> Because there were over a thousand transcripts and most were extremely long, we had to heavily delegate our analysis to AI agents; these agents had significantly worse judgment and reliability than human researchers, and it was challenging to spot check their work because both the underlying data and the agents’ analysis of it was often difficult to interpret.

> We estimate we spent roughly ~$400K in API credits over the six days of our investigation.

I don't understand why you think it's conceptually absurd? I use agents to analyze complex production issues all the time and they are very much capable of hallucinating a narrative.

So I'm really not sure how much of it can be believed, especially since AI agents are strongly biased about the capabilities of AI agents.

That simonw is causing labs to do extra fine-tuning runs for this seems highly probable :)

Because otherwise using a LLM to generate complex svgs is pretty niche and what I thought made this a good benchmark when it was new - generalized programming and spatial knowledge.

Obviously image gen in svg format is not a particularly hard problem if tackled directly on its own.

At some point, functionality needs to exist in user space, even if it's common.

But I don't think the takeaway is "humans are intelligent and LLMs are not", it's that our vocabulary for talking about the intersection of language, cognition and compute is not up for the task.

The alternative is to not hop on the AI bandwagon, or run an "also ran" AI story, and both those scenarios (I expect) game out worse given the current zeitgeist.

If you’re talking about trying to improve the genetics of populations at scale… yikes.

The comparison to the adoption of automobiles is apt, and something I've thought about before as well. Just because a technology can be useful doesn't mean it will have positive effects on society.

That said, I'm more open to using LLMs in constrained scenarios, in cases where they're an appropriate tool for the job and the downsides can be reasonably mitigated. The equivalent position in 1920 would not be telling individuals "don't ever drive a car," but rather extrapolating critically about the negative social and environmental effects (many of which were predictable) and preventing the worst outcomes via policy.

But this requires understanding the actual limits and possibilities of the technology. In my opinion, it's important for technologists who actually see the downsides to stay aware and involved, and even be experts and leaders in the field. I want to be in a position to say "no" to the worst excesses of AI, from a position of credible authority.

But that's a strawman. Racism is wrong, even if there are minor genetic variances across populations (which... seems obvious?) Variance within a population strongly dominates the weak cross-population effects, and personal history (nutrition, education, etc) strongly dominates that.

And that's setting aside the moral implications of judging someone or changing your behavior towards them even if you have somehow measured them to be "less intelligent," as if that was a single axis of worth.

Because, apparently, this needs to be said.

Because for the same price, you could point the small model at each function, one by one, N times each, across N prompts instructing it to look for a specific class of issue.

It's not that there's no difference between models, but it's hard to judge exactly how much difference there is when so much depends on the scaffold used. For a properly scientific test, you'd need to use exactly the same one.

Which isn't possible when Anthropic won't release the model.

The fact is, I think the art of building well with AI (and I'm not saying it's easy) is to have a heterogenously vibe-coded app.

For example, in the app I'm working on now, certain algorithmically novel parts are level 0 (I started at level 1, but this was a tremendously difficult problem and the AI actually introduced more confusion than it provided ideas.)

And other parts of the app (mostly the UI in this case) are level 7. And most of the middleware (state management, data model) is somewhere in between.

Identifying the appropriate level for a given part of the codebase is IMO the whole game.

Calculators provide a deterministic solution to a well-defined task. LLMs don't.

If I use a calculator to find a logarithm, and I know what a logarithm is, then the answer the calculator gives me is perfectly useful and 100% substitutable for what I would have found if I'd calculated the logarithm myself.

If I use Claude to "build a login page", it will definitely build me a login page. But there's a very real chance that what it generated contains a security issue. If I'm an experienced engineer I can take a quick look and validate whether it does or whether it doesn't, but if I'm not, I've introduced real risk to my application.