It’s unclear how we should interpret this. What do they mean by productivity uplift? To what extent is Anthropic’s institutional view that the uplift is 4x? (Like, what do they mean by “We take this seriously and it is consistent with our own internal experience of the model.”)
I think we’ve all seen the famous study, around a year ago, on self-overestimates of LLM productivity increases, which ultimately concluded that the actual gains were illusory. It’s true that I would expect frontier AI researchers to be smarter than ordinary programmers, but they’re also much more devoted to the idea of AI capabilities, and have stronger incentives to believe that their LLM is exceptionally good.
To be clear, LLMs have improved enormously since then, and I must expect that there are very real productivity gains now. I myself have used autoresearch to skip the grating process of manual hyperparameter optimization when there’s no elegant way to do so with a search algorithm. It does do valuable things. But there is a long, long, long history of very smart AI company employees reporting, subjectively, that their next model represents a sea change in ‘vibes-based’ performance.
My current best (low confidence, low precision) guess for the serial labor acceleration is ~1.55x (with a higher serial labor acceleration of ~1.75x for just research engineering activities).
My own guess would be somewhere in that same ballpark.
Thus, I’m pretty unhappy about a situation in which:
Anthropic seemingly claims they are getting 4x productivity uplift, but it’s publicly unclear what they mean by this or how much they believe this.
...
I don’t like it either, and I’m usually on the far tail of Anthropic-skeptical people, but I think this is unavoidable. The people saying things like this almost definitely sincerely believe it, and objections to any given quantitative metric are legitimate, as covered in your post. Even if we suddenly became a USSR-style command economy and the marketing hype cycle were abolished, we would still regularly see posts like these, simply on the basis that the most effective capabilities researchers appear to be devout optimists with regard to what they are building.
I agree on your recommendations for more transparent release of survey results, that seems obviously beneficial to me, and the vagueness is possibly a result of hype-related decision-making. I don’t think it would have a very substantial impact on the social impact, though—the portion of the public inclined to believe hype would read the headlines, the portion of the public inclined to be skeptical would likely draw the same conclusions that we draw from the lack of a survey release, barring exceptional circumstances, and the portion of the public that reflexively denies hype would just call them liars.
I agree that adjusting self-reports by the estimated discrepancy in effect sizes informed by the METR study is the way to go here—so maybe somewhere up to 1.5x is more likely. 4x speedup would require some really convincing evidence.
I suspect such evidence would involve giving away more details of Anthropic’s internal processes and workflows than Anthropic would be comfortable with.
I think we’ve all seen the famous study, around a year ago, on self-overestimates of LLM productivity increases, which ultimately concluded that the actual gains were illusory. It’s true that I would expect frontier AI researchers to be smarter than ordinary programmers, but they’re also much more devoted to the idea of AI capabilities, and have stronger incentives to believe that their LLM is exceptionally good.
To be clear, LLMs have improved enormously since then, and I must expect that there are very real productivity gains now. I myself have used autoresearch to skip the grating process of manual hyperparameter optimization when there’s no elegant way to do so with a search algorithm. It does do valuable things. But there is a long, long, long history of very smart AI company employees reporting, subjectively, that their next model represents a sea change in ‘vibes-based’ performance.
My own guess would be somewhere in that same ballpark.
I don’t like it either, and I’m usually on the far tail of Anthropic-skeptical people, but I think this is unavoidable. The people saying things like this almost definitely sincerely believe it, and objections to any given quantitative metric are legitimate, as covered in your post. Even if we suddenly became a USSR-style command economy and the marketing hype cycle were abolished, we would still regularly see posts like these, simply on the basis that the most effective capabilities researchers appear to be devout optimists with regard to what they are building.
I agree on your recommendations for more transparent release of survey results, that seems obviously beneficial to me, and the vagueness is possibly a result of hype-related decision-making. I don’t think it would have a very substantial impact on the social impact, though—the portion of the public inclined to believe hype would read the headlines, the portion of the public inclined to be skeptical would likely draw the same conclusions that we draw from the lack of a survey release, barring exceptional circumstances, and the portion of the public that reflexively denies hype would just call them liars.
I agree that adjusting self-reports by the estimated discrepancy in effect sizes informed by the METR study is the way to go here—so maybe somewhere up to 1.5x is more likely. 4x speedup would require some really convincing evidence.
I suspect such evidence would involve giving away more details of Anthropic’s internal processes and workflows than Anthropic would be comfortable with.