Personally I think 2030 is possible but aggressive, and my timeline estimate it more around 2035. Two years ago I would have said 2040 or a bit later, and capabilities gains relevant to my own field and several others I know reasonably well have shortened that, along with the increase in funding for further development.
The Claude/Pokemon thing is interesting, and overall Pokemon-playing trend across Anthropic’s models is clearly positive. I can’t say I had any opinion at all about how far along an LLM would get at Pokemon before that result got publicized, so I’m curious if you did. What rate of progress on that benchmark would you expect in a short-timelines world? If there’s an LLM agent that can beat Pokemon in six months, or a year, or two years?
Self-driving vehicles are already more of a manufacturing and regulatory problem than a technical one. For example, as long as the NHTSA only lets manufacturers deploy 2500 self-driving vehicles a year each in the US, broad adoption cannot happen, regardless of technical capabilities or willingness to invest and build.
I also don’t think task length is a perfect metric. But it’s a useful one, a lower bound on what’s needed to be able to complete all human-complete intellectual tasks. Like everything else to date, there is likely something else to look at as we saturate the benchmark.
I agree novel insights (or more of them, I can’t say there haven’t been any) will be strong evidence. I don’t understand the reason for thinking this should already be observable. Very, very few humans ever produce anything like truly novel insights at the forefront of human knowledge. “They have not yet reached the top <0.1% of human ability in any active research field” is an incredibly high bar I wouldn’t expect to pass until we’re already extremely close to AGI, and it should be telling that that late bar is on the short list of signs you are looking for. I would also add two other things: First, how many research labs do you think there are that have actually tried to use AI to make novel discoveries, given how little calendar time there has been to actually figure out how to adopt and use the models we do have? If Gemini 2.5 could do this today, I don’t think we’d necessarily have any idea. And second, do you believe it was a mistake that two of the 2024 Nobel prizes went to AI researchers, for work that contributes to the advancement of chemistry and physics?
AI usefulness is strongly field dependent today. In my own field, it went from a useful supplementary tool to “This does 50-80% of what new hires did and 30-50% of what I used to do, and were scrambling to refactor workflows to take advantage of it.”
Hallucinations are annoying, but good prompting strategy, model selection, and task definition can easily get the percentages down to the low single digits. In many cases the rates can easily be lower than those of a smart human given a similar amount of context. I can often literally just tell an LLM “Rewrite this prompt in such a way as to reduce the risk of hallucinations or errors, answer that prompt, then go back and check for and fix any mistakes” and that’ll cut it down a good 50-90% depending on the topic and the question complexity. I can ask the model to cite sources for factual claims, dump the sources back into the next prompt, and ask if there are any factual claims not supported by the sources. It’s a little circular, but also a bit Socratic and not really any worse than when I’ve tried to teach difficult mental skills to some bright human adults
I also don’t have a principled reason to expect that particular linear relationship, except in general in forecasting tech advancements, I find that a lot of such relationships seem to happen and sustain themselves for longer than I’d expect given my lack of principled reasons for them.
I did just post another comment reply that engages with some things you said.
To the first argument: I agree with @Chris_Leong’s point about interest rates constituting essentially zero evidence, especially compared to the number of data points on the METR graph.
To the second: I do not think the PhD thesis is a fair comparison. That is not a case where we expect anyone to successfully complete a task on their own. PhD students, post-docs, and professional researchers break a long task into many small ones, receive constant feedback, and change course in response to intermediate successes and failures. I don’t think there are actually very many tasks en route to a PhD tat can’t be broken down into predictable, well defined subtasks that take less than a month, and the task of doing the breaking down is itself a fairly short-time-horizon task that gets periodically revised. Even still, many PhD theses end up being, “Ok, you’ve done enough total work, how do we finagle these papers into a coherent narrative after the fact?” Plus, overall, PhD students, those motivated to go to grad school with enough demonstrated ability to get accepted into PhD programs, fail to get a PhD close to half the time even with all that.
I imagine you could reliably complete a PhD in many fields with a week-long time horizon, as long as you get good enough weekly feedback from a competent advisor. 1: Talk to advisor about what it takes to get a PhD. 2: Divide into a list of <1 week-long tasks. 3) Complete task 1, get feedback, revise list. 4) Either repeat the current task or move on to the new next task, depending on feedback. 5) Loop until complete. 5a) Every ten or so loops, check overall progress to date against the original requirements. Evaluate whether overall pace of progress is acceptable. If not, come up with possible new plans and get advisor feedback.
As far as not believing the current paradigm could reach AGI, which paradigm do you mean? I don’t think “random variation and rapid iteration” is a fair assessment of the current research process. But even if it were, what should I do with that information? Well, luckily we have a convenient example of what it takes for blind mutations with selection pressure to raise intelligence to human levels: us! I am pretty confident saying that current LLMs would outperform, say, Australopithecus, on any intellectual ability, but not Home sapiens. So that happens in a few million years, let’s say 200k generations of 10-100k individuals each, in which intelligence was one of many, many factors weakly driving selection pressure with at most a small number of variations per generation. I can’t really quantify how much human intelligence and directed effort speed up progress compared to blind chance, but consider that 1) a current biology grad student can do things with genetics in an afternoon that evolution needs thousands of generations and millions of individuals or more to do, and 2) the modern economic growth rate, essentially a sum of the impacts of human insight on human activity, is around 15000x faster than it was in the paleolithic. Naively extrapolated, this outside view would tell me that science and engineering can take us from Australopithecus-level to human-level in about 13 generations (unclear which generation we’re on now). The number of individuals needed per generation is dependent on how much we vary each individual, but plausibly in the single or double digits.
My disagreement with your conclusion from your third objection is that scaling inference time compute increases performance within a generation, but that’s not how the iteration goes between generations. We use reasoning models with more inference time compute to generate better data to train better base models to more efficiently reproduce similar capability levels with less compute to build better reasoning models. So if you build the first superhuman coder and find it’s expensive to run, what’s the most obvious next step in the chain? Follow the same process as we’ve been following for reasoning models and if straight lines on graphs hold, then six months later we’ll plausibly have one that’s a tenth the cost to run. Repeat again for the next six months after that.