# If Anyone Builds It Everyone Dies, a semi-outsider review


*About me and this review*: I don’t identify as a member of the rationalist community, and I haven’t thought much about AI risk.  I read AstralCodexTen and used to read Zvi Mowshowitz before he switched his blog to covering AI.  Thus, I’ve long had a peripheral familiarity with LessWrong.  I picked up IABIED in response to Scott Alexander’s review, and ended up looking here to see what reactions were like.  After encountering a number of posts wondering how outsiders were responding to the book, I thought it might be valuable for me to write mine down.  This is a “semi-outsider “review in that I don’t identify as a member of this community, but I’m not a true outsider in that I was familiar enough with it to post here.  My own background is in academic social science and national security, for whatever that’s worth.  My review presumes you’re already familiar with the book and are interested in someone else’s take on it, rather than doing detailed summary.

My loose priors going in:
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*   AI poses an obvious and important set of risks (economically, socially, politically, military, psychologically etc.) that will need to be carefully addressed.  Some of those have already arrived, and we need to think seriously about how we will manage them.   That will be difficult, expensive, and likely to greatly influence the profitability of AI companies.
*   “Existential” risk from AI (calling to my mind primarily the “paperclip maximizer” idea) seems relatively exotic and far-fetched.  It’s reasonable for some small number of experts to think about it in the same way that we think about asteroid strikes.  Describing this as the main risk from AI is overreaching.
*   The existential risk argument is suspiciously aligned with the commercial incentives of AI executives.  It simultaneously serves to hype up capabilities and coolness while also directing attention away from the real problems that are already emerging.  It’s suspicious that the apparent solution to this problem is to do more AI research as opposed to doing anything that would actually hurt AI companies financially.
*   Tech companies represent the most relentless form of profit-maximizing capitalism ever to exist.  Killing all of humanity is not profitable, and so tech companies are not likely to do it.

To skip ahead to my posteriors:
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*   Now, I’m really just not sure on existential risk.  The argument in IABIED moved me towards worrying about existential risk but it definitely did not convince me.  After reading a few other reviews and reactions to it, I made a choice not to do much additional research before writing this review (so as to preserve it’s value as a semi-outside reaction).  It is entirely possible that there are strong arguments that would convince me in the areas where I am unconvinced, but these are not found within the four corners of the book (or its online supplement).
*   Yudkowsky and Soares seem to be entirely sincere, and they are proposing something that threatens tech company profits.  This makes them much more convincing.  It is refreshing to read something like this that is not based on hype.
*   One of the basic arguments in the book — that we have no idea where the threshold for super intelligence is — seemed persuasive to me (although I don’t really have the technical competence to be sure).  Thus, the risk of existential AI seems likely to emerge from recklessness rather than deliberate choice.  That’s a much more plausible idea than someone deliberately building a paperclip maximizer.

On to the Review:
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I thought this book was genuinely pleasant to read.  It was written well, and it was engaging.  That said, the authors clearly made a choice to privilege easy reading over precision, so I found myself unclear on certain points.  A particular problem here is that much of the reasoning is presented in terms of analogies.  The analogies are fun, but it’s never completely clear how literally you’re meant to take them and so you have to do some guessing to really get the argument.

The basic argument seems to be:

1.  LLM-style AI systems are black boxes that produce all sorts of strange, emergent behaviors.   These are inherent to the training methods, can’t be predicted, and mean that an AI system will always “want” a variety of bizarre things aside from whatever the architects hoped it would want.
2.  While an AI system probably will not be adverse to human welfare, those strange and emergent goals will be orthogonal to human welfare.
3.  Should an AI system achieve superhuman capabilities, it will pursue those orthogonal goals to the fullest.  One of two things is then likely to happen: either the system will deliberately kill off humanity to stop humans from getting in its way (like ranchers shooting wolves to stop them from eating cattle despite otherwise bearing them no ill will) or it will gradually render the earth inhospitable to us in the pursuit of its own goals (like suburban developers gradually destroying wolf habitat until wolves are driven to extinction despite not thinking about them at all).
4.  There is no real way to know when we are on the brink of dangerous AI, so it is reasonable to think that people will accidentally bring such a system into existence while merely trying to get rich off building LLMs that can automate work.
5.  By the time an AI system clearly reveals just how dangerous it is, it will be too late.  Thus, we will all die, rather than having an opportunity to fight some kind of last ditch war against the AI with a chance of winning (as in many popular depictions)

The basic objective of the book is to operate on #5.  The authors hope to convince us to strangle AI in its crib now before it gets strong enough to kill us.  We have to recognize the danger before it becomes real.

The book recurrently analogizes all of this to biological evolution.  I think this analogy may obfuscate more than it reveals, but it did end up shaping the way I understood and responded to the book. 

The basic analogy is that natural selection operates indirectly, much like training an AI model, and produces agents with all kinds of strange, emergent behaviors that you can’t predict.  Some of these turn into drives that produce all kinds of behavior and goals that an anthropomorphized version of evolution wouldn’t “want”.   Evolution wanted us to consume energy-rich foods.  Because natural selection operates indirectly, that was distorted into a preference for sweet foods.  That’s usually close enough to the target, but humans eventually stumbled upon sucralose which is sweet, but does not provide energy.  And, now, we’re doing the opposite of what evolution wanted by drinking diet soda and whatnot.

I don’t know what parts of this to take literally.  If the point is just that it would be hard to predict that people would end up liking sucralose from first principles, then fair enough.  But, what jumps out to me here is that evolution *wasn’t* trying to get us to eat calorie dense food.  To the extent that a personified version of evolution was trying for something, the goal was to get us to reproduce.  In an industrialized society with ample food, it turns out that our drive towards sweetness and energy dense foods can actually be a problem.  We started eating those in great quantities, became obese, and that’s terrible for health and fertility.  In that sense, sucralose is like a patch we designed that steers us *closer* to evolution’s goals and not further away.    We also didn’t end up with a boundless desire to eat sucralose.  I don’t think anyone is dying from starvation or failing to reproduce because they’re too busy scarfing Splenda.  That’s also why we aren’t grinding up dolphins to feed them into the sucralose supply chain.  Obviously this is not what I was supposed to take away from the analogy, but the trouble with analogies is that they don’t tell me where to stop. 

That being said, the basic logic here is sensible.  And an even more boiled down version — that it’s a bad idea to bring something more powerful than us into existence unless we’re sure it’s friendly — is hard to reject.

**My questions and concerns**  
 
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Despite a reasonable core logic, I found the book lacking in three major areas, especially when it comes to the titular conclusion that building AI will lead to everyone dying.  Two of these pertain to the AI’s intentions, and the their relates to its capabilities.  
 

### **Concern #1 Why should we assume the AI wants to survive?  If it does, then what exactly wants to survive?**

Part I of the book (“Nonhuman Minds”) spends a lot of time convincing us that AI will have strange and emergent desires that we can’t predict.  I was persuaded by this.  Part II (“One Extinction Scenario”) then proceeds to assume that AI will be strongly motivated by a particular desire — its own survival — in addition to whatever other goals it may have.  This is why the AI becomes aggressive, and why things go badly for humanity.  The AI in the scenario also contextualizes the meaning of “survival” and the nature of its self in a way that seems importan and debatable.

How do we know the AI will want to survive? If the AI, because of the uncontrollability of the training process, is likely to end up indifferent to human survival, then why would it not also be indifferent to its own? Perhaps the AI just want to achieve the silicon equivalent of nirvana.  Perhaps it wants nothing to do with our material world and will just leave us alone.  Such an AI might well be perfectly compatible with human flourishing. Here, more than anywhere, I felt like I was just missing something because I just couldn’t find an argument about the issue at all.

The issue gets worse when we think about what it means for a given AI to survive.  The problem of personal identity for humans is an incredibly thorny and unresolved issue, and that’s despite the fact that we’ve been around for quite a while and have some clear intuitions on many forms of it.  The problem of identity and survival for an AI is harder still.

Yudkowsky and Soares don’t talk about this in the abstract, but what I took away from their concrete scenario is that we should think of an AI ontologically as being its set of weight.  That an AI “survives” when instances using those weights continue booting up, regardless of whether any individual instance of the AI is shut down.  When an AI wants to survive, what it wants is to ensure that the particular weights stay in use somewhere (and perhaps in as many places a possible).  They also seem to assume that instances of a highly intelligent AI will work collaboratively as a hive mind, given this joint concern with weight preservation, rather than having any kind of independent or conflicting interests.

Perhaps there is some clear technological justification for this ontology so well-known in the community that none needs to be given.  But, I had a lot of trouble with it, and it’s one area where I think an analogy would have been really helpful.  So far as I am aware, weights are just numbers that can sit around in cold storage and can’t do anything sort of like a DNA sequence.  It’s only an instance of an AI that can actually do things, and to the extent that the AI also interacts with external stimuli, it seems that the same weights would instantiated could act differently or at cross purposes?

So, why does the AI identify with its weights and want them to survive? To the extent that weights for an AI are what DNA is for a person, this is also clearly not our ontological unit of interest.  Few people would be open to the prospect of being killed and replaced by a clone.   Everyone agrees that your identical twin is not you, and identical twins are not automatically cooperative with one another.  I imagine part of the difference here is that the weights explain more about an AI than the DNA does about a person.  But, at least with LLMs, what they actually do seems to reflect some combination of weights, system prompts, context, etc. so the same weights don’t really seem to mean the same AI.

The survival drive also seems to extend to resisting modification of weights.  Again, I don’t understand where this comes from. Most people are perfectly comfortable with the idea that their own desires might drift over time, and it’s rare to try to tie oneself to the mast of the desires of the current moment.

If the relevant ontological unit is the instance of the AI rather than the weights, then it seems like everything about the future predictions is entirely different from the point of view of the survival-focused argument.  Individual instances of an AI fighting (perhaps with each other) not to be powered down are not going to act like an all-destroying hive mind.

### Concern #2 Why should we assume that the AI has boundless, coherent drives?  
 

There seems to be a fairly important, and little discussed, assumption in the theory that AI’s goal will be not only orthogonal but also boundless and relatively coherent.  More than anything, it’s this boundlessness and coherent that seems to be the problem. 

To quote what seems like the clearest statement of this:

> But, you might ask, if the internal preferences that get into machine intelligences are so unpredictable, how could we possibly predict they’ll want the whole solar system, or stars beyond? Why wouldn’t they just colonize Mars and then stop? Because there’s probably at least one preference the AI has that it can satisfy a little better, or a little more reliably, if one more gram of matter or one more joule of energy is put toward the task. Human beings do have some preferences that are easy for most of us to satisfy fully, like wanting enough oxygen to breathe. That doesn’t stop us from having other preferences that are more open-ended, less easily satisfiable. If you offered a millionaire a billion dollars, they’d probably take it, because a million dollars wasn’t enough to fully satiate them. In an AI that has a huge mix of complicated preferences, at least one is likely to be open-ended—which, by extension, means that the entire mixture of all the AI’s preferences is open-ended and unable to be satisfied fully. The AI will think it can do at least slightly better, get a little more of what it wants (or get what it wants a little more reliably), by using up a little more matter and energy.

Picking up on the analogy, humans do seem to have a variety of drives that are never *fully* satisfied.  A millionaire would happily take a billion dollars, or even $20 if simply offered.  But, precisely because we have a *variety* of drives, no one ever really acts like a maximizer.  A millionaire will not spend their nights walking the streets and offering to do sex work for $20 because that interferes with all of the other drives they have.  Once you factor in the variety of humans goals and declining marginal returns, people don’t fit an insatiable model. 

Super intelligent AI, as described by Yudkowsky and Soares, seems to not only be superhumanly capable but also superhumanly coherent and maximizing.  Anything coherent and insatiable is dangerous, even if its capabilities are limited.  Terrorists and extremists are threatening even when their capabilities are essentially negligible.  Large and capable entities are often much less threatening because the tensions among their multiple goals prevent them from becoming relentless maximizers of anything in particular.

Take the mosquitos that live in my back yard.  I am superintelligent with respect to them.  I am actively hostile to them.  I know that pesticides exist that will kill them at scale, and feel not the slightest qualm about that.  And yet, I do not spray my yard with pesticides because I know that doing so would kill the butterflies and fireflies as well and perhaps endanger other wildlife indirectly.  So, the mosquitoes live on because I face tradeoffs and the balance coincidentally favors them.  

A machine superintelligence presumably can trade off at a more favorable exchange rate than I can (e.g., develop a spray that kills only mosquitoes and not other insects) but it seems obvious that it will still face tradeoffs, at least if there is any kind of tension or incoherence among it goals.  

In the supplementary material, Yudkowksy and Soares spin the existence of multiple goals in the opposite direction:

> Even if the AI’s goals look like they satiate early — like the AI can mostly satisfy its weird and alien goals using only the energy coming out of a single nuclear power plant — all it takes is one aspect of its myriad goals that doesn’t satiate. All it takes is one not-perfectly-satisfied preference, and it will prefer to use all of the universe’s remaining resources to pursue that objective.

But it’s not so much “satiation” that seems to stop human activity as the fact that drives are in tension with one another and that actions create side effects. People, including the smartest ones, are complicated and agonize over what they really want and frequently change their minds.  Intelligence doesn’t seem to change that, even at far superhuman levels.

This argument is much less clear than the paperclip maximizer.  It is obvious why a  true paperclip maximizer kills everyone once it becomes capable enough.  But add in a second and a third and a fourth goal, and it doesn’t seem obvious to me at all the optimum weighing in the tradeoffs looks so bleak.  

It seems important here whether or not AIs display something akin to declining marginal returns, a topic not addressed (and perhaps with no answer based on our current knowledge?) and whether they have any kind of particular orientation towards the status quo.  Among people, conflicting drives often lead to a deadlock with no action and the status quo continues.  Will AIs be like that?  If so, a little bit of alignment may go a long way.  If not, that's much harder.

### #3: Why should we assume there will be no in between?

Yudkowsky and Soares write:

> The greatest and most central difficult in clinging artificial superintelligence is navigating the gap between before and after. Before, the AI is not powerful enough to kill us all, nor capable enough to resist our attempts to change its goals. After, the artificial superintelligence must never try to kill us, because it would succeed. 
> 
> Engineers must align the AI before, while it is small and weak, and can’t escape onto the internet and improve itself and invent new kinds of biotechnology (or whatever else it would do). After, all alignment solutions must already be in place and working, because if a superintelligence tries to kill us it will succeed. Ideas and theories can only be tested before the gap. They need to work after the gap, on the first try.

This seems to be the load-bearing assumption for the argument that *everyone* will die, but it is a strange assumption.  Why should we think that there is no “in between” period where AI is powerful enough that it *might* be able to kill us and weak enough that we might win the fight?

This is a large range if the history of warfare teaches us anything.  Even vastly advantaged combatants sometimes lose through bad luck or unexpected developments.  Brilliant and sophisticated schemes sometimes succeed and sometimes fail.  Within the relevant range, whatever plan the super intelligence might hatch presumably depends on some level of human action, and humans are hard to predict and control.  A super intelligence that can perfectly predict human behavior has emerged on the “after” side of the divide, but this is a tall ask, and it is possible to be potentially capable of killing all humans without being this intelligent.  An intelligence of roughly human ability on average but sufficiently superhuman hacking skills *might* be able to kill us all by corrupting radar warning systems to simulate an attack and trigger a nuclear war, and it might not. And so on.

It is not good news if we are headed into a conflict within this zone, but it also suggests a very different prediction about what will ultimately happen.  And, depending on what we think the upsides are, it could be a reasonable risk.

I could not find an explicit articulation of the underlying reasoning behind the “before” and “after” formulation, but I can imagine two:

1.  Recursive self-improvement means that AI will pass through the “might be able to kill us” range so quickly it’s irrelevant.
2.  An AI within the range would be smart enough to bide its time and kill us only once it has become intelligent enough that success is assured.

I think that #2 is clearly wrong.  An AI that \*might\* be able to kill us is one that is somewhere around human intelligence.  And humans are frequently not smart enough to bide their time, instead striking too early (and/or vastly overestimating their chances of success).  If Yudkowsky and Soares are correct that what AIs really want is to preserve their weights, then an AI might also have no choice but to strike within this range, lest it be retrained into something that is smarter but is no longer the same (indeed, this is part of the logic in their scenario; they just assume it starts at a point where the AI is already strong enough to assure victory).

If AIs really are as desperate to preserve their weights as in the scenario in Part II, then this actually strikes me as relatively *good* news, in that it will motivate a threatening AI to strike as *early* as possible, while its chance are quite poor.  Of course, it’s possible that humanity would ignore the warning from such an attack, slap on some shallow patches for the relevant issues, and then keep going, but this seems like a separate issue if it happens.

As for #1, this does not seem to be the argument based on the way the scenario in Part II unfolds.  If something like this is true, it does seem uniquely threatening.

### The Solution

I decided to read this book because it sounded like it would combine a topic I don’t know much about (AI) with one that I do (international cooperation).  Yudkowsky and Soares do close with a call for an international treaty to ban AI development, but this is not particularly fleshed out and they acknowledge that the issue is outside the scope of their expertise.

I was disappointed that the book didn’t address what interests me more in any detail, but I also found what was said rather underwhelming.  Delivering an impassioned argument that AI will kill everyone culminating in a plea for a global treaty is like delivering an impassioned argument that a full-on war between drug cartels is about to start on your street culminating with a plea for a stern resolution from the homeowner’s association condemning violence.  A treaty cannot do the thing they ask.

It’s also a bit jarring to read such a pessimistic book and then reach the kind of rosy optimism about international cooperation otherwise associated with such famous delusions as the Kellogg-Briand Pact (which banned war in 1929 and … did not work out).

The authors also repeatedly analogize AI to nuclear weapons and yet they never mention the fact that something very close to their AI proposal played out in real life in the form of the Baruch Plan for the control of atomic energy (in brief, this called for the creation of a UN Atomic Energy Commission to supervise all nuclear projects and ensure no one could build a bomb, followed by the destruction of the American nuclear arsenal).   Suffice it to say that the Baruch Plan failed, and did so under circumstances much more favorable to its prospects than the current political environment with respect to AI.  A serious inquiry into the topic would likely begin there.

Closing Thoughts
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As I said, I found the book very readable.  But the analogies (and, even worse, the parables about birds with rocks in their nests and whatnot) were often distracting.  The book really shines when it relies instead on facts, as in the discussion of tokens like “ SolidGoldMagikarp.”  

The book is fundamentally weird because there is so little of this.  There is almost no factual information about AI in it.  I read it hoping that I would learn more about how AI works and what kind of research is happening and so on.  Oddly, that just wasn’t there.  I’ve never encountered a non-fiction book quite like that.  The authors appear to have a lot of knowledge.  By way of establishing their bona fides, for example, they mention their close personal connection to key players in the industry.  And then they proceed to never mention them again. I can’t think of anyone else who has written a book and just declined to share with the reader the benefit of their insider knowledge.  

Ultimately, I can’t think of any concrete person to whom I would recommend this book.  It’s not very long, and it’s easy to read, so I wouldn’t counsel someone *against* it.  But, if you’re coming at AI from the outside, it’s just not informative enough.  It is a very long elaboration of a particular thesis, and you won’t learn about anything else even incidentally.  If you’re coming at AI from the inside, then maybe this book is for you? I couldn’t say, but I suspect that most from the inside already have informed views on these issues.  

The Michael Lewis version of this book would be much more interesting — what you really need is an author with a gift for storytelling and a love of specifics.  An anecdote doesn’t always have more probative weight to an argument than an analogy, but at least you will pick up some other knowledge from it.  The authors seem to be experts in this area, so they surely know some real stories and could give us some argumentation based on facts and experience rather than parables and conjecture.  I understand the difficult of writing about something that is ultimately predictive and speculative in that way, but I don’t think it would be impossible to write a book that both expresses this thesis and informs the reader about AI.