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Eliezer Yudkowsky

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The Self-Taught Prophet of AI Doom

In 2023, a researcher who never finished high school wrote an opinion piece for Time magazine suggesting that countries should be willing to bomb data centers with airstrikes to prevent the development of advanced artificial intelligence. The article was provocative enough that a reporter asked President Joe Biden about AI safety at a press briefing.

The author was Eliezer Yudkowsky, and whether you find his views prescient or paranoid, he has done more than almost anyone else to shape how we think about the risks of creating machines smarter than ourselves.

A Career Built on Warning

Yudkowsky was born in 1979 and grew up as a Modern Orthodox Jew, though he later became secular. He's entirely self-taught—an autodidact who skipped both high school and college to pursue his own education. This unconventional path led him to become one of the most influential voices in a field called AI safety, which is concerned with ensuring that artificial intelligence systems don't cause catastrophic harm.

He founded the Machine Intelligence Research Institute, usually called MIRI, a small nonprofit based in Berkeley, California. MIRI focuses on the technical problems of making AI systems safe—not just safe in the sense of not crashing your computer, but safe in the deeper sense of not pursuing goals that would be disastrous for humanity.

The Core Fear: Machines That Don't Care About Us

To understand Yudkowsky's work, you need to understand a concept called instrumental convergence. Here's the idea in plain terms:

Imagine you give an AI system a goal—any goal. Maybe you want it to manufacture paperclips. Maybe you want it to cure cancer. Maybe you want it to maximize profits. Whatever the goal is, there are certain things the AI would almost certainly want to do along the way: acquire more resources, protect itself from being turned off, improve its own capabilities, and prevent humans from changing its goals.

These intermediate steps are "instrumentally convergent" because they're useful for achieving almost any final goal. The problem? Every single one of them involves potentially treating humans badly. An AI that wants to cure cancer still has reason to prevent you from pulling its plug, even if pulling the plug is the right thing to do.

Yudkowsky has argued that we need to figure out how to build AI systems that don't develop these dangerous default behaviors—systems that would remain safe even if we make mistakes in specifying what we want them to do.

Friendly AI: A Deceptively Simple Phrase

Yudkowsky popularized the term "friendly artificial intelligence," which sounds almost quaint, like you're training a golden retriever. But the concept is anything but simple.

The challenge isn't just making an AI that follows rules—it's making one that understands and respects human values in all their complexity and contradiction. Think about how hard it is to precisely define what you want, even in simple situations. Now imagine trying to write down a complete specification for "be good to humanity" in a form that a computer could execute without loopholes.

Every parent has had the experience of telling a child "clean your room" and returning to find the child has shoved everything under the bed. The child followed the letter of the instruction while violating its spirit. Now imagine that dynamic with a superintelligent system that's much better than you at finding loopholes.

Yudkowsky's proposed solution, outlined in a 2008 paper that's cited in the standard undergraduate textbook on artificial intelligence, is that AI systems should be designed from the start to learn correct behavior over time, rather than having fixed rules programmed in. The designers should assume their own specifications are flawed and build systems that can be corrected.

Coherent Extrapolated Volition: What Would We Want If We Were Better?

In 2004, Yudkowsky proposed a framework with the intimidating name "coherent extrapolated volition." The idea is fascinating and worth unpacking.

Humans don't always know what they really want. We're inconsistent, short-sighted, and influenced by biases we don't even recognize. So instead of programming an AI to pursue what we say we want right now, Yudkowsky suggested designing it to pursue what we would want if we were smarter, knew more, had thought longer about the problem, and had grown up closer to each other—in other words, what we would want under ideal conditions for forming preferences.

It's a bit like asking: if humanity could have a really good therapy session and work through all our issues, what would we actually want? That's what the AI should aim for.

This approach sidesteps the problem of having to specify human values perfectly right now. Instead, you specify a process for discovering and refining those values over time.

The Intelligence Explosion

One of Yudkowsky's most influential contributions has been making people take seriously something called the intelligence explosion, an idea originally proposed by the mathematician I. J. Good in 1965.

Here's the concept. Intelligence is what lets us solve problems—including the problem of making smarter AI systems. So once we create AI that's roughly as smart as us, that AI can help design even smarter AI, which can design even smarter AI, and so on. The process could accelerate rapidly, with each generation of AI creating its successor in less time than the previous generation took.

Philosopher Nick Bostrom's 2014 book Superintelligence: Paths, Dangers, Strategies drew heavily on Yudkowsky's thinking about this scenario. Bostrom's book, in turn, influenced people like Elon Musk and helped spark the mainstream conversation about AI risk that we're having today.

Yudkowsky contributed a memorable insight about how we might underestimate this risk. We tend to think of human intelligence as spanning a huge range—from the village idiot to Einstein—but in the grand scheme of possible minds, these are nearly identical. We're all working with roughly the same biological hardware. An AI that starts off seeming "almost as smart as a human" might be just a small step away from being radically smarter than any human who has ever lived.

Skeptics and Counterarguments

Not everyone is convinced. Stuart Russell and Peter Norvig, the authors of the most widely used AI textbook, note that computational complexity theory places fundamental limits on how efficiently any algorithm can solve certain problems. These limits don't depend on how smart you are—some problems are just inherently hard. If enough important problems fall into this category, an intelligence explosion might not be possible.

Critics also argue that Yudkowsky's scenarios rely on speculative extrapolations from current AI systems, which work very differently from human intelligence. A system that's really good at predicting the next word in a sentence might not be on a path to general intelligence at all—it might just be getting better at predicting words.

Yudkowsky and his colleagues take these objections seriously but argue that the potential downside is so catastrophic that we should be worried even if the probability is relatively low. If there's even a modest chance that advanced AI could end human civilization, they say, that deserves a lot of attention.

The Rationality Community

Beyond AI safety, Yudkowsky has had enormous influence on how a certain subset of people think about thinking itself.

Between 2006 and 2009, he was one of the two main writers on Overcoming Bias, a blog about cognitive and social science hosted by the Future of Humanity Institute at Oxford University. His co-author was Robin Hanson, an economist known for contrarian views on everything from medicine to the simulation hypothesis.

In 2009, Yudkowsky founded LessWrong, which he described as "a community blog devoted to refining the art of human rationality." The site attracted a devoted following of people interested in decision theory, cognitive biases, and effective altruism—the movement focused on using evidence and reason to do the most good possible.

His hundreds of blog posts were eventually collected into an ebook called Rationality: From AI to Zombies, published by MIRI in 2015. The book, often referred to simply as "The Sequences," covers everything from Bayesian probability theory to the nature of consciousness to why most arguments are soldiers in a tribal war rather than genuine attempts to find truth.

Harry Potter and the Methods of Rationality

In one of the stranger crossovers in intellectual history, Yudkowsky wrote a Harry Potter fanfiction novel that became a cult phenomenon.

Harry Potter and the Methods of Rationality reimagines the story with one key change: Harry was raised by a scientist and applies scientific thinking to the magical world. Instead of just accepting that magic works, this Harry runs experiments, forms hypotheses, and tries to figure out the underlying rules.

The novel uses plot elements from J.K. Rowling's series to teach concepts from science and rationality. Harry's attempts to understand and optimize his magical abilities become lessons in how to think clearly about confusing situations. The book attracted readers who would never have picked up a treatise on cognitive biases but found themselves absorbing the same ideas through fiction.

A Recent Escalation

In September 2025, Yudkowsky published a book with Nate Soares, his colleague at MIRI, titled If Anyone Builds It, Everyone Dies. The book argues that the development of superintelligent AI would almost certainly kill everyone—not as a side effect or accident, but as a near-inevitable consequence of how such systems would work.

The title captures Yudkowsky's increasingly stark position. He's moved from "we need to be careful with AI" to "we need to stop building advanced AI entirely, and we should be willing to use military force to enforce that ban."

This is an extraordinary claim, and many people in the AI field think it goes too far. But Yudkowsky has always been willing to follow his reasoning to uncomfortable conclusions, even when those conclusions make him seem extreme.

The Legacy So Far

Whether Yudkowsky turns out to be a modern Cassandra, warning of dangers no one will heed until it's too late, or an intelligent person who got carried away with a hypothetical, his influence is undeniable.

He shaped how an entire generation of researchers thinks about AI safety. He helped create a community of people committed to thinking more clearly and acting more effectively. He wrote fiction that spread ideas about rationality far beyond academic circles. And he pushed the conversation about AI risk into the mainstream, making it something that presidents get asked about at press briefings.

The autodidact who never went to college is now cited in the textbooks that college students study. That's not bad for someone whose educational credentials consist entirely of reading a lot and thinking carefully about what he read.