If Anyone Builds It, Everyone Dies: my review

In the mathematics community, there is a popular joke about inflation in recommendation letters that goes as follows. A professor is happy about his PhD student, whom we may call Alex, and writes in a recommendation letter that Alex is arguably the most talented mathematician since Gauss. The next year, the professor has another PhD student, Robin, and writes in an even more enthusiastic letter that even though Alex is good, Robin is much better.

I am reminded about this as I now set out to review Eliezer Yudkowsky's and Nate Soares' new book If Anyone Builds It, Everyone Dies (henceforth, IABIED), and think back about my review of Nick Bostrom's 2014 book Superintelligence back when that book had just come out. The final sentence of that review reads "If this book gets the reception that it deserves, it may turn out the most important alarm bell since Rachel Carson's Silent Spring from 1962, or ever". Those are strong words, and I stand by them, and yet now I am tempted to announce that IABIED is better and more important than Bostrom's book.

Max Tegmark's praise for the book is more measured and restrained than mine.

The comparison is, however, unfair to Bostrom in two ways. First, Bostrom's book was written during the dawn of the deep learning revolution when it was not yet clear that it was about to become the paradigm that allowed AI development to really take off, and several years before the enormous breakthrough of large language models and other generative AI; while Yudkowsky's and Soares' book is jam-packed with insights coming from those recent developments, Bostrom's is obviously not. Second, while Superintelligence mostly exhibits a terse, academic style, IABIED is written with a broader audience in mind.

This last point should not be read as a disrecommendation of IABIED for AI researchers. Despite its popular style, the book argues quite forcefully and with a good deal of rigor for its central claims that (a) we seem to be on track to create superhumanly capable AIs within one or (at most) two decades, and that (b) with the current rush and consequent neglect of the safety aspect, creation of such AIs will likely spell the end of the human race. To the many AI researchers who are still unfamiliar with the central arguments for these claims and who in many cases simply deny the risk,¹ the book is potentially a very valuable read to get them more on board with the state-of-the-art in AI risk. And to those of us who are already on board with the central message, the book is valuable for a different reason, in that it offers a wealth of pedagogical devices that we can use when we explain AI risk to other audiences.

The authors are highly qualified in the field of AI safety, which Yudkowsky pioneered in the 00s.^2,3 Soares came later into the playing field, but is nevertheless one of its veterans, and currently the president of Machine Intelligence Research Institute (MIRI) that Yudkowsky co-founded in 2000 and still works at. They have both worked for many years on the so-called Al alignment problem - that of making sure that the first really powerful AIs have goals that are aligned with ours - but most of the fruits of this labor have been not blueprints for aligning AIs, but negative results, indicating how difficult the problem is, and how creating superintelligent AI without first having solved AI alignment spells disaster. This, unfortunately, reflects the situation that the entire field is facing.

In recent years (most visibly since 2021, but I suspect the insight goes back a bit further), Yudkowsky and Soares have converged on the conclusion that with the shortening of timelines until the creation of superintelligence (a time span increasingly often estimated in a single-digit number of years rather than in decades), we are very unlikely to solve AI alignment in time to avert existential catastrophe. Hence the stark book title If Anyone Builds It, Everyone Dies. They really mean it - and to emphasize this, one of the recurrent slogans during their book promotion work has been "We wish we were exaggerating". I mostly buy their message, albeit with less certainty than the authors; if I hade written the book, a more suitable title would have been If Anyone Builds It, Probably Everyone Dies. But of course, they are right to present their true judgements without softening or downplaying them, and to gesture towards what they think is the only viable solution: to pull the breaks, via binding international agreements, on frontier AI development. They are under no illusion that achieving this is easy, but insist that if we firmly decide to save our species from obliteration, it can be done.

The book is remarkably easy to read, and I have been very happy to put it in the hands of a number of non-expert friends, and to urge them to read it. The authors' most consistently recurrent pedagogical device is the use of colorful analogies and metaphors. One of my favorite passages of the book is a detailed description of how a nuclear energy plant works and what went wrong in the Chernobyl 1986 disaster. A comparison between this and advanced AI development reveals far-reaching similarities, but also differences in that the engineers at Chernobyl had a far better grasp of the principles underlying the nuclear reactions and how to stay safe - in particular by knowing the exact critical fraction of released neutrons triggering another fission event that is needed for a runaway chain reaction, along with the time frames involved - compared to present-day AI researchers who can at most make educated guesses about the corresponding runaway AI dynamics.

The authors' favorite source of analogies is not nuclear physics, however, but biological evolution. Early in the book (on p 17-18) we are treated to the following lovely illustration of the virtually unlimited powers of intelligence:

Imagine [...] that biological life on Earth had been the result of a game between gods. That there was a tiger-god that had made tigers, and a redwood-god that had made redwood trees. Imagine that there were gods for kinds of fish and kinds of bacteria. Imagine these game-players competed to attain dominion for the family of species that they sponsored, as life-forms roamed the planet below. Imagine that, some two million years before our present day, an obscure ape-god looked over their vast, planet-sized gameboard.

"It's going to take me a few more moves," said the hominid-god, "but I think I've got this game in the bag."

There was a confused silence, as many gods looked over the gameboard trying to see what they had missed. The scorpion-god said, “How? Your ‘hominid’ family has no armor, no claws, no poison.”

“Their brain,” said the hominid-god.

“I infect them and they die,” said the smallpox-god.

“For now,” said the hominid-god. “Your end will come quickly, Smallpox, once their brains learn how to fight you.”

“They don’t even have the largest brains around!” said the whale-god.

“It’s not all about size,” said the hominid-god. “The design of their brain has something to do with it too. Give it two million years and they will walk upon their planet’s moon.”

“I am really not seeing where the rocket fuel gets produced inside this creature’s metabolism,” said the redwood-god. “You can’t just think your way into orbit. At some point, your species needs to evolve metabolisms that purify rocket fuel—and also become quite large, ideally tall and narrow—with a hard outer shell, so it doesn’t puff up and die in the vacuum of space. No matter how hard your ape thinks, it will just be stuck on the ground, thinking very hard.”

“Some of us have been playing this game for billions of years,” a bacteria-god said with a sideways look at the hominid-god. “Brains have not been that much of an advantage up until now.”

“And yet,” said the hominid-god.

The book has dozens and dozens of similarly quotable gems. I love it, and I want everyone to read it.

Many other reviews of IABIED have been written. Those that resonate best with me include one by Scott Alexander, and one by Zvi Mowshowitz, who also offers a broad annotated collection of (positive as well as negative) reactions from others.

Footnotes

1) Indeed, there is still plently of such ignorance or even denialism around in the AI research community. As an illustrative example, Swedish readers may have look at the denialism pushed in public debate in August this year by a group of colleagues of mine at the Chalmers University of Technology.

2) Nick Bostrom's 2014 book can to no small extent be said to be conceived on top of Yudkowsky's shoulders.

3) Hostile critics sometimes counter this with the claim that Yudkowsky's highest academic merit is that of being a high-school dropout, which is formally true but conveys a lack of understanding of the importance of distinguishing between the social game of formal qualifications and the reality of actual competence.