Earth with ominous red glow

IF ANYONE BUILDS IT,EVERYONE DIES

Eliezer Yudkowsky & Nate Soares

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Published by Little, Brown and Company - September 16, 2025

The scramble to create superhuman AI has put us on the path to extinction — but it's not too late to change course, as two of the field's earliest researchers explain in this clarion call for humanity.

In 2023, hundreds of AI luminaries signed an open letter warning that artificial intelligence poses a serious risk of human extinction. Since then, the AI race has only intensified. Companies and countries are rushing to build machines that will be smarter than any person. And the world is devastatingly unprepared for what would come next.

For decades, two signatories of that letter — Eliezer Yudkowsky and Nate Soares — have studied how smarter-than-human intelligences will think, behave, and pursue their objectives. Their research says that sufficiently smart AIs will develop goals of their own that put them in conflict with us — and that if it comes to conflict, an artificial superintelligence would crush us. The contest wouldn't even be close.

How could a machine superintelligence wipe out our entire species? Why would it want to? Would it want anything at all? In this urgent book, Yudkowsky and Soares walk through the theory and the evidence, present one possible extinction scenario, and explain what it would take for humanity to survive.

The world is racing to build something truly new under the sun. And if anyone builds it, everyone dies.


Endorsements

"The most important book I've read for years: I want to bring it to every political and corporate leader in the world and stand over them until they've read it. Yudkowsky and Soares, who have studied AI and its possible trajectories for decades, sound a loud trumpet call to humanity to awaken us as we sleepwalk into disaster."
— Stephen Fry, actor, broadcaster, and writer
"If Anyone Builds It, Everyone Dies may prove to be the most important book of our time. Yudkowsky and Soares believe we are nowhere near ready to make the transition to superintelligence safely, leaving us on the fast track to extinction. Through the use of parables and crystal-clear explainers, they convey their reasoning, in an urgent plea for us to save ourselves while we still can."
"This is the best no-nonsense, simple explanation of the AI risk problem I've ever read."
— Yishan Wong, former CEO of Reddit

From If Anyone Builds It, Everyone Dies:

If any company or group, anywhere on the planet, builds an artificial superintelligence using anything remotely like current techniques, based on anything remotely like the present understanding of AI, then everyone, everywhere on Earth, will die.

We do not mean that as hyperbole. We are not exaggerating for effect. We think that is the most direct extrapolation from the knowledge, evidence, and institutional conduct around artificial intelligence today. In this book, we lay out our case, in the hope of rallying enough key decision-makers and regular people to take AI seriously. The default outcome is lethal, but the situation is not hopeless; machine superintelligence doesn't exist yet, and its creation can yet be prevented.

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About the Authors

Eliezer Yudkowsky

Eliezer Yudkowsky is a founding researcher of the field of AI alignment and played a major role in shaping the public conversation about smarter-than-human AI. He appeared on Time magazine's 2023 list of the 100 Most Influential People In AI, was one of the twelve public figures featured in The New York Times's "Who's Who Behind the Dawn of the Modern Artificial Intelligence Movement," and has been discussed or interviewed in The New Yorker, Newsweek, Forbes, Wired, Bloomberg, The Atlantic, The Economist, The Washington Post, and many other venues.

Nate Soares

Nate Soares is the President of the Machine Intelligence Research Institute. He has been working in the field for over a decade, after previous experience at Microsoft and Google. Soares is the author of a large body of technical and semi-technical writing on AI alignment, including foundational work on value learning, decision theory, and power-seeking incentives in smarter-than-human AIs.