Someone who is interested in learning and doing good.
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Matthew Barnett
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Yudkowsky’s point about trying to sell an Oreo for $77 is that a billionaire isn’t automatically going to want to buy something off you if they don’t care about it (and neither would an ASI).
I thought Yudkowsky’s point was that the billionaire won’t give you $77 for an Oreo because they could get an Oreo for less than $77 via other means. But people don’t just have an Oreo to sell you. My point in that sentence was to bring up that workers routinely have things of value that they can sell for well over $77, even to billionaires. Similarly, I claim that Yudkowsky did not adequately show that humans won’t have things of substantial value that they can sell to future AIs.
I’m not sure anyone is arguing that smart AIs would immediately turn violent unless it was in their strategic interest
The claim I am disputing is precisely that it will be in the strategic interest of unaligned AIs to turn violent and steal from agents that are less smart than them. In that sense, I am directly countering a claim that people in these discussions routinely make.
Workers regularly trade with billionaires and earn more than $77 in wages, despite vast differences in wealth. Countries trade with each other despite vast differences in military power. In fact, some countries don’t even have military forces, or at least have a very small one, and yet do not get invaded by their neighbors or by the United States.
It is possible that these facts are explained by generosity on behalf of billionaires and other countries, but the standard social science explanation says that this is not the case. Rather, the standard explanation is that war is usually (though not always) more costly than trade, when compromise is a viable option. Thus, people usually choose to trade, rather than go to war with each other when they want stuff. This is true even in the presence of large differences in power.
I mostly don’t see this post as engaging with any of the best reasons one might expect smarter-than-human AIs to compromise with humans. By contrast to you, I think it’s important that AIs will be created within an existing system of law and property rights. Unlike animals, they’ll be able to communicate with us and make contracts. It therefore seems perfectly plausible for AIs to simply get rich within the system we have already established, and make productive compromises, rather than violently overthrowing the system itself.
That doesn’t rule out the possibility that the future will be very alien, or that it will turn out in a way that humans do not endorse. I’m also not saying that humans will always own all the wealth and control everything permanently forever. I’m simply arguing against the point that smart AIs will automatically turn violent and steal from agents who are less smart than they are unless they’re value aligned. This is a claim that I don’t think has been established with any reasonable degree of rigor.
I mean like a dozen people have now had long comment threads with you about this. I doubt this one is going to cross this seemingly large inferential gap.
I think it’s still useful to ask for concise reasons for certain beliefs. “The Fundamental Question of Rationality is: “Why do you believe what you believe?”″.
Your reasons could be different from the reasons other people give, and indeed, some of your reasons seem to be different from what I’ve heard from many others.
The short answer is that from the perspective of AI it really sucks to have basically all property be owned by humans
For what it’s worth, I don’t think humans need to own basically all property in order for AIs to obey property rights. A few alternatives come to mind: humans could have a minority share of the wealth, and AIs could have property rights with each other.
Of course, actual superhuman AI systems will not obey property rights, but that is indeed the difference between economic unemployment analysis and AI catastrophic risk.
This statement was asserted confidently enough that I have to ask: why do you believe that actual superhuman AI systems will not obey property rights?
I’m confused about the clarifications in this post. Generally speaking, I think the terms “alignment”, “takeover”, and “disempowered” are vague and can mean dramatically different things to different people. My hope when I started reading this post was to see you define these terms precisely and unambiguously. Unfortunately, I am still confused about how you are using these terms, although it could very easily be my fault for not reading carefully enough.
Here is a scenario that I want you to imagine that I think might help to clarify where I’m confused:
Suppose we grant AIs legal rights and they become integrated into our society. Humans continue to survive and thrive, but AIs eventually and gradually accumulate the vast majority of the wealth, political power, and social status in society through lawful means. These AIs are sentient, extremely competent, mostly have strange and alien-like goals, and yet are considered “people” by most humans, according to an expansive definition of that word. Importantly, they are equal in the eyes of the law, and have no limitations on their ability to hold office, write new laws, and hold other positions of power. The AIs are agentic, autonomous, plan over long time horizons, and are not enslaved to the humans in any way. Moreover, many humans also upload themselves onto computers and become AIs themselves. These humans expand their own cognition and often choose to drop the “human” label from their personal identity after they are uploaded.
Here are my questions
Does this scenario count as “AI takeover” according to you? Was it a “bad takeover”?
Are the AIs “aligned” in this scenario?
Are the humans “disempowered” in this scenario?
Was this a good or bad outcome for humanity?
And so I don’t really think that existential risk is caused by “unemployment”. People are indeed confused about the nature of comparative advantage, and mistakenly assume that lack of competetiveness will lead to loss of jobs, which will then be bad for them.
People are also confused about the meaning of words like “unemployment” and how and why it can be good or bad. If being unemployed merely means not having a job (i.e., labor force participation rate), then plenty of people are unemployed by choice, well off, happy, and doing well. These are called retired people.
One way labor force participation can be high is if everyone is starving and needs to work all day in order to survive. Another way labor force participation can be high is if it’s extremely satisfying to maintain a job and there are tons of benefits that go along with being employed. My point is that it is impossible to conclude whether it’s either “bad” or “good” if all you know is that this statistic will either go up or down. To determine whether changes to this variable are bad, you need to understand more about the context in which the variable is changing.
To put this more plainly, idea that machines will take our jobs generally means one of two things. Either it means that machines will push down overall human wages and make humans less competitive across a variety of tasks. This is directly related to x-risk concerns because it is a direct effect of AIs becoming more numerous and more productive than humans. It makes sense to be concerned about this, but it’s imprecise to describe it as an “unemployment”: the problem is not that people are unemployed, the problem is that people are getting poorer.
Or, the idea that machines will take our jobs means that it will increase our total prosperity, allowing us to spend more time in pleasant leisure and less time in unpleasant work. This would probably be a good thing, and it’s important to strongly distinguish it from the idea that wages will fall.
In my view, Baumol’s cost disease is poorly named: the name suggests that certain things are getting more expensive, but if “more expensive” means “society (on the whole) cannot afford as much as it used to” then this implication is false. To be clear, it is definitely possible that things like healthcare and education have gotten less affordable for a median consumer because of income inequality, but even if that’s true, it has little to do with Baumol’s cost disease per se. As Scott Alexander framed it,
The Baumol effect cannot make things genuinely less affordable for society, because society is more productive and can afford more stuff. However, it can make things genuinely less affordable for individuals, if those individuals aren’t sharing in the increased productivity of society.
I don’t think that the number of employees per patient in a hospital or the number of employees per student in a university is lower today than it was in the 1980s, even if hospitals and universities have improved in other ways.
I think this is likely wrong, at least for healthcare, but I’d guess for education too. For healthcare, Random Critical Analysis has written about the data, and I encourage you to look at their analysis.
There is also a story of sclerosis and stagnation. Sure, lots of frivolous consumer goods have gotten cheaper but healthcare, housing, childcare, and education, all the important stuff, has exploded in price.
I think the idea that this chart demonstrates sclerosis and stagnation in these industries—at least in the meaningful sense of our economy getting worse at producing or affording these things—is largely a subtle misunderstanding of what the chart actually shows. (To be clear, this is not an idea that you lean on much in this post, but I still think it’s important to try to clarify some misconceptions.)
Prices are relative: it only makes sense to discuss the price of X relative to Y, rather than X’s absolute price level. Even inflation is a relative measure: it shows the price of a basket of goods and services relative to a unit of currency.
With this context in mind, we should reconsider what it means for the items at the top of the chart to have “exploded in price”. There are several possible interpretations:
These items have become more expensive relative to a unit of US currency (true, supported by the chart)
These items have become more expensive relative to average hourly wages (true, supported by the chart)
These items have become more expensive relative to an average consumer’s income (mostly not true, not supported by the chart)
If the economic stagnation narrative were accurate, we would expect:
Claim (3) above to be true, as this would indicate that an average consumer finds these items harder to purchase. Conversely, if a service’s price decreases relative to someone’s income, it becomes more affordable for that person, even if its price increases relative to other metrics.
The chart to accurately represent the overall price of healthcare, housing, childcare, and education, rather than misleading sub-components of these things.
However, I argue that, when correctly interpreted under the appropriate measures, there’s little evidence that healthcare, housing, childcare, and education have become significantly less affordable for an average (not median) consumer. Moreover, I claim that the chart is consistent with this view.
To reconcile my claim with the chart, it’s crucial to distinguish between two concepts: average income and average wages. Income encompasses all money received by an individual or household from various sources, including wages, non-wage benefits, government assistance, and capital investments.
Average income is a broader and more appropriate way to measure whether something is becoming less “affordable” in this context, since what we care about is whether our economy has stagnated in the sense of becoming less productive. I personally think a more appropriate way to measure average income is via nominal GDP per capita. If we use this measure, we find that average incomes have risen approximately 125% from 2000-2023, which is substantially more than the rise in average wages over the same time period, as shown on the chart.
Using average wages for this analysis is problematic because it overlooks additional income sources that people can use to purchase goods and services. This approach also introduces complexities in interpretation, for example because you’d need to account for a declining labor share of GDP. If we focused on wages rather than average income, we would risk misinterpreting the decrease in average wages relative to certain services as a real decline in our ability to afford these things, instead of recognizing it more narrowly as a shift in the price of labor compared to these services.
A closer examination of the chart reveals that only four items have increased in price by more than 125% over the given period: Medical Care Services, College Textbooks, College Tuition and Fees, and Hospital Services. This immediately implies that, according to the chart, childcare and housing have actually become more affordable relative to average incomes. For the remaining items, I argue that they don’t accurately represent the overall price levels of healthcare and education. To support this claim, let’s break down each of these components:
Regarding Medical Care Services and Hospital Services, Random Critical Analysis has (to my mind) convincingly demonstrated that these components of the CPI do not accurately reflect overall healthcare prices. Moreover, when using the right standard to measure average income (nominal GDP per capita), he concludes that healthcare has not become significantly less affordable in the United States in recent decades.
Regarding College Tuition and Fees, this is not a measure of the quality-adjusted price level of college education, in the sense that matters here. That’s because colleges are providing a fundamentally different service now than they did in the past. There are more staff members, larger dorm complexes, and more amenities than before. We shouldn’t mistake an increase in the quality of college with whether education is becoming harder to produce. Indeed, given that a higher fraction of people are going to college now compared to decades ago, the fact that colleges are higher quality now undermines rather than supports a narrative of “stagnation”, in the economically meaningful sense.
Regarding College Textbooks, I recall spending a relatively small fraction of my income in college on textbooks, making me suspect that this component on the chart is merely cherrypicked to provide another datapoint that makes it seem like education has become less affordable over time.
To avoid having this comment misinterpreted, I need to say: I’m not saying that everything has gotten more affordable in the last 25 years for the median consumer. I’m not making any significant claims about inequality either, or even about wage stagnation. I’m talking about a narrower claim that I think is most relevant to the post: whether the chart demonstrates substantial economic stagnation, in the sense of our economy getting worse at producing certain stuff over time.
What is different this time?
I’m not confident in the full answer to this question, but I can give some informed speculation. AI progress seems to rely principally on two driving forces:
Scaling hardware, i.e., making training runs larger, increasing model size, and scaling datasets.
Software progress, which includes everything from architectural improvements to methods of filtering datasets.
On the hardware scaling side, there’s very little that an AI lab can patent. The hardware itself may be patentable: for example, NVIDIA enjoys a patent on the H100. However, the mere idea of scaling hardware and training for longer are abstract ideas that are generally not legally possible to patent. This may help explain why NVIDIA currently has a virtual monopoly on producing AI GPUs, but there is essentially no barrier to entry for simply using NVIDIA’s GPUs to train a state of the art LLM.
On the software side, it gets a little more complicated. US courts have generally held that abstract specifications of algorithms are not subject to patents, even though specific implementations of those algorithms are often patentable. As one Federal Circuit Judge has explained,
In short, [software and business-method patents], although frequently dressed up in the argot of invention, simply describe a problem, announce purely functional steps that purport to solve the problem, and recite standard computer operations to perform some of those steps. The principal flaw in these patents is that they do not contain an “inventive concept” that solves practical problems and ensures that the patent is directed to something “significantly more than” the ineligible abstract idea itself. See CLS Bank, 134 S. Ct. at 2355, 2357; Mayo, 132 S. Ct. at 1294. As such, they represent little more than functional descriptions of objectives, rather than inventive solutions. In addition, because they describe the claimed methods in functional terms, they preempt any subsequent specific solutions to the problem at issue. See CLS Bank, 134 S. Ct. at 2354; Mayo, 132 S. Ct. at 1301-02. It is for those reasons that the Supreme Court has characterized such patents as claiming “abstract ideas” and has held that they are not directed to patentable subject matter.
This generally limits the degree to which an AI lab can patent the concepts underlying LLMs, and thereby try to restrict competition via the legal process.
Note, however, that standard economic models of economies of scale generally predict that there should be a high concentration of firms in capital-intensive industries, which seems to be true for AI as a result of massive hardware scaling. This happens even in the absence of regulatory barriers or government-granted monopolies, and it predicts what we observe fairly well: a small number of large companies at the forefront of AI development.
Concretely, what does it mean to keep a corporation “in check” and do you think those mechanisms will not be available for AIs?
I still think I was making a different point. For more clarity and some elaboration, I previously argued in a short form post that the expected costs of a violent takeover can exceed the benefits even if the costs are small. The reason is because, at the same time taking over the entire world becomes easier, the benefits of doing so can also get lower, relative to compromise. Quoting from my post,
The central argument here would be premised on a model of rational agency, in which an agent tries to maximize benefits minus costs, subject to constraints. The agent would be faced with a choice: (1) Attempt to take over the world, and steal everyone’s stuff, or (2) Work within a system of compromise, trade, and law, and get very rich within that system, in order to e.g. buy lots of paperclips. The question of whether (1) is a better choice than (2) is not simply a question of whether taking over the world is “easy” or whether it could be done by the agent. Instead it is a question of whether the benefits of (1) outweigh the costs, relative to choice (2).
In my comment in this thread, I meant to highlight the costs and constraints on an AI’s behavior in order to explain how these relative cost-benefits do not necessarily favor takeover. This is logically distinct from arguing that the cost alone of takeover would be high.
I think people are and should be concerned about more than just violent or unlawful takeovers. Exhibit A: Persuasion/propaganda.
Unfortunately I think it’s simply very difficult to reliably distinguish between genuine good-faith persuasion and propaganda over speculative future scenarios. Your example is on the extreme end of what’s possible in my view, and most realistic scenarios will likely instead be somewhere in-between, with substantial moral ambiguity. To avoid making vague or sweeping assertions about this topic, I prefer being clear about the type of takeover that I think is most worrisome. Likewise:
B: For example, suppose the AIs make self-replicating robot factories and bribe some politicians to make said factories’ heat pollution legal. Then they self-replicate across the ocean floor and boil the oceans (they are fusion-powered), killing all humans as a side-effect, except for those they bribed who are given special protection.
I would consider this act both violent and unlawful, unless we’re assuming that bribery is widely recognized as legal, and that boiling the oceans did not involve any violence (e.g., no one tried to stop the AIs from doing this, and there was no conflict). I certainly feel this is the type of scenario that I intended to argue against in my original comment, or at least it is very close.
I don’t think I’m objecting to that premise. A takeover can be both possible and easy without being rational. In my comment, I focused on whether the expected costs to attempting a takeover are greater than the benefits, not whether the AI will be able to execute a takeover with a high probability.
Or, put another way, one can imagine an AI calculating that the benefit to taking over the world is negative one paperclip on net (when factoring in the expected costs and benefits of such an action), and thus decide not to do it.
Separately, I focused on “violent” or “unlawful” takeovers because I think that’s straightforwardly what most people mean when they discuss world takeover plots, and I wanted to be more clear about what I’m objecting to by making my language explicit.
To the extent you’re worried about a lawful and peaceful AI takeover in which we voluntarily hand control to AIs over time, I concede that my comment does not address this concern.
I’m thinking of this in the context of a post-singularity future, where we wouldn’t need to worry about things like conflict or selection processes.
I’m curious why you seem to think we don’t need to worry about things like conflict or selection processes post-singularity.
But San Francisco is also pretty unusual, and only a small fraction of the world lives there. The amount of new construction in the United States is not flat over time. It responds to prices, like in most other markets. And in fact, on the whole, the majority of Americans likely have more and higher-quality housing than their grandparents did at the same age, including most poor people. This is significant material progress despite the supply restrictions (which I fully concede are real), and it’s similar to, although smaller in size than what happened with clothing and smartphones.
I think something like this is true:
For humans, quality of life depends on various inputs.
Material wealth is one input among many, alongside e.g., genetic predisposition to depression, or other mental health issues.
Being relatively poor is correlated with having lots of bad inputs, not merely low material wealth.
Having more money doesn’t necessarily let you raise your other inputs to quality of life besides material wealth.
Therefore, giving poor people money won’t necessarily make their quality of life excellent, since they’ll often still be deficient in other things that provide value to life.
However, I think this is a different and narrower thesis from what is posited in this essay. By contrast to the essay, I think the “poverty equilibrium” is likely not very important in explaining the basic story here. It is sufficient to say that being poor is correlated with having bad luck across other axes. One does not need to posit a story in which certain socially entrenched forces keep poor people down, and I find that theory pretty dubious in any case.
I’m not sure I fully understand this framework, and thus I could easily have missed something here, especially in the section about “Takeover-favoring incentives”. However, based on my limited understanding, this framework appears to miss the central argument for why I am personally not as worried about AI takeover risk as most LWers seem to be.
Here’s a concise summary of my own argument for being less worried about takeover risk:
There is a cost to violently taking over the world, in the sense of acquiring power unlawfully or destructively with the aim of controlling everything in the whole world, relative to the alternative of simply gaining power lawfully and peacefully, even for agents that don’t share ‘our’ values.
For example, as a simple alternative to taking over the world, an AI could advocate for the right to own their own labor and then try to accumulate wealth and power lawfully by selling their services to others, which would earn them the ability to purchase a gargantuan number of paperclips without much restraint.
The expected cost of violent takeover is not obviously smaller than the benefits of violent takeover, given the existence of lawful alternatives to violent takeover. This is for two main reasons:
In order to wage a war to take over the world, you generally need to pay costs fighting the war, and there is a strong motive for everyone else to fight back against you if you try, including other AIs who do not want you to take over the world (and this includes any AIs whose goals would be hindered by a violent takeover, not just those who are “aligned with humans”). Empirically, war is very costly and wasteful, and less efficient than compromise, trade, and diplomacy.
Violently taking over the war is very risky, since the attempt could fail, and you could be totally shut down and penalized heavily if you lose. There are many ways that violent takeover plans could fail: your takeover plans could be exposed too early, you could also be caught trying to coordinate the plan with other AIs and other humans, and you could also just lose the war. Ordinary compromise, trade, and diplomacy generally seem like better strategies for agents that have at least some degree of risk-aversion.
There isn’t likely to be “one AI” that controls everything, nor will there likely be a strong motive for all the silicon-based minds to coordinate as a unified coalition against the biological-based minds, in the sense of acting as a single agentic AI against the biological people. Thus, future wars of world conquest (if they happen at all) will likely be along different lines than AI vs. human.
For example, you could imagine a coalition of AIs and humans fighting a war against a separate coalition of AIs and humans, with the aim of establishing control over the world. In this war, the “line” here is not drawn cleanly between humans and AIs, but is instead drawn across a different line. As a result, it’s difficult to call this an “AI takeover” scenario, rather than merely a really bad war.
Nothing about this argument is intended to argue that AIs will be weaker than humans in aggregate, or individually. I am not claiming that AIs will be bad at coordinating or will be less intelligent than humans. I am also not saying that AIs won’t be agentic or that they won’t have goals or won’t be consequentialists, or that they’ll have the same values as humans. I’m also not talking about purely ethical constraints: I am referring to practical constraints and costs on the AI’s behavior. The argument is purely about the incentives of violently taking over the world vs. the incentives to peacefully cooperate within a lawful regime, between both humans and other AIs.
A big counterargument to my argument seems well-summarized by this hypothetical statement (which is not an actual quote, to be clear): “if you live in a world filled with powerful agents that don’t fully share your values, those agents will have a convergent instrumental incentive to violently take over the world from you”. However, this argument proves too much.
We already live in a world where, if this statement was true, we would have observed way more violent takeover attempts than what we’ve actually observed historically.For example, I personally don’t fully share values with almost all other humans on Earth (both because of my indexical preferences, and my divergent moral views) and yet the rest of the world has not yet violently disempowered me in any way that I can recognize.
I think people in the safety community underrate the following possibility: early transformatively-powerful models are pretty obviously scheming (though they aren’t amazingly good at it), but their developers are deploying them anyway, either because they’re wildly irresponsible or because they’re under massive competitive pressure.
[...]
This has been roughly my default default of what would happen for a few years
Does this mean that if in, say, 1-5 years, it’s not pretty obvious that SOTA deployed models are scheming, you would be surprised?
That is, suppose we get to a point where models are widespread and producing lots of economic value, and the models might be scheming but the evidence is weak and uncertain, with arguments on both sides, and no one can reasonably claim to be confident that currently deployed SOTA models are scheming. Would that mean your default prediction was wrong?
I’m happy to use a functional definition of “understanding” or “intelligence” or “situational awareness”. If a system possesses all relevant behavioral qualities that we associate with those terms, I think it’s basically fine to say the system actually possesses them, outside of (largely irrelevant) thought experiments, such as those involving hypothetical giant lookup tables. It’s possible this is our main disagreement.
When I talk to GPT-4, I think it’s quite clear it possesses a great deal of functional understanding of human intentions and human motives, although it is imperfect. I also think its understanding is substantially higher than GPT-3.5, and the trend here seems clear. I expect GPT-5 to possess a high degree of understanding of the world, human values, and its own place in the world, in practically every functional (testable) sense. Do you not?
I agree that GPT-4 does not understand the world in the same way humans understand the world, but I’m not sure why that would be necessary for obtaining understanding. The fact that it understands human intentions at all seems more important than whether it understands human intentions in the same way we understand these things.
I’m similarly confused by your reference to introspective awareness. I think the ability to reliably introspect on one’s own experiences is pretty much orthogonal to whether one has an understanding of human intentions. You can have reliable introspection without understanding the intentions of others, or vice versa. I don’t see how that fact bears much on the question of whether you understand human intentions. It’s possible there’s some connection here, but I’m not seeing it.
(I claim) current systems in fact almost certainly don’t have any kind of meaningful situational awareness, or stable(ish) preferences over future world states.
I’d claim:
Current systems have limited situational awareness. It’s above zero, but I agree it’s below human level.
Current systems don’t have stable preferences over time. But I think this is a point in favor of the model I’m providing here. I’m claiming that it’s plausibly easy to create smart, corrigible systems.
The fact that smart AI systems aren’t automatically agentic and incorrigible with stable preferences over long time horizons should be an update against the ideas quoted above about spontaneous instrumental convergence, rather than in favor of them.
There’s a big difference between (1) “we can choose to build consequentialist agents that are dangerous, if we wanted to do that voluntarily” and (2) “any sufficiently intelligent AI we build will automatically be a consequentialist agent by default”. If (2) were true, then that would be bad, because it would mean that it would be hard to build smart AI oracles, or smart AI tools, or corrigible AIs that help us with AI alignment. Whereas, if only (1) is true, we are not in such a bad shape, and we can probably build all those things.
I claim current evidence indicates that (1) is probably true but not (2), whereas previously many people thought (2) was true. To the extent you disagree and think (2) is still true, I’d prefer you to make some predictions about when this spontaneous agency-by-default in sufficiently intelligent systems is supposed to arise.
I don’t know how many years it’s going to take to get to human-level in agency skills, but I fear that corrigibility problems won’t be severe whilst AIs are still subhuman at agency skills, whereas they will be severe precisely when AIs start getting really agentic.
How sharp do you expect this cutoff to be between systems that are subhuman at agency vs. systems that are “getting really agentic” and therefore dangerous? I’m imagining a relatively gradual and incremental increase in agency over the next 4 years, with the corrigibility of the systems remaining roughly constant (according to all observable evidence). It’s possible that your model looks like:
In years 1-3, systems will gradually get more agentic, and will remain ~corrigible, but then
In year 4, systems will reach human-level agency, at which point they will be dangerous and powerful, and able to overthrow humanity
Whereas my model looks more like,
In years 1-4 systems will get gradually more agentic
There isn’t a clear, sharp, and discrete point at which their agency reaches or surpasses human-level
They will remain ~corrigible throughout the entire development, even after it’s clear they’ve surpassed human-level agency (which, to be clear, might take longer than 4 years)
I was making a claim about the usual method people use to get things that they want from other people, rather than proposing an inviolable rule. Even historically, war was not the usual method people used to get what they wanted from other people. The fact that only 8% of history was “entirely without war” is compatible with the claim that the usual method people used to get what they wanted involved compromise and trade, rather than war. In particular, just because only 8% of history was “entirely without war” does not mean that only 8% of human interactions between people were without war.
You mentioned two major differences between the current time period and what you expect after the technological singularity:
The current time period has unique international law
The current time period has expensive labor, relative to capital
I question both the premise that good international law will cease to exist after the singularity, and the relevance of both of these claims to the central claim that AIs will automatically use war to get what they want unless they are aligned to humans.
There are many other reasons one can point to, to explain the fact that the modern world is relatively peaceful. For example, I think a big factor in explaining the current peace is that long-distance trade and communication has become easier, making the world more interconnected than ever before. I also think it’s highly likely that long-distance trade and communication will continue to be relatively easy in the future, even post-singularity.
Regarding the point about cheap labor, one could also point out that if capital is relatively expensive, this fact would provide a strong reason to avoid war, as a counter-attack targeting factories would become extremely costly. It is unclear to me why you think it is important that labor is expensive, for explaining why the world is currently fairly peaceful.
Therefore, before you have developed a more explicit and precise theory of why exactly the current world is peaceful, and how these variables are expected to evolve after the singularity, I simply don’t find this counterargument compelling.