On o3: for what feels like the twentieth time this year, I see people freaking out, saying AGI is upon us, it's the end of knowledge work, timelines now clearly in single-digit years, etc, etc. I basically don't buy it, my low-confidence median guess is that o3 is massively overhyped. Major reasons:

• I've personally done 5 problems from GPQA in different fields and got 4 of them correct (allowing internet access, which was the intent behind that benchmark). I've also seen one or two problems from the software engineering benchmark. In both cases, when I look the actual problems in the benchmark, they are easy, despite people constantly calling them hard and saying that they require expert-level knowledge.
  • For GPQA, my median guess is that the PhDs they tested on were mostly pretty stupid. Probably a bunch of them were e.g. bio PhD students at NYU who would just reflexively give up if faced with even a relatively simple stat mech question which can be solved with a couple minutes of googling jargon and blindly plugging two numbers into an equation.
  • For software engineering, the problems are generated from real git pull requests IIUC, and it turns out that lots of those are things like e.g. "just remove this if-block".
  • Generalizing the lesson here: the supposedly-hard benchmarks for which I have seen a few problems (e.g. GPQA, software eng) turn out to be mostly quite easy, so my prior on other supposedly-hard benchmarks which I haven't checked (e.g. FrontierMath) is that they're also mostly much easier than they're hyped up to be.
• On my current model of Sam Altman, he's currently very desperate to make it look like there's no impending AI winter, capabilities are still progressing rapidly, etc. Whether or not it's intentional on Sam Altman's part, OpenAI acts accordingly, releasing lots of very over-hyped demos. So, I discount anything hyped out of OpenAI, and doubly so for products which aren't released publicly (yet).
• Over and over again in the past year or so, people have said that some new model is a total game changer for math/coding, and then David will hand it one of the actual math or coding problems we're working on and it will spit out complete trash. And not like "we underspecified the problem" trash, or "subtle corner case" trash. I mean like "midway through the proof it redefined this variable as a totally different thing and then carried on as though both definitions applied". The most recent model with which this happened was o1.
  • Of course I am also tracking the possibility that this is a skill issue on our part, and if that's the case I would certainly love for someone to help us do better. See this thread for a couple examples of relevant coding tasks.
  • My median-but-low-confidence guess here is that basically-all the people who find current LLMs to be a massive productivity boost for coding are coding things which are either simple, or complex only in standardized ways - e.g. most web or mobile apps. That's the sort of coding which mostly involves piping things between different APIs and applying standard patterns, which is where LLMs shine.