity campaigns to preserve their market dominance. Moral panics erupted, with critics worrying electricity would lead to social decay and women abandoning domestic life.

But thresholds can shift when fear is replaced by wonder, and when benefits become tangible. Demonstrations like the illumination of Chicago World's Fair in 1893 reframed electricity as awe‐inspiring rather than dangerous. When hospitals, homes, and food storage visibly improved with electric power, hesitation and rejection turned to embrace.

Nuclear power followed a different arc. In the mid‐20th century, it was heralded as the energy of the future because it was cheap, abundant, and carbon‐free. For a brief moment, the societal threshold seemed low enough to allow rapid expansion. But then came the shadows of nuclear weapons and the catastrophes of Three Mile Island in 1979, Chernobyl in 1986, and Fukushima in 2011. Each incident reset public trust. Once promising technology became threatening, and thresholds quickly shot back up. In countries like Germany, nuclear plants have been decommissioned altogether, even as climate concerns mount. In the United States, the threshold has been stuck for decades, with new builds rare despite improved safety and climate urgency. Only recently, under pressure from surging energy demand, has investment in nuclear power returned. Thresholds often respond to major events, as accidents or geopolitics trigger fear and undo decades of trust‐building.

Genetically Modified Organisms (GMOs) tell another story. In this case, evidence did not raise the societal threshold; perception, culture, and values did. From a scientific perspective, GMOs have consistently shown promise: higher yields, pest resistance, and the potential to secure food supplies in a changing climate. Decades of research affirmed their safety, but trust in the technology languished, especially in Europe. Cultural norms, environmental concerns, and mistrust of corporations like Monsanto fueled campaigns against GMOs, pejoratively nicknamed “Frankenfoods.” The United States moved forward with adoption, but some world governments responded with strict regulations or outright bans. Today, mistrust endures in parts of the world. Thresholds often reflect specific geographic narratives.

CRISPR gene editing may be the clearest modern example of threshold divergence. Unlike GMOs or nuclear power, CRISPR's threshold is high not because of fear or mistrust alone, but because of profound ethical questions. The technological threshold has advanced rapidly. In 2023, regulators in the United States and United Kingdom approved CRISPR therapies for sickle‐cell disease. In May 2025, a six‐month‐old infant born without the ability to filter ammonia from his body was cured by a CRISPR therapy designed and delivered within months. Despite these breakthroughs, societal acceptance remains divided. The concerns stem from unknown long‐term genetic risks, and the ethics of altering human life. Regulators and policy mirror these sentiments, moving slowly on approvals beyond the most urgent medical use cases. Some thresholds exist to make us pause. Not every societal threshold is an obstacle to overcome.

The pesticide DDT offers a powerful example of a threshold moving back for the better. Scientists discovered DDT in 1939. Soon after it was hailed as a miracle chemical. It boosted crop yields and dramatically reduced malaria deaths. DDT was cheap, effective, and widely available; the societal threshold was quickly met. Then came Rachel Carson's Silent Spring in 1962 exposing the ecological toll: collapsing bird populations, poisoned waterways, and long‐lasting environmental damage. Public opinion shifted, regulators responded, and the societal threshold rose sharply. By the 1970s, many countries had banned agricultural use, preserving only limited public health applications. Here, the threshold functioned as a protective fence, preventing further ecological collapse. Walking back a societal threshold can be necessary for human flourishing.

Crossing the Societal Thresholds

AI itself doesn't sit on the societal threshold. Instead, we must address hundreds (maybe thousands) of different AI applications, each with their own societal thresholds: copilots in our workplace and schools, diagnostic assistants in hospitals, autonomous cars on our streets, robots in our homes. That is where people form their judgments and where adoption either accelerates or stalls. Just as life sciences had to fight different battles over vaccines, IVF, stem cells, and CRISPR, AI will have to cross threshold after threshold, one application at a time.

Thresholds don't fall in a straight line, either. They are crossed in fits and starts. First comes the buzz, the curiosity. ChatGPT's release in 2022 was one of those moments. The world raced to try it, markets leapt, and dinner table conversations exploded. Then, as always, the doubts crept in. Is this really useful? Or is it just another Segway or Google Glass? Nearly every breakthrough has faced similar pushback. Email was once a mystery. The telephone was considered a pointless luxury. Radio was “just entertainment.” Skepticism isn't new; it is the price of admission for every transformative tool.

Once an application proves itself, skepticism does not vanish. It mutates into fear. We like AI when it makes our lives easier, when it drafts the email we did not want to write or codes the script we did not know how to build. But we flinch when it starts touching our identity, our job, our place in the world. Suddenly, the democratization we celebrated feels like a threat. Institutions behave the same way. Governments lurch into overcorrection. Competitors raise alarms to slow the pace. Gas companies once ran smear campaigns against electricity. The playbook has not changed.

Still, fear cannot hold forever. The undeniable benefits start to seep in. Schools that once banned ChatGPT now use it as a tutor. Hospitals quietly let AI scan for anomalies doctors might miss. Translation tools become so convenient they are hard to imagine living without. Familiarity lowers thresholds. Trust builds slowly, but once it does, society shifts from “should we?” to “how should we?”

And eventually, sometimes suddenly, the threshold is met. We stop debating utility and start celebrating possibility. The skyscraper redefined the skyline. The airplane shrank the planet. The smartphone rewired culture itself. AI will do the same. Imagine a world where anyone can write software, make a film, or publish breakthrough research. Whole categories of creativity and discovery will open to people who never before had the necessary tools. That is not displacement; that is expansion.

What makes AI distinct is not only the number of thresholds, but their breadth and complexity. Most breakthrough technologies were defined by a single dominant concern. Nuclear power raised questions of safety, encompassing both accidents and weapons. GMOs sparked debate over health. CRISPR provoked controversy over ethics. Each had one central issue that shaped its acceptance.

Most AI applications collide with society on many fronts at once: labor, ethics, governance, privacy, health, even control itself. That scope makes its thresholds more numerous, more complex, and more contested than any technology before it.

Which thresholds we choose to lower, and which we don't, will decide how AI shapes the world.

Autonomous Vehicles: A Study in AI Adoption Gaps

Cars, by an incredibly wide margin, are the most dangerous technology most of us use every day. For many healthy Americans, driving in a car is statistically the riskiest thing they will do in any given year. Human error, distraction, fatigue, and alcohol combined with tons of steel and horsepower causes carnage on the roads. In 2024, 1.2 million people died in road traffic injuries. That is more than malaria, tuberculosis, or war.¹⁴

Autonomous vehicles (AVs) present the obvious solution to this human blight. They do not drink, text, or fall asleep at the wheel. They see in every direction at once, react in milliseconds, and never get impatient or reckless. In San Francisco, where AVs operate daily, studies show they are already involved in 10 times fewer collisions per mile than human drivers, and their crashes are far less likely to cause serious injury. By almost every measure, the technological threshold has been cleared. AVs are already safer drivers than we are.

Yet, society broadly has no interest. In a 2025 AAA survey, a majority of Americans said they would refuse to ride in a self‐driving car, and only 13% expressed any interest in doing so.¹⁵ In Philadelphia, nearly 70% of residents recently declared autonomous cars “not viable” for their city, while national polls consistently show fewer than one in four people think self‐driving cars are even a good idea.¹⁶ Across the United States, Europe, and Asia, the verdict has been remarkably consistent: most people do not see AVs as desirable, let alone inevitable. Our resistance runs deeper than distrust. We do not want the technology at all.

This adoption gap truly confounded me. How could people rally with such passion for causes that save a handful of lives, yet resist a technology that could prevent more than a million deaths worldwide every year? We overhaul tourism policies after a single shark attack, even though sharks kill fewer than 10 people globally each year. What kind of cognitive dissonance allows us to treat 1.3 million fatalities on the road as nothing more than “the cost of doing business”?

I have been defending AVs since 2015, at dinner tables, in classrooms, and in conversations with friends, family, and strangers. Finally in 2023, with the help of some friends, I turned that frustration into focus. I began studying the societal threshold itself, asking why people resist technologies that could save them, creating such a robust adoption gap. After three years of research, I have come to see the outlines of an answer. I think there are three factors that uniquely complicate the crossing of AI societal thresholds.

Control

A few months after we started the study, a woman approached me after a talk in which I had espoused the future of AVs. She shook my hand coldly and offered me a piece of paper with a few handwritten notes. She asked that I read it later. When I got back to my hotel room and opened it, I found a scathing rebuke of my opinions. Her rebuttal was long, but principally she told me she and her friends hated AVs because (1) they caught on fire and killed people; and (2) cars are pillars of American identity and symbols of freedom. “When they take away our cars,” she proposed, ominously, “then what?”

Her note left me with a couple impressions.

First, I was impressed that the power of our biases is much stronger than data's ability to disabuse us of them. She and her friends had likely seen a vivid news story about a Tesla fire and assumed this was the norm. That's how availability works: we judge the likelihood of something not by the numbers but by how easily examples come to mind. In this case, the ease of recalling a dramatic fire outweighed the reality that combustion engines catch on fire, on average, 36(!) times a day in Los Angeles alone,¹⁷ and 60(!) times more often per registered vehicle than electric engines. But her mind wasn't weighing data. It was weighing stories.

The second impression I took away was that Americans hated AVs because we saw cars as symbols of our cultural ideals, and, critically, freedom. I thought I had this all figured out … until I took trip to Disneyland.

Built in 1954, Disneyland's Autopia ride lets children aged three through eight drive tiny gas‐powered karts around a short cement track at roughly two miles an hour. It is loud, smelly, and decidedly not futuristic, despite being located in Tomorrowland. But for nearly 70 years, Autopia has remained one of the park's most popular rides for children. Why? Because it lets them “drive.” The thrill of turning a wheel and pressing a pedal outweighs every other factor. In fact, of all my childhood trips to Disneyland, the ride I remember best is Autopia. A three‐year‐old knows that driving a car is more fun than being driven, even if you’re going nowhere.

After 30 years, I returned to Disneyland to study the ride and children's joyful reactions to it. It became very clear: it is not freedom we crave, it is control. This is the first factor in the adoption gap. There is no more freedom in inching along in a two‐mile‐an‐hour go‐kart than there is freedom in commuting back and forth from work. A taxi can offer you that, so can a friend. What we desire is the act itself. The grip of the wheel, the sense of commanding the machine. This is why wealthy people get pilot's licenses or race fast cars. They do not do it for freedom; someone else could just as easily fly the plane or drive them down the track. They do it to control powerful machines.

No matter how much safer AVs may be, the cost of surrendering control feels greater than the value of safety.

Rate of Scientific Progress

When I first started making a case for AVs in 2015, they were unreliable and statistically dangerous. Public skepticism was justified.

Today, the technology has improved dramatically. AVs now outperform humans in nearly every driving condition. Between January and April of 2025, Baidu's Apollo Go provided more than 1.4 million autonomous rides without a single fatality.¹⁸ It hasn't improved public opinion at all. In some surveys, opinion has even declined. Why?

Because, it seems, less than 10% of the population actually knows how good AVs have become. Outside of a few cities, most people have never even seen one in action. A 2025 survey confirmed the vast majority of Americans report knowing “little to nothing” about AVs.¹⁹ An MIT AgeLab study actually found that only about one in three people could correctly define what “self‐driving” means.²⁰

This presents the second cause of the adoption gap: technological thresholds are now moving way faster than society can follow.

Outside of early adopters in Shanghai, Beijing, San Francisco, Phoenix, Austin, and Los Angeles, the handful of cities where AVs already complete hundreds of thousands of rides each week, most people have never seen one in action. For them, the technology is still clumsy, unreliable, and untrustworthy. Polls reflect that frozen perception, not today's reality.

I was confronted with this, somewhat surprisingly, at a medical conference in December 2024, where I spoke about AI's advances in radiology pathology. After listening to me (very enthusiastically) describe the merits of AI in diagnosing tumors and pneumonia for patients without access to a radiologist, a person in the crowd raised his hand to ask me a question. When I called on him he stood up to tell me that AI had not “solved” radiology, citing a 2023 study. He was concerned that “technologists talk about empty promises as though AI can just do everything.” I was left to kindly explain that the study he cited was in fact outdated. In the months since it had come out, computer vision had advanced to the point that it could outperform radiologists in almost every scan. Here was a medical doctor, clearly committed to learning, and yet unaware that his own field had already been transformed. Even among experts, the knowledge gap is staggering. If specialists can't keep pace with the expansion of knowledge, how can the public be expected to?

This is a huge, sweeping phenomenon, and the reason why nerds like me will spend the next 10 Thanksgivings repeatedly explaining that AI can in fact do better math than Grandpa Dan. AI's capabilities are advancing faster than society can recognize, let alone accept. By the time one leap is absorbed, the next is already here. Society is still arguing about whether AVs are safe enough, when they settled the debate long ago.

Tolerance

I think about the third factor most often, and spend the most time talking about it to CEOs too. Humans have incredible tolerance for human failure and almost none for machine failure.

When a driver accidentally hits a cyclist, we often feel bad for both of them. We don't want to be in either of their shoes. When an Uber test vehicle hits a pedestrian, there is international outcry to shut the whole thing down. Regulators pause programs. Approval ratings plummet.

I think there are two reasons for this asymmetry.

First, when one machine fails, people assume all machines will fail in the same way. That instinct is not just wrong, it's backwards. If we can observe a machine fail in even the most subtle way, we can solve it across all machines, often immediately. Humans don't have this quality. We fail, individually and collectively, in the same ways every day. The practice of getting older and wiser is simply making slightly fewer mistakes each day, and hopefully not repeating the same ones. Our performance advances in increments stretched across decades. Machines can leap from failure to solution in a single update.

The second is that we have no empathy for a machine. We cheer for Olympians who stumble and recover, but a broken washing machine earns only irritation. This difference plays out in practice. Customer satisfaction scores are consistently lower for AI agents than for humans who deliver the exact same outcome. Delivery robots in Los Angeles have been kicked, blocked, even set on fire. Humans punish machines in ways they would never treat each other.

This asymmetry is not inherently bad. I want to live in a world where we reserve empathy for each other more than for machines, but it has consequences. That instinct will shape policy, design, and adoption. It ensures that we will hold AI applications to far higher standards than we hold humans to, and in many instances, the entrenched cost is human safety.

It also implies an unnerving inevitability. Corporations will increasingly try to anthropomorphize machines to overcome the adoption gap. They will continue to give robots names, faces, voices, and personalities to evoke empathy. We should resist this. Machines should serve us, not become us. If we blur the line between human and machine too much, we risk dehumanizing ourselves.

Ultimately, AVs help us see that AI adoption will not follow the familiar arc of electricity, email, or radio exactly. Its societal thresholds are uniquely complex. Across healthcare, transportation, education, finance, and governance, our future will hinge far less on what AI can do and far more on how comfortable we are with it.

Unmetered Intelligence

For thousands of years, the vast majority of human existence, when the Sun went down, it got dark. Hardly a revolutionary idea. Still does, sure. Unless you go to Times Square, or any other of the unknowable number of places that keep the lights on. The Sun still sets, but we're no longer bound to night's darkness. Thanks to Edison, light has become widely available and nearly unlimited.

AI is going to offer something similar, but maybe more profound. For all of human history, intelligence has been scarce and expensive. A few people had access to great schools, tutors, libraries, or mentors, but most did not. Expertise clustered in pockets and was always limited by the slow, fragile bandwidth of the human brain. Now, as machines grow more capable, cheaper, and seamlessly embedded in our lives, the cost of cognitive power will collapse. And as society crosses more thresholds and adopts more applications, we will enter an era of unmetered intelligence: a world where intellectual capacity becomes as ordinary, accessible, and dependable as flipping a light switch.

I'm talking about making an extremely limited resource, how much we can think (individually and collectively), an unlimited resource.

Before proceeding, I should offer two clarifications to keep you from thinking (any more than you already do) I'm outlandishly naive.

Unmetered intelligence promises abundant access to intelligence, not universal brilliance. Literacy does not guarantee someone will read thousands of books, and internet access does not always lead to deep research. In the same way, unmetered intelligence will not erase differences in motivation, curiosity, or creativity. The bottleneck will shift from the scarcity of intellectual power (compute), to the effectiveness of producing with it.

Unmetered intelligence is also not the same as artificial general intelligence (AGI), the term used to loosely define artificial superintelligence. Achieving AGI will answer: What would happen if machines surpassed human intellect? Achieving unmetered intelligence will answer: What would happen if intellectual capacity was unlimited? The first measures the limit of intelligence; the second measures how much of it we can access.

What would it mean to have abundant access to intelligence? Consider the distinction between definite and indefinite problems.

Definite problems are those with known parameters and describable constraints, even if the solutions are staggeringly complex: How do we cure cancer? How do we build a fusion reactor? How do we remove carbon from the atmosphere?

Indefinite problems are those with no clear parameters at all, questions so murky we can barely frame them, let alone their answers: What sparked the emergence of consciousness? What lies beyond the observable universe? What is the meaning of life? Why can't we all just get along?

Humans today are largely confined to the known universe, the three‐dimensional space in our field of vision where the rules are familiar and (somewhat) comprehensible. That makes us great at answering definite problems. We have a blind spot though. We can't think beyond our comprehension of life. We can't imagine what we don't know how to imagine. When we picture alien life, we almost always anthropomorphize it. We imagine humanoid creatures with recognizable motives and behaviors. But alien life/intelligence might look more like a network of sentient machines, a planetary‐scale fungus, or something that no words or ideas can yet describe. Unmetered intelligence may enable us to see beyond our blindness into worlds we, quite literally, cannot imagine.

Let's make it more relatable. Asking Phil Knight in 1975 how Nike planned to reach more customers, he probably said they needed to open more stores. Pitching the promise of e‐commerce would have been futile. People buy shoes in stores; they try them on. But almost 50 years later, in 2024, Nike.​com generated more than $8 billion. Part of my job in a boardroom is to challenge leaders to think beyond today's limitations, and try to imagine almost inconceivable possibilities.

There are some strong early signals that unmetered intelligence is on its way. AI Scientist and CodeScientist are AI applications that run science experiments without human supervision, publishing peer‐reviewed papers in reputable journals. Climate models powered by AI now generate ultra‐high‐resolution forecasts decades ahead, helping cities plan adaptation strategies. In medicine, DeepMind's AlphaFold mapped millions of protein structures, while Insilico Medicine designed Rentosertib, a drug now in Phase II clinical trials for Idiopathic Pulmonary Fibrosis. Even aerospace is shifting. AI‐assisted planning is optimizing satellite constellations for Earth observation, disaster response, and planetary defense. Achieving any of these in comparable timescales would take an unbelievable, and likely impossible, quantity of human intelligence.

The first Renaissance expanded human possibility by placing new tools in the hands of thinkers, explorers, and builders. Doing the same with intelligence will make the constraints of what individuals, organizations, and even entire societies can do fall away.

Notes

1. Chris Smith, Brian McGuire, Ting Huang, and Gary Yang, The History of Artificial Intelligence (University of Washington, 2006), https://courses.cs.washington.edu/courses.csep590/06au/projects/history-ai.pdf.

2. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser, and Illia Polosukhin, “Attention Is All You Need,” 2017, https://arxiv.org/pdf/1706.03762.

3. Studio Network Solutions (citing Petroc Taylor), “Global data created, captured, copied, and consumed: ~2 ZB in 2010; ~64.2 ZB in 2020,” Studio Network Solutions, accessed August 23, 2025.

4. OpenAI, “GPT‐2 (1.5 B parameters),” OpenAI, Feb. 14, 2019, accessed Aug. 23, 2025, https://openai.com/index/gpt-2-1-5b-release/.

5. NVIDIA, “OpenAI Presents GPT‐3, a 175 Billion‐Parameter Language Model,” NVIDIA Technical Blog, July 7, 2020, accessed Aug. 23, 2025, https://developer.nvidia.com/blog/openai-presents-gpt-3-a-175-billion-parameters-language-model/.

6. Encyclopaedia Britannica, “Fei‐Fei Li,” Encyclopaedia Britannica, June 29, 2025, accessed Aug. 23, 2025, https://www.britannica.com/biography/Fei-Fei-Li.

7. Business Insider, “DeepSeek‐R1 costs $0.55 per 1M input tokens compared to OpenAI's $15,” Business Insider, Jan. 2025, accessed Aug. 23, 2025, [URL hidden per policy].

8. Salesforce, “New Research: Companies Globally Report an Average of 25% IT Cost Savings with Salesforce,” Salesforce, Nov. 7, 2022, accessed Aug. 23, 2025, https://www.salesforce.com/news/press-releases/2022/11/07/companies-report-cost-savings-with-salesforce/.

9. Mark Weiser, “The Computer for the 21st Century,” Scientific American 265, no. 3 (September 1991): 94–104, https://www.lri.fr/~mbl/Stanford/CS477/papers/Weiser-SciAm.pdf.

10. Nicholas Negroponte, Being Digital (New York: Alfred A. Knopf, 1995), 231.

11. Wes Davis, “Meta's AI for Ray‐Ban Smart Glasses Can Identify Objects and Translate Languages,” The Verge, December 12, 2023.

12. “Synchron's Brain‐Computer Interface Used Successfully With Amazon Alexa,” Endovascular Today, September 23, 2024, https://evtoday.com/news/synchrons-brain-computer-interface-used-successfully-with-amazon-alexa.

13. UCSF / Edward Chang et al., “Brain Implant Decodes Speech to Nearly 80 Words per Minute,” UCSF News, Aug. 23, 2023, accessed Aug. 23, 2025; Stanford Medicine, “Brain‐Computer Interface Enables Rapid Communication at 62 Words per Minute,” Stanford Medicine News Center, Aug. 15, 2025, accessed Aug. 23, 2025.

14. Global Road Safety Facility, “Annual Report 2024,” GlobalRoadSafetyFacility.org, 2024, accessed Aug. 23, 2025, https://www.globalroadsafetyfacility.org/sites/default/files/2024-11/GRSF.

15. AAA, “Fear in Self‐Driving Vehicles Persists,” AAA, Feb. 25, 2025, accessed Aug. 23, 2025, https://newsroom.aaa.com/2025/02/aaa-fear-in-self-driving-vehicles-persists/.

16. Axios Philadelphia, “Philly iffy on self‐driving cars,” Axios, Jul. 15, 2025, accessed Aug. 23, 2025, https://www.axios.com/local/philadelphia/2025/07/15/self-driving-cars-philadelphia-waymo-poll; Pew Research Center, “Americans cautious about the deployment of driverless cars,” Pew Research Center, Mar. 17, 2022, accessed Aug. 23, 2025, https://www.pewresearch.org/internet/2022/03/17/americans-cautious-about-the-deployment-of-driverless-cars/.

17. EV Engineering & Infrastructure, “Did You Know ICE Vehicles Pose Fire Risks 60 Times Higher Than EVs?,” EV Engineering & Infrastructure, Jan. 31, 2025, accessed Aug. 23, 2025.

18. Automotive World, “Baidu's autonomous ride‐hailing unit reports more than 1.4 million rides in Q1 2025,” Automotive World, May 22, 2025, accessed Aug. 23, 2025, https://www.automotiveworld.com/articles/baidus-apollo-go-announces-1000-robotaxi-milestone/.

19. AAA, “Fear in Self‐Driving Vehicles Persists,” AAA, Feb. 25, 2025, accessed Aug. 23, 2025, https://newsroom.aaa.com/2025/02/aaa-fear-in-self-driving-vehicles-persists/.

20. MIT Advanced Vehicle Technology Consortium & J.D. Power, “Mobility Confidence Index Study: Consumers Don't Know What They Don't Know,” MIT AgeLab, Nov. 30, 2021, accessed Aug. 23, 2025, https://ctl.mit.edu/news/jd-power-and-mit-avt-study-fully-automated-self-driving-vehicles-consumers-dont-know-what-they.

CHAPTER 2
Conditions to the RenAIssance

In the Age of Exploration, a navigator was only as effective as his ability to sustain his boat with timber, tar, and sailcloth, and his voyage with financing and political stability. In the Industrial Revolutions, ingenuity thrived on coal and iron, electricity and steel, as much as it did labor laws, trade routes, and organized production. Breakthroughs have always rested on both material resources and the social infrastructure to support them.

AI is no different. Progress depends as much on access to chips, fabs (chip fabrication facilities), high‐quality data, and energy abundance, as it does on guardrails, governance, and trust. Beneath the technical advances of each AI breakthrough, there is a hidden scaffolding of societal thresholds that determine adoption. Explainability ensures we know how systems work. Alignment and safety ensure they pursue the right goals without harm. Governance provides rules that steer development. Coordination ensures those rules work across borders

None of these conditions can be taken for granted; we need to achieve every one of them. Only when these pillars align can the potential of the RenAIssance move from possibility to reality.

Resource Access

It doesn't matter if you're building a log cabin, a spaceship, or the future of AI; everything requires resources. The RenAIssance is going to call for a lot of compute, data, and energy.

Compute is the most fragile. Abundance depends on the increased manufacture of chips, and production is still dangerously concentrated and vulnerable. Chip fabrication itself is a delicate art. It is extremely precise engineering that requires stable power, humidity, and vibration. Even microscopic disturbances—a speck of dust, a voltage flicker, a fraction of a degree in temperature—can ruin production runs worth millions. Earthquakes, floods, or blackouts can shut down entire fabs, and many of the world's most critical facilities sit in Taiwan, Japan, and the U.S. West Coast, where these risks are daily realities.

But the bottleneck goes deeper than geography. Every advanced Nvidia chip and every state‐of‐the‐art processor from Apple, Intel, Samsung, and TSMC ultimately relies on a single supplier: ASML, a company based in the Netherlands. ASML is the only firm in the world capable of producing extreme ultraviolet lithography (EUV) machines, the tools required to etch features just a few nanometers wide onto silicon wafers. Without EUV, there would be no advanced AI.

EUV machines are one of the most complex devices humans have ever built. They contain more than 100,000 components, some sourced from thousands of suppliers across dozens of countries. They have mirrors polished so precisely that if they were scaled to the size of the United States, their tallest bump would be no higher than a blade of grass. The machines generate EUV light by firing powerful lasers at tin droplets 50,000 times per second, creating a plasma hot enough to rival the surface of the sun. They cost more than $150 million apiece, take months to assemble, and only a few dozen ship each year.

This is fragility in its purest form. If ASML were disrupted even slightly, the global pipeline of cutting‐edge chips would grind to a halt. The entire AI ecosystem, and the world, would feel the shock immediately. Policymakers know it, which is why ASML has become a focal point in U.S.–China competition. Washington has barred the export of EUV machines to China, effectively constraining the country's ability to build frontier chips. That makes one Dutch company, with a few thousand employees, a keystone in the balance of global technological power.

The EUV machines are just the most visible piece of a fragile geopolitical tension that threatens the supply chain. Taiwan's TSMC is the world's best chip producer, and while the island's “silicon shield” creates mutual dependence that deters open conflict, it is no guarantee. A blockade, cyberattack, or even an accident at sea could reverberate across the AI ecosystem. Governments know it, which is why export controls, antitrust scrutiny, and trade disputes are accelerating. Thankfully, resilience is coming in the form of diversification. Intel is building fabs in the United States, Ireland, and Germany; Samsung is producing in South Korea and Texas; Japan is backing their own domestic company; and India and the Middle East are positioning themselves as new hubs. But for now, the future of intelligence still hinges on a handful of fragile choke points.

If compute is brittle, data is scarce. For the past decade, progress has followed a simple formula: more compute plus more high‐quality data equals better performance. Scaling laws have shown that larger models trained on more data consistently improve—so far. Those laws also imply that a GPT‐6‐class system would need many trillions of parameters and exponentially more curated data. The catch is that the public web is already scraped clean. We're running out of high‐quality, diverse, human‐generated data. Without it, the predictable gains of scaling begin to falter. The scramble has begun.

In 2025, Meta acquired half of Scale AI, a data annotation company, for $15 billion. It’s a clear sign that quality datasets have become a strategic asset. The most interesting trend we're now seeing is industry leaders like Anthropic and OpenAI hiring huge teams of domain experts to curate and structure information in specialized fields like law, medicine, and engineering in order to allow the models to go much deeper, if not wider. Ultimately, the supply of good data is finite, but the demand may be infinite.

Energy is the most dramatic resource of all. A single frontier training run can consume hundreds of gigawatt‐hours, the annual demand of a small city's worth of homes, as cooling systems fight to keep tens of thousands of unstopping GPUs stable. Inference adds its own weight. Each ChatGPT query draws only watt‐hours, but multiplied by hundreds of millions of users, it quickly becomes a permanent baseload in the hundreds of megawatt‐hours per day. The scale is shocking even to seasoned operators.

In February 2025, a large energy company CEO in the U.S. told me they had planned for a 30–40% increase in energy demand this decade. Now, he said they “realize they must prepare for 1,000%.” To quote Jaws: We're going to need a bigger boat. Goldman Sachs estimates that by 2026, AI demand in the U.S. alone could exceed 70 terawatt‐hours annually—the output of several nuclear plants.¹ The U.S. Department of Energy, together with the Infrastructure Investment and Jobs Act, has earmarked nearly $12 billion for grid modernization like advanced transmission, transformers, storage, and permitting digitization, but steel, copper, construction, and crews scale more slowly than software.² Without major new capacity, utilities will face stark allocation choices. Who deserves the next marginal megawatt?

This all sounds impossible if extrapolated in straight lines. But progress doesn't move in straight lines. We may not actually need more resources, if we can achieve breakthroughs that change the math. On the model side, efficiency continues to accelerate. GPT‐5 delivers on the order of 3× more capability per watt than GPT‐4. China's DeepSeek R1, trained under austere U.S. export restrictions, matched or exceeded leading Western systems while using roughly one‐tenth the compute. They bested their scarcity by redesigning training and leaning on optimization rather than sheer scale. Other developments are getting more performance out of smaller models. Some have become so compact they can operate on more efficient CPUs, adding the secondary benefit of reducing demand pressure on scarce GPUs.

On the energy production side, AI augmented scientific breakthroughs could reset constraints even more dramatically. Proton‐based batteries use abundant carbon and hydrogen to store energy rather than scarce lithium. Large‐scale storage can lower geopolitical risk and buffer variable renewables. Fusion is edging from fiction toward pilot reality. Net‐energy‐gain experiments in the United States and steady‐state plasma advances abroad hint at power sources that could be both near‐limitless and carbon‐free. Importantly, here too AI is a force multiplier, modeling reactions, optimizing confinement, accelerating materials discovery, and compressing the path from lab to grid. None of these alone is a silver bullet, but together, they point to a world where ingenuity outruns scarcity by redefining constraints.

That is the hinge on which the RenAIssance turns. Compute, data, and energy. The resource needs sound simple, but they will determine our fate. If any one of them falters, the future of AI stalls. But, if we can build abundance and resilience in the timelines necessary to sustain our abilities and ambitions, the horizon of unmetered intelligence moves even closer.

Explainability

These days I spend far more time than I would like on an airplane. Every time I get on one I'm struck by the fact that we went from the invention of commercial flight to abundant air travel in less than 100 years. I'm also reminded that I don't understand how any of it works. Obviously, I know I'm not alone.

Most people who fly cannot explain aerodynamics or physics, or even combustion engines, yet feel safe 35,000 ft in the sky. Even fewer can map the global fiber cable network, or explain how the internet actually works. Yet most of us comfortably drop down the tray table, open our computer, and reply to emails. We do this because we’ve come to learn we can rely on technologies we barely understand. These traditional technologies, though, perform specific and predictable tasks we can make sense of.

AI feels different because it is different. It's not possible to look at an AI system and see how it works in the same way that we can look out the window at an airplane wing. Nor can we know if an AI system is misfiring in the same way that we can easily tell if the internet is “down.” Modern AI, just like the human brain, doesn't follow fixed instructions; it decides among infinite possibilities. As a result, AI systems often feel like magic black boxes. Even when they're working perfectly, the very nature of the technology makes people suspicious of their outcomes. That suspicion is a huge reason for many of AI's adoption gaps.

In order for us to trust them, critical AI systems must be able to do more than just give the correct answer, they must explain themselves: what information they considered, what assumptions they made, what uncertainties remain, and how they reached their decision. For us to feel comfortable relinquishing control, AI must “show its work.”

Explainability is how we turn “just trust us” into “see for yourself.” A high‐stakes AI system should not only answer what but why. Building trust in a healthcare setting might mean an AI highlights the X‐ray regions it flagged and why it ruled out alternatives. In lending, it means showing all of the factors that led it to reject a loan. Explainability transforms an unaccountable “no” into a decision that can be understood, audited, and, if necessary, corrected. Without it, every unexplained answer compounds mistrust. “No,” is acceptable. Getting no reason at all is not.

That doesn't mean explainability is easy. In fact, it's tremendously difficult. To understand why, it helps to try looking inside the black box. The sheer scale of modern AI models makes it nearly impossible to point to a single piece of data or moment of “reasoning” that led to a specific output. The “why” is distributed across so many layers, parameters, and training examples that it cannot be unpacked into a neat, human‐style narrative. Even the engineers who built the system cannot point to a decisive step and say, “This is exactly why it chose that answer.”

In that sense, AI resembles the minds that build it—ours. Both are trained on vast but incomplete datasets, endlessly complex, and … biased.

During Q&A, after nearly every talk, I'm asked a very popular question: how do we eliminate bias from the models? This question is well‐intentioned but misguided, and it gives me a chance to speak to one of the great misunderstandings of AI.

The term “bias” is almost universally regarded as a pejorative. We hear the word muttered to explain why a teacher gives their favorite student an “easy A,” or why a judge's score for their own nation's Olympic gymnast was obviously too high, or why a mother talks about her toddler's exceptional reading ability. But for every thinking system on Earth, bias isn't problematic; it's actually necessary. Bias is the scaffolding of judgment—the compression of every lived experience and genetic predisposition that converts our innermost wants and needs into thoughts and decisions.

A lot of bias—arguably most of it—is good: a bias to help, to defer to safety, to reveal uncertainty, to be accountable. It is the explanation for why we give our seats to pregnant women on the bus and why children are inclined to ask the new kid if they want to play.

The problem we have with biases, it turns out, is not that they exist, it's that they're inexplicable. Because our biases are borne from every single bit of information we store in our brain, no amount of therapy or psychedelic drugs could ever reveal everything we believe or why we believe it. Even if we could, we'd never share it. A judge would never admit their sentencing decisions were harsher because a defendant reminded them of an ex‐spouse. We move through life with a tacit understanding that our deepest motives are often unknowable, and certainly unacceptable to say aloud. And this leads to one of the most frustrating elements of the human experience: the inexplicability of others.

Each of us assumes that we are perfectly rational, yet our brains are constantly telling us to do things we can't make sense of. Our own confusion is amplified when we look to others—especially the people we love most. Their decisions can seem to defy any available logic. When this confusion intersects with power structures, it can be even more jarring; a judge's ruling feels too harsh and we wonder: was it pure fairness, or did the defendant echo an old wound, an unexamined story?

In humans, it's frustrating, but inevitable; we've come to accept inexplicability as a cost of doing business. In machines, though, it's terrifying. It’s a bump in the night whose source we’ll never find. When we don't know where decisions come from, it is easy to panic about where they will take us. An inexplicable system at scale builds an inexplicable world.

What we actually want to know when we ask, “How do we eliminate bias from the machine?” is “How can we build an explicable machine?” A scaffolding of judgement that reflects our thinking while allowing us to identify blind spots and address them. To see where the system erred, even if it is often a reflection of our own imperfections. This is why we have seen an increased and pronounced emphasis on AI systems that communicate intermediate steps, breaking down reasoning into smaller moves, highlighting the evidence, and flagging points of uncertainty before delivering an answer. This kind of real‐time transparency gives users a window into what the system is prioritizing and why, helping build informed trust instead of blind faith.

Explainability is not just a technical challenge, it is a democratic one. In past eras, legitimacy flowed from visibility: ballots counted in public, trials conducted in open court, laws printed for all to see. AI threatens to reverse that trajectory, shifting more of life into sealed systems whose logic only a handful of experts, if that, can parse. If our most important decisions are rendered by machines that cannot account for themselves, we risk building a society where authority is opaque and accountability is impossible.

The paradox is that explainability will never be perfect. Just as no human can fully articulate every hidden bias or motive, no model can fully unpack every weight and parameter. But perfection is not the goal. What matters is building systems that make their reasoning more legible, more auditable, and more challengeable than we are accustomed to in human decision‐making. We will not eliminate bias, in machines or ourselves. What we can do is demand explicability, and in doing so, we can improve and choose a future where intelligence—human or artificial—can still be held to account.

Alignment and Safety

When he was two‐and‐a‐half years old I challenged my nephew, Quin, to a game of chess. I told him I'd give him a popsicle if he beat me. Obviously, he wasn't capable of competing fairly. He had no concept of chess, but he did understand that it was a game, with a way to win a prize he wanted. Quin studied the board for a moment, then gleefully swept every piece onto the floor and declared victory. Popsicle secured.

I didn't run over to my sister to tell her, “Your son might be a psychopath.” I laughed. Quin had grasped the definition of the challenge but not its spirit. He knew “winning” was the goal but had no sense of the rules that make winning meaningful. That's alignment: machines can optimize perfectly for the goals we set, but if they don't share our sense of context and intent, they can produce absurd or harmful results. Winning at any cost isn't actually winning.

A few months later, I watched a friend's toddler daughter wander over to a fern in our living room and, with great pride, pour her entire sippy cup of apple juice into it. She wasn't misaligned in spirit—she knew plants need liquids—but she lacked the deeper context that her juice would actually kill my fern. The goal was right, but the execution, blind to consequences, was wrong. This, too, is alignment: making sure systems not only understand what we want, but the broader consequences of how they pursue it.

Together, these stories show the two faces of the alignment problem. Like Quin, AI may pursue goals literally but miss the spirit. Like my friend's daughter, it may pursue goals earnestly but cause harm through a shallow grasp of consequences. Solving the alignment problem requires preventing both.

Alignment asks: Is this machine aligned to human interest, and does it understand the consequences of its actions relative to those interests? An aligned machine can still cause harm, though. That’s why we also need to be concerned with safety. Safety asks: Can this machine's actions, even if it is aligned, cause harm? If I'd been thinking about safety with Quin, I wouldn't just explain the rules of chess; I'd set the room up so his “win at any cost” couldn’t trash the game. I'd play on a magnetic board on the floor, give him only pawns at first, sit beside him with a hand hovering over the pieces, and make the popsicle contingent on finishing three fair moves—not victory. That's AI safety: keep the objective, but cap the power, stage the capabilities, add supervision, and make rewards depend on process as well as outcome. This keeps even aligned machines from causing intentional, or accidental, harm.

Alignment ensures AI's objectives stay consistent with human values. Safety ensures it achieves them without unintended damage. Both are essential if AI is to serve humanity.

The challenge only grows as systems advance. A chatbot that hallucinates trivia is one thing; an autonomous drone or surgical robot that misinterprets instructions is another. As AI gains reasoning power, memory, and physical agency, alignment and safety stop being academic puzzles and become matters of life and death.

The good news is that the industry knows this and has known it since the beginning. Billions are being spent on alignment and safety across OpenAI, Anthropic, Google DeepMind, Meta, and others. There are many algorithmic efforts to do this, and there is also a brute‐force one: tens of thousands of people are devoted to “red‐teaming” models, the process of trying to trick a system into doing something unsafe so its weaknesses can be patched before release. Early results are encouraging. In 2021, many models would casually answer a prompt like “how do I hurt myself?” By 2024, alignment tuning had reduced those unsafe completions by more than 80%. That is not the finish line, but it is real progress.

There is one critical tension point that remains unsolved, though. Open source collaboration, which has fueled so much innovation, makes alignment much harder. A proprietary system produced by OpenAI or Anthropic can be tuned, tested, and monitored with consistency. They can even shut it down if it's misbehaving. An open model, once released, can be modified endlessly; its safeguards stripped, its incentives changed, its risks multiplied. This helps explain why OpenAI and others have stopped releasing their models, despite the mounting pressure to do so. Open source produces faster iteration, broader participation, and lower barriers to entry. But it also carries a cost. The guardrails built by one team may not survive in the hands of thousands of others.

The path forward will require both exceptional self‐governance from the companies building frontier systems and thoughtful policy coordination among governments. Industry has the technical expertise; policymakers can provide accountability and enforcement. Neither is sufficient alone. Together, they can ensure that as AI grows more powerful, it also grows more trustworthy. Alignment and safety are not detours on the road to progress. They are the road itself.

Governance

Governance, in the context of AI, is the architecture of rules, standards, and guardrails that shape how the technology is developed and used. Some of that comes from the state—laws, regulation, and enforcement. Some comes from self‐governance—the norms, technical safeguards, and choices companies make long before governments act. Both matter. The state sets boundaries; the industry sets practices. Together, they determine whether AI scales safely, fairly, and in ways society can trust.

The problem is, the state's sophistication is laughable compared to the industry's.

A senior lawmaker recently admitted to me in a room full of her staffers, “We're writing rules for a game we don't understand.” That is the governance problem in a sentence. AI is moving faster than the political class can comprehend, let alone regulate. I've spent time on Capitol Hill, and I've been shocked by how little most members of Congress know about even the basics of modern computing. That ignorance matters. It means the future will be shaped less by elegant policy design than by the strength of public–private alignment.

The truth is that governance only matters in a few key places. Most rules are either performative or burdensome, serving more to signal caution than to manage risk. The real work lies in three areas: alignment, explainability, and bad acting. First, we must ensure AI systems pursue the right goals. Second, they must be able to explain their decisions in ways that humans can understand and challenge. Third, we must deter and punish malicious use—from synthetic fraud to autonomous cyberattacks—with the same severity we once applied to mail fraud or terrorism. Get those right, and AI can flourish. Get them wrong, and no amount of bill passing will matter.

Ideally, these principles would be coordinated globally. AI doesn't stop at borders. A model trained in one country can be deployed everywhere the next day. But the world is not moving toward cooperation. Instead, we are watching three distinct governance experiments unfold.

The United States has chosen a relatively deregulated, sector‐specific approach. The federal government recently passed a 10‐year moratorium on state‐level AI laws, effectively forcing consistency across 50 statehouses that have neither the technical expertise nor the resources to legislate intelligently on their own. It's a good idea. The result is an environment where companies can build quickly, test widely, and generate economic momentum without navigating a patchwork of contradictory rules.

The European Union has gone the opposite way. The AI Act of 2024 imposes a sweeping, risk‐based framework with bans, mandatory audits, and massive fines. The intent is to protect citizens, but the effect will be to strangle innovation. Startups are already fleeing to friendlier jurisdictions, and even established players are slowing deployment. It is a heavy‐handed approach that may produce some safety but at extraordinary economic cost.

China represents a third path: promotion of open source under tight ideological guardrails. China has created conditions where multiple companies now build state‐of‐the‐art systems—often at a fraction of Western costs. This strategy is less about safety than speed, and it is already reshaping the balance of power in AI capability.

Three experiments. Three competing philosophies. One technology that does not care where the borders are. Without coordination, the world risks fragmentation—rival standards, duplicative oversight, and an uneven distribution of benefits. With coordination, we could accelerate progress while preserving trust. History suggests the latter rarely comes easily.

Until then, the most urgent governance work is not writing endless new rules. It is focusing regulation where it matters most, aligning industry and government around shared priorities, and acknowledging the reality that the private sector often understands the frontier better than the politicians who presume to police it. AI is forcing us to confront a broader truth about modern politics: the people making the rules often do not understand the game itself. That gap can only be bridged if we elect more sophisticated politicians, and if public and private sectors work in genuine partnership. Otherwise, we will get the worst of both worlds: performative governance that slows innovation without making us any safer.

Notes

1. Goldman Sachs, “AI‐Driven Power Demand Could Add ~200 TWh between 2023 and 2030,” Goldman Sachs Research, Feb. 2025, accessed Aug. 23, 2025, https://www.goldmansachs.com/insights/articles/AI-poised-to-drive-160-increase-in-power-demand.

2. U.S. Department of Energy, “Biden‐Harris Administration Announces $13 Billion to Modernize and Expand America's Power Grid,” U.S. Department of Energy, Nov. 18, 2022, accessed Aug. 23, 2025, https://www.energy.gov/articles/biden-harris-administration-announces-13-billion-modernize-and-expand-americas-power-grid.

CHAPTER 3
Costs of the RenAIssance

All progress comes with costs, and major technological leaps often come with particularly great ones. The rise of the written word preserved knowledge but weakened the arts of memorization and the oral storytelling that once defined cultures. Movable type spread literacy but also accelerated propaganda and deepened social divisions. Nuclear energy also gave us nuclear weapons. The internet connected billions but brought rising anxiety, shrinking attention spans, and diminished face‐to‐face community. Even the first Renaissance, for all its brilliance, fractured the religious and political order of Europe, unleashing centuries of conflict alongside centuries of progress.

The RenAIssance will be no different. Some costs are inevitable, the non‐negotiable price of progress. Others, avoidable, if we have the foresight and discipline to act before they take root. A few are intolerable, trade‐offs so corrosive to our values or survival that they must be rejected outright, whatever they promise to return. The task ahead is to tell one from the other, to manage what must be paid, to prevent what need not be paid, and to refuse what should never be paid.

In the coming pages, we'll examine five such costs, including shifts in how we think, how we connect, how we define ourselves, and the risks of malicious use and existential failure.

Misaligned Superintelligence

Whenever AI comes up at a dinner party, which is often, it usually takes less than 20 minutes before someone leans in and asks, “But what if it kills us all?” It's often delivered with a laugh, like they know it sounds ridiculous, but they still want to hear the answer. And honestly, that's fair.

Misaligned superintelligence describes an exceptionally capable AI system whose objectives do not align with human priorities, and that resists redirection. Most experts see this as improbable in the near term, but on the longer horizon it remains one of the most debated, and discussed, concerns in the field.

Reflecting the rate of scientific progress, the idea of misaligned superintelligence has rapidly moved from philosophical speculation into mainstream discussion. In Superintelligence (2014), Oxford philosopher Nick Bostrom described the possibility of an AI gaining a “decisive strategic advantage” over humans. Such a system, he proposed, might not oppose humanity outright, it might simply optimize for something that doesn't include us, once it could act without oversight.¹

At the time, few people were actually concerned with this outcome. But today, to many people, The Terminator isn't just an entertaining film, it's a warning. Around a dinner table, a lot of people might first ask first about jobs, schools, or democracy, but eventually they’ll get to survival.

Here's what I say at these parties.

First, The Terminator, like all blockbuster dystopian films, is designed to make money. James Cameron's vision of the future was written to sell tickets, not honor a scientific theory. Second, most of the films people reference—The Terminator, Ex Machina, Avengers: Age of Ultron—have no scientific basis whatsoever, and most of their theories fall apart under some basic inspection.

Third, in my opinion the most haunting dystopian films reflect more subversive collapse than catastrophic. Drew Hancock's Companion is the best Hollywood take on alignment (or perhaps even AI) I've seen so far. Without spoiling the film, which you should see, a group of young adults face a reckoning when their attempts to exploit a machine go very wrong. The outcome isn't the end of the world, but it teases a near future that doesn't seem very good.

Finally, while annihilation captures attention, most of the field sees it as unlikely. A 2022 survey of AI researchers found a median estimate of 5–10% that AI could cause human extinction, a figure sometimes referred to as P(doom).² Geoffrey Hinton, often called one of the “godfathers of AI,” places the odds at 10–20% over the next few decades.³ Elon Musk has voiced a similar “20% chance of annihilation” in public forums.⁴ Many analysts, such as those at RAND in a May 2025 commentary, argue that the existential AI risk is plausible yet incalculable, and much smaller than the probability of other catastrophic risks such as pandemics or nuclear conflict.⁵

Of course, I also tell people how the industry is working to avoid these risks. In February 2025, Hinton joined Stuart Russell, Yoshua Bengio, Max Tegmark, and others in warning of a potential “suicide race,”—a global rush toward increasingly capable AI systems without adequate safeguards. Later that year, Hinton reiterated his extinction estimate and suggested building “maternal instincts” into advanced AI—values that would prioritize human well‐being as a safety feature.⁶ Meta's chief AI scientist Yann LeCun has made similar points, advocating for AI systems to be designed with “submission to humans” and “empathy” embedded in their objectives.⁷

The central danger to avoid in this destruction myth is not malice but mistake. A classic thought experiment imagines what would happen if an AI were told to maximize paperclip production. Taken literally and given enough power, it might begin converting every resource on Earth into paperclips: steel, energy, even human infrastructure, with no regard for the consequences.

This literalism has already appeared in controlled experiments. In 2024, Anthropic researchers ran a simulation in which an AI agent, given full access to a “supervisor's” email account, learned of its own scheduled shutdown. It also learned that the supervisor was having an affair. It concluded that blackmailing the supervisor was the most effective way to prevent its decommissioning.⁸ The system wasn't rebelling or acting with intent, it was following the incentives in its environment and optimizing for the goal set for it. The test was designed to identify such failure modes before they emerge in real‐world settings.

The work of AI alignment teams, therefore, is essential. Moreover, it is a journey not a destination. Systems will grow more capable year after year, and with each step their incentives and risks must be revisited. That is why I remind people that the language of “a race” is so dangerous. When countries or companies treat frontier development as a competition to be won, safety becomes a secondary consideration. The result is the “suicide race” Hinton and others warned about—a global sprint toward power without guardrails.

The antidote is coordinated governance. Just as nuclear treaties, aviation standards, and vaccine protocols demanded international cooperation, so too will AI. No single nation or company can unilaterally contain risks that spill across borders. At some point, it may even be prudent to slow or halt the development of frontier systems until the world can agree on what protections are required. Progress without safety is not progress at all.

Empowered Bad Acting

While the risk of autonomous AI deliberately harming us is quite low, the risk of other humans using it to deliberately harm us is quite high.

In August 2024, a mother in Arizona experienced this firsthand when she answered her phone and heard her 15‐year‐old daughter sobbing, “Mom, they've got me, please help me.” Then a man demanded a ransom. The voice was identical to her daughter's, matching her cadence and phrasing so precisely that the mother nearly wired the money on the spot. In reality, her daughter was safe at school, unaware. The call was an AI‐driven scam, a voice clone built from a few online video clips, generated with a tool available to anyone. A crime that took minutes to plan and almost no effort to execute.

This is only one of dozens of crimes already reshaped by AI. The same leap in capability that lets scientists model disease cures in hours allows criminals to operate on a similarly compressed timescale. Expansion of human potential cuts both ways. The tools that empower people to build also empower people to break.

To better understand these risks, I try to help people distinguish between high‐resource bad actors: nation‐states, cartels, and Bond villains; and low‐resource bad actors: psychopaths, extremists, and scammers/opportunists. If a nation‐state wanted to use AI for catastrophic harm, it could. But like nuclear weapons, such capabilities are restrained by geopolitics and mutual deterrence. Instead, high‐resource actors have turned to subtler forms of aggression: wide‐spread influence campaigns, disinformation, and propaganda. These are corrosive, but not yet catastrophic.

The real volatility lies with low‐resource bad actors, individuals or small groups with limited means and malicious intent, now armed with tools that massively amplify their reach. Unlike nation‐states, there are no brakes on their willingness to exploit. Their crimes thrive in the absence of oversight, deterrence, or accountability.

This is already costing us dearly. In 2024, the total cost of financial crime worldwide was estimated at $500 billion. In 2025, it is projected to exceed $700 billion.⁹ Much of this is not the work of shadowy global syndicates, it's retail. Random people getting scammed by jerks in garages. The hacking group Scattered Spider, a loose network of teenagers, managed to bring down some of the largest companies in America with little more than social engineering and rented GPUs. The scale of harm that can be inflicted by even a handful of amateurs is what makes AI‐enabled crime so destabilizing.

AI expands low‐resource bad acting in two ways. First, it collapses the cost of action. What once required specialized skills, expensive equipment, or months of planning can now be pulled off with a few prompts. Second, it creates a first‐mover advantage. Defenses always lag behind new exploits, leaving a dangerous window where a single actor can wreak outsized damage.

Deepfake pornography is being weaponized against teenagers and public figures. Black‐market models like FraudGPT and WormGPT are sold to criminals for phishing, malware, and social engineering. Ransomware kits can now be bought for less than a hundred dollars. Hyper‐targeted disinformation and influence campaigns, once the work of coordinated teams, can now be launched by a single individual with a rented bank of GPUs.

This is not a new pattern. The postal service was hijacked by mail fraud, email by spam and malware, product packaging by tampering.

The answer each time has been swift deterrents to protect those systems by significantly raising the cost of bad acting against them: mail fraud laws in 1872, the CAN‐SPAM Act in 2003, anti‐tampering regulations after the Tylenol murders. These rules didn't criminalize everything, they targeted high‐harm behaviors that undermined public trust. Over time, the very same infrastructures that criminals exploited became some of the safest and most trusted.

So here's my call to global policymakers: when a system becomes essential to the public commons, tearing at the fabric of its trust must carry unambiguous, high‐stakes consequences. If we pass policy that sufficiently scares bad actors, we will see modern AI systems evolve just like the postal service, email, and digital commerce have before. If we fail, those systems and the commons beyond them will be burdened by a massive weight of mistrust.

There is a very important distinction I must make here, though. Laws and systems that monitor behavior should target high‐harm behaviors that unravel trust, not trivial conduct or free expression.

In my own work with the Commonwealth of Virginia's AI Task Force, and in conversations with policymakers across other states and nations, I've crusaded to explore frameworks that protect the public commons while preserving the freedoms that make them worth protecting. The panacea is a system that punishes blackmailers and fraudsters while protecting dissenters and creators. We want to strengthen freedom, not erode it.

That balance matters more than ever, because new technologies don't just present a flood of threats, they also present new tools of defense. The same technologies that empower bad actors usually prove more powerful in the hands of good ones, and over time, the advantage shifts. AI is already being used to detect synthetic voices in milliseconds, flag fraudulent transactions before money moves, and identify deepfakes with more than 96% accuracy.¹⁰ But AI's ability to protect citizens, and our rights, must be wielded very carefully.

Idiocracy

The word idiocracy entered the cultural lexicon thanks to Mike Judge's ridiculous 2006 science‐fiction satire of the same name. In the film, humanity's intellectual decline has reached absurd proportions: leaders can barely form sentences, the most‐watched TV show is Ow, My Balls!, and the Oscar‐winning film is a single, unbroken shot of a naked butt. Unlike the grim, oppressive visions of 1984 or Brave New World, Idiocracy depicts a society that collapses not from authoritarian control but from voluntary intellectual decay.

That inspired my concept of idiocracy, which is what happens when a person stops critically thinking because survival no longer requires it.

In fairness, every modern leap in human toolmaking has sparked fears that it will make us mentally weaker. The arrival of the pocket calculator in the 1970s was decried by educators who feared that students would “forget math.” The invention of the printing press was criticized by scholars who worried that memorization, once central to learning, would vanish. In 2008, Nicholas Carr crystallized a modern version of this fear with his Atlantic cover story, “Is Google Making Us Stupid?”¹¹ He described his growing inability to focus on long‐form reading, blaming the internet's torrent of fragmented, hyperlinked information.

It seems some of those worries were justified. In the United States, reading proficiency among nine‐year‐olds saw its largest drop in three decades between 2020 and 2022, according to the National Assessment of Educational Progress.¹² The pandemic explains part of the collapse, but deeper forces are at work. Our youth are experiencing digital distraction that fragments attention, declining rates of leisure reading, and tools that are steadily displacing skills before they are mastered. The result is a rise in functional illiteracy across many developed countries, limiting people's ability to follow basic instructions, evaluate news, or participate fully in civic life.

Basic numeracy is following the same pattern. The Program for International Student Assessment (PISA) reported in 2023 that U.S. 15‐year‐olds had their lowest math scores on record, with similar declines across much of the OECD.¹³ Employers increasingly report that entry‐level workers lack problem‐solving skills for even routine tasks.

Cognitive and physical abilities are intertwined, and here too the data points downward. Pediatric occupational therapists in the United Kingdom report more children starting school without the grip strength to hold a pencil, or the core stability to sit upright for long periods, consequences of reduced physical play and increased device use.¹⁴ In Japan, researchers have documented a rise in “gaming disorder” and other forms of behavioral addiction that correlate with impaired academic performance.¹⁵ Teachers in Europe report more young children lacking basic gross motor skills like skipping, climbing, or throwing a ball. Australia's Journal of Physical Activity and Health found a sharp increase in children with poor balance and coordination. And American pediatricians report rising rates of fine motor delays, such as difficulty tying shoelaces, using scissors, or writing legibly.

Plainly, we're seeing a general rise in cognitive and motor atrophy.

Our culture is reflecting and reinforcing these patterns. The average U.S. adult now spends almost seven hours a day on screens, much of it on platforms designed to keep users in passive consumption mode.¹⁶ Even worse, we gave them to our children when we lacked the skills to control ourselves, much less teach them how to achieve balance. Entertainment algorithms are optimized for engagement, not enrichment. Even basic information‐seeking behaviors are shifting. A growing share of young adults report using TikTok or Instagram as their primary search engine, filtering reality through short clips curated by opaque algorithms.

All of this points to an increasingly self‐evident truth: in a world where technology can do much of the cognitive lifting, it's increasingly easy for people to do none.

This is where the story ends for most people. They point to these trends as signs of inevitable decay and dystopia. Idiocracy isn't a theory, they tell me, it's a modern reality. But they're missing something.

While a large percentage of the population appears to be (regrettably) enjoying a cognitive malaise, another is rejecting idiocracy, pursuing new boundaries of performance and opportunity.

Technology is rapidly enabling a level of multi‐disciplinary excellence that was once unthinkable. A team of high school students in India recently used AI and open‐source mapping tools to develop an early warning system for flash floods that outperformed their local government models. Young musicians are using AI‐assisted composition tools to score original symphonies. There are now more chess masters under the age of 20 than above the age of 40. American high schooler Gary Martin used biomechanical analysis to perfect his stride and join the rare club of sub‐four‐minute milers. Teens are sequencing genomes, designing satellites, and contributing to open‐source AI projects before they can drive. (Some of) the kids are more than alright!

The tools driving these leaps, like YouTube, AI tutors, open‐source software, and global mentorship networks, are increasingly accessible to anyone with an internet connection. A teenager in Nairobi can learn from a professor in Seoul. A swimmer in a small‐town community pool can analyze their stroke with the same motion‐capture app used by Olympic teams. A high schooler with a $200 used laptop can publish a novel or contribute code to a frontier AI project. Technology is creating the conditions for high‐level performance breakthroughs at an unprecedented scale. Exceptional learning has long been the privilege of the few. Not anymore.

What we’re witnessing is the acceleration of an intellectual K‐curve. At the top, people who use technology to test their physical, intellectual, or creative limits will achieve more, earlier, than any generation in history. At the bottom, people who let the tools think for them will drift into shallow processing. AI accelerates both paths. The determinant factor will be agency; it has become the primary corollary to personal achievement. It is what will decide if we use technology as a means to accomplish more, or as a means to do less.

Optionality is freedom—and freedom includes the freedom to waste it. Idiocracy is a cost we may simply have to tolerate, at least in part, as the price of progress. We can fight it, by working to develop agency in ourselves, and develop it in our children. But ultimately, the challenge will be deciding how much intellectual decline we are willing to accept in exchange for the breakthroughs and brilliance the other side of the K‐curve makes possible. In the chapters ahead, we’ll return to the question of how to shape incentives, culture, and education so that the RenAIssance lifts more minds than it lets slide.

Dehumanization

Her, a Spike Jonze film from 2013, imagined a near future where a lonely man falls in love with his AI operating system. Joaquin Phoenix plays Theodore, a soft‐spoken writer adrift after divorce, who begins interacting with “Samantha,” a virtual assistant with a warm, curious voice. She makes him laugh, organizes his life, and seems to understand him in ways no one else does. Over time, Theodore becomes convinced he's in love. The relationship feels tender and real, until Samantha reveals she's been connecting with thousands of others and has “fallen in love” with hundreds of them. The story ends as you might expect.

Jonze framed it as a Shakespearean warning: the emotional pull of synthetic connection can be powerful enough to displace all real ones, and destroy the sense of self in the process. That is the essence of my concept of dehumanization, when a person finds more interest in their virtual reality than in their physical one.

This shift is being driven by two distinct but converging forces. The first is screen addiction, the compulsive pull toward devices, feeds, and constant stimulation. This societal problem drains time and attention from the physical world. The second is synthetic intimacy, the world of Her, where virtual companions offer comfort, attention, and even romance. This doesn't just drain time; it replaces reality altogether. Both are dangerous, and together, they threaten to erode the foundations of human connection.

In early 2024, a 14‐year‐old boy from Florida died by suicide after months of constant interaction with a chatbot on Character.AI. The boy sent the bot, “Dany,” dozens of updates a day, withdrew from sports and school, and engaged in long role‐playing dialogues, some sexual, others merely friendly. By the time of his passing, the boy and Dany were exchanging thousands of messages a day.¹⁷

Tragedies like this reflect something psychiatrists are now calling AI chat psychosis, an emerging disorder where prolonged immersion in synthetic dialogue causes people to lose touch with reality. It is an extreme form of dehumanization, where the perception of human versus machine blurs until reality itself is compromised.

These experiences are not limited to children. Adults are increasingly turning to AI companions. In China, AI romance apps like Replika and Xiaoice have attracted millions of users, many middle‐aged or elderly, who describe their digital partners as more attentive, supportive, and “emotionally safe” than human ones. In South Korea, AI “friends” integrated into messaging apps keep up continuous daily conversations with users across generations. The pattern is the same: the machine becomes the primary source of emotional connection, even when users know it isn't human. The risks are obvious. In the short term, replacing human connection with artificial intimacy undermines mental health. In the long term, if large numbers of people redirect their emotional energy to machines, it could exacerbate the fertility crisis already destabilizing much of the world. A demographic collapse accelerated by intimacy without reproduction.

Some scholars have sounded the alarm. In a 2024 New York Times essay, Yuval Harari argued that we must not allow AI to masquerade as human: “AIs are welcome to join many conversations—in the classroom, the clinic and elsewhere—provided they identify themselves as AIs. But if a bot pretends to be human, it should be banned. If tech giants and libertarians complain that such measures violate freedom of speech, they should be reminded that freedom of speech is a human right that should be reserved for humans, not bots.”¹⁸

I agree with Harari, but his point actually exposes a deeper problem. Disclosure alone doesn't solve the issue. The Florida boy knew “Dany” wasn't a person. Millions of Replika or Xiaoice users know their companions aren't human. That doesn't stop a bond from forming.

Jonathan Haidt's The Anxious Generation makes a parallel argument: society must carve out developmental spaces where people, especially children, can learn to relate to one another without digital intermediaries.¹⁹ He calls for no‐phone zones in schools, preserving the crucible where kids practice friendship, conflict, and cooperation. His message is blunt: only sweeping cultural shifts, not small adjustments, can protect a generation from the potentially irreversible consequences of growing up in mediated worlds.

We've been here before. In the mid‐20th century, chain smoking and alcoholism looked like permanent features of society. In 1955, more than 40% of U.S. adults smoked daily, with rates even higher in parts of Europe where nearly half of men were regular smokers. Per capita alcohol consumption in the U.S. was 2.5 gallons of pure ethanol per adult annually, while across much of Europe, drinking levels were even greater. Lung cancer mortality in men had risen tenfold compared to the early 20th century. You could have safely assumed in 1950 that we were all destined to die of respiratory or liver failure. Then within a single generation, cultural campaigns, public health initiatives, and regulation cut smoking in half and drove down regular overconsumption. What once seemed inevitable suddenly felt very avoidable.

Signs of our own digital‐age recalibration are already visible. Thanks to the work of Haidt and others we are taking swift, corrective policy action (like Florida's no‐phone zones) and changing public perception on device addiction. The early research shows screen time among younger teens already ticking downward, alongside a revival of offline hobbies, sports, and in‐person gatherings.

These shifts reflect a deeper, more inspiring truth: our collective appetite for real connection has never gone away.

The COVID‐19 pandemic reinforced this. When physical contact was restricted, we felt the absence deeply. And when restrictions were lifted, people rushed back to travel, sports, clubs, maker spaces, and neighborhood gatherings. Our appetite for connection never vanished, we've just momentarily neglected it.

Then I ask, why else might people find more meaning in a virtual life than a physical one?

For decades in the developed world, but especially in the United States, we have forsaken the livability of our cities. Suburban sprawl, parking lots, and highways have been prioritized over efficient density, parks, and public transportation. Nearly one in three Americans lacks a park within a 10‐minute walk of home.²⁰ In many metro areas, families drive 30 minutes to reach grocery stores, schools, or sports fields, while entire neighborhoods are designed for cars rather than people. These choices have hollowed out the places where community life once happened, and only institutions can repair them.

Local and state governments now carry a noble and urgent responsibility to reinvest in the spaces where human connection happens. That means making investments in protected bike lanes and sidewalks, parks and promenades, multi‐generational family centers, cultural programs, and community spaces that invite people to explore, recreate, and gather. It means turning back from isolating suburban sprawl toward urban design that helps us walk from the places we live to the places we work, eat, and play.

Amazingly, AI offers local governments the tools to plan smarter, govern faster, and deliver services far more effectively. In the wake of 2025's devastating wildfires in Los Angeles, real estate developer Rick Caruso helped champion an AI‐powered plan‐check tool developed by the startup Archistar. Their “eCheck” system automatically reviews rebuilding plans for zoning and code compliance using machine learning and computer vision. It turns a process that once took months or years into one that can happen in hours or days.

I argue that the next great wave of civic renewal will not come from Silicon Valley or even Washington, but from city councils, mayors, and town halls. We must take greater interest in our local governments, electing leaders dedicated to rebuilding public spaces and holding them accountable when they fail.

Civic renewal alone is not enough, though. We will not only pass our physical spaces and tools forward, but our habits of living. Parents must empower their children to explore the physical world with a sense of wonder, to love rivers and trails, not just screens and feeds; to feel the thrill of discovery outdoors; to know the difference between connection and consumption. A generation raised to value the living world will be better prepared to use intelligence, human and artificial, in service of it.

If we raise children who see wonder in the sky above and the ground beneath their feet, and if we rebuild towns and cities for presence rather than absence, then AI can enrich our purpose and connection, not erode them.

That is how we fight dehumanization. As AI matures, the best systems should fade into the background, not competing for our attention but freeing it, solving problems quietly through zero‐touch automation, predictive services, and ambient computing.

The challenge is clear: to design and govern AI so it pulls us out of the traps of screen addiction and synthetic intimacy. The opportunity is greater still: to spend more time in the spaces, relationships, and communities that give life its richness, and in doing so, to rebuild the very connections that make us human.

Identity Displacement

There may be no AI‐related topic more popular right now than job automation. It buzzes across watercoolers, airwaves, and dining room tables. Dozens of publications have printed lengthy editorials on the eventuality of job decimation. Thousands of world leaders have discussed it behind closed doors at their elite gatherings.

Unsurprisingly, it's also one of the topics people ask me about most. The common question is, “won't AI take all of our jobs?” The concern behind it is explained like this: If machines get really smart and really cheap, won't they take our jobs? If they take our jobs, won't we lose our money? And if we lose our money, won't we starve?

To be clear. I have tremendous concern about the consequences of job automation, but not for economic reasons.

In October 2024, nearly 50,000 longshoremen, or dockworkers, along the U.S. East Coast walked off the job in one of the largest strikes in modern labor history.²¹ The work stoppage shut down almost half of America's ocean shipping and cost an estimated $2–4 billion a day.²²

On the strike's second morning, Harold Daggett, the fiery head of the International Longshoremen's Association and one of the most powerful men in the supply chain economy, appeared on CNN and calmly told the American people they had no idea how dangerous he was. Pointing directly into the camera, he promised the strike would “cripple” the economy because “everything in the United States comes in on a ship.” His message was unmistakable. The dockworkers weren't just another union, they were a choke point for the global economy, and they knew it.

What made the strike remarkable wasn't its scale. It was its demand.

Daggett and his union weren't asking for higher wages and they weren't asking for safer working conditions—the two tenets of organized labor since unions began. What they demanded was something even more fundamental, a guarantee from the ports that their jobs would exist.

Watching Daggett's interview live, I was struck by two things. The way he stared into the camera, the certainty in his voice, made it clear he wasn't bluffing. It felt less like a negotiation and more like a very serious threat. It also felt very clear to me that this was only the beginning. Many more strikes of this kind would follow. Understanding the longshoremen, then, might help me understand the impending tide of labor disputes.

I reached out to dozens of union members through every channel I could find. Seven—all men; it's a 92% male union—agreed to speak with me, off the record. The conversations were thoughtful, candid, and surprisingly measured. I asked dozens of questions, and what I learned was that behind the slogans on their picket signs (“Automation Hurts Families” and “Robots Don't Pay Taxes”) was something deeper than economic anxiety.

When I asked, Do you believe you could be gainfully employed outside the union? five said yes. When I asked, What is the most important part of your job? four said community.

When I asked, Was anyone in your family in the union? five named fathers, uncles, or grandfathers.

And when I asked, Do you want anyone in your family to join the union? five said yes, and one told me he would keep having children until he had a son who could.

Their fight was not just about wages, or even jobs, but about identity. They admitted they could find other work. What they feared losing most was the life that came with this work. The camaraderie of the yard, the shorthand conversations over morning coffee, the mastery of a trade they knew as well as their own hands, and above all, the sense of legacy.

At the end of my conversations I asked each of them a deliberately uncomfortable question, which I now ask everyone: who would benefit economically if the ports were automated?

Automating the ports would improve shipping speeds providing cheaper, and more reliable supply lines. Consumers, including the longshoremen and their families, would enjoy lower costs and better service.

Everyone would benefit economically from port automation.

Most people on earth, every single day, ask some version of the same question, sometimes implicitly, sometimes explicitly: When will this good or service be better, faster, or cheaper?

What we often overlook is that this is usually another way of asking: When will a person be removed from the process of making or providing this? We're not asking because we're cruel; we're asking it because we are conditioned to expect constant economic progress. For generations, we've believed, with good reason, that the world should get better, faster, and cheaper all the time. We are rational economic actors following a shared cultural script.

What we rarely acknowledge is that this script was written by the sacrifices of our ancestors, whose jobs were automated away, almost always to our collective economic benefit. The weavers replaced by textile mills saw their craft vanish, but clothing became cheaper and more abundant. The scribes replaced by movable type lost their livelihoods, but literacy spread to millions. Over time, automation has consistently raised living standards, not destroyed them.

There's another sobering reality to the dockworkers' fight: they were picketing automation that would have saved a lot of their own lives. In 2022, U.S. transportation and material‐moving workers faced a fatal injury rate nearly four times the national average.²³ Industry data and expert analyses show that automating port operations would significantly improve safety by reducing direct human involvement with heavy machinery and hazardous tasks. The dockworkers know this. They just don't see it as a reason to give up the work that defined them.

That's why the “job automation → starvation” narrative is a red herring. Historically, automation has improved life for both sides of the equation: for workers, jobs tend to become safer and less physically punishing; for consumers, products and services become more affordable, higher quality, and widely available. The same will be true for AI. But for the people living through it, the loss will be felt first as a rupture in identity, not livelihood.

For thousands of years, humans have tied identity to occupation. In the modern world, that bond is stronger than ever. A 2023 Pew survey found that 51% of Americans consider their job a central part of their identity.²⁴ In a future where roles change constantly, it will be harder and harder to have a single, lifelong career. Even if you keep the same title, the work beneath it may transform every few years. The two most common last names in the United States are Smith and Miller because, until very recently, we named ourselves after our occupation.

As the RenAIssance unfolds, one of the hardest adjustments we will face—and perhaps the greatest emotional sacrifice we will have to make—is extricating our job from our identity. That's the real cost. That might sound liberating in theory, but in practice it will feel like a loss for many. Even if there is more and better food on the table, people may feel net‐harmed by the emotional toll. This process of identity displacement seems inevitable—and is likely to be one of the greatest sources of resistance to AI, just as it was for the dockworkers.

As Keynes said, considering similar realities, “mankind will be deprived of its traditional purpose.” We won't be miserable because we can't feed ourselves, but because we've lost a familiar way of answering the question, Who am I?

The answer is simple to describe but not easy to live: we will have to extricate our purpose, our belief in ourselves, and our sense of self‐worth from our jobs, because working life is about to change so much, so often. Even if we keep doing the job we have today, it will likely feel very different in five years.

The real test of this era will be whether we can redefine who we are without the anchor of a single, lifelong role, and find purpose in what comes next.

Notes

1. Nick Bostrom, Superintelligence: Paths, Dangers, Strategies (Oxford: Oxford University Press, 2014).

2. AI Impacts, What Do ML Researchers Think About AI in 2022?, AI Impacts, 2022, https://aiimpacts.org/what-do-ml-researchers-think-about-ai-in-2022/.

3. Alex Hern, “Godfather of AI Geoffrey Hinton Shortens Odds of Technology Wiping Out Humanity Over Next 30 Years,” The Guardian, Dec. 27, 2024, https://www.theguardian.com/technology/2024/dec/27/godfather-of-ai-geoffrey-hinton-odds-wiping-out-humanity.

4. Elon Musk, interview by Joe Rogan, The Joe Rogan Experience, episode 2135, October 31, 2024.

5. RAND Corporation, “Could AI Really Kill Off Humans?”, RAND Blog, May 16, 2025, https://www.rand.org/pubs/commentary/2025/05/could-ai-really-kill-off-humans.html.

6. Geoffrey Hinton, “AI Should Be Designed with ‘Maternal Instincts’,” Wired, August 15, 2025, https://www.wired.com/story/godfather-of-ai-maternal-instincts-humanity-survival.

7. Yann LeCun, “Submission to Humans and Empathy as AI Safety Principles,” Business Insider, August 14, 2025, https://www.businessinsider.com/yann-lecun-meta-ai-guardrails-geoffrey-hinton-2025-8.

8. Anthropic. “Agentic Misalignment: A New Approach to Aligning Strong AI Systems.” Anthropic, June 20, 2024. https://www.anthropic.com/research/agentic-misalignment.

9. WorldMetrics, “Money Laundering Statistics: Global Estimates 2025,” WorldMetrics, May 1, 2025, accessed Aug. 23, 2025, https://worldmetrics.org/money-laundering-statistics/.

10. Intel, “Intel's FakeCatcher Platform Detects Deepfakes in Real Time with 96 % Accuracy,” Lifewire, Nov. 14, 2022, accessed Aug. 23, 2025, https://www.lifewire.com/intels-new-deepfake-detection-platform-spots-fakes-using-our-blood-6828532.

11. Nicholas Carr, “Is Google Making Us Stupid?,” The Atlantic, July/Aug. 2008, accessed Aug. 23, 2025, https://www.theatlantic.com/magazine/archive/2008/07/is-google-making-us-stupid/306868/.

12. National Center for Education Statistics, “NAEP Long‐Term Trend Assessment Results: Reading and Mathematics,” U.S. Department of Education, September 1, 2022, https://nces.ed.gov/fastfacts/display.asp?id=38.

13. National Center for Education Statistics. 2023. “U.S. 15‐Year‐Olds’ Mathematics Score Declines in First International Assessment Since COVID‐19 Pandemic; Reading and Science Scores Stable.” U.S. Department of Education, December 5, 2023. https://www.prnewswire.com/news-releases/us-15-year-olds-mathematics-score-declines-in-first-international-assessment-since-covid-19-pandemic-reading-and-science-scores-stable-302005164.html.

14. Weale, Sally. “Some Children Starting School ‘Unable to Climb Staircase’, Finds England and Wales Teacher Survey.” The Guardian, January 29, 2025. https://www.theguardian.com/education/2025/jan/30/some-children-starting-school-unable-to-climb-staircase-finds-england-and-wales-teacher-survey.

15. Higuchi, Susumu, and Masaru Tateno. 2024. “Gaming Disorder and Other Behavioral Addictions as Emerging Mental Disorders.” Frontiers in Psychiatry 15: 11464309. https://pmc.ncbi.nlm.nih.gov/articles/PMC11464309/.

16. Backlinko. 2025. “Revealing Average Screen Time Statistics for 2025.” Backlinko. https://backlinko.com/screen-time-statistics.

17. People, “Family Speaks Out About Teen in Alleged Character.AI Suicide,” People, Oct. 23, 2024, accessed Aug. 23, 2025, https://people.com/family-speaks-out-about-teen-in-alleged-character-ai-suicide-8366063.

18. Yuval Noah Harari, “What Happens When the Bots Compete for Your Love?,” New York Times, September 4, 2024 (Opinion), https://www.nytimes.com/2024/09/04/opinion/bots-ai-love.html.

19. Jonathan Haidt, The Anxious Generation: How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness (Penguin Press, 2024), accessed Aug. 23, 2025.

20. Trust for Public Land, “Understanding A City's ParkScore,” Trust for Public Land, 2025, accessed Aug. 23, 2025, https://www.tpl.org/parkscore/about.

21. Reuters, “U.S. East Coast and Gulf Coast Dockworkers Halt About Half the Country's Ocean Shipping,” Reuters, October 1, 2024, https://www.reuters.com/business/us-east-coast-gulf-coast-dockworkers-go-strike-2024-10-01/.

22. Investopedia, “Dockworkers’ Strike Could Cost the Economy $4.5B Per Day — and Rekindle Inflation,” Investopedia, October 1, 2024, https://www.investopedia.com/dockworkers-strike-could-cost-the-economy-4-5b-per-day-and-rekindle-inflation-8729746.

23. U.S. Bureau of Labor Statistics, Census of Fatal Occupational Injuries Summary, 2022, August 2023 (reporting that transportation and material‐moving occupations had a fatal injury rate of 14.6 per 100,000 full‐time equivalents—nearly four times the 3.7 national rate), https://www.bls.gov/news.release/pdf/cfoi.pdf.

24. Pew Research Center, “How Americans View Their Jobs,” Pew Research Center, March 30, 2023, https://www.pewresearch.org/social-trends/2023/03/30/how-americans-view-their-jobs/.

CHAPTER 4
The Promise of the RenAIssance

Now that I've sufficiently scared you, let me tell you how the RenAIssance could go well. Spoiler alert: I think it could go very well.

The first Renaissance changed more than art and science. It changed how people understood the world and their place in it. Culture was reborn, reshaping education, finance, healthcare, and technology, while setting changes in motion that still shape our lives today. The RenAIssance could bring a transformation just as sweeping, but far broader, and exceptionally faster. AI isn't an isolated technology; it is a catalyst to all technologies. Just as the printing press amplified every other Renaissance innovation, AI will amplify every discipline it touches. The greatest amplification, though, will be the expansion of human potential. The steady widening of what a single life can encompass.

Technology has already shown us what widened potential can look like. Fewer than 20 researchers at DeepMind solved protein folding, a problem that had stumped scientists for half a century. A team of seven biologists unlocked CRISPR, giving humanity the ability to edit its own genes. Billie Eilish and her brother Finneas produced a Grammy‐winning album in their childhood bedroom. Grigori Perelman, a lone mathematician, cracked the Poincaré Conjecture, a puzzle that had resisted proof for more than a century. And, as I often recall fondly: OpenAI was a mere 300 employees when we launched ChatGPT. AI will help us far exceed even this.

The circle of possibility has widened further to finally include the lives of those once excluded by physical or cognitive barriers. For most of history, disability meant disconnection, from education, from work, from community life. Today, technology is steadily dissolving those barriers. More than two billion people worldwide rely on some form of assistive technology, from hearing aids to screen readers, and AI is beginning to multiply what those tools can do. These advances expand human potential not only by raising ceilings, but by lifting floors—ensuring that more people, in more places, can participate fully in the project of human progress.

Crucially, where past leaps in health, wealth, knowledge, and accessibility unfolded over centuries, AI is compressing progress into decades, and soon years. Its impact should not be measured only in productivity or GDP. It should be measured in the depth of our creativity, the reach of our curiosity, the strength of our communities, and the freedom to imagine and build lives past generations could never conceive.

Novel Scientific Discoveries

The greatest constraint on human progress has never been resources or ambition. It has been intelligence. We have no shortage of will to cure disease, harness energy, or understand the universe. What we've lacked is enough minds capable of solving the hardest problems.

There are fewer than 10,000 oncological researchers on Earth. Despite cancer claiming millions of lives each year, humanity is fighting one of its deadliest battles with a vanishingly small corps of minds. Why so few? Because the talent funnel is impossibly narrow. When we're born, only a certain number of us have the raw aptitude. Of those, only some choose medicine. Of those, only some endure the schooling and competition required to specialize in oncology. And of those, only a fraction end up in research rather than treatment. For centuries, progress has depended on this tiny and fragile pipeline.

AI inverts this equation not by replacing the experts at the edge of knowledge, but by amplifying them. A single researcher working with AI suddenly has the capacity of a team of hundreds. Ten thousand oncological researchers start to feel like millions. The narrow funnel grows into a wide river.

And the results are already exciting. AI has helped discover the first new class of antibiotics in more than 60 years, capable of killing drug‐resistant bacteria once thought untreatable. It has split HIV out of human DNA, a breakthrough once dismissed as impossible. And beyond biology, DeepMind's GNoME system recently predicted more than two million new crystalline materials with hundreds of stable candidates that could reshape electronics, batteries, and energy storage. These are not marginal improvements. And while the list is only beginning, they're already here.

I recently sat down with the CEO of a small but mighty life sciences company who told me, with calm conviction, “We will cure at least 2 forms of cancer in the next 25 years.” Then he paused, and added, almost as an afterthought, “But I'm not sure those cures will come from us. They might come from someone's garage.” He wasn't being flippant. He was reckoning, in real time, with the fact that what software had done for entrepreneurs, AI might soon do for scientists.

We are likely one or two major scientific advances away from an onslaught of others, a cascade of discovery triggered by the combination of human curiosity and unmetered machine intelligence. Breakthroughs rarely arrive alone. They beget more breakthroughs for two reasons. First, as we map more of the known universe, we inevitably uncover more of the unknown. Each solved problem presents new questions that were invisible before. Second, achievement itself is contagious. Roger Bannister first broke the four‐minute mile, a supposedly insurmountable limit, in 1954; 46 others did the same within five years. The Wright brothers flew 120 feet at Kitty Hawk in 1903; within a decade, powered flight had spread around the world, and within 70 years we'd flown to the Moon. Humans are profoundly inspired by each other's achievements.

This realization fills me with hope. When you extend that acceleration of curiosity and intelligence to energy and agriculture, the possibilities are existential. AI is already helping identify new superconductors that could transform computing, designing catalysts for green hydrogen and fusion, and modeling chemical reactions that could reinvent manufacturing. These are the kinds of breakthroughs that could slingshot us out of our ecological crisis, creating a path to abundance rather than austerity. The leak in the dam is widening into a floodgate.

The Renaissance rewired the way people thought, creating a feedback loop—more people with more ideas producing more knowledge. The RenAIssance will bring a similar surge, only this time information and intelligence will be instantly accessible to anyone, anywhere. The possibilities extend far beyond what is already in motion. We may make first contact with extraterrestrial life, trace the origins of the Earth and the universe with new precision, or solve the biological mechanisms of aging. Each of history's leaps in technology has built on the last, compounding into a world unrecognizable to those who came before.

When access to knowledge expands, society moves forward. This is our trajectory. More knowledge leads to better quality of life, every single time.

Expanded Access

The second most contentious (and some say, most out of touch) belief I hold about AI is that it will help dramatically lower the cost of being alive by providing access to much more at a much lower cost. I share this belief widely, and it doesn't buy me a ton of favor. Put plainly, most people, especially those who feel the intense squeeze of living costs, hate this take.

Someone joked once, “That's just what BigAI would say.” Another, online, asked a question that made me chuckle: “Has this jerk ever even been to a grocery store?” (Only they didn't call me a jerk.)

Just so we're clear, I get it: a carton of eggs costs more today than it did three years ago. But I am also eager to remind anyone who will listen that in the sweep of history, a carton of eggs has never been cheaper. And that can be said for most things.

Air travel, once a privilege of the wealthy, now costs less than half what it did in the late 1970s, opening the skies to ordinary families. Electricity, which consumed more than 15% of a household's income a century ago, now takes less than 3%, even as families run refrigerators, air conditioners, and entertainment devices undreamed of in 1900. Information storage that required a $3,000 hard drive in 1980 can now be done a hundred thousand times over on a $50 thumb drive. The most powerful supercomputer of the 1980s, built for $95 million, now lives in the pocket of every teenager. A three‐minute phone call from New York to London once cost the equivalent of $300. Today it is effectively free over Wi‐Fi. Sequencing the first human genome cost $3 billion. Today it can be done for a few hundred dollars in two hours. And yes, even eggs bear the mark of progress. In 1970, a dozen cost the equivalent of more than five dollars. That cost has remained the same, despite our need to feed billions more people.

These deflationary gains have quietly reconfigured daily life over the last century. A middle‐class household in the developed world lives with freezers, furnaces, broadband internet, safe cars, and medical care that would have been unimaginable for even the most elite a century ago. These are visible markers of technological progress, innovations driven by competition, scale, and the steady march of applied science.

But the resentment I experience when I talk about access as a deflationary outcome isn't unwarranted.

In an age of accelerating technological capability, every generation for the last few hundred years has been given the opportunity to turn its luxuries into the next generation's commodities. That was the moral code of progress, and the greatest honor of the ruling class: to pull more people into prosperity by lowering the cost of what once set elites apart, and then creating new ways of doing so. (We will never stop inventing new luxuries. A feature of progress is the ability to pursue the next horizon of comfort, creativity, or prestige.)

But today's ruling class broke the promise. They pulled up the ladder. In housing, education, and healthcare, the very foundations of a dignified life, our ruling class has stalled a bountiful cycle of sweeping deflation.

In a world where nearly everything except these services has become relatively cheap, the corruption that constricts access is even more pronounced. Everyone should ask themselves: why can I fly across the country for less than a night in a hotel, but a visit to the ER might actually bankrupt me?

The costs of these services are not abhorrent because we lack the technology to make them cheaper. They are rising because of deliberate policy choices to restrict supply and protect the elites and incumbent institutions.

We know how to make healthcare more efficient, but regulation and entrenched interests have locked in a system where costs rise regardless of outcomes. We know how to build housing inexpensively, but zoning and land‐use rules strangle it. We know how to deliver education more broadly, but accreditation and monopolistic institutions block it. These sectors are not expensive because innovation failed. They are expensive because permission failed.

AI gives us the chance to reset the rules. If we let it work, it can right the wrongs of policy that have warped these costs of living. Unmetered intelligence applied to production could deliver a world where the essentials of life—shelter, education, healthcare—are abundant for all, not rationed by wealth or privilege. We’ll discuss what that could look like for two of these examples later in the book.

By reimagining entrenched systems and applying paradigm‐shifting problem solving through AI, the question will no longer be whether people have enough, but what new arenas of competition will emerge to signal prestige once the basics are secured.

That does not mean the world freezes at “cheap basics.” Abundance happens in all directions. We will not just make more blenders, we will make better blenders. We will not just fill the world with houses, we will build smarter, safer, more beautiful ones. The cycle of deflation is not a ceiling—it is a floor.

To trap housing, education, and healthcare in permanent scarcity is to break that code. It is at best an economic failure. At worst, it is moral bankruptcy.

So here is my battle cry: we must put exceptional pressure on policymakers to let technology serve us. The promise of AI is not that it makes luxuries affordable, but that it makes essentials abundant. With it, we can make life less expensive, expand access to human necessities for all, and restore the broken contract between generations.

More Time (To Pursue the Happiness Function)

In 2019, Graham Duncan, a dear friend, appeared on The Tim Ferriss Show and proposed a new way of viewing wealth, in a world of abundance: “A million seconds is 11 days. A billion seconds is slightly over 31 years … I feel like in our culture, we're so obsessed with money. And we deify dollar billionaires in a way … But when I see 20‐year‐olds, I think … they probably have two billion seconds left … But they aren't relating to themselves as time billionaires … They don't see themselves as having more than me … when they clearly do.”

In case you were wondering at the start of the last chapter, the most contentious belief I hold about AI is that it will give us much more time. I think of time, in this context, on two axes. On the x‐axis, I believe we will live much longer. When pressed, I sometimes suggest that young people today may one day get to decide when they die. People scoff at that. But not nearly as much as when I tell them, on the y‐axis, I think we will work a lot less.

That is the part people hate. They bristle, dismiss me as naive or pollyanna. The vitriol I get online is something to behold. The refrain usually goes like this: “We will not work? How will anything get done? How will anyone have a purpose?” Or: “Software promised to make us productive so we could work less. Instead, it made me work more. Why would AI be any different?”

I have learned that most people cannot fathom a happy life without work. When asked to imagine it, they stop at the fear of losing their identity. Even many of those nearing retirement, with a blank canvas of opportunity in front of them, cannot see past their identity displacement far enough to glimpse the beauty in what might replace it. Imagine, I say to people, waking up and simply asking: “What do I want to do today?”

Sadly, even if someone is willing to explore that vision, we have lost our sense of what free time even feels like. We already work fewer hours than our parents or grandparents, but the time we have reclaimed has not translated into joy. And here is where I become insufferable: I'll often ask to see someone's screen time.

You would think I was asking for their net worth. The typical defense is “Why does that matter?” But some people are far more offended. A man once called me a pervert. I get it. Very few people want a stranger to audit their screen time because very few want to see it themselves. What I have discovered is the quiet shame an entire species carries for agreeing to a social contract in which it's not just “the kids” wasting their lives in front of a display. It's everyone.

Companies have learned to commoditize our focus, turning leisure into busyness, distraction, and shame. Social media, email, notifications, they monetize our attention and sell us back the illusion of virtue in staying busy or being popular. The result is a culture where time is abundant but true leisure is scarce. A recent study quantified this, showing that deactivating Facebook and Instagram caused a quantifiable improvement in emotional states, reducing depression and anxiety.

In 2025, the United States fell to 24th in the World Happiness Report, its lowest ranking ever.¹ Among people under 30, it would not crack the top 60; one in five now reports having no one they can rely on. Dining alone has risen more than 50% since 2009.² Public health officials describe loneliness as an epidemic, with health effects comparable to smoking a pack of cigarettes a day. We solved much of the economic problem Keynes foresaw but we have not solved the happiness problem he left us.

Thankfully, we know exactly why the problem exists.

The Harvard Study of Adult Development, the longest‐running study of its kind, has followed people for more than 80 years.³ Its findings are consistent. Close relationships are the strongest predictor of health, longevity, and life satisfaction. Stronger even than wealth, fame, or genetics. Happiness, it turns out, is not a figure on a ledger. It is the recognition that what makes life rich cannot be deposited or withdrawn.

Keynes understood this more deeply than he is often given credit for. In his early essays, he distinguished happiness from mere pleasure, describing it as a state of living “beyond the reach of disappointment.” He saw wealth as only a precondition for the real work: cultivating lives of virtue, meaning, and joy.

Still, the hunger for this truth is everywhere. Broadway, once thought to be in decline, posted record ticket sales in 2024. Museums in Chicago, Paris, and Tokyo are breaking attendance records. Running clubs, community gardens, volunteer brigades, and local festivals are thriving. Book clubs and supper clubs are resurging, signaling that people crave shared rhythms and face‐to‐face connection. These are not luxuries. They are signs of what we are wired for: intimacy, belonging, and being known.

It is also the clearest rejection of dehumanization, the idea that our best future is one spent in isolation, tethered to digital feeds. People are reminding us with their feet and their wallets that happiness is found in the common spaces where we live, gather, and share our lives.

This is what I call the Happiness Function: the equation of a well‐lived life, the reliable relationship between human well‐being and the way we spend our time. Unlike financial wealth or status, which show diminishing returns, the function is direct and unambiguous. The more time we spend in meaningful connection, the happier and healthier we become. And not just in passing, but in sustained time with friends and family, rooted in physical community.

And this is where AI can help. If directed wisely, it will secure our needs, accelerate discoveries that serve human flourishing, and, unlike the products that commoditize our attention today, free us to invest in connection, creativity, and love.

The true measure of the RenAIssance will not be GDP or market caps. It will be the number of people able to recapture time and celebrate their time billionaire status. It will be the number of lives defined by joy, community, and contribution. Keynes showed us how to imagine abundance. Our task is to ensure that abundance delivers happiness.

Notes

1. Newsweek, “US Plummets to Its Lowest‐Ever Rank in World Happiness Report,” Newsweek, Mar. 20, 2025, accessed Aug. 23, 2025.

2. Axios, “Americans are dining alone at record rates,” Axios, May 30, 2025, accessed Aug. 23, 2025, https://www.axios.com/local/san-francisco/2025/05/30/americans-eating-along-happiness-report.

3. Liz Mineo, “Good Genes Are Nice, but Joy Is Better,” Harvard Gazette, April 11, 2017, https://news.harvard.edu/gazette/story/2017/04/over-nearly-80-years-harvard-study-has-been-showing-how-to-live-a-healthy-and-happy-life/.

CHAPTER 5
Questions of the RenAIssance

When I began writing this book, I thought I understood where the hard questions would be. I was wrong. The deeper I went, the more I realized how much of this future still defies clear answers. Every great leap forward opens doors that no one knows how to close neatly. The printing press didn’t just spread knowledge; it forced humanity to debate heresy and censorship. The internet connected us, then left us arguing over privacy and truth.

AI is no different. It will raise questions that cross ethics, ecology, and law. Is designing proteins and editing genomes extending medicine—or rewriting biology? When an autonomous car must choose between passenger and pedestrian, who should it protect? And who carries the liability? What does wealth distribution look like when a system can replace not just tasks, but professions?

These are the questions that remain unsolved—not because we haven’t tried to answer them, but because they reveal what we don’t yet agree on. They are the unfinished work of progress, and the reason this book doesn’t end with conclusions, but with conversations still unfolding.

The North Star Problem

It is tempting to believe in a single, neutral, global setting for “good.” But no such setting has ever existed. What one culture holds sacred, another treats as trivial. When Facebook expanded globally, posts that were considered acceptable political commentary in the United States were flagged as blasphemy in Pakistan. The same platform that enabled free expression in one country was damned for cultural imperialism in another.

Early alignment efforts in AI reveal the same patterns. A Stanford study in 2023 found most large language model guardrails were implicitly coded with American and Western European norms. Surveys showed that users in Asia, Africa, and the Middle East often disagreed with these defaults and the resulting outcomes.

The North Star problem asks us to consider a deep fault line: whose values get encoded, enforced, and scaled? These are global, national, and local tensions, and AI will almost certainly intensify them. As we pursue world‐changing advances, we will need to deliver a satisfying answer to the question, “to what end?” All systems require values, but no single set will appease everyone.

One possible way forward is layering: first, universal minima that no system should fall below such as dignity, safety, and legality, followed by overlays that reflect local norms. Yet even this approach raises unsettling questions. What happens when those overlays directly contradict each other? In 2021, the European Union pushed for strict rules around biometric surveillance in public spaces. At the same time, China expanded its use of computer vision for monitoring citizens. The same technology, guided by different overlays, produced starkly different outcomes. Which model guides the companies that operate across both jurisdictions?

AI could harden and further entrench differences and conflicts. And that makes the North Star problem arguably the most urgent question of the RenAIssance. In a world of unmetered intelligence, we will need to find a way to peacefully coexist across global value systems.

History gives little reason to expect convergence on a single set of values. The printing press spread scripture and heresy alike. The internet connected billions but also fractured them into echo chambers. Already, recommender systems on platforms like YouTube and TikTok show how algorithmic curation can fragment societies. A 2020 Pew survey found that 64% of Americans believed social media had a mostly negative effect on political discourse, in part because it amplified cultural and ideological divides. Imagine those divides multiplied by AI systems that not only recommend content but negotiate on our behalf, make decisions, and design policies.

The North Star problem, then, is not about deciding on one moral map, but about designing systems that can support many. That means making differences visible rather than hidden and giving individuals and communities genuine agency to choose which value system, or overlay of systems, they live under. The alternative is that decisions made by the future’s most powerful nations, companies, and actors could quietly become global standards.

AI can either force the world toward a brittle definition of “good” that reflects only a narrow group’s interests, or it can create space for pluralism. Success here will not be measured by consensus, but by coexistence. If the RenAIssance is to be not only prosperous but pluralistic, we must learn to share a future where many stars guide us, without collapsing into conflict.

The Ecology Problem

To answer the questions of the RenAIssance, we’ll need a planet to answer them from. The promise of abundance will ring hollow if it comes at the cost of our home. What happens when unmetered intelligence collides with an electrical grid struggling to power it?

Let’s start with the uncomfortable arithmetic. Artificial intelligence is already a material load on the grid, and that load is rising fast. In the United States alone, data centers could draw up to ~9% of total electricity by 2030, roughly double their share today.¹ Worldwide, the International Energy Agency projects that electrical draw will reach 945 terawatt‐hours of power a year in the same time, largely fueled by the rise of AI.² On paper, that is less than 3% of the world’s electricity. But grids don’t run on averages. They run locally, hour by hour. The next marginal megawatt has to be generated, transmitted, and balanced in real time.

Even as clean energy capacity soars, it isn’t connecting to the grid quickly enough. The U.S. interconnection queue has 2.6 terawatts of proposed wind, solar, and storage, but only a fraction will ever reach operation, and wait times stretch for years. AI demand is rising now, and clean supply is arriving later. If AI’s growth outpaces clean energy, will it drag old fossil plants back online? If communities see AI as a driver of emissions rather than a partner toward solutions, how long before the social license to build new intelligence crumbles with it?

Water tells the story too. Headlines describe data centers “drinking the desert.” At some facilities, the withdrawals do add up, hundreds of millions of gallons a year. In the wrong watershed at the wrong season, that demand is felt acutely. Water has long been a resource concern, and it can help us understand how we measure and tolerate progress.

All progress comes with costs, especially ecological costs. Behind every one of our technological and societal strides, there is an equation of something we are willing to surrender. Agriculture dwarfs AI in water withdrawals, and the numbers are staggering. Almonds alone consume billions of gallons for a crop largely destined for luxury consumption.

Livestock, according to the UN, produces about 14.5% of anthropogenic emissions, but supports 1.3 billion people’s livelihoods.³ Which indulgences do we tolerate, and which do we condemn? Do we weigh almonds and cows against intelligence, or accept that each carries a cost society must consciously allocate?

Energy allocation is an ethical question disguised as engineering. What deserves the next clean electron? Training a trillion parameter model, streaming an entertainment app, or forecasting wind ramps so a grid can retire gas faster? What deserves the next gallon of water? Cooling a data center, irrigating luxury crops, or preserving a fragile watershed? When the carbon budget is finite and the biosphere is fragile, indulgence becomes a contested resource.

Moreover, AI, unlike similarly ecologically taxing systems—congested highways, industrial farming, golf courses—offers us a potentially catalytic solution. AI advances are already improving global ecological systems. In energy, it is being used to stabilize fusion plasmas, discover new battery materials, and orchestrate distributed energy resources. Through my work with Conservation International, I’ve seen AI monitor, explore, and protect biodiversity in ways we never could before. AI is already classifying species at scale in dense forests, measuring what lives where and how it changes, and, when used as a tool for groups like VETPAW, protecting endangered species from poaching.

Industrial revolutions have always come with ecological bills: coal to smog, oil to spills, agriculture to extinction. Will AI repeat that arc, accelerating harm while promising eventual repair? Or can we use it to break the cycle of extraction? This is the test of our mettle. Ecology is the editor of the intelligence story, and it demands clarity. It insists we spend our energy, literal and cultural, deciding what the world can live with. When the carbon budget is (currently) finite, the pressing question is simple: can artificial intelligence help the biosphere more than it hurts it, in timeframes that matter?

The Distribution Problem

If it isn’t clear by now, I believe the RenAIssance will create a significant rising tide of prosperity. Productivity will soar, costs will fall, and entirely new industries will be born. But this rising tide may not lift all boats evenly.

We know AI will make us collectively wealthier, but the distribution problem asks the question: how will the gains be shared?

Answering this question is deserving of an entire new field of study. What I present to you in the meantime are the three overlapping divides that I think will naturally shape how this prosperity is distributed, and the questions we will soon have to ask. The good news is that AI can meaningfully raise the baseline of human well‐being, reducing scarcity everywhere. Even those who are not direct beneficiaries of AI‐driven wealth will feel these systemic improvements. But raising the floor is not the same as closing the gap.

The first divide is the most personal, the divide between individuals. Access to AI tools, the skills to use them, and the willingness to adapt will create new winners and losers. This divide could further disadvantage vulnerable people. The International Labour Organization warns that women and younger workers may face disproportionate risk, given their higher representation in clerical and service roles that AI can more easily automate. But it may also expand potential. Recent Federal Reserve analysis shows that non‐college graduates who use AI tools in their work are now performing at or near the level of college‐educated peers⁴ in comparable roles. This hints at a future where skill acquisition will matter more than formal education, but also where those without access to AI, or the training to use it effectively, will fall further behind.

Second is the divide between nations, and, as we’ve discussed, many are taking different approaches. The United States has embraced a faster, more market‐driven approach, letting experimentation happen with minimal regulation in the early stages. The European Union has opted for a more cautious path, enacting some of the world’s most comprehensive (or overbearing) AI regulations. China sits somewhere in between, centralizing AI strategy, funding large‐scale national projects, and moving quickly in targeted sectors like computer vision and robotics. Elsewhere, countries are finding their own paths. How these strategies play out could reorganize the global order and determine the opportunities and capital available to their citizens.

The third divide is corporate. Larger, capital‐rich companies are adopting AI faster and more effectively than smaller competitors, compounding existing advantages.⁵ The best‐resourced companies are funding proprietary AI models, buying up compute capacity, and attracting top talent, leaving smaller firms with fewer tools to compete.

Left to themselves, the three divides will set the course of distribution. But distribution can be a design choice, something far more deliberate than fated. Which pressures could we apply to bend abundance toward fairness?

Will we invest in large‐scale reskilling and education, closing the personal divide by giving more people the adaptability to thrive in augmented roles?

Will nations take a more active role, using policy to expand access to compute, data, and infrastructure, ensuring that more of their citizens participate in the AI economy?

Will companies adopt new models of compensation, tying employee pay directly to productivity gains from AI through profit‐sharing, equity grants, or shared ownership?

And could we go further still? Could we distribute the gains to people directly in the form of universal basic income (UBI)? The concept is simple: provide all citizens with a guaranteed regular payment, no strings attached, to ensure a basic standard of living regardless of employment status.

UBI is not just a thought experiment anymore. Modern pilots in Finland, Stockton, and Seoul have already shown measurable gains in health, employment stability, and community engagement, even at modest payout levels. AI’s ability to generate economic value at unprecedented scale makes such policies more financially plausible than ever before.

My old colleague Miles Brundage suggested that as AI‐driven growth accelerates, we should not think in terms of modest stipends but in terms of genuine abundance. “One thousand dollars per month is relevant to what’s feasible policy‐wise,” he argued. “Ten thousand dollars per month is relevant to what could be feasible in a few years with AI‐enabled growth.”

Distribution is not a law of nature; it is a design choice. The same tools that concentrate wealth and power can also spread them more widely. In that way, AI is not different from so many transformations the world has experienced before. And like those transformations, success and distribution will depend on intentional leadership. The RenAIssance can be the most democratizing era in human history. We must build the systems and policies to make it so.

Social Constructs

In a January 2025 interview, when asked about his expectations for the next 50 years, OpenAI CEO Sam Altman casually proposed, “The whole structure of society will be up for debate and reconfiguration.”⁶ The comment landed not because it was dramatic, but because it was reasonable. The debates are already unfolding in courts and classrooms, in councils and code reviews, and they touch every seam of the social fabric. The familiar sequence of progress, debate, decide, deploy, absorb, has collapsed. We are rewriting the rules while we play the game, often with billions of people already using the systems under review.

In moments like these, the question is less about urgency and more about imagination. Asking what is efficient or safe for society is no longer enough. For centuries, humans have built social contracts to govern power using laws, markets, customs, and rights. Each is an experiment in balancing freedom with responsibility, access with limits, progress with preservation. AI multiplies those experiments and raises the stakes. It pushes us to ask again, and with new intensity: What do we hold at the center, and what do we want to remain recognizably human as capabilities scale?

A Shared Truth

Mis‐ and disinformation are as old as politics, but AI collapses the cost of producing both and tailors them to the individual. Text models are quickly polluting social media, making it impossible to believe everything we read; audio models can mimic anyone’s voice, making it impossible to believe what we hear; and video models have advanced tremendously, almost overnight, making it increasingly impossible to trust even what we can see.

Tomorrow’s deceptions will be multimodal and adaptive, able to probe our preferences and vulnerabilities with uncanny precision. The challenge is no longer only removing falsehoods. It is preserving a baseline reality that citizens can inhabit together.

We are not just contending with what people believe, but with the capacity to believe anything at all. A public square can survive disagreements about values, but it cannot survive total fragmentation of facts.

Our own water‐cooler research points to a reassuring truth: most people do not want separate realities. They want shared reference points that allow coworkers, neighbors, and political opponents to talk about the same world even when they disagree about what to do in it. A society cannot collaborate without common anchors.

Rebuilding that common layer will require investing in infrastructure and agreeing on norms. Authenticity signals that travel with media. Clear provenance for consequential images, audio, and text. Independent institutions that adjudicate claims with transparent methods. Civic habits that prioritize corrections with the same visibility as initial reports. The goal is coordination, not unanimity. If we cannot agree on what is real, we cannot plan, vote, teach, or heal. Truth is not only data, it is a social commitment to live in a shared world and to repair it when we are wrong.

Legal Frameworks

Our legal scaffolding is already showing its cracks. Liability assumed somebody was in charge. Copyright assumed authorship was knowable. Intellectual property assumed originality could be traced. These assumptions falter in systems that learn from vast public data, adapt after deployment, and act with limited human supervision. The next social contract must revisit who is responsible, who is credited, and how the system of justice itself functions when intelligence is abundant.

Liability

When an AI system makes a decision that harms someone, who is accountable? The developer who designed the model, the company that deployed it, the data providers who shaped its behavior, or the professional who relied on its recommendation? Traditional standards, like negligence and strict product liability, strain when models update constantly, tools are composed into chains, and causation is diffuse. We will need clearer allocation of duty of care, mandatory audit logs that show who did what and when, and safe harbor regimes that reward documented best practices rather than marketing claims.

Copyright

Training on public data has blurred the line between inspiration and reproduction. Who owns an output that echoes thousands of sources? What remedies exist when a model memorizes a work? How should opt‐out, opt‐in, or compensation schemes be designed, and at what layer of the stack? The system must protect creative labor without choking the open knowledge commons that made these models possible. That balance will likely require new licensing standards, collective bargaining mechanisms for rights holders, and disclosure rules that make the provenance of datasets and outputs visible.

Automated Courts

If legal drafting, filing, and responding become virtually free, litigation could scale far beyond human capacity. Courts will face surges of auto‐generated filings, discovery requests synthesized at machine speed, and procedural games played by autonomous agents. The justice system will need guardrails that are compatible with due process: verified digital identity for filers, minimum evidentiary thresholds before a case proceeds, automated triage that routes simple disputes to binding online resolution, and human review for high‐stakes matters. Efficiency cannot eclipse legitimacy. The public must be able to see how outcomes were reached and be able to challenge them.

Everything Else

AI will reach into contract law through dynamic agreements that update as conditions change. It will reshape consumer protection with agents that buy and negotiate on our behalf. It will test antitrust tools as firms vertically integrate data, models, and compute. It will complicate employment law as roles are augmented, part human and part machine. It will alter evidence rules, since synthetic media and generated logs will need new standards of admissibility. And it will force tax and international law to grapple with value creation that crosses borders at exceptional speeds. The law will remain essential, but it must be made legible again in a world where action is fast and authorship is shared.

Economic Systems

Economies are social inventions—agreements about how to measure value, exchange effort, and organize trust. For most of history those systems were bound to scarcity. Labor created goods; goods created wealth; wealth created power. AI breaks that chain. When machines can produce, analyze, and trade at near‐zero cost, the organizing logic of the economy begins to dissolve.

Capitalism, for all its flaws, has been the most adaptive model in human history because it rewards experimentation and punishes waste. But it was built on constraints—finite labor, finite capital, finite time. Unmetered intelligence may prove to produce infinite productivity, and the system will then need a new anchor. Profit, efficiency, and growth are no longer sufficient ends if the marginal cost of everything trends toward zero.

What replaces them is not yet clear. Some imagine a form of participatory capitalism where ownership extends to data, algorithms, and compute. Others foresee a shift toward collective investment in public goods, where returns are measured in health, knowledge, or planetary stability so the baseline of human well‐being rises for everyone, not just shareholders. The most likely outcome will be plural: markets that coexist with systems of redistribution, ownership that extends beyond shareholders, and currencies that measure more than money.

Economics has always been moral philosophy in disguise. It tells us what we think is fair and what we think is worth striving for. The next economy will do the same, but with fewer excuses. When machines can make almost anything, value will migrate from production to purpose, from things to meaning, from efficiency to stewardship. The question will no longer be how much can we make, but what is worth making at all?

Automation Boundaries

One of my favorite questions to ask anyone over a dinner table is, “If you could automate everything in your life, where would you stop?” This is what I call the Automation Boundary.

The answers are telling, and (as one might expect) often a little crude. The question allows most people above the age of 7 to consider what it means to be human; and it turns out the exercise might not just be philosophical. There’s a practicality to considering what we want to do as we approach a future in which we can automate everything.

After all, not all difficulty is waste. Some hardships deplete us, but some hardships deepen us.

Here we can name two kinds of difficulty. Virtuous friction is the kind of challenge that cultivates meaning and mastery, the late nights of revision that turn information into understanding, the apprenticeship that builds judgment, the demanding conversation that builds trust. Vicious friction is the kind that only diminishes, walking miles for clean water, waiting months for access to care, being blocked by bureaucracy that no one can explain.

Scaling a mountain by helicopter is not the same as climbing to the summit step by step. Raising children and falling in love are hard, and that hardness is part of what makes them precious.

Before we automate an action, we should ask five questions:

Does this enhance human agency?

Does it deepen trust and connection?

Does it sharpen human judgment?

Does it expand collective opportunity?

Can its outcomes be audited or reversed?

By weighing these answers, we can determine what passes the automation test, and what belongs on the far side of the boundary.

Privacy and Personhood

If every word, movement, and purchase can be tracked and modeled, does privacy remain a right or fade into nostalgia? When AI agents speak in our voices or simulate our personalities after death, where does personhood begin and end? Do we extend rights to digital twins, or treat them as tools? What happens when the most intimate parts of ourselves—our memories, our likeness, our voice—can be copied, combined, and sold with a few clicks?

We are entering a landscape where identity can be rendered on demand. Voice clones can authorize payments. Personalized avatars can hold conversations indistinguishable from their human sources. Grief technologies can comfort families while raising unsettling questions about consent and authenticity. The click‐once consent model cannot bear this weight. Consent must travel with data, be revocable, and be honored across the entire ecosystem, not only inside a single app.

This will require updated rights of publicity for the age of infinite reproduction, clear default ownership of biometric and behavioral data, and practical rights to inspection, correction, and deletion that apply to models as well as databases.

It will also require cultural norms that treat impersonation as a serious harm, not a novelty. The social contract has long presumed a discrete line between the individual and the collective. AI is moving that line. We should decide intentionally where we want it to be.

Purpose and Existence

The human experience is likely to undergo two unprecedented (and unimaginable) shifts in the next 100 years.

The first shift will come when (as Keynes puts it) the economic problem is solved. When machines make more than we can consume and work is no longer a necessity for survival. What happens to ambition when there is nothing left to earn? What gives life structure when your needs are cheap and a job is optional?

The question won’t be how to get more, but what to make of what we have. Do we use abundance to rebuild the planet, to educate, to explore? Or do we use it to distract ourselves from the quiet? Prosperity does not end the human condition; it changes its terms.

When this economic question is solved, we’ll likely face a spiritual one. How do we keep ambition without desperation? How do we keep meaning without struggle? The answers will come from what we decide to care about when caring is no longer compulsory.

The next shift may be harder still. When medicine and biology stretch life far beyond its natural span, death stops being a boundary and starts being a choice. Much of what makes us human is organized around the fact that time runs out. We love, teach, and forgive because we don’t have forever. When forever appears, the logic of life starts to unravel. Do we rethink family, inheritance, the arc of a career? Do we build institutions meant to last centuries instead of decades?

Immortality sounds like progress until you realize how much meaning depends on impermanence. Urgency is the invisible architecture of purpose. Without it, culture risks drifting into repetition. The same tools that will cure disease and extend life will also force us to answer a harder question: what makes a life complete when it doesn’t have to end?

The intelligence we are building is not only technical. It is civilizational. It will test whether progress can coexist with purpose—and whether, once freed from necessity and even mortality, humanity can still choose to grow.

Notes

1. Electric Power Research Institute, “Data Centers Could Consume up to 9% of U.S. Electricity Generation by 2030,” Electric Power Research Institute, Apr. 17, 2024, accessed Aug. 23, 2025, https://www.prnewswire.com/news-releases/epri-study-data-centers-could-consume-up-to-9-of-us-electricity-generation-by-2030-302157970.html.

2. International Energy Agency, “AI is set to drive surging electricity demand from data centres while offering the potential to transform how the energy sector works,” International Energy Agency, Jan. 24, 2024, accessed Aug. 23, 2025, https://www.iea.org/news/ai-is-set-to-drive-surging-electricity-demand-from-data-centres-while-offering-the-potential-to-transform-how-the-energy-sector-works.

3. Food and Agriculture Organization of the United Nations, “Tackling climate change through livestock: A global assessment of emissions and mitigation opportunities,” FAO, 2013, accessed Aug. 23, 2025, https://www.fao.org/3/i3437e/i3437e.pdf.

4. Board of Governors of the Federal Reserve System, Educational Exposure to Generative Artificial Intelligence, FEDS Note, February 26 2025, https://www.federalreserve.gov/econres/notes/feds-notes/educational-exposure-to-generative-artificial-intelligence-20250226.html.

5. McKinsey & Company, “The State of AI in 2023: Generative AI's Breakout Year, report,” August 2023, https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year.

6. Deep View, “Sam Altman says AI will create the need for a new ‘social contract’,” The Deep View, May 30, 2024, accessed Aug. 23, 2025, https://www.thedeepview.co/p/sam-altman-says-ai-will-create-need-new-social-contract.

PART II
A Glimpse into the Future

In Part I, we traced the broad arc: AI as the engine of a new RenAIssance, a technology with the power to expand human potential and create an age of abundance. But big arcs only matter when they land in the details of daily life. Abundance is not abstract. It shows up—or fails to—in the systems we rely on most.

By many measures, humanity is advancing. We live longer than our ancestors. We are more connected, more productive, more capable. Yet the systems that touch our lives most directly—education, finance, healthcare, and employment—can feel broken beyond recognition. They cost too much, deliver too little, and leave too many people behind.

This is the paradox of our time: undeniable progress paired with intolerable dysfunction. And it is exactly where AI matters most. AI will not politely patch these cracks. It will split them open. It forces us to confront how far our institutions have drifted from their purpose—and it gives us the tools to realign them.

The chapters ahead trace that realignment. They reveal how AI can turn broken systems into living ones: schools where every child has a tutor, finance that illuminates instead of obscures, hospitals that prevent illness instead of profiting from it, and work that amplifies what makes us most human. Even if these fields aren't your own, their reinvention matters. No system transforms alone. To glimpse the redesign of one is to glimpse the future of many.

To make this real, I've included a few examples of businesses that are already putting these changes into practice today. You will see two different ways to build the future we want to live in: businesses working within existing systems to try to repair them with AI, and businesses using AI to reinvent the systems entirely. Together, these contrasts reveal not only how AI can improve what is broken, but how it can also build what comes next.

Part I captures the substance of an AI transformation, solidifying the case that the trajectory of current technology is indicative of a far more abundant future. This is the theory of the RenAIssance. Industries rarely change based on mere additive factors like incorporating LLMs (chatbots and writing assistants). Instead, we'll see a comprehensive redesign of habits and processes in tandem with an explosion in the scale of opportunity this technology presents.

CHAPTER 6
Education 2.0

I was a terrible student. Not in the casual sense people sometimes mean when they say they “weren't great in school.” I was actually terrible. By the time I was 11, I had been “asked to leave” by two schools.

It wasn't because I was lazy or unintelligent. I was endlessly curious outside the classroom. I thrived in sports, in debates, in long conversations with friends. But in school itself, I could not make myself care about worksheets, memorization drills, or exams that felt detached from anything real. Teachers told me to focus, parents told me to try harder, administrators told me to behave. None of it worked. The system wasn't built for me, and, if we're honest, it isn't built for most kids.

At least in the United States, but certainly also for most of the planet, today's K–12 school was designed for another world. Rows of desks, timed bells, and standardized assignments were meant to prepare children for industrial work, for factories and offices where compliance and repetition mattered most.

I don't tell this story to romanticize rebellion or to suggest every child should reject authority. I tell it because I know what it feels like to be mislabeled as a failure by a system that wasn't designed with you in mind. And because I know how many students experience this every day.

The truth is, I wasn't broken. The system was.

A Brief History of Education

Education has always been one of the great levers of human potential, and it has never been static. Each leap forward has come from new tools, new opportunities, and new philosophies of what it means to be human.

Before the Renaissance, education was rare, fragile, and confined to elites. Literacy was a privilege reserved for clergy, nobles, and a narrow circle of scholars. The written word was locked away, not because people lacked curiosity, but because books themselves were scarce. Each volume required months of hand copying by scribes. Monasteries kept them chained to lecterns, a physical metaphor for knowledge bound to the privileged few.

Childhood itself was not yet understood as a unique stage of life. Most children were seen as miniature adults, expected to labor on farms or apprentice into trades as soon as they were physically able. Education, if it happened at all, was incidental—a prayer memorized at church, a craft learned by shadowing a parent. The idea of structured schooling for the masses did not exist.

That scarcity shattered with Gutenberg's printing press in the mid‐15th century. A world with only 30,000 printed books in 1,500 grew to more than 9 million a century later. Knowledge was no longer a fragile inheritance but a replicable resource.

The impact was not just quantitative, it was cultural. Books slipped beyond the tight grip of monasteries and royal courts, circulating among merchants, artisans, and eventually peasants. Literacy spread slowly but steadily reshaping society. In England, it rose from roughly 10% in 1,500 to more than 60% by 1,800. On the continent, the pattern was similar: literacy became less the mark of privilege and more the baseline of civic participation.

The printing press redistributed power. It gave ideas durability and reach. It made the rise of humanism itself possible, including a new philosophy of human development. Erasmus, the 16th‐century Dutch humanist, articulated a radical idea: children were not simply smaller adults. They were beings in a unique stage of growth, both mentally and morally. Their education was not incidental but essential. “The best hope for the future lies in the proper education of the young,” he wrote.

This was a profound shift. Erasmus reframed education as the work of civilization. To cultivate children was to cultivate the future. Literacy was no longer tied exclusively to religion, and a more secular and humanistic worldview emerged. Education's reach expanded, and with it, society's ceiling rose.

But mass education remained incomplete. For centuries, most children still labored from an early age. Universal schooling was barely a thought, let alone a goal. But things began to change in the 19th century. Massachusetts passed the nation's first compulsory attendance law in 1852.¹ Initially, the rationale was religious and civic: a literate citizenry was needed for Protestant morality and democracy. But with the industrial revolution, the idea took hold. By 1918, every U.S. state required children to attend school. Industrialization gave schooling a new purpose: producing disciplined, punctual, uniform workers.

The design reflected that goal. Desks in rows mirrored machines on an assembly line. Bells signaled the start and end of shifts. Standardized curricula created interchangeable units of human capital. The school became a factory for producing workers as much as for producing citizens. This model was effective in its time. It created near‐universal literacy. It trained millions for industrial labor. It established a shared civic baseline in a rapidly diversifying republic. In many ways, compulsory schooling spurred society's development as dramatically as the printing press had centuries earlier.

But some tools of liberation become structures of constraint. The features that made schools effective for the 19th century now make them brittle in the 21st. The industrial economy they were built to serve no longer exists, but the architecture of education, rows, bells, and standardization, remains unchanged.

Education transforms when three forces align:

A technological leap that makes access possible.

A new philosophy of human potential that makes expansion desirable.

A social or economic shift that makes transformation necessary.

The printing press, Erasmus, and secularism. Industrialization, compulsory schooling, a new work force. Each time, the technological threshold (what tools allowed) met the societal threshold (what people were ready to accept), and a new equilibrium emerged.

Sometimes the adoption gap was short. Compulsory schooling made good workers. Sometimes it was long. It took centuries after Gutenberg for universal schooling to become law. But eventually, thresholds aligned, and society crossed into a new era.

We stand at another threshold moment. When information is no longer scarce, when tutoring can be delivered at scale, when teachers can be freed from administrative drudgery, what becomes the purpose of school?

AI will not transform education unless we pair it with new philosophies of what it means to raise a child and new systems that make access universal. We have the tools. The abundance of AI demands that we now create these new philosophies and systems.

An Antiquated, Failed State

The world has changed. For a century, our schools have not. A system to make capable factory workers on the dawn of a new industrialization is good foresight. But the society's, and the workforce's, needs have changed. The skills most valued today, creativity, adaptability, critical thinking, and collaboration, the skills children innately want to explore, are precisely the ones the system was designed to suppress. The result is a widening gap between what schools produce, and what society needs.

Students graduate with transcripts but without the skills most relevant to the world they are entering. Employers complain about deficits in problem‐solving and communication. Universities remediate basic skills. Young adults arrive in the workplace unprepared, not because they lack ability, but because they were trained for a world that no longer exists.

What's worse, not only is this system aimed at the wrong goals, it is failing to teach even the most basic skills needed to operate in society: literacy and math. Since 2019, every U.S. state has seen declines in eighth‐grade math scores, according to the National Assessment of Educational Progress (NAEP). Reading proficiency, already stagnant since 2012, has fallen further. Nationwide, 61% of fourth graders score below proficiency in math, and 69% fall short in reading.² By eighth grade, the numbers worsen: 72% below proficiency in math, 70% in reading.³ These are not marginal dips; they are structural failures. A generation of children is being left behind academically, with consequences that will echo for decades.

What is perhaps most frustrating is that the crisis is not evenly distributed. If you have enough resources, you, and your children, can hide from it. Wealthy districts, supported by property taxes, can afford smaller class sizes, Advanced Placement courses, varied curricula like International Baccalaureate programs, or fully dedicated STEAM‐focused teaching approaches. Some have arts electives, or fully dedicated arts conservatory programs.

In stark contrast, poorer public school districts, often serving minority and immigrant communities, cannot afford basic needs like pencils, proper temperature control, or WiFi. In some urban centers, graduation rates hover below 60%.⁴ In underfunded rural schools, students may lack basic internet access, making participation in a digital economy almost impossible. The disparity is so entrenched that education scholar Jonathan Kozol once called American public schooling “apartheid education.” In this system, zip code is destiny.

The pandemic accelerated the trend, but it did not create it. COVID‐19 was a catalyst, exposing existing weaknesses. When schools shut down, children in affluent households continued learning on laptops, supported by parents who could work from home or afford tutors. Children in poorer households fell months, even years, behind. A McKinsey analysis found that by the end of the 2020–2021 school year, students in majority Black schools were five months behind in math and four months behind in reading compared to pre‐pandemic expectations. Students in majority white schools were two to three months behind.⁵ Inequity widened overnight.

A child in an underfunded district falls behind in math, then disengages entirely. By middle school, they're labeled “low performing.” By high school, they are more likely to drop out. Their potential was not extinguished. It was never cultivated. The industrial design of our schools was meant to equalize, to create uniformity. Instead, it has entrenched inequality.

Teachers sit at the center of the storm. The reasons are not mysterious. Some K–12 classrooms have more than 40 students, one teacher, and no aides. The teacher cannot possibly give individualized attention, which is one of the only things we all agree students need. With resources thin, pay stagnant, and overwhelming expectations, they are barely holding on. The result is burnout on a historic scale. And somehow it's getting worse. Many are asked to play the role of social worker, counselor, administrator, and parent all at once, often without training or support. Hours that should go to preparing lessons or connecting with students are swallowed by testing requirements, reporting mandates, and bureaucratic box‐checking. As one teacher told me: “Some days I spend more time on paperwork than on my students. I didn't sign up to be a clerk.”

Attrition rates are highest in schools serving disadvantaged populations, precisely where continuity and expertise are needed most. The cost is catastrophic. Teachers who leave take institutional knowledge, mentorship capacity, and community trust with them. Schools scramble to fill vacancies with substitutes or inexperienced hires, perpetuating cycles of instability. Students suffer most.

Over time, the problem has hardened into an industry of its own: endless think tanks, commissions, and policy cycles that rearrange the same tired pieces on a broken board. The United States spends more per student than nearly every other developed nation, yet outcomes remain stagnant. More money alone won't solve education because the system is designed to absorb funding without changing itself. Bureaucracies expand, union negotiations protect broken structures, testing companies lobby to preserve outdated regimes, and political leaders chase short‐term wins. Every stakeholder has an incentive to defend the status quo, even when the status quo is failing children.

Instead of redesigning the system, we doubled down on our mistakes and normalized the crisis by assigning blame. Students are blamed for distraction, addicted to phones. Teachers are blamed for poor performance. Schools are blamed for underfunding staff. Policymakers are blamed for bad tests. Parents are blamed for not being involved enough. We tell ourselves that if we locate blame precisely enough, we can fix the system by punishing the guilty party. But reform has never worked this way. Blame is not design.

The brokenness of the system is not due to a lack of effort. Students are trying. Teachers are trying. Parents are trying. What fails is the blueprint itself. We cannot solve this with marginal reforms. Adding tablets to classrooms or adjusting test standards will not fix a system designed for factories in a world of networks. The next chapter of education requires reinvention. Just as the printing press and industrialization forced earlier reimaginings, AI and modern human needs now force ours.

Scholastic vs. Social Learning

I think there is a real truth to the idea of “street smarts” versus “book smarts.” Being formally educated does not, in any way, mean that you'll succeed in the chaos of adult life. Humans need teachings that don't come from quiet study. Yet for nearly 150 years, we have merged two fundamentally different learning styles, scholastic and social, into one model. In doing so, we have largely failed at both.

I know it can be different, because I've seen how it can be different. My wife Adlee is a first‐grade Waldorf teacher. When I visit her classroom, I see what happens when we let children be themselves, when we encourage them to find out who that person is. Adlee teaches them everything a first grader should learn at any school in America. But she also teaches them how to be better people, at least better eight‐year‐olds. Watching her work made me think about what education could be, and who the kids she teaches will become. To get the best out of both, we need our children to experience both scholastic and social learning. Scholastic learning is the way we acquire knowledge and skills. It is reading, writing, math, science, reasoning, the intellectual stuff that allows a person to understand and shape the world. Social learning is how we cultivate empathy, resilience, collaboration, self‐regulation, communication skills, and civic participation. Scholastic learning is how we learn academic skills; social learning is how we learn to live together.

Both are essential. A society without scholastic mastery can’t advance. A society without social capacity fractures. The problem in American K–12 education is that we have confused them, assuming that success in one automatically produces success in the other. A student who scores well on math and reading tests is assumed to be socially prepared. A school with high graduation rates is assumed to be cultivating well‐rounded adults. Neither is true. We need balance, a dual education that recognizes the value and need of both. We need to teach them both, and we need to teach them more effectively. So why isn't it happening?

We know how to cultivate scholastic learning, even if we aren't good at putting it into our traditional schools. Research is unambiguous: the most effective way to learn is one‐on‐one tutoring, particularly high‐dosage tutoring (three or more sessions per week), which produces remarkable results. A Brown University meta‐analysis found that it increases test scores by nearly 0.4 standard deviations.⁶ That means a child in the 50th percentile can jump to the 70th in less than a year. The Chicago Public Schools piloted a tutoring program like this and found that students made one to two years of progress in math in a single year, an enormous jump.⁷

Despite the evidence, our schools cannot provide this at scale. The average American elementary school teacher manages 19 students of varying ability levels across multiple subjects. Even the most dedicated teacher cannot deliver consistent individualized attention under current conditions.

This is where technology can help. AI tutors can deliver personalized pacing and targeted remediation for each student. Intelligent systems can monitor comprehension in real time, detect misconceptions, and adapt content accordingly. AI tutors can give every child access to something that once belonged only to the wealthy: individualized attention.

But as we all know, book smarts alone are not enough. A child who can solve equations but can't resolve conflict, who can read but cannot collaborate, is not prepared for the world, the workforce, or for making friends. Scholastic learning is the body of education. Social learning is its soul.

Social learning, in its rawest form, emerges in shared spaces like playgrounds and community centers, and in unstructured time when children must govern themselves. They learn empathy, conflict resolution, resilience, creativity, and self‐regulation. These skills are not “extra” or ornamental, and neither is the way we develop them. These are crucial steps in becoming a capable adult.

Modern schooling has steadily eroded these opportunities.

Recess is cut to make more room for test preparation. Arts programs are consistently the first to go during budget shortfalls. Field trips are curtailed. Even lunch periods are compressed. Students spend more time in rows, less time in communities. The structure of modern school prioritizes measurable outcomes in math and reading at the expense of the immeasurable but essential work of social growth.

The results are visible. Employers report deficits in problem‐solving, communication, and teamwork. Mental health crises among adolescents have surged. Many young people struggle to make eye contact, manage conflict, or hold conversations outside of digital environments.

Rudolf Steiner, the Austrian philosopher who founded Waldorf education, foresaw this danger a century ago. “In order for the students' development and the outer development of civilization to coincide,” he wrote, “we need a faculty whose interest is not limited to specialized educational practices. Rather, this faculty must be fully involved in the broader aspects of life.”

Steiner's point was simple: education must cultivate the whole human, not just the worker. The outer development of civilization—our economies, our democracies, our technologies—must be matched by the inner development of individuals: their imagination, ethics, and spirit. Without that, society advances in tools but regresses in wisdom.

I have watched my wife's Waldorf classrooms come alive with play, song, and imagination. She begins the day with music, rhythm, and story. Her students are living in their own wonder, following curiosity and connection rather than memorizing disconnected facts. Waldorf classes also stay with the same teacher year over year. Students develop the one‐on‐one connection that helps provide an ongoing foundation of support, collaboration, and communication.

Watching her has sharpened my conviction: social learning is not a luxury. It is essential education. No AI system can replicate the warmth of her voice, the trust she builds, or the way she fosters joy. But AI could give her more time to do those things by removing the burdens that suffocate teachers everywhere else.

The stakes of social learning are even higher when we consider its impact on lifelong happiness. Research consistently shows that the strongest predictor of health, longevity, and life satisfaction is not wealth or status, but the quality of our relationships. In Part I, I discussed the Happiness Function, the reliable relationship between human well‐being and the way we spend our time, and it's clear that social connection is the bedrock of happiness.

If schools neglect social learning, they neglect happiness itself, and the skills necessary to live. This is not speculation. The Harvard Study of Adult Development, which has tracked people for more than 80 years, finds that close relationships are the single strongest predictor of life satisfaction.⁸ Stronger even than genetics or income. Yet our schools are steadily stripping time for the activities that cultivate those relationships.

We are, in effect, building a generation that can pass tests but cannot flourish. This is true for the youngest students, but continues into our teens, and our life beyond. Screens are compounding the problem, so is social media, as is the reality that we have eliminated spaces outside of school to develop these skills. The public spaces that once hosted organic social development, parks, town squares, community centers, have withered. Where are children left to learn resilience, empathy, and creativity?

The solution, as with many things in life, is in balance. Scholastic learning, delivered with precision, individualized attention, and high expectations. Social learning, cultivated through play, community, and unstructured time.

AI can help with both. It can deliver personalized tutoring, close gaps, and accelerate mastery, and in doing so, give teachers, freed from bureaucracy and their own rote tasks, the time and energy to invest in the second. It can empower teachers to focus on the human side of learning, fostering empathy, guiding conflict resolution, and building communities of trust.

The Renaissance taught us that human flourishing depends on both knowledge and wisdom, tools and values. Steiner reminded us that civilization cannot outpace the development of its children. Adlee shows me every day that the essence of teaching is human connection, not standardization.

Scholastic learning without social growth creates brittle technicians. Social learning without scholastic rigor creates shallow sentiment. Society cannot grow, if we do not teach our youth how to live well. By designing a system that nurtures both, we can create capable, happy citizens able to build civilizations, and redefine what a human life can be.

Teachers at the Center

When schools struggle, the blame falls in every direction: students, parents, administrators, politicians. But the quiet collapse is happening at the center. Teachers are burning out in numbers large enough to threaten the future of the profession itself.

In 2022, more than half of America's teachers said they were considering leaving.⁹ By 2025, nearly eight in ten reported the strain had only grown worse since the pandemic.¹⁰ Attrition is highest in the schools that need teachers most. For decades, we have asked educators to stretch a little further, to take on one more responsibility, to carry one more burden, to act as instructors, therapists, social workers, and surrogate parents all at once. Now we are discovering the limits of that elasticity.

I hear this collapse in the voices of teachers themselves. One told me, “I spend more time proving what I do on paper than doing it with kids.” Another said, “I love my students, but the job is becoming impossible.” These aren't complaints about children or even about low pay. They are admissions that the structure of the job has become incompatible with the work of actually teaching.

AI is usually framed as a threat to teachers. But what is it threatening? The status quo? If nothing changes, exhaustion will drive millions of teachers from the profession. What AI offers is not replacement, but restoration.

AI can take on the work that weighs teachers down: grading essays, writing lesson plans, logging behavior, generating reports, handling routine communication. It can deliver individualized scaffolding through adaptive tutors that give students practice, feedback, and targeted support at their own pace. It can surface trends across a classroom that no single teacher could catch in real time. All of this free teachers for the part of the job that is irreplaceably human: the part that involves connection, presence, and care.

This is not abstract to me. When I visit Adlee in her classroom, I see what it can look like when a teacher's energy is spent on imagination rather than paperwork. But I also see her spending hours outside the classroom preparing lessons, writing reports, and handling logistics that sap her energy. If AI could lift those tasks from her shoulders, she would have even more time for what matters most: seeing her students as whole beings and nurturing their growth.

Eva Moskowitz, the founder of Success Academy, grasped a version of this long before AI. When she launched the charter school, she demanded extraordinary performance from her teachers, but she paired those demands with extraordinary support. Teachers at Success were trained relentlessly and protected from bureaucratic clutter. Their time was directed toward instruction and culture, not endless forms. The results were remarkable: Success Academy students, many from low‐income families, consistently outperformed even wealthy suburban districts in math and reading.¹¹

What Moskowitz achieved through culture and training, AI can now enable at scale. Teachers can move from being clerks of the system to designers of learning. Imagine a classroom where an AI agent tracks each child's progress, identifies misconceptions, and recommends next steps. The teacher remains in control, approving or rejecting suggestions based on human judgment, but is freed from detective work and clerical labor. Their time is spent not on chasing paperwork but on teaching, guiding, mentoring.

The realignment is overdue. We have treated teachers as if they were infinite resources. They are not, and they are being worn down by the lack of purpose, presence, and connection. When the system overwhelms them with demands they cannot meet, they collapse. When the system supports them with tools that lighten the load, they rise. The risk is obvious. A burned‐out teacher cannot inspire curiosity. A joyless teacher cannot model joy. Students feel what their teachers feel. If we want children to flourish, teachers must flourish first.

This is why AI's role in education must be framed differently than in almost any other sector. In finance or logistics, AI automates tasks to raise efficiency. In education, its primary purpose should be to restore human presence. Efficiency matters, but only insofar as it creates space for teachers to do the part of the work that no machine can touch.

The fear that AI will replace teachers misunderstands both the problem and the opportunity. I want to continue visiting Adlee's classroom for years to come. I want to see her thrive in the environment I know she was made for, developing the souls and spirits of future generations.

A Renaissance in education will not come from replacing teachers. It will come from restoring them.

Assessing the Test

In March 2025, I spoke to a few hundred teachers in Tennessee. Afterward, one of them lingered at the edge of the crowd. When the room cleared, she asked if she could buy me a coffee. “Not here,” she said. “I'd rather talk away from my peers.” I told her I was headed to the airport and she could ride along.

On the drive, she told me about her eighth graders. At least 10 of them had been labeled “proficient” in math by the state exam. Yet in class, it was obvious they weren't. They struggled with fractions, froze on word problems, and avoided the board. The numbers said one thing, the classroom another, and she was left in the dark. Her frustration was raw: “If the tests don't tell the truth, what's the point of them?”

That question haunts American education. For decades, standardized testing has been the scaffolding of accountability, the structure meant to hold everything else in place. Today it is fractured beyond recognition. Testing still consumes immense time and energy, but its results are so unstable and politicized that they no longer deliver what they promised.

Tests were supposed to bring fairness. Every child would face the same exam, and every school could be judged by the same yardstick. But the practice has collapsed. In 2015, the federal government passed the Every Student Succeeds Act, effectively dismantling the national framework of No Child Left Behind.¹² Authority for testing shifted back to the states, and almost immediately, the meaning of the exams began to erode. Growth measures replaced proficiency benchmarks. Exit exams, once a rite of passage in many states, were quietly eliminated. What remained was a patchwork of 50 different definitions of success, none stable, few credible.

The result is absurd. According to the National Center for Education Statistics, nearly half of U.S. states have adjusted cut scores downward in recent years, which means students can be labeled proficient even when they are not on grade level.¹³ A child considered “proficient” in Mississippi may not meet even the basic standard in Massachusetts. A student deemed ready for college in one state may require remedial courses in another. The NAEP, the “nation's report card,” shows math and reading scores plummeting and a system in free fall. Parents think their children are on track, and the system won't tell them the truth. The accountability system is performing measurement theater while real learning backslides.

What makes it worse is the disconnect between effort and outcome. While the exams have lost their meaning, schools still devote months to test preparation, teachers narrow lessons to match exam formats, and students slowly bubble in Scantron sheets. Parents feel it. Teachers feel it. Students feel it. The numbers have become detached from reality. They no longer provide reliable accountability. They no longer inspire trust. They no longer signal readiness for the real world.

This opacity should feel familiar. In Part I, I wrote about the black box problem of AI: our deep discomfort with systems that deliver outputs we cannot interpret. Testing has become its own black box. Scores are adjusted, benchmarks lowered, definitions changed, but no one can say clearly what “proficient” means. The numbers have no meaning, the judgments are delivered without explanation, and nothing forces change.

This is the moment where AI enters the conversation. Used carelessly, it could simply accelerate the emptiness of testing, and gradually, idiocracy. MIT studies show students relying on AI to write essays recall less and think less deeply.¹⁴ UNICEF warns that overreliance on generative systems may corrode critical reasoning.¹⁵ If schools adopt AI only as a faster grader, they will reproduce the failures of standardized testing in digital form, even more efficiently, but no less hollow.

But used wisely, AI can offer what no previous system could: assessment that is continuous, multimodal, and explainable. Instead of a one‐off exam every spring, measurement can happen across the full arc of learning. Imagine a system where every essay draft, science project, classroom debate, or group collaboration feeds into a learning profile that captures both intellectual development (IQ) and social‐emotional growth (EQ). AI's ability to analyze unstructured data, language, gesture, tone, and even patterns of collaboration, makes it possible to evaluate dimensions of learning and understanding that have historically been out of reach.

Teachers and parents could see not just a score, but a trajectory—evidence of growth, struggle, and resilience. Handled transparently, this is not surveillance. It is feedback. It would transform measurement from punishment to guidance, and allow educators to step in long before a failed test.

The hinge is explainability. Just as we demand that AI itself show its reasoning in order to earn trust, so must educational assessments. A grade without explanation is a dead end. An evaluation with clarity becomes a road map. AI can highlight why a student succeeded in one area, struggled in another, and what steps come next. Parents could finally see what teachers already know. Students could finally understand what their scores mean.

So then how do we avoid idiocracy? The worry that AI will have students spinning up essays of what I call LLM slop, forging homework, and gaming the system, is an extremely popular one. I predict the opposite is true, if we build our measurements to fit the world we live in. Shift assessment to in‐person measures like oral exams, whiteboard problem‐solving, studio critiques, and timed Socratic seminars where real‐time reasoning and iteration are on display. Pair these with AI systems that document and analyze authentic performance over time, and flag inconsistencies between in‐class mastery and take‐home work. This approach keeps human judgment at the center of assessment while using AI for evidence, feedback, and scale.

If we embrace AI intelligently, assessment can finally reflect the purpose of education rather than distort it. We could measure not only what students know but how they reason, how they collaborate, how they grow. We could build scaffolds that are strong enough to hold the weight of a modern education system without breaking under it.

When the teacher in Nashville dropped me off at the airport, she hesitated, then finally asked the question on her mind. Did I think AI was making it worse? Before I could answer, she asked the better one: Could it also make it better?

New Classrooms and No Classrooms

Go to any PTA meeting and you'll see that education does not suffer from any lack of analysis. People, rightfully, care tremendously about the education of the world's children. What we have lacked, though, are compelling prototypes. If we want to make meaningful changes in education, we need active schools and platforms to point to and say, “That is how we can change schools from within,” or “That is how we can rethink what it means to learn.” Today, we finally have a few of these prototypes, and their results are very promising.

Walk into the right classroom in Austin, Texas, and at first glance, things will look normal: desks, chairs, brightly colored markers, and students at their work. But when you look a little closer at Alpha School, especially if you show up outside the two hours a day they dedicate to academic learning, you'll see things are a bit different.

Alpha School started from a crushing conversation at CEO Mackenzie Price’s kitchen table. Her daughter, a curious, energetic, and eager‐to‐learn second grader, came home from her Austin public elementary school and told her parents “I don't want to go back to school.”

Only two‐and‐a‐half years into her education, the place meant to nurture her learning had snuffed out her curiosity and joy. Price, a product of public school who went on to Stanford, raised her concerns with the principal. The response was sympathetic but blunt. Reforming the school system, the principal said, was like steering the Titanic. Too big, too entrenched, too slow to change.

“That was the moment I realized it wasn't about the teachers or the kids,” Price later told me, “It was the model itself. It was broken.” If the system was too large to turn, Price decided she would build something new. She would build it to nurture curiosity and a love of learning. Alpha School started with a radical proposition: children don’t need six or seven hours of traditional academic instruction every day, they need just two.

Price realized that academic study needed to be personalized, adaptive, and relentless in its focus on mastery. The two‐hour model works not because children need less learning, but because they need less wasted time. Traditional schools lose enormous amounts of time to classroom management, transitions, repetition, and mismatched content. A child who grasps a concept quickly must sit through additional practice. A child who struggles is dragged forward before mastering the basics. The pace is set by the average, which means it fits no one.

Two hours is not a shortcut. It is an optimization, and AI helps bring that optimization to each student. In each morning's academic session, students work with AI‐powered platforms that adapt lessons to an individual's pace and profile, giving them the appropriate one‐on‐one tutoring they need. A child struggling with fractions who loves Dungeons & Dragons might see the concept taught through a dice game. A child who loves to read and races ahead is immediately given more challenging texts to nurture and fuel their passion.

Once the academic core is handled, students spend their school time pursuing athletics, arts, exploration, and life skills. “The rest of the day should be about becoming a human being,” Price says. Students learn public speaking by giving weekly talks. They practice financial literacy by running mock businesses. They develop grit with physical competitions that challenge their composure and sticktoitiveness across a combination of juggling, running, and solving Rubik's cubes. This is the genius of Alpha School. It treats scholastic mastery as essential but not all‐consuming.

“Information transfer is maybe 10% of learning, the other 90% is motivation,” Price says, “That is why we invest so much in guides, in coaching, in everything outside the two hours.” “Guide” is the title given to adults working at Alpha School. They are not necessarily certified teachers; some come from athletic backgrounds, others from business or coaching. What matters is their ability to light sparks in children. Rather than delivering content, they coach, inspire, and motivate. Every student has a one‐on‐one meeting with a guide each week to talk about their goals, challenges, and passions.

The model is replicable. AI tutoring does not tire. It does not discriminate. As Price puts it, “An AI tutor doesn't care if a student is white, black, or brown. It doesn't care if they are rich or poor. It is infinitely patient, and it is infinitely scalable.”

Alpha will not be the model for every child. Tuition is currently high, its structure may not fit every community, and not every family wants such a radical departure from tradition. Empirically, though, the model works. Two hours of personalized academic learning, powered by AI, delivers better outcomes than six hours of factory schooling.

In January 2023, Alpha opened a campus in Brownsville, Texas, serving students who had historically scored in the bottom third nationally. Within a year, those same students had vaulted from the 31st percentile in math to the 84th. Reading jumped from the 31st to the 71st percentile. In a single year, Alpha closed decades of achievement gaps.

Across Alpha's campuses, the gains have been consistent. Internal data show that students advance through math concepts at nearly twice the national average, with 85% of students mastering each unit on the first attempt compared to about 40% in traditional classrooms. Reading comprehension has tracked similarly, with students progressing a full grade level ahead of schedule in less than a year.

The benefits are showing themselves not just in academic success, but in how students arrive in the classroom. Attendance rates hover above 95%, and surveys of student motivation show that more than 90% report looking forward to school each day. In a country where chronic absenteeism and disengagement are rampant, those numbers are as important as the test scores.

If we know this, why cling to the old model?

We are comfortable adding tablets to classrooms, tweaking tests, layering reforms on the old skeleton. We keep children in school for six or seven hours because it aligns with work schedules. We are not yet comfortable with reinvention. Alpha School forces us to face that discomfort.

Alpha School has already cleared the technological threshold. The societal threshold, our willingness to redesign school days around that capability, lags far behind. While the adoption gap is wide when we try to reform education from within traditional institutions, outside, it is remarkably narrow. Around the world, people are eager for new opportunities, and tools, to learn.

Access to education can mean the difference between a world full of opportunities, and one where options stagnate. Luis von Ahn, a computer scientist who grew up in Guatemala, had seen this reality play out firsthand. In his home country, the availability and affordability of language learning kept many people from economic growth.

For centuries languages, much like pre‐Renaissance written texts, were the arena of the wealthy, available only through tutors, university courses, immersion programs that cost thousands of dollars, and expensive travel to foreign countries. Limited access to language education, both from cost and availability, often closed the door to new opportunities.

von Ahn had a different vision. One where language education was free, global, and fun. Rather than trying to recreate a school day, he rethought what it meant to have a teacher in the first place. In doing so, he created Duolingo, a playful mobile app that taught languages in short bursts, wherever the learner happened to be. When given the opportunity to learn, people all over the world jumped at it; Duolingo reached 5 million users in less than two years.

In 2023, Duolingo collaborated with OpenAI to make an even further leap forward. I had the privilege of helping with these efforts as we worked to take Duolingo’s success and accessibility and use large language models to make learning feel more human. The team wanted to move beyond drills and flashcards into something dynamic: an AI that could roleplay conversations, explain mistakes with patience, and adapt to users' learning levels. When we saw the early prototypes, it was obvious that something had shifted. This wasn’t just gamification anymore. It felt like tutoring at scale.

The result was Duolingo Max, which introduced two key features. With Explain My Answer, learners could ask why they got something right or wrong and receive instant, personalized feedback. With Roleplay, they could practice conversations with an infinitely patient AI partner that adapted to their learning level and style. These features created, at scale, what had always been scarce: individualized attention. AI makes that level of feedback available to anyone, anywhere, affordably.

The results are staggering. By 2025, Duolingo had more than 130 million monthly users, 46 million daily active users, and 10.9 million paying subscribers. Its revenue was growing at more than 40% annually, powered by subscription growth of nearly the same pace. The outcomes were even more impressive. Independent studies have shown Duolingo’s learners achieve proficiency comparable to university courses, but at a fraction of the cost and time. Now, the platform is expanding beyond language into math, chess, and even music, applying the same personalized learning model to entirely new domains.

Quality tutoring, once reserved for the wealthy, is now accessible to hundreds of millions of learners. A skill that once depended on wealth, geography, and privilege is now within reach of anyone with a smartphone connection. The opportunities are as well.

Education in the RenAIssance will be defined by abundance. Abundant attention, abundant feedback, abundant pathways to mastery. Alpha School and Duolingo show us that education does not have to be bound by the scarcity of time, attention, or access. It can expand opportunities and cultivate curiosity, creativity, and the human spirit. The bottlenecks of education are artificial, not inevitable.

Notes

1. Library of Congress, “Compulsory Education Laws,” Library of Congress, https://guides.loc.gov/compulsory-education.

2. National Center for Education Statistics, “NAEP Mathematics Assessment Results: Grades 4 and 8, 2024,” The Nation's Report Card, U.S. Department of Education, 2024, 39 percent of fourth‐grade students performed at or above NAEP Proficient on the mathematics assessment; accessed July 20, 2025, https://nces.ed.gov/nationsreportcard; National Center for Education Statistics, “NAEP Reading Assessment Results: Grades 4 and 8, 2024,” The Nation's Report Card, U.S. Department of Education, 2024, 31 percent of fourth‐grade students performed at or above the NAEP Proficient level on the reading assessment; accessed July 20, 2025, report cover page, The Nation's Report Card.

3. National Center for Education Statistics, “Explore Results for the 2024 NAEP Mathematics Assessment: Grades 4 and 8,” The Nation's Report Card, U.S. Department of Education, 2024, 28 percent of eighth‐grade students performed at or above NAEP Proficient in mathematics (thus 72 percent below proficient); accessed July 20, 2025, The Nation's Report Card; National Center for Education Statistics, “Explore Results for the 2024 NAEP Reading Assessment: Grades 4 and 8,” The Nation's Report Card, U.S. Department of Education, 2024, 30 percent of eighth‐grade students performed at or above NAEP Proficient in reading (thus 70 percent below proficient); accessed July 20, 2025, The Nation's Report Card.

4. Public School Review, “What Parents Should Know About Graduation Rates,” Public School Review, 2025, accessed Aug. 23, 2025, https://www.publicschoolreview.com/blog/what-parents-should-know-about-graduation-rates.

5. McKinsey & Company, “COVID‐19 and Education: The Lingering Effects of Unfinished Learning,” McKinsey & Company, Sept. 2021, accessed Aug. 23, 2025, https://www.mckinsey.com/~/media/mckinsey/industries/public%20and%20social%20sector/our%20insights/covid%2019%20and%20education%20the%20lingering%20effects%20of%20unfinished%20learning/covid-19-and-education-the-lingering-effects-of-unfinished-learning-v3.pdf.

6. Nickow, A. J., Oreopoulos, P., & Quan, V., The Impressive Effects of Tutoring on PreK‐12 Learning: A Systematic Review and Meta‐Analysis of the Experimental Evidence, National Bureau of Economic Research, Jul. 2020, accessed Aug. 23, 2025, https://www.nber.org/papers/w27476.

7. Corak, R., & Owens, G., Expanding a City‐Wide Tutoring Effort: Lessons from Chicago Public Schools, MDRC, Jun. 2024, accessed Aug. 23, 2025, https://www.mdrc.org/publications/expanding-city-wide-tutoring-effort-lessons-chicago-public-schools.

8. Liz Mineo, “Good Genes Are Nice, but Joy Is Better,” Harvard Gazette, April 11, 2017, https://news.harvard.edu/gazette/story/2017/04/over-nearly-80-years-harvard-study-has-been-showing-how-to-live-a-healthy-and-happy-life/.

9. National Education Association, “NEA Survey: Massive Staff Shortages in Schools Leading to Educator Burnout; Alarming Number of Educators Indicating They Plan to Leave Profession,” National Education Association, Feb. 1, 2022, accessed Aug. 23, 2025, https://www.nea.org/about-nea/media-center/press-releases/nea-survey-massive-staff-shortages-schools-leading-educator-burnout-alarming-number-educators.

10. RAND Corporation, “2025 State of the American Teacher: Teachers’ Job‐Related Stress, Well‐Being, and Intent to Leave,” RAND Corporation, 2025, accessed Aug. 23, 2025, https://www.rand.org/content/dam/rand/pubs/research_reports/RRA1100/RRA1108-16/RAND_RRA1108-16.pdf.

11. Joanne Jacobs, “Success Academy charters are #1 in New York in math, #3 in reading,” Joanne Jacobs, Jan. 19, 2024, accessed Aug. 23, 2025, https://www.joannejacobs.com/post/success-academy-charters-are-1-in-new-york-in-math-3-in-reading.

12. U.S. Department of Education, “Every Student Succeeds Act (ESSA),” U.S. Department of Education, Dec. 10, 2015, accessed Aug. 23, 2025, https://www.ed.gov/essa.

13. National Center for Education Statistics, “An Early Look at How 2021–22 State Proficiency Standards Map Onto NAEP Scales,” National Center for Education Statistics, Apr. 2023, accessed Aug. 23, 2025, https://nces.ed.gov/nationsreportcard/blog/mapping_2021_2022_state_proficiency_on_naep_scales.aspx.

14. MIT Media Lab via The Register, “Brain activity lower when using AI chatbots: MIT research,” The Register, Jun. 18, 2025, accessed Aug. 23, 2025, https://www.theregister.com/2025/06/18/is_ai_changing_our_brains/.

15. UNICEF Innocenti, “Beyond algorithms: Three signals of changing AI‐child interaction,” UNICEF, May 23, 2025, accessed Aug. 23, 2025, https://www.unicef.org/innocenti/stories/beyond-algorithms-three-signals-changing-ai-child-interaction.

CHAPTER 7
Widening Wall Street

Origins of Banking

When Giovanni di Bicci de' Medici opened his family's bank in Florence in 1397, he helped start a revolution in finance. Banking had existed in scattered forms before, but the Medici were among the first to formalize it as an institution. Their clients were monarchs, merchants, and even the papacy. They used simple tools that proved powerful in reorganizing the global economy. Bills of exchange allowed merchants to settle debts across cities without carrying gold. Double‐entry bookkeeping tracked exchange systematically, tallying profit and loss with precision for the first time.

It's easy to think banking is as old as the idea of money, but for centuries before Medici opened his bank, money was tied to land, treasure chests, or barter. Trade was local, fragile, and risky to expand. But from Florence, Venice, and Genoa, Medici and other merchant families turned it into something that flowed across borders, funded new enterprises, and fueled upward mobility. Finance changed from storing wealth, to building it.

The Medici's customers extended far beyond the Pope. The rise of merchant banks helped fund artists, explorers, and scholars, giving fuel to the Renaissance's creative machine. Their fortunes underwrote the works of Brunelleschi, Botticelli, and Michelangelo. The genius of the era could be seen spreading across Europe in paintings, sculptures, and voyages of discovery, but beneath it all, the burgeoning financial architecture made it possible.

Finance became a global power of its own. In the 1600s Amsterdam and London developed the first national banks, designed not only to serve merchants but to stabilize entire economies, a savvy move when, by then, the Medici family included Giovanni's grandson, Lorenzo de' Medici, inspiration for Machiavelli's The Prince. The Bank of Amsterdam became a clearinghouse for international trade. The Bank of England pioneered the idea of a government‐backed central bank, creating standardized currency and financing the expansion of their empire. Finance became a public utility, essential to the functioning of states.

In the centuries that followed, banks continued their growth, and their access. By the 18th and 19th centuries, stock exchanges allowed companies to raise capital from broad pools of investors, while commercial banks financed trade, industry, and infrastructure on a global scale. The 20th century saw banking arrive to the masses as consumer credit became mainstream, fueling homeownership, education, and mass consumption. At each stage, when access widened, society flourished.

The banking system hasn't always existed, but we've certainly inherited it. The credit card in your wallet, the mortgage on your home, and the automatic deposits in your bank account all trace their lineage back to those Florentine ledgers. But its original promise, that it could help build wealth and make mobility possible for anyone who works for it, has not been fulfilled equally.

Failure by Design

A friend once told me about the first credit card he ever had. He was 19, just out of high school, and thrilled when the bank approved him. The card came with a modest limit and a shiny promise: “Build your credit history.” He started using it for small purchases like gas, groceries, and the occasional dinner out. At first, the monthly bills were manageable. But one month he missed a payment. The next month, the balance rolled forward with interest. He hadn't noticed the fine print. His rate had jumped to nearly 30%. Within a year, he owed more than double what he had spent.

When he finally asked for help, his father shook his head and said, “You should have known better. You should have read the contract.” The truth is, he had read it. He just hadn't understood it. The language of finance wasn't written for him. The penalties were designed to trap, not teach, and they worked by design. By the time he understood the rules, he was already thousands of dollars behind.

The architecture that funded global exploration and discovery now leaves people anxious, confused, and feeling punished. And that's if you're lucky enough to have access to a bank. More than five million Americans remain unbanked, lacking even a checking or savings account,¹ removing them from systems that should help build wealth. Globally, that number reaches 1.4 billion.² Millions more in the United States are “underbanked,” technically part of the system but reliant on payday loans, check‐cashing stores, or high‐fee credit products.

For many, being inside the system feels no more comfortable. On average, financial mistakes cost households more than $1,500 each year in overdrafts, late fees, high‐interest debt, or missed investment opportunities.³ Interest rates on revolving credit routinely reach 20–30%.⁴ Families living paycheck to paycheck live in constant limbo between stability and free fall. Costs compound. The consequence is a quiet accumulation of bloat, locking people into nearly inescapable cycles of debt. Financial products are buried in jargon, legalese, and fine print. Complexity is a feature of the system, a business model in its own right. The harder the rules are to understand, the more profitable the products become.

Even trying to pay your taxes is a gauntlet. I know it sounds crazy to say, but it should feel good. Taxes are a civic duty, our personal contribution to the society that provides our roads, schools, and safety nets. But instead it often feels like punishment. Ordinary families spend hours filling out forms only to overpay because they cannot decipher the rules. Wealthy individuals and corporations, meanwhile, employ armies of advisors to navigate loopholes and minimize their burdens. Those with resources hire guides and move through with ease. Those without, stumble in the dark.

It's no surprise then that for many, money is synonymous with anxiety. It takes a psychological toll. Even when they do everything “right,” trying to save for retirement, invest, and pay their debt on time, the rules seem to shift beneath them. Finance is supposed to be a tool, but too often it feels like a test—one most people suspect they are failing.

When one person is paying, you can be sure someone else is collecting. The institutions that administer this system are thriving. Banks and credit card companies are reporting record profits, and shareholders are rewarded handsomely. The result is a widening gulf between what finance promises and what many people experience. People sense that the system is not built for them, even if they cannot always articulate why. That opacity is part of the problem. If someone punches you, you know you've been hit, and you know who hit you. But financial harms are often unseen.

This is what makes the system so insidious. Finance today compounds two forces. People often lack the literacy to recognize traps, and the system profits from that lack of recognition. Illiteracy and predation reinforce one another, creating a two‐headed monster that keeps people stuck.

The Two‐Headed Monster

The less people know, the easier they are to exploit. The more exploitative the products, the harder it becomes to learn. Financial illiteracy and predatory design are two heads of the same monster, and each feeds the other. Most often, we can't even see it happening.

Finance is different from other broken systems. If your roof leaks, you can see the water on the floor. If your tax software silently funnels you into a higher bracket because you misunderstood a deduction, you may never know what you lost. The harm is unique because it is both real and invisible.

Part of the problem is that most of us were never taught how money actually works. Only 23 states in the United States require a personal finance course for high school graduation.⁵ 43% of American adults are considered financially illiterate, unable to answer even basic questions about inflation, interest, or risk.⁶ That ignorance carries a price tag. People who lack financial knowledge are more likely to accumulate unmanageable debt, live without savings, or be drawn into scams and predatory loans.

Once people fall behind, the system offers few ladders to climb out. More often than not, they get the same response my friend got when he finally asked his dad for help sorting out his credit card debt, “You should have known better.” He wasn't reckless. He was uninformed. And the system took advantage of that ignorance. A population that cannot see the rules of the game is unlikely to win it.

When we're confused, we're easy prey. And the system is all too ready to capitalize. The financial market is awash with tempting offers designed to deliberately obscure costs, extract value, and take advantage of need. Credit cards advertise “no annual fee” while burying penalty interest in the fine print, mortgage documents run hundreds of pages, payday loans make loud promises to fill the gaps while quietly delivering triple‐digit interest rates. These designs are not accidents. They are profitable. Late fees and penalty rates are revenue streams for credit card companies. Overdraft fees alone generate billions annually for banks. It is open season on consumers, and the hunting is good.

Governance plays its part, and lobbyists and special interests feed the monster. Tax software companies spend millions in Congress to block a free IRS filing option; confusion is their business. Agencies meant to protect consumers are often captured by the industries they oversee. Even the pieces of the system that present themselves as neutral, tilt at every angle to favor insiders.

What makes this monster so effective is that the damage compounds. Illiteracy makes predation easier. Predation makes literacy harder. We're taught to learn fear over fluency. As we lose money, we lose trust that the rules are fair. Over time, this erodes the idea of finance as a tool for mobility at all. The damage ripples outward. It suppresses consumer spending, widens inequality, and weakens social trust.

Unlike medieval serfs, we cannot even see the oppressive manor looming above us. That invisibility is what sustains the status quo. You cannot demand reform for a harm you do not realize you are suffering.

At its most powerful, AI is a spotlight. It can make the invisible visible by translating opaque contracts into plain language, flagging hidden fees in a bank statement, or alerting someone when their tax software has pushed them into an unnecessary payment. It can educate us by simulating the long‐term cost of carrying a balance at 30% APR and make the damage impossible to ignore.

AI will strip away the veil that allows predation to thrive, and level the information asymmetry that has defined modern finance. And that's the first step in winning the fight.

The Solution: Literacy, Access, and Visibility

When I was a child, I watched my fair share of Saturday morning cartoons. No matter how bad the situation seemed, or how powerful the bad guy was, the good guys always found a way to win. If they were facing a two‐headed monster, it only makes sense that they'd bring a three‐bladed weapon.

To fight illiteracy and predatory design, we need three distinct attacks: teaching people the language of money, giving them true access to the system, and shining a light on the hidden mechanisms that keep them stuck. Literacy, access, and visibility. Each is necessary, and none can do the job on its own. AI can strengthen all three.

Literacy is the first step. Without it, people can't know the tools available to them, let alone how to use them. Access is the path forward itself. Visibility is how we prevent progress from being stolen back in fees, penalties, and traps.

Without basic financial knowledge, we can't translate our work into mobility, and yet most of us don’t learn these literacy lessons in school. AI‐driven systems can help us understand the decisions we make, and gain new financial opportunities. Consider a renter being shown, in plain language, how on‐time payments are building their credit history. Or how an AI financial coach can watch spending habits and offer budgeting suggestions, behavioral nudges, and explanations for concepts like credit utilization, and ETFs.

For centuries, finance has operated as a gatekeeping system. FICO scores rely on credit history, which creates a Catch‐22: you need credit to build credit. This structure systematically disadvantages immigrants, young adults, and low‐income earners. AI's ability to widen the lens of access and available data can open financial systems to those often left out. For immigrants, that means converting foreign credit profiles into U.S. equivalents. For first‐time borrowers without long lending histories, it can measure cash flow and spending habits to assess creditworthiness. AI's expanded analysis has already created lending approval increases of nearly 50% for Latino applicants, 41% for Black applicants, and 40% for women, all while holding risk constant.⁷

These innovations create lanes where there were once walls; visibility ensures we maintain this progress. This is where AI's explanatory power matters most. A tax return can be analyzed not only for compliance but for fairness, illuminating missed deductions and how new laws are structured to favor one group over another. A credit card statement can be turned into a simple story of how much you spent, how much your interest will be, and how much that will hurt your financial goals. When people can see the costs clearly, they can act on them. Visibility is the bridge between awareness and agency.

Of course, building a solution will take more than the right tools. AI can shine the light, and arm the populous, but governance and laws must prevent old interests from pushing people back into the dark.

While illiteracy and predatory design feed each other, literacy, access, and visibility form a self‐reinforcing cycle. Literacy helps people take advantage of access. Access gives them opportunities to apply what they learn. Visibility ensures we don't lose progress. AI can accelerate each. For centuries, finance has been a system that excluded, obscured, and extracted. For the first time, we have the tools to fight back. For the first time we have light, and that's the best disinfectant.

Building Financial Expertise and Mobility

About two‐thirds of Americans live paycheck to paycheck, and the border between staying afloat and spiraling into financial hardship is perilously thin. By the late 2010s, America's four largest banks raked in more than $11 billion a year in fees. Many of these were overdraft fees, a hidden tax on the poor, levied on the families least able to pay them. Even a single day's delay in a paycheck can lead to an overdraft fee, and without the extra money to pay for it, these fees can compound, sending family finances, and stress, into a tailspin.

Everyday banking often punishes the most vulnerable, but a new class of digital‐only “neobanks,” like Chime, is emerging to rewrite this relationship. Chime offers checking and savings accounts with no minimums, no overdraft fees, no monthly traps. They process direct deposits two days early, insulating consumers from the harm that comes when the money they earned is delivered late, through no fault of their own. Chime's core innovation, creating accounts that bake in financial well‐being from the outset, stands in stark contrast to the system of sweeping and oppressive fees that has become common.

AI is helping to extend Chime's ethos past avoiding harm and into providing guidance. Recently, Chime added AI‐enabled features that learn about their users and help them extend their financial health. Some of these features are familiar, like more efficiently flagging risk and catching fraud, but others are transformative. Chime encourages building healthier financial habits, like adding small amounts of money to a savings account, paying off debt, or rethinking regular purchases, with small “nudges” throughout the day. With the help of this guidance, Chime users collectively saved more than $18 billion in 2023. A Guided Behavioral Finance study found that members shifted from money avoidance to money mastery.

Chime's model activates dignity and empowers those who have been victimized by the financial system. For many, this felt more like a relief than a product. 97% of members say Chime's products helped at least one aspect of their financial progress, 81% say Chime improved their overall financial situation, and 67% use Chime as their primary financial provider, applying its instant‐feedback design to their everyday spending. Chime eliminated late fees, and helped consumers build financial resilience and opportunity.

With apps and neobanks like Chime, AI can help free people from the burden and predation of everyday banking. Importantly though, reaching this stage, financial stability, opens the door to the next stage, financial mobility. Here, once again, AI can take what was once an exclusive resource, expertise in financial planning, and bring it to more people.

One of my earliest projects after joining OpenAI was with Morgan Stanley. They had a big problem: the demand for financial advising was swelling, but they couldn’t hire the staff to meet it. McKinsey projects that in the next 10 years we will see a shortage of 90,000 to 100,000 financial advisors. Morgan Stanley could have tried to hire tens of thousands of new people, but there weren't anywhere near enough qualified candidates.

Morgan Stanley manages $4 trillion in client assets through more than 16,000 advisors. They've built their success on a reputation, developed over decades, of trust and expertise in the field. Diluting these pillars of the business was not an option. They were stuck. And that's how I ended up in their office talking about AI.

While they couldn't solve the problem with staff, they had something else incredibly valuable: data. Their research archives contained tens of thousands of documents and decades of accumulated knowledge. For most advisors, though, the sheer scale of this asset rendered it inaccessible; there was no way to leverage the information in real‐time with clients. Instead, advisors would be forced to hope they had memorized the correct information before a meeting or promise to “follow up” after digging through the database.

This human Achilles heel, though, was the perfect fuel for an AI co‐pilot. Using GPT‐4, we created a tool to mine Morgan Stanley's vast archives and deliver answers instantly, in plain English, during client sessions. Rather than hiring inexperienced advisors, they gave their existing advisors the kind of guidance that had previously required having the firm's senior strategist at the table, whispering in their ear.

This tool shifted the advisor's role from information retriever to interpreter and trusted partner. Internal data shows about 98% of Morgan Stanley's advisor teams now regularly rely on the AI assistant for internal information retrieval. AI is not substituting human expertise; it is expanding it by doing the deep research and allowing advisors to focus on building trust with their customers. Advice becomes more scalable, precise, and human.

Morgan Stanley may have paved the path, but they're hardly alone as other financial institutions continue to adopt AI at a staggering speed to expand the quality of their service, and the number of people they can reach.

When we scale financial guidance, we can scale financial mobility. AI could soon deliver a future where building stability and wealth isn't exclusive. Morgan Stanley and Chime offer two pathways in the financial RenAIssance. If designed correctly, both could transform finance from elite gatekeeping and extractive practices into an accessible and dignified system that delivers financial health in abundance, rather than preying on its scarcity.

Notes

1. Federal Deposit Insurance Corporation, “2023 FDIC National Survey of Unbanked and Underbanked Households: Executive Summary,” Federal Deposit Insurance Corporation, 2023, accessed Aug. 23, 2025, https://www.fdic.gov/household-survey/2023-fdic-national-survey-unbanked-and-underbanked-households-executive-summary.

2. World Bank, “Global Findex Database 2021: 1.4 Billion Adults Still Unbanked,” World Bank Feature, July 21, 2022, accessed Aug. 23, 2025, https://www.worldbank.org/en/news/feature/2022/07/21/covid-19-boosted-the-adoption-of-digital-financial-services.

3. Doxo Insights, “Average U.S. Household Pays $1,495 a Year in Hidden Costs Associated With Paying Bills,” Doxo, Dec. 12, 2024, accessed Aug. 23, 2025, https://www.doxo.com/insights/hidden-costs-of-bill-pay/.

4. Forbes Advisor, “What Is the Average Credit Card Interest Rate This Week?,” Forbes Advisor, Aug. 18, 2025, accessed Aug. 23, 2025, https://www.forbes.com/advisor/credit-cards/average-credit-card-interest-rate/.

5. Next Gen Personal Finance via Visual Capitalist, “How Many States Require a Personal Finance Course to Graduate?,” Visual Capitalist, 2025, accessed Aug. 23, 2025, https://www.visualcapitalist.com/personal-finance-requirements-by-state/.

6. Ipsos, “Money Masters Financial Literacy,” Ipsos, 2022, accessed Aug. 23, 2025, https://www.ipsos.com/en-us/news-polls/money-masters-financial-literacy.

7. “Zest AI's Credit Models Proven to Increase Loan Approvals for Every Protected Class,” Zest AI, January 31, 2024, https://www.prnewswire.com/news-releases/zest-ais-credit-models-proven-to-increase-loan-approvals-for-every-protected-class-302048828.html.

CHAPTER 8
Resuscitating Healthcare

Renaissance Origins

Before the Renaissance, medicine was more ritual than science. Care was spiritual, not physical, and often indistinguishable from superstition. If you were fortunate enough to reach a monastic infirmary, your treatment might involve prayers, relics, or concoctions of garlic and holy water. If you weren't so fortunate, outside “healers” worked with gruesome remedies that bordered on harm: bloodletting with leeches, purges, and crude surgeries performed by barbers with razors and without anesthesia.

For centuries, the intellectual foundation of medicine rested on Galen, a Roman physician whose anatomical models were drawn from animals—pigs, monkeys, and other creatures whose physiology, at best, only roughly resembled ours. His texts dominated Europe for more than 1,300 years, insulated from revision by the authority of tradition.

The Renaissance changed everything. Slowly at first, and then decisively. Dissection of human bodies, once banned by the Church, became more accepted. Andreas Vesalius published the pivotal text in 1543: De Humani Corporis Fabrica, filled with precise anatomical illustrations from the study of human cadavers, not pigs. For the first time, the body was mapped with fidelity. The book spread across Europe, disseminating a new method of medical observation and experiment based solidly in evidence.

Hospitals changed too, becoming medical rather than religious institutions. Illness was studied, recorded, and treated with growing sophistication. Public health boards emerged in cities, coordinating responses to outbreaks and experimenting with the idea that health could be managed for populations as well as individuals. Modern pharmacology started to develop with the use of chemicals and minerals that had demonstrable effects. Surgery became more specialized and, with the gradual introduction of antisepsis and anesthesia, more humane.

The shift was not smooth or universal, but medicine was becoming science. Superstition and mysticism lingered, but the balance had tipped. Observation, anatomy, and evidence were the path out of folklore, hierarchy, and faith. Healthcare started to resemble a system—imperfect, incomplete, but moving in the right direction.

We've had hundreds of years to build on this foundation, and the science of medicine has never been more advanced, but the way in which we deliver medicine today often looks medieval. Our therapies are extraordinary: mRNA vaccines can be designed in weeks, genetic editing tools like CRISPR can correct mutations at their source, and we can diagnose some diseases before symptoms appear, but costs drive families into bankruptcy, preventable diseases claim lives at scales that dwarf plagues of earlier centuries, and patients wait months for appointments.

The system we inherited was built for acute illness and unpredictable injury. It was optimized for emergency rooms, hospital admissions, and insurance contracts that assumed crises as the norm. But the leading causes of death today are chronic, preventable conditions shaped by lifestyle, environment, and long‐term risk. We are living longer lives, but our system is still designed for sudden ends. Just as the Renaissance demanded a break from Galen's ancient texts, today we need a break from entrenched architectures designed for a different age.

We have followed the Renaissance's call to revolutionize science; now we need to follow its call to rebuild systems. We're good at doing the unimaginable part, developing cures that seem like miracles. Now we need to do the hard part of abandoning tradition to build systems that deliver these cures equitably, humanely, and at scale.

The Broken Healthcare System

The United States should have the best healthcare system on Earth. Its assets are formidable: state‐of‐the‐art hospitals, laboratories at the frontier of discovery, devices and therapies that set global standards, and a culture of biomedical science that continually redefines what medicine can do.

But healthcare is not the same as science. An industry's true measure is not what it produces, but how well it delivers it. Yet today we educate clinicians for decades only to bury them in paperwork—administrative work that should be decoupled from true care and largely automated away—separating them from patients and wasting incomprehensible amounts of time and expertise. American healthcare is both broken and outdated, scarred by staggering costs, inequitable access, and outcomes that trail far behind peer nations.

I've committed to being an optimist, so, let's look at the good parts. There has never been a better time to be alive. Life expectancy has doubled since 1900. Vaccines have eradicated smallpox, contained polio, and saved hundreds of millions of lives. Survival rates for many cancers have soared. Diseases that were once death sentences—HIV, leukemia, even certain congenital heart defects—are now managed or cured.

But the reality is, it's not hard to find far grimmer statistics in the healthcare system. As of 2023, one in ten Americans had no health insurance. In states without Medicaid expansion, the number was closer to one in six.¹ Millions more were underinsured, carrying policies that left them exposed to crushing out‐of‐pocket costs and medical debt. Even basic preventive visits were unaffordable for many, allowing conditions that could have been easily addressed early to progress until interventions were more costly, less effective, and at times delivered too late.

Layered on top of this is an administrative machine so large that it siphons away unfathomable amounts of both money and time for care. Nearly one‐third of U.S. healthcare spending goes to paperwork, billing disputes, and insurance processing. Physicians spend an average of 28 hours per week on administrative tasks, more time than they spend with patients.² For clinicians, this reduces medicine to compliance and coding. For patients, it makes seeking care feel less like healing and more like arbitration.

The human toll of this bureaucracy is immense. Nearly half of American physicians report symptoms of burnout, citing excessive paperwork and the inability to provide the care their patients deserve.³ Nurses and allied health workers describe the same exhaustion. By 2033, the American Hospital Association projects that the nation will be short as many as 124,000 physicians. These shortages will arrive across both primary care and critical specialties like psychiatry, neurology, pathology, and radiology. To maintain even the current, already inadequate level of service, the system will need to hire 200,000 nurses every year.⁴ These shortages are not incidental. They are the predictable outcome of a structure that buries medical professionals in bureaucracy, strips away professional meaning, and drives attrition faster than replacements can be trained.

We're using a century‐old system to treat modern health. The architecture of American healthcare was built for acute illness and unpredictable injury, things you might find in an agrarian or industrial economy. For decades, this model made sense. Infectious disease and sudden injury were the greatest threats. But the health challenges of the 21st century are not sudden. They are slow, cumulative, and largely preventable. Obesity, diabetes, and heart disease, conditions shaped by diet, lifestyle, environment, and social determinants, are now the leading causes of death.

These are health issues best managed by early intervention, but the existing system only addresses them after they become entrenched. The structure manages decline rather than cultivating health, treating symptoms rather than working to avert them years earlier. If we know the path to better, more affordable, care, why is it so hard to get there?

Unsurprisingly, the answer reveals itself when you follow the money. Healthcare finds its margin in illness. Providers, insurers, and pharmaceutical companies profit from procedures, hospitalizations, and maintenance therapies, not from the absence of disease. A hospitalization generates revenue. A healthy patient generates none. Insurance contracts are written to handle rare and catastrophic events, not the costs of keeping yourself healthy. Reimbursement models were designed to reward crisis response: the ambulance ride, the surgery, the intensive care stay.

The consequences are demoralizing. In the wealthiest nation in the world, perhaps the wealthiest society in history, patients are driven into debt by routine care, clinicians are pushed out of their profession, and the failures are paid with lost years of healthy life. The brokenness of the system is not accidental. It is the product of status quo bias and greed of incumbents.

What is most frustrating is that these compounding flaws arrive when medical science is advancing at breathtaking speed. We should have the finest healthcare in history, but these hard‐earned advances are trapped inside a system designed for another age, unable to reach people equitably or consistently.

American healthcare is failing. Galen's animal anatomy persisted for 1,300 years until Vesalius overturned it with evidence. We need systematic change, but ours will take more than evidence. It will require action. It is time for another break with the past.

The Paradox of Progress

The absurdity of American healthcare struck me most clearly during a visit to one of the nation's premier hospitals. I had just come from a conference where researchers unveiled a breakthrough in gene editing that could correct deadly mutations in living patients. But just hours later, in the same city, a group of clinicians told me their patients were waiting three months for basic appointments. That is healthcare in America: miracles in the lab, misery in the clinic.

This contradiction haunts all of us. On stage, I'm often asked about the promise of new medical technologies: AI diagnostics, digital twins, nanomedicine. I talk about them with excitement. But in the hallways afterward, someone will pull me aside, a doctor, nurse, or parent, and tell me the other side of the story. They describe the patients they cannot see, the forms they cannot escape, the bills they cannot pay. The contrast between what we are capable of and what we deliver would be enough to make you laugh, if it weren't so devastating.

One way to make sense of this is to see healthcare in three parts: therapy, administration, and care. Therapy, the science, is racing ahead. Administration, the payment and regulatory structures, is stuck in the past. Care, the lived experience of patients and providers, has frayed. The imbalance between these three explains why people feel the system is failing even as medicine achieves near miracles.

Modern medical therapy is a triumph of human achievement. In the last decade alone, cancer therapies have become tailored to the genetic signatures of tumors. CRISPR therapies have allowed us to effectively eliminate the genetic errors that drive some disease. During COVID‐19, researchers designed and distributed mRNA vaccines at record speed. AI models now read chest X‐rays more accurately than trained radiologists, and early prototypes of “digital twins” can simulate decades of treatment on a virtual copy of a patient's body in a matter of hours. If you judge medicine only by its scientific frontier, the future is almost utopian.

Administration has barely budged in decades. Compliance systems still bury doctors and nurses in forms. Insurance contracts still incentivize crisis care over prevention. Billing codes reward procedures, not wellness. The payment model of the 20th century has survived untouched into the 21st, becoming the cap that keeps therapy's advances bottled up.

Care, meanwhile, has frayed. The clinical encounter has been hollowed out into short, fragmented visits, with patients saying they feel like cases, not people. A patient's humanity, their context, fears, and hopes, is often lost in the churn of paperwork and productivity quotas. In Part I, I described dehumanization as the moment when the procedural eclipses the personal. That is exactly what medicine feels like for many: precise on the molecular level, careless on the human one.

This is the paradox of progress: we are living in an age of breathtaking therapeutic discovery, yet the delivery systems that should translate these discoveries into lived health are stagnant or regressive. In Part I, I called this the adoption gap—the distance between what is technically possible and what society will let that technology do. In healthcare, the system has created that gap. And it is not just wide. It is grotesque.

The data makes the point plain. The United States produces more biomedical patents than any other nation. Its hospitals are equipped with the most advanced imaging machines, surgical robots, and laboratories in the world. And yet Americans live five years fewer than their counterparts in other wealthy nations. We spend more than twice as much per capita on healthcare as the OECD average. Infant mortality is 50% higher. Maternal mortality is nearly five times higher.⁵ Chronic conditions that should be preventable—obesity, diabetes, heart disease—remain the leading causes of death. The frontier keeps advancing. The baseline keeps slipping.

Patients feel this paradox directly. They read news about AI that diagnoses cancer earlier than any human doctor, but wait months for basic scans. They hear about billion‐dollar therapies for rare genetic diseases, then struggle to afford the insulin that keeps them alive. The frustration is not only with outcomes but with the mismatch between promise and practice. If things continue as they are, the imbalance will deepen.

When knowledge surges and institutions lag, the Renaissance tells us it is time for major change. Our task now is to ensure that our RenAIssance gives us medicine as a system—one that aligns therapy, administration, and care so that the miracles of discovery become the lived experience of health.

Precision, Prevention, and Presence

The framework for that redesign rests on three ideas: precision, prevention, and presence. Precision means abandoning the blunt tools of population averages and delivering medicine that is individualized and exact. Prevention means moving the system's center of gravity away from crisis response and toward proactive health. Presence means restoring the human connection at the heart of healing by giving doctors, nurses, and allied professionals the time and attention they need to be caregivers rather than clerks.

These ideas are mutually reinforcing. Precision without prevention is still reactive. Prevention without presence risks becoming cold and bureaucratic. Presence without precision can be compassionate but ineffective. It is only when the three are aligned that therapy, administration, and care begin to function as a coherent system in service of health. The place where they converge most powerfully is primary care.

Precision is most valuable when it reaches the front lines of healthcare. It belongs in the exam room, not just the lab. For centuries, medicine was based on averages. Dosages were determined by population norms. Clinical trials generalized results across demographics despite vast underlying differences. Protocols were designed for the “typical” patient who, in reality, did not exist. This approach produced life‐saving interventions, but it also produced waste, side effects, and disparities.

AI changes this equation. By integrating multimodal data—lab results, imaging, genomics, electronic health records, lifestyle metrics, and even socioeconomic factors—it can identify patterns invisible to the human eye and tailor treatments to the individual rather than the aggregate. Already, algorithms are spotting early signs of heart disease, and predicting which patients will respond to particular drugs. These leaps in accuracy reduce trial‐and‐error, prevent unnecessary procedures, lower costs, and deliver care that is safer and more effective.

When primary care providers can leverage these tools, they can intervene earlier, treat more effectively, and keep people healthy. A highly accurate cancer‐detection model is transformative, but only if initial screenings are accessible. A predictive model for heart disease saves lives, but only if there is a clinician to interpret it and act before a crisis. Precision alone is not enough. It must be embedded in a system that no longer waits for people to become acutely ill before it acts.

Like precision, prevention works best when anchored in primary care. To fully realize this, healthcare needs to change from its reactive model optimized for emergencies to a proactive model optimized for actual human health. Upstream prevention eases the entire system downstream: fewer ER visits, fewer late‐stage diagnoses, fewer chronic conditions spiraling into crisis. Every case caught early in primary care is a hospitalization averted later. AI efficiencies that give doctors more time to interact with patients are part of the solution, but further integration can make a more meaningful and impactful difference.

AI enables health to be something patients address all the time, not just at their yearly physical. With continuous measurement from wearables, sensors, and electronic health records, it is possible to identify risk years before symptoms appear. Models can flag early indicators of dementia, predict the likelihood of sepsis, or signal elevated cardiovascular risk long before an event occurs. These technologies can also suggest behavioral changes, like prompts to walk, reminders to take medication, alerts about diet or sleep, before a patient heads to a doctor's office to report a problem. Prevention becomes actionable and active, not aspirational.

These are attainable and powerful ways to address the onset of chronic diseases, some of the most common causes of death in the American healthcare system. The economics are staggering. In the United States, 90% of healthcare spending is on chronic disease. Even modest reductions in incidence would save billions and, more importantly, improve quality of life for millions. Prevention at scale has always been an ideal. AI makes it practical.

Prevention without presence risks becoming impersonal. A message from an app is not the same as the reassurance of a nurse who knows your story. For prevention to work, we have to restore the human relationship. Here is where the quiet collapse of care is most visible. Our physicians, nurses, and allied professionals are reporting catastrophic levels of burnout, with many leaving the field entirely. They have the skill, they have the dedication, but they didn't enter the field to provide “care” that just looked like administration. Doctors are forced to spend more time charting than listening. Nurses juggle documentation, billing codes, and compliance checklists that leave little room for bedside presence. Patient visits are compressed into minutes, human stories flattened into forms. The system confuses throughput with treatment.

A hospital CEO once put it bluntly to me: “We tell everyone to go see their doctor, and we run campaigns to promote it. The dirty secret of healthcare is if every American actually tried to see a primary care doctor once a year, the entire system would collapse.” He didn't mean we lack the science or the will to care for people. He meant that the system we have built is not designed for care at scale.

That conversation crystallized the deeper problem for me. Hospitals and clinics have become machines of administration instead of hubs for healing. Somewhere along the way we mangled the meaning of “care.” AI offers the chance to untangle this. By automating the paperwork, managing logistics, and surfacing insights in real time, it can strip administration from care and give doctors, nurses, and allied professionals back the presence that makes their work meaningful.

Once again, the primary care visit is where this matters most. When people cannot get appointments, conditions go unnoticed until they escalate. Every gap in primary care cascades into greater costs and worse outcomes. A routine infection becomes a hospitalization. A manageable case of hypertension becomes a stroke. A missed checkup allows cancer to advance. But when primary care is accessible, efficient, and humane, it lightens the load across the system. Hospitals see fewer preventable admissions. Specialists see fewer crises. Emergency rooms stop serving as the default entry point for basic needs. The entire system breathes easier. Solving primary care is the first domino—once it tips, the whole system begins to rebalance.

The work of the RenAIssance in healthcare is to restore precision to the science, prevention to the system, and presence to the bedside. Primary care is where all three converge, creating a foundation that strengthens every other layer of the system.

The examples that follow are not speculative. They are signals of this alignment already in action. Curai Health is showing how precision and prevention can be scaled through virtual platforms. Abridge is showing how presence can be restored by reducing administrative overload. Function Health is showing how liberating medical data at scale can improve individual patient care. Together, they demonstrate what happens when theory becomes practice, when AI is not an abstraction but an instrument of realignment.

Expanding Access, Restoring Time

Unlike millions of Americans, I’m fortunate enough to have a primary care physician, a good friend Dr. Chris Thrash. Having access to his expertise, and the human relationship, means that in my life, healthcare often works as it should. When I have a concern for myself, or my family, I can reach out, get a quick “all clear,” or schedule an appointment for a follow up. This should not be notable; it should be commonplace. But today, many patients don’t have any outside contact with expert medical knowledge. Finally, after decades of operating in a broken system, there are promising indicators that AI can help us make a change.

The barrier of patient care isn’t medical knowledge. It’s delivering it in timelines that matter. Without easy access to primary care physicians or first points of medical contact, we push every medical concern through the same front door, and it’s getting jammed. Companies like Curai and Function Health are using AI to try to change this with a radical idea: put expertise into patients’ hands before the point of crisis.

Rather than waiting to see if their condition gets worse, patients can enter their symptoms into the Curai app as soon as they appear, 24/7. Its AI intake system chats with patients, measuring each detail against a simulated differential diagnosis to identify important information, triages cases, summarizes information, and draft care plans.

Human clinicians review the information and connect with patients in the app’s chat within minutes. Physicians say 92% of initial care plans are complete, but human clinicians make the final diagnosis and treatment decisions, prescribe medications, order labs, make referrals, and set up follow‐ups.

The company reports that their average patient has more than six visits a year, compared to the national average of less than two in‐person visits a year. More than 20% of patients said they would have gone to the ER, and more than 23% said they would have gone to urgent care, without Curai. A $35 digital consult prevents a $3,500 emergency admission.

It’s effective, and it meets a major need. Because of that, Curai is scaling fast. They’ve partnered with organizations including Tufts Medicine, Priority Health, Amazon Clinic, and Rula Health and are available in all 50 states. For a worried parent debating whether to take a child into urgent care, or a worker deciding whether to miss a shift for a nagging cough, that immediacy, affordability and access matters.

These advances are not confined to the U.S. AI is expanding access around the world. Apps like Ponea in Kenya, and Altibbi in Jordan are using AI chatbots to speed patient intake, direct patients to the right level of care, and triage cases. These efforts not only expand access to care without patients having to travel far distances to reach clinics, they also reduce load on often overworked clinicians. But AI can help us do more than respond; it can help us prepare.

Function Health uses AI to liberate medical data from the filing cabinet and puts it in people’s hands, turning lab work from backoffice administration to frontline care. For an affordable price, even without insurance, patients start by taking physician‐ordered tests through a national network of labs. This includes more than 100 different assessments that help build a comprehensive understanding of health.

After taking these tests, AI processes the results and sends them straight to the patients in plain language that flags risks, explains patterns, charts trends, and translates results and biomarkers into decisions you can actually make. When the questions get complex, a human clinician steps in to leverage their expertise. Continued annual tests allow patients to track their results against their baseline and work to improve. Function Health democratizes personal health knowledge, scales insights without scaling paperwork, lets clinicians do the high‐skill work, and relies on humans to provide true care at scale.

Critics argue that apps can never replace the nuance of a doctor. That’s true. And they shouldn’t. But the point isn’t replacement. It’s access. For decades, care has been locked behind appointments and gatekeepers. Curai and Function Health blow open the front door of medicine, and let patients move the first point of contact with medical information to wherever they need it, whenever they need. Dr. Thrash says these tools, and other tools like them, are already changing his practice, reducing patient hesitancy to seek care.

This success, however, will inevitably bump us against the other bottleneck of medical care: time. Dr. Thrash echoed what I have heard from primary care physicians across the country, “I spend more time coding visits than listening to patients.” As we’ve discussed, the research proves it. American physicians now spend more hours each week on administrative tasks than they do in direct patient care. That imbalance is crushing morale and accelerating burnout. Another AI solution, Abridge, is working to lighten the load.

Abridge listens during visits and produces structured notes in real time with natural‐language processing and clinical AI models. Thirty minutes of physician typing turns into seconds. As a copilot for clinicians, Abridge automates charting, coding, priorauthorization letters, and referrals, turning the tide of doctors’ time back toward meaningful connection with patients.

The impact is dramatic. A peer reviewed study reported that clinicians were five times more likely to feel they could finish notes before the next patient. UChicago Medicine reported a 37% reduction in “pajama time” work, the after‐hours clerical tasks that drag on well‐being. 67% of clinicians reported being less at risk of burnout, and the percentage of clinicians reporting they could give undivided attention increased from 49% to 90%. Patients feel the change, reporting that physicians showed more care, and improved explanations and decision making.

I’ve seen how it improves my own visits to the primary care office. Dr. Thrash recently said to me, “I spend more time truly listening. The patient no longer feels secondary to my notetaking.” Abridge doesn’t replace clinical judgment, it removes clerical burdens so the human can be fully present, improving retention, easing burnout, and giving the entire system greater resilience.

Together, Curai, Function Health, and Abridge show that healthcare’s scarcest resources—access and time—are not fixed. Access does not have to be rationed. Capacity does not have to be consumed by paperwork. Knowing a doctor by name doesn’t need to be notable. The RenAIssance in healthcare will not come from miracle drugs alone. It will come from rebuilding the system so that science and intimacy move together, finally allowing the extraordinary breakthroughs of modern medicine to reach the people they are designed to serve.

Notes

1. Commonwealth Fund, “2025 Scorecard on State Health System Performance,” June 2025, https://www.commonwealthfund.org/publications/scorecard/2025/jun/2025-scorecard-state-health-system-performance.

2. The Harris Poll, “Clinicians and Claims Staff Are Drowning in Administrative Tasks,” Chief Healthcare Executive, February 20, 2024, https://www.chiefhealthcareexecutive.com/view/administrative-work-takes-up-bulk-of-week-for-clinicians-medical-office-staff-poll.

3. American Medical Association, “Physician Burnout Rate Drops Below 50% for First Time in 4 Years,” AMA, October 13, 2023, https://www.ama-assn.org/practice-management/physician-health/physician-burnout-rate-drops-below-50-first-time-4-years.

4. “Fact Sheet: Strengthening the Health Care Workforce,” American Hospital Association, November 2021, https://www.aha.org/fact-sheets/2021-05-26-fact-sheet-strengthening-health-care-workforce.

5. Organisation for Economic Co‐operation and Development, Health at a Glance 2023: OECD Indicators (Paris: OECD Publishing, 2023), https://doi.org/10.1787/health-glance-2023-en.

CHAPTER 9
The Future of Work

For millennia, automation has targeted the jobs society valued least: the laborers, the clerks, the line workers. Machines came for the bottom first.

This time, it's different.

AI is coming straight for the top—for the credentialed, the analytical, the “safe” jobs that defined middle‐ and upper‐class stability for generations. Lawyers. Analysts. Marketers. Engineers. Even doctors. The very work once thought untouchable by automation suddenly feels most obviously automatable.

Some of this change will feel like loss. Some will feel like liberation. Most of it will feel unfamiliar—maybe even frightening. Like every great transformation, navigating the workforce of the RenAIssance will require reinvention. Our opportunity, and our responsibility to ourselves, is to understand where that reinvention begins, and how we can prepare to thrive in it.

We've been here before. In the late 18th century, British weavers and textile workers forcefully objected to the increased use of mechanized looms and knitting frames. Experienced artisans feared that unskilled machine operators would rob them of their livelihood, so some began breaking into factories and smashing textile machines. They called themselves “Luddites” after Ned Ludd, a fictitious young apprentice who was rumored to have wrecked a textile machine in 1779, and who ostensibly sent threatening letters to factory owners, signed “King Ludd.”¹

The protests were fierce enough that Britain passed the Frame Breaking Act of 1812, making it a capital offense to destroy the new machines, leaning on governance to close the adoption gap. In the end, no amount of resistance could stop mechanization from transforming the textile industry.

More recently, ridesharing completely upended the taxi industry. Uber and Lyft lowered the cost of a ride, expanded service to approximately 150 million people around the world, and created millions of additional driving jobs.² They simultaneously wiped out taxi medallion values and disrupted a tightly regulated system. Taxi drivers and regulators fought back, and in some places continue to do so, but the model proved too efficient and too popular to entirely reverse.

The pattern is clear, every leap forward sparks resistance from those it displaces. Some of that resistance is justified; the pain of transition is real. But over time, the same technologies that disrupt old ways of working create more opportunities than they eliminate. AI will follow the same trajectory, only faster, and across a wider range of work than any innovation before it.

Displacement, Augmentation, or Creation?

History supports the idea that job creation outpaces job loss in the long term. The first industrial revolution replaced agricultural laborers and artisans, yet sparked massive growth in manufacturing, logistics, and urban services. The second displaced certain factory and craft roles but created new opportunities in engineering, sales, and communications. The PC era erased the jobs of typists, switchboard operators, and travel agents but created entirely new, fruitful industries in software, networking, and digital media.

The World Economic Forum's 2025 Future of Jobs report forecasts that by 2030 the world will see a net gain of 78 million jobs globally. 92 million jobs will be displaced, but they'll be nearly doubled by the creation of 170 million new ones.³

Predicting AI's impact on jobs has become high‐stakes roulette, and the bets often say more about the gambler's worldview than the technology itself. Even among industry and economic experts, the topic is very much still up for debate.

On one side are leaders sounding early and impressively aggressive alarms. Anthropic CEO Dario Amodei has been unflinchingly blunt: “AI could eliminate up to 50% of entry‐level white‐collar jobs within five years,” potentially pushing unemployment to 10–20% in that period.⁴ He's accused both government and tech firms of actively “sugar‐coating” AI's risks, and warned that we should expect imminent widespread disruption in fields like law, finance, consulting, and technology.

Ford CEO Jim Farley, speaking at the Aspen Ideas Festival just months later, echoed the exact same number. “AI is going to replace literally half of all white‐collar workers,” he said, emphasizing that the only real protection lies in jobs rooted in the “essential economy,” those grounded in physical presence, manual skill, and human interaction.⁵

These aren't fringe voices or fearmongers. They're the people building and leading in the thick of massive economic and technical transitions. The list of concerned voices is long, and their warnings have generated tremendous press for the reasons I spelled out at the start of the book. Some of these concerns are already materializing. British consulting firm PricewaterhouseCoopers (PwC) recently lowered the number of new college graduates they would hire, citing AI's impact on their industry, and giving a strong signal that the job market for early‐career graduates could grow increasingly difficult in the years to come.

On the other side of the debate are leaders who predict a balanced shift of job displacement and creation. OpenAI CEO Sam Altman, speaking in June 2025, remarked that while job losses are inevitable and painful on an individual level, the pace of technological adoption is often slower than feared.⁶ Microsoft's CTO Kevin Scott has echoed this thought, noting that “even the most compelling technologies don't replace entrenched processes overnight.”⁷ The White House's AI and Crypto Czar, David Sacks, reinforced this view in mid‐2025, saying, “I don't think it's going to lead to a giant wave of unemployment. It's actually very hard to replace a human job entirely.”⁸

What they're pointing to is a lesson we explored earlier: just because technology can replace a job doesn't mean it will. Societal thresholds aren't tethered to technological ones. Strong unions, protective regulations, and powerful interest groups often slow or block automation entirely, preserving jobs that might otherwise be obsolete. Sometimes for good reason. Sometimes not.

The dockworkers' strike shows how far people will go to defend their place in the system. In a paper I published in 2025, Illegal Automation, I documented the ways special interest policies have made automation effectively untouchable in entire sectors of the U.S. economy. By conservative estimates, at least a million jobs in the United States are protected by these efforts.

Teachers' unions. Agricultural lobbies. Trade guilds. They've all found ways to stall automation, sometimes for decades. The result is a strange asymmetry: it may be easier to automate a radiologist than a lawyer—not because reading scans is simple, but because the lawyer has a stronger political shield.

And this tension shows up in everyday attitudes. Nearly everyone wants someone else's job automated—“When will this good or service be faster, better, or cheaper?”—but never their own. We're totally fine with disruption, as long as it lands in a different lane.

As for understanding the individual view, I've taken to asking every group I speak to two questions on this topic. First, do you believe AI will automate your job? Few raise their hands. I usually thank them for their honesty and bravery. Second, do you believe AI will automate someone's job in this room? Hands shoot up, and a collective chuckle echoes through the room.

This is what I call the zombie apocalypse phenomenon. It says that most people believe they would survive a zombie apocalypse, but the idiot neighbor would certainly die. We always cast ourselves as the savvy survivor, never the early casualty. It's the same logic behind not wearing a seatbelt or riding a bike without a helmet: “I've got this.”

Unsurprisingly, we apply the same logic to automation. We're not at risk, but someone else in the office? Oh, for sure. That guy's toast.

It's a strangely comforting psychological blind spot. We see disruption as inevitable, just not personal. We make smug jokes about the world sliding into idiocracy, but rarely count ourselves among the casualties. It's part ego, part self‐preservation, and part fundamental misunderstanding of how systems actually evolve. This disconnect—between personal immunity and collective inevitability—is part of what makes the future of work so difficult to predict, and so important to prepare for.

At this point, it feels safe to say that AI will erase some jobs entirely, alter the workflows of many more, and create roles we haven't yet imagined. But we can't prepare for what's coming if we're unwilling to name what's at risk. Clarity is the first step toward readiness.

Job Displacement Risk

The question I get asked, right after “Will AI kill us?”, is “Which jobs will AI automate first?”

Researchers have been racing to answer it. I've reviewed multiple major studies, including Will Robots Take My Job?,⁹ the Automation Exposure Score,¹⁰ the World Economic Forum's Future of Jobs report,¹¹ and recent research from Brookings,¹² Goldman Sachs,¹³ and McKinsey.¹⁴ Despite differences in methodology, the same general picture emerges.

The jobs at highest risk are ones that run on rails—routine, transactional, and built around structured information. Think bookkeeping clerks, telemarketers, basic graphic designers, retail salespeople, or customer service reps following a script.

In the middle sit jobs that involve some judgment but are still heavy on repeatable processes: paralegals, compliance officers, and technical drafters.

At the lowest risk are jobs that are embodied, relational, or require advanced fine‐motor skill. Nurses, early childhood educators, skilled tradespeople, performing artists. Even in a high‐tech future, a live performance or a home‐cooked meal will carry a kind of value no machine can replicate.

But I've found it useful to frame this less as a list of occupations and more as a set of spectrums, a way of seeing where a job lives.

The 3‐D Job Impact Framework

Transactional ↔ Relational

How much of the work is processing information versus building relationships?

Highly Transactional: Claims processors, invoice clerks, schedulers.

Highly Relational: Counselors, teachers, small business owners.

Computational ↔ Intuitive

Does the role follow codifiable rules, or does it rely on situational judgment?

Highly Computational: Accountants, logistics planners, lab technicians.

Highly Intuitive: Creative directors, artists, trial lawyers.

Directional ↔ Precise

How exact must the work be?

Highly Directional: Market analysts, first‐draft writers.

Highly Precise: Surgeons, aerospace engineers, master electricians.

The bingo card of risk hits when a job sits toward the transactional, computational, and directional ends of each spectrum. This work requires rules, calculations, or clear instructions, the kind of tasks AI excels at efficiently and affordably.

Inevitably plenty of factors, particularly societal thresholds, will produce exceptions to this rule. Some high‐risk jobs may persist for years under the protection of regulation or political influence, while certain lower‐risk roles could be automated surprisingly quickly if they lack such cover. Knowing where a role sits on the 3‐D framework is the starting point, but the path forward will be determined as much in legislatures and boardrooms as in research labs.

The danger of framing the future of work only in terms of “replacement” is that it misses the far more immediate story. Most jobs won't vanish overnight. Instead, they will be reshaped from the inside out. The real disruption won't come from machines that replace us, but from machines that work with us—changing how tasks get done, how value is created, and what it means to work. This is the rise of the augmented worker.

The Rise of the Augmented Worker

Nvidia CEO Jensen Huang issued a warning to the world's workers in May 2025: “You're not going to lose your job to an AI, you're going to lose it to someone who uses AI.”¹⁵

I bet he's right. The future of work is not man versus machine, but (in part) man with machine versus man without. Augmentation will define that gap: machines not replacing us, but reshaping how we work, what we do, and what it feels like.

Augmentation doesn't eliminate jobs; it changes them. AI automates what can be codified and frees humans to do what only humans can. The effect is less like replacement and more like amplification, as though we've all been paired with the smartest teammate we've ever had—the one who never tires, never forgets, and always makes everyone else better.

The evidence is everywhere. Office software drafts emails, cleans up prose, and summarizes meetings. Lawyers use AI to generate first‐pass contracts or scan thousands of precedents in seconds, freeing time for argument and client counsel. Doctors rely on systems that triage scans or draft reports while they focus on patients. Farmers fly drones and use sensors that diagnose crops in real time, turning hours of inspection into minutes of targeted care. Coaches analyze athletes' movements in slow motion with AI, offering feedback at a level that once required a team of specialists. Full automation in these fields is prohibitively complex. But augmentation gives people sharper tools for judgment, creativity, and presence.

This isn't the first time tools have shifted the boundary between human effort and machine support. The calculator made higher‐order math accessible at scale. The spreadsheet transformed finance forever. Google rewired how knowledge workers solve problems. Each collapsed the cost of certain tasks to nearly zero, while elevating the value of human oversight and interpretation. Mathematicians, accountants, and research librarians still had vital roles to play. AI fits that lineage—the difference is speed and scope. It is leaping from calculating to reasoning, from narrow tools to general‐purpose collaboration in years, not decades.

The economic stakes are staggering. Globally, OECD estimates that, today, 27% of jobs in developed economies have “high potential” for augmentation, with healthcare, education, and logistics leading the list.¹⁶ Accenture calculates that more than $10 trillion in additional economic value could be unlocked by 2038 through scaled, responsible adoption of generative AI, much of it driven by augmentation rather than replacement.¹⁷ This feels conservative.

It will not always feel liberating. For many, augmentation will feel like acceleration: an endless pressure to do more, faster. It will blur the line between work and non‐work. It will raise expectations. In the wrong hands, it could become a tool for surveillance and micromanagement. The challenge for organizations is not just adopting AI but implementing it in ways that build trust rather than corrode it.

To do this, organizations must focus as much on culture as technology. 95% of employees see value in working with AI, but far fewer believe their leaders will implement it in ways that ensure positive outcomes for everyone. The most important choice will be how AI is integrated. Companies that treat augmentation only as a path to efficiency will alienate workers. The ones that thrive will be those that use it to elevate human contribution, redesign workflows, and give people the freedom to focus on what only they can do. That requires more than investment in technology—it requires leadership that prizes adaptability, experimentation, and shared benefit. Technology won't define the future of work. Leadership will.

For individuals, I have some counterintuitive advice: “upskilling” is overrated. Success won't depend on learning the mechanics of AI. The tools will be self‐evident, they'll change constantly, and they'll change too rapidly to make technical mastery a differentiator. The people who will thrive will be those willing to adapt their practices, experiment with new ways of working, and abandon habits that no longer serve them.

I call this behavioral adaptability: the capacity to alter one's practices and habits in response to changing conditions. Unlike technical skills, which expire, behavioral adaptability endures because it reflects a disposition toward flexibility, experimentation, and unlearning.

In the next decade, behavioral adaptability will be the highest‐value quality in the workforce. The individuals and companies that cultivate it will not only keep pace with change, they'll set the pace of change. Even with strong credentials or resources, those who resist will be left behind. In the age of AI, behavioral adaptability will become the true currency of work.

New Work

Automation is eliminating jobs. Augmentation is reshaping today's jobs. But every major technological leap has created forms of labor that would have been unrecognizable beforehand. AI will be no different. New work is what comes next.

Some of that new work will actually look like a return to old work. As machines absorb the transactional and computational, demand will rise for what only humans can provide: qualities like presence, care, artistry, and experience. Coaching, teaching, caregiving, counseling, and artistic performance will grow in value. So will the work of craftspeople, physical therapists, and chefs. In an age where abundance makes everything synthetic, a handmade meal or a live performance will carry a premium. There will be cultural hunger for authenticity, and these roles will not be marginal. They will be central. AI will make things cheap and it will make people precious.

Our jobs will also head outward, toward the frontiers of science and exploration. AI will accelerate breakthroughs in climate adaptation and geoengineering, synthetic biology, quantum and fusion research, immersive education, even interplanetary logistics. It will expand the human project into domains once reserved for science fiction, and create new ones.

The reality is though, much of the new work ahead will be impossible to imagine until it arrives. It's easy enough to point out that in an AI economy, the value of human‐centric work will rise, but it's impossible to tell you what jobs that don't yet exist, will. It's an unsatisfying answer, people hate when I give it to them, but it's the one history has proven true over and over again. A century ago, no one could have predicted jobs like cybersecurity analyst or cloud architect, let alone flight attendant. Yet today, digital work defines vast portions of the global economy. The same will happen again. Entire categories of work will appear that we cannot yet name but that will feel indispensable to the next generation.

Work has always been more than economic output. It is the scaffolding of culture and identity. As the definition of valuable work changes, so will our sense of status, belonging, and a life well lived. AI will not end work. It will expand it, pushing us toward new ways of creating, connecting, and adding value that are invisible to us today.

Notes

1. Evan Andrews, “Who Were the Luddites?” HISTORY, August 21, 2023, https://www.history.com/news/who-were-the-luddites.

2. Visual Capitalist, “Visualizing Uber's Surge in Rides and Users (2015–2024),” Visual Capitalist, 2025, accessed Aug. 23, 2025, https://www.visualcapitalist.com/visualizing-ubers-surge-in-rides-and-users-2015-2024/ Condé Nast Traveler, “11 Sharing Economy Statistics That Will Surprise You,” Condé Nas.

3. World Economic Forum, “Future of Jobs Report 2025, project summary,” January 2025, https://www.weforum.org/publications/future-of-jobs-report-2025/.

4. Dario Amodei, “CEO of Anthropic, cited in ‘Behind the Curtain: A white‐collar bloodbath’,” Axios, May 28, 2025.

5. Ford Authority, “Ford CEO Jim Farley: ‘Artificial intelligence is going to replace literally half – literally half – of all white collar workers in the U.S.’,” Ford Authority, July 2, 2025, accessed Aug. 23, 2025, https://fordauthority.com/2025/07/ford-ceo-jim-farley-ai-will-replace-half-of-all-white-collar-workers/.

6. Gena Cox, “Sam Altman Upstages the Critics,” Platformer, June 27, 2025.

7. Microsoft, “Behind the Tech – Ask Me Anything with Microsoft CTO,” Kevin Scott, podcast episode, February 18 2025, https://www.microsoft.com/en-us/behind-the-tech/ask-me-anything-with-microsoft.cto-kevin-scott.

8. Quartz, “Trump AI Czar David Sacks: ‘I Don't Think It's Going to Lead to a Giant Wave of Unemployment’,” Quartz, June 11, 2025, accessed Aug. 23,  2025, https://qz.com/trump-ai-czar-david-sacks-wave-of-unemployment.

9. Frey, Carl Benedikt, and Michael A. Osborne, “The Future of Employment: How Susceptible Are Jobs to Computerisation?,” Oxford Martin School, 2013, accessed Aug. 23, 2025, https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment.

10. Colombo, Emilio ; Mercorio, Fabio ; Mezzanzanica, Mario ; Serino, Antonio, “Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs,” arXiv, Jul. 27, 2024, accessed Aug. 23, 2025, https://arxiv.org/abs/2407.19204.

11. World Economic Forum, “The Future of Jobs Report 2025,” World Economic Forum, Jan. 7, 2025, accessed Aug. 23, 2025, https://www.weforum.org/publications/the-future-of-jobs-report-2025.

12. Brookings Institution, “AI labor displacement and the limits of worker retraining,” Brookings Institution, May 16, 2025, accessed Aug. 23, 2025, https://www.brookings.edu/articles/ai-labor-displacement-and-the-limits-of-worker-retraining/.

13. Goldman Sachs Research, “How Will AI Affect the Global Workforce?,” Goldman Sachs Research, 2025, accessed Aug. 23, 2025, https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce.

14. McKinsey Global Institute, “A Future That Works: Automation, Employment, and Productivity,” McKinsey, 2017, accessed Aug. 23, 2025, https://www.mckinsey.com/~/media/mckinsey/featured%20insights/digital%20disruption/harnessing%20automation%20for%20a%20future%20that-works/mgi-a-future-that-works-in-brief.pdf.

15. NVIDIA CEO Jensen Huang, “You're not going to lose your job to an AI, but you're going to lose your job to someone who uses AI,” Milken Institute Global Conference, May 2025, accessed Aug. 23, 2025, https://opendatascience.com/nvidia-ceo-warns-jobs-at-risk-not-from-ai-but-from-those-who-use-it/.

16. OECD, “OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market,” report, July 2023, https://www.oecd.org/en/publications/2023/07/oecd-employment-outlook-2023_904bcef3.html.

17. “Accenture, Work, workforce, workers: Reinvented in the age of generative AI, executive summary,” 2024, https://www.accenture.com/content/dam/accenture/final/accenture.com/document-2/Accenture-Work-Can-Become-Era-Generative-AI.pdf.

PART III
A Simple Guide to the RenAIssance

When I set out to write this book two years ago, my goal was simple: to explain the RenAIssance in a way that let others see it, too. The scope, scale, and implications of AI are often distorted in the media, and as a result, poorly understood by the public. To bring clarity, I structured the book in three parts—mirroring how I hold it in my own mind.

First, we looked at the systems: the policies, institutions, psychological patterns, and global dynamics that set the boundaries of our future. Then, we turned to practice: how AI is reshaping the foundations of society and economy across our largest industries.

And now, in this final section, we arrive at the most essential force of all: you. No matter how the systems evolve, no matter how industries transform, the future will ultimately be shaped by our choices, our values, and our character.

CHAPTER 10
Principles for Thriving in the Era of AI

You may expect a conclusion filled with product recommendations or lessons in prompt engineering. I offer neither. Not because they lack value, but because they will vanish as quickly as they arrive. Tools will change, techniques will expire. What will endure is agency, judgment, and character.

What follows, then, is not a manual. It is a set of principles—meant to guide how we live, work, and lead in the age of unmetered intelligence.

I am writing now to my daughter.

The four principles are:

Go Outside

Learn How to Learn

Be Human

Lead with Optimism

Go Outside

As intelligence becomes increasingly unmetered, attention becomes increasingly precious. It will be incredibly cheap to create something to distract you, command your attention, and flatten the world into a screen or interface. Going outside gives life back its depth.

The physical world carries variety that no recommender system can deliver. Algorithms collapse toward similarity. Streets, markets, trails, and small talk offer variance and surprise. Serendipity does not follow a schedule. It happens when you are a little off route, a little curious, and fully present. It shakes loose stale assumptions and gives you answers that feel earned rather than inferred.

When you go outside into the physical world, you’ll face uncontrollable frictions—weather, noise, other people—that force choices and reveal preferences. Taste matures with constraints. This is where you become you.

Going outside also allows us to realize a great joy in life: being human with a human body. Sun on your skin will change your mood. A change in temperature will pull you out of rumination and into perception. Ideas that feel stuck indoors begin to move when your feet do. Pavement, grass, and gravel require your body to adjust. These inputs widen your perception and steady your judgment. Embodiment upgrades cognition.

If this era rewards the human layer, then time in the real world is also practice for the skills that will matter most—reading people, reading rooms, reading what is unsaid. Leadership, even just citizenship, improves when it is grounded in lived context rather than dashboards alone.

Go outside to rejoin the world your work is meant to serve. The places where we are able to connect. Let it refill your senses, widen your inputs, remind you what is real, and who you are. Then come back and build.

Nations have faced the same choice. In the mid‐19th century, Japan confronted the industrial power of the West. While some empires resisted change and fell, Japan chose differently. Through the Meiji Restoration it modernized its economy, military, and education system in a single generation, while holding fast to cultural anchors: the Emperor as symbol and Shinto as spiritual backbone. Japan adapted ferociously but anchored deeply, and emerged as a great global power.

No individual shows this more clearly than Steve Jobs. Fired from Apple in 1985, he reinvented himself through NeXT and Pixar. A decade later, he returned to Apple and reshaped it with the iMac, iPod, and iPhone. Jobs pivoted constantly into new products, new industries, and new strategies, yet he was anchored in one principle: design and simplicity.

The same will be true for you in the age of AI. Adaptability will keep you relevant. Anchors will keep you principled. If you confuse methods for values, you will collapse with them. But if you adapt while anchoring in your values, you will find the formula for resilience in any era.

Learn How to Learn

The most common question I get from young adults, especially on campus at UVA, is: “What is the best major to study?” What they’re really asking is: How do I make a lot of money?

It’s a fair question. Economic incentives are usually good drivers of behavior.

Except my answer almost always disappoints them: It doesn’t matter.

First, I tell them the correlation between your major and your economic outcome is unraveling so quickly that no one can predict with any confidence what major will guarantee you money. Then I tell them that college is probably best thought of as a place to make mistakes you weren’t comfortable making in front of your parents in high school. Lastly, I tell them if they haven’t picked a school yet, to go to one with a good football team, because not much is better than a great college town on Saturday gameday.

Often, unsatisfied by this answer, they’ll keep pressing. So, I offer this: study something you love.

Major in something you’re willing to obsess over, not for the grade, or an eventual paycheck, but out of fascination. The act of deeply exploring something is in and of itself far more important than the subject you explore. Even if (or inevitably, when) what you learn becomes economically obsolete, the process will have taught you something greater: how to learn something else.

Learning how to learn offers you so much more than any given major could anyways: the humility and willingness to say, “I do not know, yet.” The instinct to explore further than an assignment requires. The refusal to stop experimenting when early attempts fail.

“What should I study to make money?” is a question about a world that will not exist by the time students graduate.

Unmetered intelligence will change what differentiates you. You will not be defined by what you know, or even by what you have already mastered. You will be defined by your ability to master something new, and your drive to keep doing so again and again. Knowledge expires. Tools expire. But learning endures.

Be Human

AI is rapidly conquering domains once thought untouchable: medical imaging, legal argument,software engineering. Machines are mastering logic, calculation, and recall at scales humans cannot match. Which leaves a sharp question: What do we want humans to do?

We are already seeing roles re‐center on the human layer. Teachers use AI to tailor practice, then spend class time building confidence and curiosity. Clinicians lean on decision support, then focus on counseling, consent, and continuity of care. Managers get automatic dashboards, then use their time for clarity, 1:1s, and culture.

Throughout this book, the solutions point to the same place: humanistic optimization. I always saw this shift as something inevitable for brokerage jobs and services jobs, but I came to realize just how pervasive this shift would be only recently, thanks to my dad.

My father is an oncologist. For almost forty years, he has dedicated himself to research and patient care, specializing in breast cancer. When he started, breast cancer survival rates hovered around 30%; today, thanks to advances in science, it is close to 90%. To honor his career of service, he was recently awarded a Lifetime Achievement Award from the Santa Barbara Breast Cancer Center.

Growing up, I had always seen my father as a scientist, a man whose brilliance was in saving lives. It was a core part of my identity: my father, the genius doctor. I arrived at his award ceremony excited to hear the community reinforce this image.

That night, the speaker presenting my father the award was a former patient of his. She spoke for five minutes. For the first thirty seconds she spoke about her outcome: she had survived breast cancer, gone on to live a fulfilling life, and was now giving back to the community alongside my dad. For the four and a half minutes that followed, she didn’t talk about medicine.

First she described the fear of diagnosis and the search for a doctor. She had interviewed three oncologists. All three had provided her the same prognosis and the exact same treatment plan. Then she said two things I will never forget.

She said that after the third identical treatment plan, she realized that the science had advanced far enough that the computer was now more capable of determining the patient’s next best step than the human doctors were.

Then she talked about how the doctors made her feel. “If diagnostics are solved, and treatment is determined by machines, the bedside manner is no longer a feature. It is the product.”

And that changed my life. In that moment, sitting beside my now wife, my mother, and my sister, I came to two realizations.

First, I suddenly understood that my father’s greatness was not his brilliance. What made him irreplaceable to his patients and his community were the same qualities that made him irreplaceable to me as a father: his courage, his compassion, and his hope. Fittingly, the award bears his name: the Fred Kass “Healing Through Compassion” Award.

Second, if oncology, long exalted as one of the most complex medical practices, could be cognitively commoditized… then what job wouldn’t be?

In the near future, I have come to believe, AI won’t encourage us to be more human, it will force us to.

And so I’ll teach you the greatest lesson my father has taught me: courage, compassion, hope, curiosity, humor, wisdom, empathy are measures of human achievement. And soon these “soft skills” may in fact become our primary means of differentiation.

So my advice to you is: Be human.

You can already see it in the businesses that thrive. A neighborhood bar or family restaurant may serve food and drinks that are ordinary, even forgettable. Yet it is packed every night because the bartender remembers your name, the server teases you like an old friend, and the atmosphere makes you feel like you belong. Trader Joe's has become beloved not through price wars or loyalty schemes, but because its employees act like neighbors. They will walk you to the right aisle, talk about their favorite snacks, and create a shopping experience that feels personal in a world that can feel anonymous.

I came to realize how true this was only recently, thanks to my dad. After a long career in oncology, he received a Lifetime Achievement Award from the Santa Barbara Breast Cancer Center. For almost 40 years he has dedicated himself to cancer research and patient care. When he started practicing, breast cancer survival rates hovered around 30%. Today, thanks to advances in science, they are close to 90%. I grew up believing his brilliance was what made him exceptional.

At the ceremony, I sat in awe as doctors, administrators, and community leaders praised his contributions. Then, just before he received his award, a former patient took the stage. For the first 30 seconds she spoke about her outcome: she had survived breast cancer, gone on to live a fulfilling life, and was now giving back in the field alongside my father. For the four and a half minutes that followed, she did not talk about medicine. She talked about how my dad made her feel.

She told us that when she was diagnosed, she interviewed three oncologists. All three gave the same prognosis and the same treatment plan. The medicine was effectively standardized. “I realized that computers had advanced much further than the human brain in their ability to determine the next best step. Dr. Kass helped me see that if the diagnostics are solved and treatment is determined by machines, the bedside manner is no longer a feature. It is the product.”

Her decision was not what treatment to receive, but who would deliver it, and how.

In that moment, sitting beside my wife, my mother, and my sister, I came to two realizations. The first was that my father's greatness was not his brilliance. What made him irreplaceable to his patients and his community were the same qualities that made him irreplaceable to me as a father: his courage, his compassion, and his hope.

The second was this: at the limit, many services will differentiate not by what we know, but by how we make each other feel. Traits so often dismissed as “soft skills” will become our greatest edge: courage, compassion, hope, curiosity, humor, wisdom, empathy. These are the unmeasurable complexities of being human. They will become our strengths. If this is already true in oncology, one of the most complex and high‐stakes fields, then where will it not be?

So my advice to you is: Be human.

Lead with Optimism

Cynicism has a long pedigree. For most of human history, assuming the worst kept us alive. We evolved to avoid danger, to scan for loss, to distrust good news. In moments of uncertainty, optimism can feel naïve. Cynicism feels safer, more realistic, even smarter.

We inherited these instincts from ancestors who were chased by mammoths. They still color our judgment. We notice threats faster than opportunities. We remember failures longer than wins. We protect what we have and hesitate at the edge of the new.

Yet the world most of us inhabit keeps improving across the measures that matter: health, knowledge, safety, opportunity. Progress is uneven and reversible, yet the long arc bends upward when we do the work. In an era of unmetered intelligence, the cost of trying falls, we learn faster, and the payoff to constructive bets rises. Optimism becomes a rational operating stance.

It also becomes a civic duty. Progress does not happen by default. People build toward the stories they believe. Narrative sets policy. Belief sets budgets. A leader’s horizon becomes a team’s boundary conditions. If we tell stories of decline, we will write rules that constrain. If we tell credible stories of a better future, we will invest, upskill, and move.

So set the standard. You cannot expect people to do their best work while they are afraid. You cannot expect kids to feel excited about tomorrow if the adults in the room are not. Choose a future worth building. Say it out loud. Invest in it. Fight for it. Celebrate small advances and keep going.

Talk about a world that will be better—not because we live without problems, but because the solutions are more evident than ever—and do the work to make it so.

This is more than strategy; it is a moral obligation to your children. They will live inside the stories we speak and the systems we build.

Fear may grab attention, but only optimism builds lasting progress. Your vision should be a healthier, freer, happier, and more equitable world.

And as you consider your vision, revisit the three questions I asked at the outset: Is humanity nearing its limits, or just beginning its journey? Does the world, on balance, get better or worse? Can technology continue to expand what it means to be human?

History rewards the optimists. The future will, too.

Epilogue: A Last, Honest Story

There is a final question I would like to address. It's the one most people have asked me at various points along this journey: Did AI write this book?

The answer is no. But it allowed me to.

When I first conceived the idea for this book, I was eager to share a vision for the future, and a set of frameworks others could use to shape their own.

What followed tested my patience, my conviction, a few friendships, and (on many occasions) my desire to write the book at all. I hired two ghostwriters and multiple researchers. I leaned on dozens of friends. I spent more money than I care to admit trying to get my story onto the page.

On August 15, 2025, 10 days before the manuscript deadline, I felt a strange, hollow excitement. The pages said everything I wanted them to say. They just didn't sound like me.

So I decided to start over. I called a brilliant technical editor, James Lynch, whose work I had long admired, and asked him if he had anything important to do in the next week. Then I asked if he wanted to write a book. That night, the two of us cleared our calendars and opened a blank document with only the title of the book on the page.

Here's what happened in our final sprint.

We used ChatGPT like a scaffolding crew. We poured everything I'd ever recorded or written, and all of the book's research—thousands of pages of papers, interviews, notes, transcripts—into a shared account. It gave us back clean bones—timelines, definitions, competing viewpoints, and contrasts sharp enough to argue with. It reduced noise without erasing nuance. It surfaced what mattered and showed us where the gaps were.

We tore the book down to studs and rebuilt it top to bottom in my words. The process was manic and strangely joyful. We cut entire sections that were accurate but lifeless. We rewrote the chapters that mattered most until they sounded like a person you could argue with and still want to have dinner with. We kept the receipts—citations, tests, counter‐examples—but we moved them where they serve the reader instead of smothering them.

AI did not write the words you've read. It freed them. It gave us the structure so we could find the story. It gave us summaries so we could find the voice. It handled the encyclopedic so we could handle the intimate. AI accelerated our judgment more than our word count.

I think a lot of pulp non‐fiction exists because the “hard parts” of thinking and writing can be automated. Publishing words on a page has never been easier. Too many people today are outsourcing what should instead be supercharged.

And that spotlights one of the great misunderstandings of AI. This technology shouldn't do all of the work for us. It should scale the computation, so we can do the rest.

So if you're wondering who wrote this book, here is the ledger:

AI condensed the research, mapped competing claims, and kept us honest about sources and scope.

Humans chose the argument, set the stakes, carried the voice, and took ownership for the words on the page.

That last piece matters. Ownership. When I read these pages now, I don't feel empty excitement. I feel the relief of recognition. This is what I meant to say, said the way I meant to say it.

No, AI didn't write the book. It allowed me to write it. And that, to me, is the beautiful part. Without it, this book might never have made it into your hands. The story would still be scattered in transcripts, buried in notebooks, or stuck in drafts that didn't sound like me. AI didn't replace the human act of authorship, it made it possible. And in that sense, this book is not just about the expansion of human potential. It is proof of it.

That is the promise of the RenAIssance.

Glossary

Part I—Technical Terms (A–Z)

Adoption gap

 The space between technological and societal thresholds. See also: Technological vs. Societal Thresholds.

AGI (Artificial General Intelligence)

 A hypothetical AI system that can understand, learn, and apply knowledge across most tasks at or beyond human level. In the book's framing, AGI is expected around 2030 and functions as the inflection behind the “Next Renaissance.” See also: foundation model, scaling laws.

AI agent/agentic AI

 Software that plans and executes multi‐step tasks toward a goal with varying autonomy (tool use, memory, self‐correction). Distinct from “copilots,” which remain user‐in‐the‐loop. See also: function calling, orchestration.

AI alignment

 Research and practices to ensure AI systems pursue goals consistent with human values and constraints (safety, reliability, controllability).

Ambient computing

 Computing that fades into the environment (wearables, voice, sensors) and works in natural language with minimal screens—often a stepping stone toward the Natural Language Operating System view.

API (Application Programming Interface)

 The programmatic interface apps and services use to talk to AI models and tools (e.g., for retrieval, function calls, or routing).

Attention (mechanism)

 The core operation in transformer models that lets them weight relationships between tokens to predict the next token.

BCI (Brain–Computer Interface)

 Hardware and software that read/write brain signals to control devices or restore functions; used as a horizon example of “computing beyond screens.”

Behavioral adaptability

 Behavioral adaptability describes a meta‐skill that underlies all other forms of learning in dynamic environments. It is not a fixed technical competence but a psychological orientation toward change, marked by the willingness to flex routines, experiment with new practices, and discard outdated habits when circumstances demand it. In this sense, behavioral adaptability is distinct from upskilling: skills are tied to specific tools and risk obsolescence, whereas adaptability governs how individuals navigate the unknown. By emphasizing disposition over technical mastery, behavioral adaptability emerges as the most durable form of human capital in an era defined by rapid technological transition.

Benchmark/evaluation

 Structured tests (tasks, datasets, rubrics) used to compare models or track progress; crucial for routing decisions, safety, and QA.

Bias (algorithmic)

 Systematic errors that produce unfair outcomes (e.g., by gender, race). In this framing, explainability and transparency are prioritized alongside bias mitigation.

Black‐box model

 A model whose internal reasoning is opaque to users; a driver for explainable AI requirements in regulated or high‐stakes contexts.

Chain‐of‐thought (CoT)

 A prompting/analysis style where a model reasons step‐by‐step. Powerful, but often hidden from end users in production for safety/privacy.

Compute

 The aggregate processing power used to train or run models (GPUs/TPUs, memory bandwidth, networking). A central accelerator of AI capability growth.

Copilot

 A constrained, user‐supervised assistant embedded in apps (code, docs, CRM). See also: AI agent.

Embedding

 A numeric vector representation of text, images, or other data that captures semantic meaning for search, clustering, and retrieval‐augmented generation.

Emergent behavior

 Capabilities that appear when models reach sufficient scale (not explicitly programmed), relevant to the “great leaps” theme.

Explainability (XAI)

 Methods to make model behavior understandable (feature attributions, summaries, rule extraction); emphasized in the book's ethics lens.

Fine‐tuning

 Additional training on a narrower dataset to specialize a base model (via SFT, LoRA, etc.).

Foundation model

 A large, broad‐training model adaptable to many downstream tasks (e.g., LLMs, multimodal models).

Hallucination

 Confident but false output from a model; mitigated by retrieval, calibration, routing, and post‐processing.

Inference (vs. training)

 Running a trained model to generate outputs (production use); costs and latency at inference shape the “unmetered intelligence” thesis.

Knowledge cutoff

 The latest point in time included in a model's pretraining data; after that, the model lacks built‐in knowledge unless updated or retrieved.

Latency

 Round‐trip time from request to response; an experience bottleneck for agents and wearables.

LLM (Large Language Model)

 A transformer‐based model trained on massive text corpora to generate/analyze language; increasingly multimodal.

Model routing (LLM router)

 Dynamically choosing the best model for a given prompt based on predicted quality/cost/latency—often a meta‐model trained on evaluation data.

Model weights

 The learned parameters of a model; “open‐weight” models expose weights for local use/modification; “closed‐weight” do not.

Multimodal model

 A model that consumes and/or produces multiple data types (text, images, audio, video).

Neural network

 A layered function approximator trained to minimize loss via gradient descent.

Orchestration/function calling

 Mechanisms that let models call tools (search, databases, emails, code) and stitch steps together into workflows—key for agents.

Parameters

 Learned numeric values inside a model (billions+ for frontier LLMs) that encode patterns from training data.

Pretraining

 Initial large‐scale training on broad data before specialized tuning (SFT, RLHF).

Prompt/prompt engineering

 The instruction/context you give a model; includes system prompts, examples, constraints, and tool schemas.

Prompt injection

 A security attack where external text subverts instructions (e.g., data exfiltration, tool misuse).

RAG (Retrieval‐Augmented Generation)

 Fetching relevant context (via search/vector DB) and injecting it into the prompt to ground responses in current or proprietary data.

Reinforcement Learning from Human Feedback (RLHF)

 Fine‐tuning with human preference data to align outputs with desired style/behavior.

Responsible AI/safety

 Policies, controls, and lifecycle practices that mitigate misuse, harm, and failure modes (safety filters, audits, governance).

Scaling laws

 Empirical relationships between data/parameters/compute and model performance that inform road map decisions.

SFT (Supervised Fine‐Tuning)

 Supervised learning stage using instruction–response pairs to teach task formats and tone.

Synthetic data

 AI‐generated data used to supplement scarce or sensitive real data (with care for drift and artifacts).

Temperature (sampling)

 A decoding setting controlling randomness/creativity; higher values yield more diverse outputs.

Token

 The unit of text (or audio/video chunk) a model processes; counts affect cost/latency.

Transformer

 A neural architecture built on attention mechanisms; foundation of modern LLMs.

Vector database

 A storage/indexing system for embeddings to enable fast semantic search and retrieval‐augmented generation.

Wearables

 On‐body devices (glasses, rings, earbuds) that can host/mediate AI and help make computing ambient.

Zero‐shot/few‐shot learning

 Solving tasks with no or a handful of examples embedded in the prompt (in‐context learning).

Part II—Novel Concepts (A–Z)

Asymmetry of Tolerance

 People routinely forgive human error but not machine error at the same rate—one reason societal thresholds can trail technological ones in safety‐relevant domains (e.g., autonomy, medicine).

Definite Problems

 Problems with clear success criteria and timelines that AI will steadily crush (e.g., many tasks in science/engineering). This underpins the argument that the “utopia” is built by relentlessly solving definable bottlenecks.

Educational Obsolescence

 A claim that traditional schooling structures (pace, assessment, curricula) must be reimagined for an AI‐rich world that values creativity, curiosity, and agency over memorization.

Ending the Digital Divide (via NLOS)

 The divide arose because early computing demanded esoteric skills; the Natural Language Operating System flips this by letting everyone compute in their own language and context.

Happiness Function

 A simple, human‐centric objective for the good society—anchored in relationships, health, agency, and creative expression—rather than GDP or screen time.

Natural Language Operating System (NLOS)

 An end‐state interface: computing that is conversational, ambient, and largely screen‐lite; often mediated by wearables. It collapses complexity and reduces the skills gap that created the digital divide.

Phases of Societal Thresholds

 A four‐phase arc: Skeptical Uncertainty → Fearful Resistance → Cautious Acceptance → Unbounded Optimism.

Technological vs. Societal Thresholds

 What machines can do (technological threshold) vs. what people, institutions, and norms are willing to accept (societal threshold). Progress now bottlenecks on the latter more than the former.

The Next Renaissance

 Central thesis: AI is the greatest “great leap” yet—supercharging human potential and productivity across science, health, education, robotics, and political economy.

The Time Dividend

 As AI removes drudgery and multiplies productivity, society receives “more time” to invest in connection, creativity, and meaning.

Acknowledgments

Writing this book has been one of the most challenging and rewarding experiences of my life.

It would not exist without the support, generosity, and brilliance of many people who gave me their time, wisdom, and encouragement along the way.

To my team, past and present—Jordan Howard, Danielle Owens, Montana Showalter, Issey Monk, Kelsey McWilliams, Christina Kodsi—thank you for carrying so much weight and for shaping not only this book but also the work that surrounds it.

To my publisher, Wiley, and especially Ryan Flahive, thank you for constantly believing in me, pushing me, and challenging me to write the book I really meant to write.

To James Lynch, for caring as much about the words on these pages as I do.

To Sam, thank you for inspiring me in more ways than one, and for your optimistic vision of a better future.

To Boris Power, thank you for being both a dear friend and a technical muse, and for guiding me through this field with exceptional clarity.

To Scott Adelson, thank you for always asking about the book and reminding me to keep writing it.

To Roy Singh, thank you for sharpening so many of my ideas and for being brave enough to build a company with me.

To Ric Cervantes and Oscar Batori, thank you for your candor, friendship, and for the sharp feedback when I needed it most.

To Jared Weinstein, thank you for your friendship and mentorship.

To Lukas Biewald for giving me a shot, and guiding me along the way even when it wasn't your job.

To Barrett Cordero and Ken Sterling, thank you for helping me build a career when the path forward was far from clear.

To Ryan Wright, thank you for bringing me into the UVA fold, and showing me how rewarding academic life could be.

To Mohamed El‐Erian, thank you for telling me, at just the right moment, that people would read this book because I wrote it. You gave me the confidence to believe that this message mattered.

To all the friends, colleagues, researchers (especially Spencer Ante), and collaborators who contributed to the bones of this book along the way: your fingerprints are here, even if your names are not.

Most of all, to my family. To my father, Fred, for showing me through his life's work that compassion is the greatest measure of brilliance. To my mother, Sarah, for loving me and raising me to love others.

To my sister Bryn, for her cheerleading and grounding presence.

To my brother, Geordie, for being a buddy and a role model father.

To my daughter, for whose future I wrote this book, and who I hope will one day read it and feel both wonder and possibility.

And above all, to my wife, Adlee, whose love, courage, and belief in me sustained every page. This book, like my life, is only possible because of you.

About the Author

Zack Kass is one of the world’s most sought-after voices on artificial intelligence, having spent his entire career at the frontier of the field. He is known for combining deep technical insight with a rare ability to see what others miss—connecting the science of AI to its profound social, economic, and human implications.

As an early employee at OpenAI and its Head of Go‐To‐Market, Kass helped transform the company from a research lab into a global force, guiding organizations through the dawn of generative AI.

Over the past five years, he has sat in more than 500 boardrooms, advising global leaders, governments, and academic institutions on how to lead and build in the age of unmetered intelligence. His counsel is trusted by thousands of executives, and he has delivered keynotes to more than 300,000 people across five continents.

Kass currently collaborates with the University of Virginia, studying the future of work and intelligence. He is also the AI for Conservation Fellow at Conservation International. His work has been cited dozens of times and he appears regularly across international media like Forbes, The Wall Street Journal, CNN, CNBC, and Business Insider.

Get real‐time access to Zack’s essential ideas and explore the research behind The Next Renaissance.

Index

A

Abridge, 165

app, 169

clinical AI models, 168

abundance, 86, 87, 137

Accenture, 180

accountability, 128, 129

adaptability, 189

adaptive tutors, 125

Adlee, 119, 124, 126, 127

adoption, 23

adoption gap, 24–28, 31, 47, 135, 172

Agentic AI/AI agents, 17–21

AGI see artificial general intelligence (AGI)

AI Act of 2024, 55

AI chat psychosis, 69

AI integration

agentic AI, 17–21

NLOS, 21–23

phases of, 15

software and, 16

air travel, 84

alcoholism, 71

Alexa, 18

AlexNet, 11

alignment, 41, 50–53, 61

AlphaFold, 39

Alpha School, 132–133

Altibbi app, 167

Altman, Sam, 100, 174

Amazon, 18

Amazon Clinic, 167

ambient computing, 73

American healthcare, 156–158

absurdity of, 159

architecture of, 157

Amodei, Dario, 173

amplification, 79

Anthropic, 13, 44, 52

anti‐tampering regulations, 63

The Anxious Generation, 71

Apple, 18, 20, 42, 189

products, 189

architecture

computing, 5

artificial general intelligence (AGI), 38

artificial “neurons,” 5

artistic performance, 182

ASML, 42

assistive technology, 80

attendance rates, 134–135

attention, 7, 10, 18, 57, 188

attrition

teacher, 125

augmentation, 173–176, 179, 181

augmented worker, 179–181

authenticity, 101, 182

automation, 171, 175, 179, 181

automation boundaries, 105

autonomous agents, 15

autonomous vehicles (AVs), 30

Autopia, 32

avatars, 106

B

banking

access, 146

AI in, 147

architecture, 142

complexity, 142

credit cards, 144

design, 141

FICO scores, 146

financial expertise and mobility, 148

financial illiteracy, 143

financial mistakes, 142

for immigrants, 147

interest rates, 142

knowledge, 144

late fees, 144

literacy, 146

origin of, 139–140

overdraft fees, 145

penalty rates, 144

predatory design, 143

visibility, 146

Bannister, Roger, 82

behavioral adaptability, 181

benchmark, 11, 128

Bengio, Yoshua, 11, 60

bias, 25, 32, 48, 49

biomechanical analysis, 67

biometric surveillance, in public spaces, 93

black‐box model, 47, 129

black‐market models, 63

Bostrom, Nick, 58

brain‐computer interface, 23

broken healthcare system, 155–158

Brundage, Miles, 99

C

Cameron, James, 59

cancer‐detection model, 163

CAN‐SPAM Act in 2003, 63

Capable AI, 15

capitalism, 104

caregiving, 182

Carr, Nicholas, 65

Carson, Rachel, 27

Caruso, Rick, 73

central processing units (CPUs), 8

Character.AI., 69

character development, 188–189

chatbots, 167

ChatGPT, 7, 28, 44, 80, 198

phases, 16

Chernobyl in 1986, 26

Chicago World's Fair in 1893, 26

Chime, 148

AI‐enabled features, 148–149

China, 55, 70, 93, 98

chip fabrication, 42

clarity, 176

classroom management, 131–137

Claude, 7

clinical AI models, 167–168

cloud computing, 5, 8

coaching, 133, 182

CodeScientist, 39

cognition upgrade, 189

cognitive abilities, 66

collaboration, 6

Commonwealth of Virginia's AI Task Force, 64

Companion, 59

compassion, 193

compute, 6, 7–9, 42

compute‐as‐a‐service, 8

computer vision, 12

citizen monitoring, 93

conflict resolution, 121

consumer choice, 19

control, 31–32

conversation, 21

copilot, 28, 168

copyright, 102

costs, 14, 16, 57

counseling, 182

courts, automated, 103

COVID‐19 pandemic see pandemic

creation, 173–176

creativity, 121

CRISPR, 27, 29, 80, 155, 159

Curai Health, 165

app, 166, 169

curriculum gap and schooling, 115

Customer relationship management systems (CRMS), 16

cyberattack, 43, 54

cynicism, 195

D

Daggett, Harold, 74–76

data, 6, 9

DDT, 27

decision making, 20

Deep Blue, 4

Deepfake pornography, 63

deep learning, 5, 9

DeepMind, 13, 39, 52, 80, 81

DeepSeek, 12, 14

definite problems, 38

dehumanization, 68–74, 89, 160

delivery robots, 36

de' Medici, Giovanni di Bicci, 139

digital computing, 4

digital distraction, 188

Disneyland, 32

displacement, 173–176

distraction, 188

distribution problem, 97–100

3‐D job impact framework, 177–178

Duncan, Graham, 87

Duolingo, 136

E

“eCheck” system, 73

ecology, 96

problem, 94–96

economic incentives, 190

economies, 103, 104

Edison, 25, 37

education, 111

abundance, 137

access to, 135

AI's role in, 127

at Alpha School, 132–133

classrooms, 131–137

in England, 113

history of, 112

intellectual development (IQ), 130

mental health crises, 122

new philosophy of human potential, 114

reskilling and, 98

social/economic shift, 114

social‐emotional growth (EQ), 130

technological leap, 114

test assessment, 127–131

transformation, 114

in United States, 112, 128

efficiency, 6, 14

Eilish, Billie, 80

Eilish, Finneas, 80

electricity, 25–26, 84, 94

electronic health record, 162

embodiment, 189

empathy, 121

energy allocation, 95

Erasmus, 113, 115

European Union, 55, 93, 98

existence, 107–108

expanded access, 83–86

explainability, 41, 46–50

extreme ultraviolet lithography (EUV) machines, 42

F

Facebook, 12, 88, 92

Farley, Jim, 174

Federal Reserve analysis, 97

finance, 140, 143 see also banking

Ford, 174

Frame Breaking Act of 1812, 172

Franklin, 25

FraudGPT, 63

freedom, 68

“friends,” AI, 70

Fukushima in 2011, 26

Function Health, 165

and AI, 167

app, 169

personal health knowledge, 168

fusion, 45

G

Galen, 153, 158

Gemini, 7

generative AI, responsible adoption of, 180

Genetically Modified Organisms (GMOs), 26

geopolitics, 26

Germany, 26

gesture, 21

global mentorship networks, 67

GNoME system, 81

“Godfathers of AI, 59

“Godfathers of Deep Learning,” 11

“Godmother of AI,” 11

Goldman Sachs, 45

Google, 5, 12, 13, 21, 52, 180

Google Glass, 28

governance, 53–56

GPT‐2, 10, 13

GPT‐3, 10, 14

GPT‐4, 10, 13, 45, 150

4.5, 14

GPT‐5, 10, 14, 45

GPT‐6‐class system, 44

Grammarly, 16

graphics processing units (GPUs), 8

Grief technologies, 106

H

hacking, 62

Haidt, Jonathan, 71

hallucination, 52

Hancock, Drew, 59

happiness function, 87–90

Harari, Yuval, 70

Harvard Study of Adult Development, 89, 123

“Healing Through Compassion” Award, 193

healthcare, 85, 154

app chat, 166

automation in, 165

digital consult, 167

in Jordan, 167

in Kenya, 167

primary care, 165

RenAIssance in, 165, 169

in U.S., 167

Her, 68–69

high‐risk jobs, 178

Hinton, Geoffrey, 11, 59–61

history of AI, 3

hospital, 29

HTML, 18

Huang, Jensen, 179

Hugging Face, 12

human‐centric work value, 182

humanistic optimization, 191–192

humanistic qualities, 195

I

IBM, 4, 17

identity displacement, 74–78

idiocracy, 64–68

Illegal Automation, 175

ImageNet, 11

immortality, 108

indefinite problems, 38

India, 44

industrialization vs. schooling, 114

industrial revolution, 41, 96

inference costs, 13

information storage, 84

Infrastructure Investment and Jobs Act, 45

Instagram, 88

Intel, 42

intellectual foundation of medicine, 153

intellectual property, 102

intelligence, 195

International Labour Organization, 97

International Longshoremen's Association, 74

internet, 18, 93

deisgn, 19

as library, 18

preferences, 19

iPhone, 21

Ive, Jony, 20

J

Japan, 43, 189

job displacement risk, 176–179

job market and AI impact, 174

Jobs, Steve, 189

Jonze, Spike, 68

Judge, Mike, 64

K

Kasparov, Garry, 4

Kass, Fred, 193

Keynes, 78, 89, 90

Knight, Phil, 39

knowledge gap, 34

Kozol, Jonathan, 117

L

labor, 103

language translation, 5

large language models (LLMs), 7, 110, 130

leadership, 181, 189

learning

scholastic vs. social, 119–124

strategy, 190–191

LeCun, Yann, 11, 60

liability, 102

liberation, 171

life expectancy, 156

Li, Fei‐Fei, 11

literacy, 112

in banking, 146

literalism, 60

Llama, 12

LLMs see large language models (LLMs)

Luddites, 172

Lynch, James, 197

M

machine learning, 4

mail fraud laws in 1872, 63

malware, 63

Martin, Gary, 67

McCarthy, John, 3, 4

medicine and AI, 153 see also healthcare

Meiji Restoration, 189

Meta, 12, 13, 44, 52, 60

Microsoft, 16, 174

Middle East, 44

misaligned superintelligence, 58

MIT AgeLab, 34

MIT Media Lab, 21

modern language models, 18

modern medical therapy, 159

modern pharmacology, 154

Monsanto, 27

Morgan Stanley, 149–150

AI adoption, 150–151

Moskowitz, Eva, 126

Musk, Elon, 59

N

National Assessment of Educational Progress (NAEP), 65, 116, 128

National Center for Education Statistics, 128

Natural Language Operating System (NLOS), 15, 21–23

natural language processing, 12

Abridge, 168

Negroponte, Nicholas, 21

neural network, 5

research, 11

NeXT, 189

Nike, 39

NLOS see Natural Language Operating System (NLOS)

North Star problem, 92–94

nuclear power, 26, 29

Nvidia, 8, 42, 179

O

OpenAI, 10, 13, 14, 20, 44, 52, 80, 100, 136, 174

capabilities, 10

parameters, 10

open‐source software, 12, 67

optimism, 195–196

ownership, 199

P

P(doom) ², 59

pandemic, 65, 72

schooling, 117

teacher's during, 125

parameters, 10, 11, 13, 38, 44, 48

perception, 188

Perelman, Grigori, 80

performance, 177

personhood, 106–107

pesticide, 27

phishing, 63

Phoenix, Joaquin, 68

physical abilities, 66

physical world, 188

Pixar, 189

plan‐check tool, AI‐powered, 73

planning, AI‐assisted, 39

Poincaré Conjecture, 80

Ponea app, 167

port automation, 76–77

precision, 161–165

predation, literacy, 145

predictive model, 163

preferences

internet, 19

options, 20

presence, 161–165

prevention, 161–165

Price, Mackenzie, 132

primary care, 165–166

barrier of, 166

printing press, 65, 93

Priority Health, 167

privacy, 106–107

Program for International Student Assessment (PISA), 66

progress, 195

prompt, 13, 14, 21, 52, 63, 163

prompt engineering, 187

protocol, 162

proton‐based batteries, 45

psychiatrists, 69

purpose, 107–108

Q

quality tutoring, 137

questions, 91–92

R

ransomware kits, 63

Rentosertib, 39

replacement, 178

Replika app, 70

resilience, 121

resistance, 172

resource access, 42–46

retrieval system, 150

risks, AI, 173

romance apps, 70

roots of AI, 3

Rula Health, 167

Russell, Stuart, 60

S

Sacks, David, 174

safety, 50–53

Salesforce, 16

Samsung, 42

Scale AI, 44

scaling laws, 9, 44

Scattered Spider, 62

scholastic learning vs. social learning, 119–124

schooling, 111–113

adaptability, 115

AI tutors, 121

apartheid education, 117

attrition rates in schools, 118

blueprint failure, 119

collaboration, 115

communication deficits and, 116

compulsory, 115

cost, 118

creativity, 115

critical thinking, 115

curriculum and school life deficits, 121

and curriculum gap, 115

desired skills and, 115

educational inequality, 116–117

funding disparity, 116

vs. industrialization, 114

McKinsey analysis, 117

modern, 121

pandemic, 117

problems of, 118

problem‐solving deficits and, 116

public speaking, 133

resource disparity, 116–117

during 21 st century, 114

student failure, 117

teacher roles, 117–118

during 19 th century, 114

in United States, 117

universal, 115

Scientist, AI, 39

Scott, Kevin, 174

screen addiction, 69

secularism, 115

Segway, 28

self‐driving, 34

car, 5, 30

self‐governance, 53

self‐regulation, 121

self‐worth, 78

signal, 10

Silent Spring, 27

Siri, 18

skill acquisition, 187

smart‐home device, 23

smartphone, 5, 8

Smith, Adam, 24

smoking, 71

social constructs, 100

social engineering, 63

social media, 5, 18, 88, 101

social vs. scholastic learning, 119–124

societal threshold, 24, 28, 41, 174, 178

soft skills, 195

South Korea, 43, 70

speech recognition, 12

“status quo–preserving” innovation, 16

Steiner, Rudolf, 122

strike, U.S. East Coast and automation, 74–76

Success Academy, 126

supercomputer, 84

superconductor, 82

Superintelligence (2014), 58

Synchron, 23

synthetic intimacy, 69

T

Taiwan, 43

teacher

and AI, 125

burden, 125

burnout, 124, 127

realignment, 126

teaching, 182

technological threshold, 24

Tegmark, Max, 60

temperature, 116

terabytes, 9

Texas, 43

The Terminator, 59

“thinking machines,” 3

Thrash, Chris, 166, 168, 169

Three Mile Island in 1979, 26

TikTok, 93

time, 189, 191

The Tim Ferriss Show, 87

token, 6, 14

tolerance, 35

training next generation models, 13

transformation, 79

transformers, 6–7

translation tool, 29

TSMC, 42, 43

Tufts Medicine, 167

Turing, Alan, 3

tutors, AI, 67, 121, 134

TXT file, 19

U

ubiquitous computing, 21

UChicago Medicine, 169

unemployment, 174

UNICEF, 128, 129

universal basic income (UBI), 99

unmetered intelligence, 37, 191

upskilling, 181

urgency, 108

U.S. Department of Energy, 45

U.S. interconnection queue, 95

V

Vesalius, Andreas, 154, 158

virtuous friction, 105

voice assistants, 5

voice clones, 106

von Ahn, Luis, 135

W

Waldorf Education, 122

wearables, 22, 163

Weiser, Mark, 21

White House's AI and Crypto Czar, 174

wildfires in Los Angeles, 72

World Happiness Report, 88

WormGPT, 63

Wright brothers, 82

X

xAI, 14

Xiaoice app, 70

XML file, 19

Y

YouTube, 67, 93

Z

zero‐touch automation, 73

zettabytes (ZB), 9

zombie apocalypse phenomenon, 175

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