This article is really well written. Thanks for sharing.

PSA: This is a very dated take from September; Ramez says himself that the new data has his attention and he’s digging into it now: https://x.com/ramez/status/2118472046812213577?a=KjLVobKV0gb...

In the original article he noted that getting to a 2x increase in capabilities would be crazy and require ~40x researcher productivity; that’s exactly the increase the new data now shows, and thus a huge number of the assumptions made in this earlier post about how it’s unlikely we would get to this point have literally already been proven wrong less than two months later.

I expect to see an update from Ramez moving substantially more in the direction of RSI in fact very much increasing the odds of reaching ASI soon.

I’m very curious what the major bottleneck is in the current landscape. I can only think of three possibilities: 1) model capabilities, 2) human researchers, or 3) available computers resources.

Given that frontier models are leapfrogging and massive infrastructure build is ongoing, I don’t see why any of these three couldn’t be overcome.

Why would one expect humans to get more intelligent as they offload their thinking to something else?

Interesting write-up, but I feel like a lot of its argument relies on the interpretation of ECI scores as merely showing linear improvement in AI capabilities. Sure, Epoch AI themselves claim that ECI's scale is linear, meaning that a large increase in capabilities should result in a large increase in ECI. Yet, the widely perceived jump in coding abilities in late 2025 that started making agentic coding actually productive doesn't really show on the software engineering ECI.

The ECI is basically an aggregate of benchmark scores, but benchmark complexity might not increase linearly. In fact, if LLM capabilities grew exponentially, then benchmarks would also need to increase their difficulty / complexity exponentially to stay relevant.