Doesn't the human brain's structure provide something closer to an upper bound rather than a lower bound on the number of parameters required for higher reasoning? 

Higher reasoning evolved in humans over a short period of time. And it is speculated that it was mostly arrived at simply by scaling up chimp brains.

This implies that our brains are very far from optimised for higher reasoning, so we should expect that to whatever extent factors other than scale can contribute to higher-reasoning ability, it is possible for brains smaller than our own to engage in higher reasoning. 

The human brain should be seen as evidence that a certain scale is ~sufficient, but not that it is necessary. 

X-risk is one of the most severe threats faced by minorities. 

OSWorld isn't in machine learning or mathematics, so we don't have much data to go on.

But what we do have suggests ~4 month doubling time from which we arrive at an ~8 minute 50% time horizon by EOY, Given:
> # Difficulty Split: Easy (<60s): 28.72%, Medium (60-180s): 40.11%, Hard (>180s): 30.17%

This does suggest greater than 80% by EOY, but this depends on model release cadence etc.

I also forward-predicted it based specifically on METR research. 

I think many thresholds in machine learning and mathematics can be analysed this way. The main barriers are (a) modeling hyperexponentiality as you get further out in time and (b) modeling things like RLVR hitting the compute ceiling, the new RL methods being hinted at by OA, etc.

Let's take replacement of AI researchers as an example. AI research tasks in frontier labs rarely extend beyond 200 hour of working hours. Let's ask for an 50% success rate. Let's assume 3 month doubling times to take into account hyperexponential progress. Current time horizon is 180 minutes. We need 12000 minutes. We need 6 doubles (-1-> 360 -2-> 720 -3-> 1500 -4-> 3000 -5-> 6000 -6-> 12000). 6 * 3 = 18 months = early 2027. Therefore, AI-researcher level AIs by early 2027 seems not unlikely. 

Can you be more specific about what you think the issue is?

LLM Gold on the IMO was predictable using METR HCAST extrapolation:

o3's 80% success time-horizon was 20 minutes. 

o3 came out in ~3 months ago. Add 6 months for the lab-to-public delay: 9 months of progress.

This is ~3 doublings in the current RLVR scaling paradigm with a buff for being mathematics (more verifiable) specific rather than ML (~4 month doubling time -> 3 month doubling time). 

3 doublings of 20 minutes gets us to 160 minutes (-> 40 -> 80 -> 160)

IMO participants get an average of 90 minutes per problem.

The gold medal cutoff at the IMO 2025 was 35 out of 42 points (~83%)

So, by trusting HCAST extrapolation, we could have predicted that a pure LLM system getting gold was not unlikely.

Edit: some unstated premises of this analysis:

•  80% doubling times are similar to 50% doubling times (see https://arxiv.org/pdf/2503.14499)
• math horizons are generally above HCAST (https://x.com/METR_Evals/status/1944817692294439179)
• math doubling times are generally shorter than HCAST's (https://x.com/METR_Evals/status/1944817692294439179)

Assuming the o3 date is accurate, I think IMO Gold shouldn't have been surprising.

(the initial version of this comment had a math error, fixed now)

On the other hand, I am pretty surprised by regular reasoning LLMs getting gold in the way openai described that they did.

Yes, seems slightly surprising. Let's see whether METR doubling-time extrapolation predicted this.

o3's 80% success time-horizon was 20 minutes. o3 came out in ~3 months ago. Add 6 months for the lab-to-public delay: 9 months. This is ~3 doublings in the current RLVR scaling paradigm with a buff for being mathematics (more verifiable) specific rather than ML. 3 doublings of 20 minutes gets us to 160 minutes. IMO participants get an average of 90 minutes per problem. (although more relevant is the time taken on the more difficult questions, say 120 minutes).

OK, on reflection this wasn't much of a surprise at all. Interesting!

I doubt ASI will think in concepts which humans can readily understand. It having a significantly larger brain (in terms of neural connections or whatever) means native support for finer-grained, more-plentiful concepts for understanding reality than humans natively support. This in turn allows for leaps of logic which humans could not make, and can likely only understand indirectly/imperfectly/imprecisely/in broad strokes.

which was clearly supposed to have been GPT-5

I have seen people say this many times, but I don't understand. What makes it so clear?

GPT-4.5 is roughly a 10x scale-up of GPT-4, right? And full number jumps in GPT have always been ~100x? So GPT-4.5 seems like the natural name for OpenAI to go with.

I do think it's clear that OpenAI viewed GPT-4.5 as something of a disappointment, I just haven't seen anything indicating that they at some point planned to break the naming convention in this way.