The Economic Singularity

What would happen if machines could do every job better, faster, and cheaper than you? Would you still have a place in the economy? In The Economic Singularity, Calum Chace asks one of the most profound questions of the 21st century: what happens when artificial intelligence becomes so capable that human labor is no longer required? His answer is both exhilarating and terrifying. Chace contends that we stand on the brink of a radical transformation—the economic singularity—in which accelerating technological progress renders most people unemployable. Yet he also argues that this future could be immensely positive, if handled well.

Chace takes us on a journey through history—from the agricultural and industrial revolutions to the current information revolution—showing how each has reshaped human life. Technologies once feared to destroy jobs eventually created more of them, enriching society overall. But this time, he warns, things may be different. Machines are not just replacing our muscles—they’re starting to outperform our minds. Artificial intelligence can now recognise faces, understand language, drive cars, diagnose diseases, write newspaper articles, and even fold shirts. The result is nothing less than the decoupling of income from employment, a disruption so profound that Chace calls it a singularity—an event horizon beyond which human economic life becomes unpredictable.

From the Technological to the Economic Singularity

The term “singularity” is borrowed from physics, where it means a point beyond which normal rules break down. In technology, it’s often used to describe the moment when machines surpass human intelligence. Chace distinguishes between two types: the technological singularity, where AI becomes superintelligent, and the economic singularity, where automation makes work obsolete. The economic version, he argues, will arrive long before superintelligence does. As self-driving cars, automated factories, and intelligent software spread across industries, millions will lose their jobs, and many will never find new ones.

Chace doesn’t predict exactly when this will happen—he's careful to note that futurists often overestimate short-term changes while underestimating long-term ones (a phenomenon known as Amara’s Law). But he believes the transition could begin within decades. Like the industrial revolution, the shift will start slowly but accelerate inexorably. Once AI can perform most cognitive tasks, the traditional labor market may collapse, forcing societies to reimagine how people earn, spend, and define their lives.

Why It Matters

This question isn't just for economists—it’s for everyone. Your career, your family’s future, and even your sense of identity may depend on how we navigate this transformation. Most of us define ourselves by what we do. Work gives structure, meaning, and connection. Without jobs, what happens to self-worth—and to social cohesion? Chace argues that capitalism and liberal democracy, which have thrived in an age of work, may no longer suffice in a post-automation world. We’ll need entirely new systems of distribution and meaning. That’s where ideas like the Universal Basic Income (UBI) come in—a guaranteed payment to all citizens simply for being alive.

Yet UBI alone may not be enough. If most people live on handouts while a tiny elite owns the machines that run everything, the gap between rich and poor could become unbridgeable. Historian Yuval Harari warns that humanity could split into “the gods and the useless”—the augmented and the obsolete. Chace echoes this fear but also sees hope. Technology, he reminds us, has always both destroyed and created worlds. The economic singularity could usher in an age of radical abundance, in which machines produce almost everything we need, freeing humans to pursue creativity, learning, and joy. But this outcome will require foresight, policy, and imagination.

Preview of What’s Ahead

In this summary, you’ll explore the key ideas behind Chace’s argument. First, you’ll see how the history of automation—from horses to robots—reveals the pattern of progress. Then you’ll examine why AI-driven automation may be fundamentally different this time. You’ll learn about exponential technological growth (Moore’s Law), the coming wave of artificial intelligence, and how industries like transportation, healthcare, and law are already transforming. Later sections dive into potential solutions: Universal Basic Income, virtual reality lifestyles, new economic systems beyond capitalism, and the philosophical question of meaning in a world without work. Finally, you’ll see Chace’s possible futures—ranging from collapse to

Chace begins his argument by reminding us that automation has always been the double-edged sword of progress. From the spinning jenny to the smartphone, machines have changed what it means to work and live. But until now, automation has been a story of displacement and replacement: while some jobs vanished, new ones always appeared. The industrial revolution shifted labor from fields to factories; the information revolution shifted it from factories to offices. Yet the next wave—driven by artificial intelligence—may take away human economic relevance itself.

From Mechanisation to Automation

Mechanisation replaced human muscles; automation replaces human minds. The industrial revolution’s machines transformed agriculture so thoroughly that America’s farm workforce shrank from 41% of employment in 1900 to under 4% by 1970. But it was still humans operating those machines. The real change comes when machines no longer need guidance. In the 1940s, General Electric coined the word “automation” to describe systems that could correct themselves without human intervention. With the arrival of the programmable logic controller (PLC) in 1968, industry began creating factories where machines made decisions in real time.

The Luddite Fallacy

Every revolution sparked fear of unemployment. In the early 1800s, British textile workers—nicknamed Luddites—destroyed machines they believed threatened their livelihoods. Economists later called this fear the “Luddite fallacy,” arguing that innovation ultimately raises productivity, creates wealth, and thus more jobs. Historical data seemed to confirm this: the more we automated, the richer and busier society became. But Chace cautions that history might not repeat itself. Up until now, humans had something to offer that horses didn’t—intelligence. Once machines have that too, there may be nowhere left for us to go "up the value chain."

The Rise of the Information Age

In the 20th century, knowledge became the new factory floor. Economists like Fritz Machlup and Alvin Toffler declared that information itself was becoming an economic product. Toffler coined the term “prosumer” for consumers who also participate in production—think self-service gas stations and online shopping. But as Chace notes, the more our economy depends on data and algorithms, the easier it becomes to automate. Artificial intelligence doesn’t just shift tasks—it absorbs them entirely.

Is This Time Different?

Chace’s central theme echoes the warning from Oxford economists Carl Benedikt Frey and Michael Osborne, whose 2013 report estimated that 47% of US jobs are at high risk of automation. In previous eras, machines replaced physical effort but couldn’t think or learn. Now they can. When machine learning and deep neural networks started outperforming humans at image recognition and speech translation, the foundation of white-collar work began to crumble. The industrial revolution had “peak horse.” The next revolution may bring “peak human.”

“Put simply,” Chace concludes, “if the Luddite fallacy were correct, we would all be unemployed by now—but we’re not. That may be about to change.”

Artificial intelligence is the engine driving the economic singularity. Chace dives deep into its workings, tracing its origins from the 1956 Dartmouth conference to today’s breakthroughs in machine learning and deep learning. The story is not just about smarter machines—it’s about exponential progress, the kind that turns slow trends into revolutions.

From Symbolic AI to Deep Learning

Early AI sought to replicate human reasoning through symbols and logic—a method now known as “Good Old-Fashioned AI.” It failed because it couldn’t handle the messy complexity of real-world perception. Machine learning flipped the script: instead of programming rules, it trained systems to find patterns in massive amounts of data. The rise of neural networks allowed algorithms to “learn” like brains do—layer by layer. Geoff Hinton’s 2012 win at the ImageNet competition marked a turning point. From then on, deep learning became the lingua franca of machine intelligence.

Exponential Growth

Chace illustrates exponential growth through vivid metaphors: the football stadium filling with water in 49 minutes, or 30 paces that reach the moon. Moore’s Law—Gordon Moore’s observation that computing power doubles every 18 months—remains the force behind this acceleration. Innovation compounds upon itself, creating deceptive lulls followed by explosive leaps. By the time AI systems reach human parity in perception and reasoning, they won’t stop—they’ll continue improving exponentially. As Kevin Kelly (author of The Inevitable) notes, each new technology creates almost as many problems as it solves, but always expands choice.

Machines Learn to See, Hear, and Drive

Today’s AI systems already outperform humans at specific tasks. Google’s DeepMind built AlphaGo, which defeated a world champion in 2016—a feat once thought decades away. IBM’s Watson won Jeopardy! by analysing natural language questions. Microsoft and Baidu’s neural networks surpassed humans at image and speech recognition. Google’s self-driving cars travelled millions of miles with fewer accidents than human drivers. Each step eroded the boundary of what “only humans can do.”

Human-Level and Beyond

Chace distinguishes between narrow AI—machines that outperform us in one domain—and general AI, which matches us across the board. He warns that we don’t need general AI to face an economic singularity. Narrow AI is enough. Once systems can read, recognise, diagnose, and decide, most human roles become redundant. As Elon Musk and Stephen Hawking have noted, superintelligence may pose existential risks, but the immediate challenge is economic: what happens when these machines no longer need us?

“Moore is more,” writes Chace, “and more is better. Even if progress slows, it will continue to accelerate relative to us.”

If machines can do everything, what’s left for you? Chace confronts this existential question head-on. Work doesn’t just pay bills—it defines identity and purpose. Without it, many fear emptiness or chaos. Yet history and psychology suggest otherwise: humans thrive when freed from necessity.

The Meaning of Work

Sociologist Simon Sinek says working for something we don’t love is called stress; working for what we love is called passion. Chace agrees, but points out that fewer people feel passion for their jobs than pretend to. Work, he argues, is often just a survival mechanism. When that mechanism disappears, culture will need new ways to provide meaning. Some will turn to art, learning, and relationships. Others will struggle.

Lessons from the Rich and Retired

Chace looks to history for clues. Aristocrats of Europe lived jobless lives for centuries, yet many contributed to the arts and sciences. Retirement studies show a similar pattern: happiness follows a U-shape, dipping during middle age and rising after work ends. Old age brings peace, not despair. Maybe leisure isn’t the problem—it’s poverty and isolation. In a well-designed post-work world, freed humans might do their best thinking, creating, and connecting.

Virtual Reality and New Frontiers of Meaning

Enter virtual reality (VR). Chace envisions a future where immersive digital worlds offer endless exploration and creativity. Philosopher Robert Nozick’s “experience machine” imagined a device that could simulate perfect happiness—but he doubted anyone would choose illusion over reality. Chace disagrees. If VR feels real and meaningful, it is real for its participants. He cites Oculus Rift founder Palmer Luckey, who argues it’s a moral imperative to democratise such experiences. Rather than an escape, VR might become the place we find purpose.

“If people are having a virtually happy life,” Luckey says, “they are having a happy life. Period.”

To survive the economic singularity, Chace argues, we must decouple income from employment. His proposed remedy is the Universal Basic Income (UBI): a guaranteed payment for every citizen. UBI is not socialism revived—it’s an adaptation to a world where machines do all productive work. He explores past and present experiments, from Canada’s Dauphin trial to Y Combinator’s Silicon Valley pilot project, asking whether people can thrive—creatively, socially, and economically—without jobs.

The Logic of UBI

At first glance, UBI seems radical. How can people earn a living if machines earn it all? But Chace reminds readers of Walter Reuther’s retort to a Ford executive in the 1950s: “How will your robots buy cars?” Without incomes, there’s no demand, and economies collapse. UBI solves that by creating permanent purchasing power. It’s also administratively efficient—no means-testing, no bureaucracy, no stigma. Everyone receives enough to live decently.

Objections and Answers

Critics fear laziness, inflation, and unaffordability. Yet evidence suggests the opposite. The Dauphin experiment found that only new mothers and students worked less—with positive results. Inflation, notes UBI advocate Scott Santens, didn’t rise in Alaska’s or Kuwait’s versions. Funding remains contentious: should we tax the wealthy, or replace welfare systems entirely? Chace sees both as partial solutions and proposes gradual trials—begin with modest payments, then scale up as automation spreads.

Beyond UBI

UBI may not be enough if inequality widens. If ownership of AI and robotics remains concentrated, societies risk splitting—the “gods and useless” scenario. Chace hints at deeper reforms: new economic systems uniting abundance with fairness. He imagines decentralised, blockchain-based collective ownership, where everyone holds shares in the machines that sustain them. Economic survival then becomes universal participation in progress itself.

“A world of abundance will need a new form of economy,” Chace writes, “not capitalism as we know it.”

Chace closes by charting six possible futures—each a response to the economic singularity’s challenge. These scenarios range from denial to transcendence, reminding readers that the future is open, not fixed.

1. No Change

The comforting view: progress slows, society stabilises. Journalist John Markoff doubts major innovation ahead, claiming 2045 will look much like today. Chace respectfully disagrees. History’s exponential momentum makes stasis the least likely outcome.

2. Racing with the Machines

Optimists like MIT’s Brynjolfsson and McAfee believe we’ll collaborate with AI like chess “centaurs”—humans plus machines beating either alone. Yet Chace warns that the icebergs of new work eventually melt. Machines will outlearn us even in creative and caring roles.

3. Capitalism + UBI

A transitional model: keep capitalism but add a universal income floor. This could sustain stability, at least until automation becomes total. Chace sees this as plausible but temporary—a bridge, not the destination.

4. Fracture and Collapse

The worst-case scenarios: humanity splits between enhanced elites (“the gods”) and the irrelevant masses (“the useless”); social order disintegrates as wealth concentrates. Chace fears that unprepared societies could regress or self-destruct.

5. Protopia

Borrowing Kevin Kelly’s term, Chace envisions protopia—a future that’s slightly better each year, not perfect. Machines create abundance, humans find meaning through leisure, creativity, and learning. Wealth becomes decentralised via technologies like blockchain, enabling collective governance of shared resources. The economic singularity, successfully managed, becomes humanity’s greatest triumph.

“Relatively speaking,” writes Chace, “the technological singularity is more important but less urgent. The economic singularity is less important—but far more urgent.”