I didn’t anticipate this as a factor at all. My off-the-cuff counterargument would be that the actual habitable portion of the Stelliferous Era may be much shorter than we would think, given that the relative abundance of elements and what type of star you have depends on how early or late you are. For niche example, the proportion of radioactive elements and therefore heat you have depends on where you are in the “birth order” of planets, and if your planet is relatively cold for this reason it can no longer be earth-like. I could try to put together the same idea for the biochemistry, since the normal organic elements will vary too, but the difficulty there is less obvious to me.
SE Gyges
Karma: 153
Might An LLM Be Conscious?
Alignment Is Proven Tractable
Most Observers Are Alone: The Fermi Paradox as Default
At a meta level, “publishing, in 2025, a public complaint about OpenPhil’s publicly promoted timelines and how those may have influenced their funding choices” does not seem like it serves any defensible goal.
Let’s suppose the underlying question is “why did OpenPhil give money to OpenAI in 2017”. (Or, conversely, not give money to some other venture in a similar timeframe). Why is this, currently, significantly important? What plausible goal is served by trying to answer this question more precisely?
If it’s because they had long timelines, it tells you that short timeline arguments were not effective, which hopefully everyone already knows. This has been robustly demonstrated across most meaningful groups of people controlling either significant money or government clout. It is not information. I would not update on this.
If they did this because they had short timelines, they believed in whatever Sam was selling for that. I would not update on this either. It is hopefully well understood, by now, that Sam is good at selling things. “You could parachute him into an island full of cannibals and come back in 5 years and he’d be the king.”
If they did this for non-timeline reasons, I might update on, idk, some nebulous impression about how OpenPhil’s bureaucracy worked before the year 2020 or so. How good Sam (or another principal) was at convincing people to give them money. I don’t see how this is an important fact about the world.
Generally my model is that when people do not seem to be behaving optimally, they are behaving close to optimal for something, but that something is not the goal I imagine they are pursuing. I am imagining a goal like “being able to influence future events more effectively”, but I can’t see how that’s served here, so I imagine we’re optimizing for something else.
I have a very simple opinion about why pushes for AI regulation fail. Ready?
Because nobody knows what they are asking for, or what they are asking for is absurd.
Here are the absurdities:
“Stop all AI research forever”
“Stop all AI research until it’s safe to do, and I say when it’s safe (i will never say it’s safe)”
“Stop all AI research except mine, where I sit around and think about it”
“Pause all AI research until we have a functional genetic engineering project so our smarter descendants can do AI research”
Once we get past absurd asks we get into easily coopted asks, like “restrict compute” which turns into “declare an arms race with the largest industrial power on the planet” and “monitor training runs” which turns into “tell every potential competitor you are going to have the government crush them”.
What will convince me there is a sane block pushing for AI-related regulations is when they propose a regulation that is sane. I cannot emphasize enough how much these things have failed at the drafting stage. The part everyone is allegedly good at, that part, where you write down a good idea that it might be a good idea to do? That’s the part where this has failed.
This is just accurate and I have nothing interesting to add to it. I basically think that considering the thing in the abstract as an idea as opposed to the thing in the real world as a social grouping, scene, place, group of people, set of practices, etc is kind of wasted. As soon as you make a point of not just taking some part of the ideas seriously but identifying yourself as a rationalist and participating in any rationalist space (including this one) the thing you are doing is not, primarily, an abstraction that lives entirely in your head.
It’s “notably and especially” part OF YUDKOWSKY’S WORK AND WHAT HE PROMOTES. It is a minor part of the characterization of rationalism.
This is legitimately such poor reading comprehension that I assume malice.
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This is a category argument that I explicitly avoid making and don’t think is meaningful: the word itself does not mean anything and arguing over it is meaningless. You seem to really want to do that anyway because you can support your argument better on basically definitional grounds than factual ones.
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This is also being supported by a category of argument for “things people say about stuff”, which is both not a method that you will ever find the truth by (“what do people say the most often? i dunno, i can go find some things I think are similar and then decide how similar they are by how similar the things people say sometimes are”) and cherry picked to support your definition.
Like: this method cannot even in principle tell us anything about rationalism, and if it did, by choosing Islam, Judaism etc as comparison points instead of Scientology, Mormonism or any other smaller, newer or more modern movement is just assuming your conclusion. If you compare the things people say for or against new, modern, small religions (ie, cults) to the things they say about rationalism, then on the terms of “what’s in the discourse is the definition of the thing”, which is in any case trash, rationalism is clearly a new age California cult. So I don’t know why you think that—completely logically invalid, nonsensical—criteria for truth is the one you’d want to use, since it very clearly indicates rationalism is a standard California cult.
edit: and this style of discourse around cults is SO COMMON THERE IS A MEME FORMAT ABOUT IT. It is a King of the Hill joke about cults, because this discourse surrounding cults was so common that King of the Hill made a joke about it, and then it became a meme because enough people found the joke funny AND expected to have occasion to use it in the future that they clipped a meme format about it. You do not want “what does discourse around the thing look like? that clearly defines what it is” to be your source of truth here and it’s insane to me that you imagine you would
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True! It is very often used pejoratively. I am mostly uninterested in whether or not it’s pejorative. I think it’s descriptively accurate. I think the place in thingspace where rationalism lives is close to religion.
How many people have DENIED that transhumanism, social justice, liberalism, conservatism, libertarianism, communism, capitalism, objectivism, apple or unix is a religion? Is this a common feature for people interested in those things?
If the standard is “very prominently and loudly denies being a religion” I think you will find that like 10:1 loud denials of being a religion come from cults, not from non-religions.
As I see it, that you have to argue that it’s a religion is a bad sign. It means that rationality isn’t passing the smell test. That is, you needed to write a post to argue that rationality is a religion, which I view as evidence against it being a religion, since most religions are clearly religions and no one writes posts arguing that Islam or Hinduism is a religion (if anything, people sometimes write the opposite for various reasons!). If it’s in the category of religions, it’s a marginal case at best.
How many non-religions have had people write thousand-word posts denying they are religions? Does it come up often? “The fact that someone even wrote <this thing>” does not prove the point you think it does. It proves the opposite point.
This is so common, in fact, that there’s a meme format for it.
Did I say that rationalism is about Bayes’ theorem? No. Bayes’ theorem is a minor descriptive detail of Yudkowky’s focus on being more rational.
Did I say that rationalism is religious because it is about Bayes’ theorem? Also no. In fact you can remove the mention of Bayes’ theorem and it changes basically nothing.
Is it true that rationalism inherits its obsession with ‘being more rational’ from Yud’s writings, which are also accurately described as emphasizing Bayes’ theorem? Yes.
Is talking about p(doom), p(anything), or “updating” in a certain direction a cultish and religion-like use of the language of Bayesian probability? Yes.
Do religions often fetishize or have in their holy writings things which they do not actually practice or have constructed out of practice? Also yes.
Is Rationalism a Religion
in my view, there doesn’t seem to be such a thing as bad publicity for AGI, and I still don’t know for sure why that’s happening. And that seems like where most of the value is in figuring out this discussion, to me, at least.
It’s an incentive problem.
There is no way to discuss something being dangerous that does not also render it valuable. People are incentivized to seek out value; our entire economy is based on it. It works beautifully, but it is terrible at mitigating externalities. We only dial back from dangerous or bad things after the disaster; so long as doing things is profitable, rational economic actors seek out high-risk activities as far as permitted, because they alone get the profit and the majority of the risk is to other people.
In my view Yudkowsky’s body of work has had two main effects, which run in opposite directions:
Convincing many people that AI is extremely valuable, which is a large part of why we currently are where we are.
Convincing many people that AI is dangerous, which shows no signs of paying off yet but which may be crucially important at some future juncture. I am willing to pronounce it a complete failure at actually causing any regulatory regime whatsoever to come into existence thus far.
I think these are blind guesses and relying on the benchmarks is the streetlight effect, as I think we talked about in another thread. I am mostly explaining in as much detail as I can the parts I think are relevant to Neel’s objection, since it is substantively the most common objection, ie, that paying attention to financial incentives or work history is irrelevant to anything. I am happy that I have addressed the scenario itself in enough detail
AI 2027 Response Followup
These issues specifically have been a sticking point for a number of people, so I should clarify some things separately. Probably this is also because I didn’t see this earlier so it’s been a while and because I know who you are.
I do not think AI 2027 is, effectively, OpenAI’s propaganda because it is about a recursively self-improving AI and OpenAI is also about RSI. There are a lot of versions (and possible versions) of a recursively self-improving AI thesis. Daniel Kokotajlo has been around long enough that he was definitely familiar with the territory before he worked at OpenAI. I think that it is effectively OpenAI propaganda because it assumes a very specific path to a recursively self-improving AI with a very specific technical, social and business environment, and this story is about a company that appears to closely resemble OpenAI [1]and is pursuing something very similar to OpenAI’s current strategy. It seems unlikely that Daniel had these very specific views before he started at OpenAI in 2022.
Daniel is a thoughtful, strategic person who understands and thinks about AI strategy. He presumably wrote AI 2027 to try to influence strategy around AI. His perspective is going to be for playing as OpenAI. He will have used this perspective for years, totaling thousands of hours. He will have spent all of that time seeing AI research as a race, and trying to figure out how OpenAI can win. This is a generating function for OpenAI’s investor pitch, and is also the perspective that AI 2027 takes.
Working at OpenAI means spending years of your professional life completely immersed in an information environment sponsored by, and meant to increase the value of, OpenAI. Having done that is a relevant factor for what information you think is true and what assumptions you think are reasonable. Even if you started off with few opinions about them, and you very critically examined and rejected most of what OpenAI said about itself internally, you would still have skewed perspective about OpenAI and things concerning OpenAI.
I think of industries I have worked in from the perspective of the company I worked for when I was in that industry. I expect that when he worked at OpenAI he was doing his best to figure out how OpenAI comes out ahead, and so was everyone around him. This would have been true whether or not he was being explicitly told to do it, and whether or not he was on the clock. It is simpler to expect that this did influence him than to expect that it did not.
Quitting OpenAI loudly doesn’t really change this picture, because you generally only quit loudly if you have a specific bone to pick. If you’ve got a bone to pick while quitting OpenAI, that bone is, presumably, with OpenAI. Whatever story you tell after you do that is probably about OpenAI.
I think the part about financial incentives is getting dismissed sometimes because a lot of ill-informed people have tried to talk about the finances in AI. This seems to have become sort of a thought-terminating cliche, where any question about the financial incentives around AI is assumed to be from uninformed people. I will try to explain what I meant about the financial influence in a little more detail.
In this specific case, I think that the authors are probably well-intentioned. However, most of their shaky assumptions just happen to be things which would be worth at least a hundred billion dollars to OpenAI specifically if they were true. If you were writing a pitch to try to get funding for OpenAI or a similar company, you would have billions of reasons to be as persuasive as possible about these things. Given the power of that financial incentive, it’s not surprising that people have come up with compelling stories that just happen to make good investor pitches. Well-intentioned people can be so immersed in them that they cannot see past them.
It is worth noting that the lead author of AI 2027 is a former OpenAI employee. He is mostly famous outside OpenAI for having refused to sign their non-disparagement agreement and for advocating for stricter oversight of AI businesses. I do not think it is very credible that he is deliberately shilling for OpenAI here. I do think it is likely that he is completely unable to see outside their narrative, which they have an intense financial interest in sustaining.
There are a lot of different ways for a viewpoint to be skewed by money.
First is to just be paid to say things.
I don’t think anyone was paid anything by OpenAI for writing AI 2027. I thought I made enough of a point of that in the article, but the second block above is towards the end of the relevant section and I should maybe have put it towards the top. I will remember to do that if I am writing something like this again and maybe make sure to write at least an extra paragraph or two about it.
I do not think Daniel is deliberately shilling for OpenAI. That’s not an accusation I think is even remotely supportable, and in fact there’s a lot of credible evidence running the other way. He’s got a very long track record and he made a massive point of publicly dissenting from their non-disparagement agreement. It would take a lot of counter-evidence to convince me of his insincerity.
You didn’t bring him up, but I also don’t think Scott, who I think is responsible for most of the style of the piece, is being paid by anyone in particular to say anything in particular. I doubt such a thing is possible even in principle. Scott has a pretty solid track record of saying whatever he wants to say.
Second: what information is available, and what information do you see a lot?
I think this is the main source of skew.
If it’s valuable to convince people something is true, you will probably look for facts and arguments which make it seem true. You will be less likely to look for facts and arguments which make it seem false. You will then make sure that as many people are aware of all the facts and arguments that make the thing seem true as possible.
At a corporate level this doesn’t even have to be a specific person. People who are pursuing things that look promising for the company will be given time and space to pursue what they are doing, and people who are not will be more likely to be told to find something else to do. You will choose to promote favorable facts and not promote bad ones. You get the same effect as if a single person had deliberately chosen to only look for good facts.
It would be weird if this wasn’t true of OpenAI given how much money is involved. As in, positively anomalous. You do not raise money by seeking out reasons why your technology is maybe not worth money, or by making sure everyone knows those things. Why would you do that? You are getting money, directly, because people think the technology you are working on is worth a lot of money, and everyone knows as much as you can give them about why what you’re doing is worth a lot of money.
Tangentially, this type of narrative allows companies to convince staff to take compensation that is more heavily weighted towards stock, which tends to benefit existing shareholders in cases where they prefer to do that. They know employees will probably sell it back to them well below value at public sale or acquisition, or they know the stock is worth less than salary would be.
For a concrete example of this that I didn’t dig into in my review, from the AI 2027 timelines forecast.
We first show Method 1: time-horizon-extension, a relatively simple model which forecasts when SC will arrive by extending the trend established by METR’s report of AIs accomplishing tasks that take humans increasing amounts of time.
We then present Method 2: benchmarks-and-gaps, a more complex model starting from a forecast saturation of an AI R&D benchmark (RE-Bench), and then how long it will take to go from that system to one that can handle real-world tasks at the best AGI company.
Finally we then provide an “all-things-considered” forecast that takes into account these two models, as well as other possible influences such as geopolitics and macroeconomics.
Are either RE-Bench or the METR time horizon metrics good metrics, as-is? Will they continue to extrapolate? Will a model that saturates them accelerate research a lot?
I think the answer to all of these is maybe. If you’re OpenAI, it is pretty important that benchmarks are good metrics. It is worth a ton of money. So, institutionally, OpenAI has to believe in benchmarks, and vastly prefers if the answer is “yes” to all of these questions. And this is also what AI 2027 is assuming.
I made a point of running this point into the ground in writing it up, but essentially every time we break a “maybe” question in AI 2027, the answer seems to be the one that OpenAI is also likely to prefer. It’s a very specific thing to happen! It doesn’t seem very likely it happened by chance. In total the effect is that “this is a slight dissent from the OpenAI hype pitch”, in my opinion.
This isn’t even a problem entirely among OpenAI people. OpenAI has the loudest voice and is more or less setting the agenda for the industry. This is both because they were very clearly in the lead for a stretch, and because they’ve been very successful at acquiring users and raising money. There are probably more people who are bought into OpenAI’s exact version of everything outside the company than inside of it. This is a considerable problem if you want a correct evaluation of the current trajectory.
I obviously cannot prove this, but I think if Daniel hadn’t been a former OpenAI employee I probably would have basically the same criticism of the actual writing. It would be neater, even, because “this person has bought into OpenAI’s hype” is a lot less complicated without the non-disparagement thing, which buys a lot of credibility. I honestly didn’t want to mention who any of the authors were at all, but it seemed entirely too relevant to the case I was making to do it.
That’s two: being paid and having skewed information.
Third thing, much smaller, just being slanted because you have a financial incentive. Maybe you’re just optimistic, maybe you’re hoping to sell soon.
Daniel probably still owns stock or options. I mentioned this in the piece. I don’t think this is very relevant or is very likely to skew his perspective. It did seem like I would be failing to explain what was going on if I did not mention the possibility while discussing how he relates to OpenAI. I think it is incredibly weak evidence when stacked against his other history with the company, which strongly indicates that he’s not inclined to lie for them or even be especially quiet when he disagrees with them.
I don’t think it’s disgraceful to mention that people have direct financial incentives. There’s I think an implicit understanding that it’s uncouth to mention this sort of thing, and I disagree with it. I think it causes severe problems, in general. People who own significant stock in companies shouldn’t be assumed to be unbiased when discussing those companies, and it shouldn’t be taboo to mention the potential slant.
My last point is stranger, and is only sort of about money. If everyone you know is financially involved, is there some point where you might as well be?
JD Vance gets flattered anonymously by describing him using his job title, but we flatter Peter Thiel by name. Peter Thiel is, actually, the only person who gets a shout-out by name. Maybe being an early investor in OpenAI is the only way to earn that. I didn’t previously suspect that he was the sole or primary donor funding the think tank that this came out of, but now I do. I am reminded that the second named author of this paper has a pretty funny post about how everyone doing something weird at all the parties he goes to is being bankrolled by Peter Thiel.
This is about Scott, mostly.
AI 2027’s “Vice President” (read: JD Vance) election subplot is long and also almost totally irrelevant to the plot. It is so conspicuously strange that I had trouble figuring out why it would even be there. I didn’t learn until after I’d written my take that JD Vance had read AI 2027 and mentioned it in an interview, which also seems like a very odd thing to happen. I went looking for the simplest explanation I could.
Scott says whatever he wants, but apparently by his accounting half of his social circle is being bankrolled by Peter Thiel. This part of AI 2027 seems to be him, and he seems to be deliberately flattering Vance. Vance is a pretty well known Thiel acolyte. On the relatively happy ending of AI 2027 they build an ASI surveillance system, and surveillance is a big Peter Thiel hobby horse.
I don’t know what I’m really supposed to make of any of this. I definitely noticed it. It raises a lot of questions. It definitely seems to suggest strongly that if you spend a decade or more bankrolling all of Scott’s friends to do weird things they think are interesting, you are likely to see Scott flatter you and your opinions in writing. It also seems to suggest that Scott’s deliberately acting to lobby JD Vance. If it weren’t for Peter Thiel bankrolling his friends so much that Scott makes a joke out of it, I would think it just looked like Scott had a very Thiel-adjacent friend group.
In pretty much the same way that OpenAI will tend to generate pro-OpenAI facts and arguments, and not generate anti-OpenAI facts and arguments, I would expect that if enough people around you are being bankrolled by someone for long enough they will tend to produce information that person likes and not produce information that person doesn’t like.
I cannot find a simpler explanation than Thiel influence for why you would have a reasonably long subplot about JD Vance, world domination, and mass surveillance and then mention Peter Thiel in the finale.
I don’t think pointing out this specific type of connection should be taboo for basically the same reason I don’t think pointing out who owns what stock should be. I like knowing things, and being correct about them, and so I like knowing if people are offering good information or if there is an obvious reason their information or arguments would be bad.
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A few people have said that it could be DeepMind. I think it could be but pretty clearly isn’t. Among other things, DeepMind would not want or need to sell products they considered dangerous or to be possibly close to allowing RSI, because they are extremely cash-rich. If the forecast were about DeepMind, it would probably consider this, but it isn’t, so it doesn’t.
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If you present this dichotomy to policymakers the pause loses 100 times out of 100, and this is a complete failure, imho. This dichotomy is what I would present to policymakers if I wanted to inoculate them against any arguments for regulation.
What would be a major disagreement, then? Something like a medium scenario or slopworld?
Possibly, but in my own words, on technical questions, purely? That an LLM is completely the wrong paradigm. That any reasonable timeline runs 10+ years. China is inevitably going to get there first. China is unimportant and should be ignored. GPUs are not the most important resource or determinative. That the likely pace of future progress is unknowable.
Substantive policy options, which is more what I had in mind:
1) For-profit companies (and/or OAI specifically) have inherently bad incentives incompatible with suitably cautious development in this space.
2) That questions of who has the most direct governance and control of the actual technology are of high importance, and so safety work is necessarily about trustworthy control and/or ownership of the parent organization.
3) Arms races for actual armaments are bad incentives and should be avoided at all costs. This can be mitigated by prohibiting arms contracts, nationalizing the companies, forbearing from development at all, or requiring an international agreement & doing development under a consortium.
4) That safety work is not sufficiently advanced to meaningfully proceed
5) That there needs to be a much more strictly defined and enforced criteria for cutoff or safety certifying a launch.Any of the technical issues kneecaps the parts of this that dovetail with being a business plan. Any of these (pretty extreme) policy remedies harms OAI substantially, and they are incentivized to find reasons why they can claim that they are very bad ideas.
Follows various bits about China, which I am going to avoid quoting because I have basically exactly one disagreement with it that does not respond to any given point:
The correct move in this game is to not play. There is no arms race with China, either against their individual companies or against China itself, that produces incentives which are anything other than awful. (Domestic arms races are also not great, but at least do not co-opt the state completely in the same way.) Taking an arms race as a given is choosing to lose. It should not, and really, must not be very important what country anything happens in.
This creates a coordination problem. These are notoriously difficult, but sometimes problems are actually hard and there is no non-hard solution. Bluntly, however, from my perspective, the US sort of unilaterally declared an arms race. Arms race prophecies tend to be self-fulfilling. People should stop making them.
My argument for, basically, the damnation by financial incentive of this entire China-themed narrative runs basically as follows, with each being crux-y:
1) People follow financial incentives deliberately, such as by lying or by selectively seeking out information that might convince someone to give them money.
2) This is not always visible, because all of the information can be true; you can do this without ever lying. You can simply not try hard to disprove the thesis that you are pushing for.
3) People who are not following this financial incentive at all can, especially if the incentive is large, be working on extremely biased information regardless of whether they personally are aware of a financial incentive of any kind. Information towards a conclusion is available, and against it is not available, because of how other people have behaved.
4) OpenAI has such an incentive, and specifically seems to prefer to have an arms-race narrative because it justifies government funding and lack of regulation. (e.g., this op ed by sam altman)
5) The information environment caused by this ultimately causes the piece to have this overarching China arms race theme, and it is therefore not a coincidence that it is received by US Government stakeholders as actually arguing against regulation of any kind.I think that this specifically being the ultimate cause of the very specific arms race narrative now popular and displayed here is parsimonious. It does not, I think, assume any very difficult facts, and explains e.g. how AI 2027 manages to accomplish the exact opposite of its apparently intended effect with major stakeholders.
[quoting original author] in our humble opinion, AI 2027 depicts an incompetent government being puppeted/captured by corporate lobbyists. It does not depict what we think a competent government would do. We are working on a new scenario branch that will depict competent government action.
I would read this.
We don’t actually have any tools aside from benchmarks to estimate how useful the models are. We are fortunate to watch the AIs slow the devs down. But what if capable AIs do appear?
Hoping that benchmarks measure the thing you want to measure is the streetlight effect. Sometimes you just have to walk into the dark.
So your take has OpenBrain sell the most powerful models directly to the public. That’s a crux. In addition, granting Agents-1-4 instead of their minified versions direct access to the public causes Intelligence Curse-like disruption faster and attracts more government attention to powerful AIs.
I am actually not sure this requires selling the most powerful models, although I hadn’t considered this.
If there’s a -mini or similar it leaks information from a teacher model, if it had one; it is possible to skim off the final layer of the model by clever sampling, or to distill out nearly the entire distribution if you sample it enough. I do not think you can be confident that it is not leaking the capabilities you don’t want to sell, if those capabilities are extremely dangerous.
So: If you think the most powerful models are a serious bioweapons risk you should keep them airgapped, which means you also cannot use them in developing your cheaper models. You gain literally nothing in terms of a safely sell-able external-facing product.
So you want to reduce p(doom) by reducing p(ASI is created). Alas, there are many companies trying their hand at creating the ASI. Some of them are in China, which requires international coordination. One of the companies in the USA produced MechaHitler, which could imply that Musk is so reckless that he deserves having the compute confiscated.
This is about right. I do not think P(ASI is created) is very high currently. My P(someone figures out alignment tolerably) is probably in the same ballpark. I am also relatively sanguine about this because I do not think existing projects are as promising as their owners do, which means we have time.
That’s what the AI-2027 forecast is about. Alas, it was likely misunderstood...
I think the fact that tests for selling the model and tests for actual danger from the model are considered the same domain is basically an artifact of the business process, and should not be.
The scalar in question is the acceleration of the research speed with the AI’s help vs. without the help. It’s indeed hard to predict, but it is the most important issue.
A crux here: I do not think most things of interest are differentiable curves. Differentiable curves can be modeled usefully. Therefore, people like to assume things are differentiable curves.
If one is very concerned with being correct, something being a differentiable curve is a heavy assumption and needs to be justified.
From a far-off view, starting with Moore’s Law, transhumanism (as was the style at the time) has made a point of finding some differentiable curve and extending it. This works pretty well for some things, like Kurzweil on anything that is a function of transistor count, and horribly elsewhere, like Kurzweil on anything that is not a function of transistor count.
Some things in AI look kind of Moore’s-law-ish, but it does not seem well-supported that they actually are.
This is likely a crux. What the AI-2027 scenario requires is that AI agents who do automate R&D are uninterpretable and misaligned.
Yes.
If a corporation plans to achieve world domination and creates a misalinged AI, then we DON’T end up in a position better than if the corp aligned the AI to itself. In addition, the USG might have nationalised OpenBrain by that point, since the authors promise to create a branch where the USG is[5] way more competent than in the original scenario. [6]
Added note to explain concern: What type of AI is created is path-dependent. Generically, hegemonic entities make stupid decisions. They would e.g. probably prefer if everyone shut up about them not doing whatever they want to do. Paths that lead through these scenarios are less likely to produce good outcomes, AI-wise.
This is the evidence of a semi-success which could be actually worse than a failure.
Yes. I hate it, actually.
DeepSeek outperformed Llama because of an advanced architecture proposed by humans. The AI-2027 forecast has the AIs come up with architectures and try them. If the AIs do reach such a capability level, then more compute = more automatic researchers, experiments, etc = more results.
This is cogent. If beyond a certain path all research trees converge onto one true research tree which is self-executing, it is true that available compute and starting point is entirely determinative beyond that point. These are heavy assumptions and we’re well past my “this is a singularity, and its consequences are fundamentally unpredictable” anyway, though.
“Selling access to a bioweapon-capable AI to anyone with a credit card” will be safe if the AI is aligned so that it wouldn’t make bioweapons even if terrorists ask it to do so.
I actually don’t think this is the case. You can elide what you are doing or distill it from outputs. There is not that much that distinguishes legitimate research endeavors from weapons development.
Finally, weakening safety is precisely what the AI-2027 forecast tries to warn against.
I very much do not think it succeeds at doing this, although I do credit that the intention is probably legitimately this.
As long as c is the speed limit it doesn’t matter that much