that was always the promise wasn't it? linear increase in intelligence in exchange for exponential increase in compute?

It's not really if you mean to point to the bitter lesson, it was models/intelligence scales better with general/simple methods with more compute better than it scales with any hand-crafted human program/method.

Ala intelligence is a function of compute, there was no guarantee that you need to 10x compute for 10% better performance or something like that.

If you ask the labs they will say the scaling is fairly linear or atleast sub-quadratic, they definitely won't say it's exponential.

No one was thinking we need to 10x compute from here again for another 10-20% increase in performance.

It's only been since Opus 4.6 that the compute growth vs improvements are starting to feel this way.

For instance GPT-3 vs GPT-4 was practically a 10-100x jump for a 10-100x increase in compute, we went from broken sentences to fully formed essays writing, speech and so on.

GPT-4 vs GPT-5 was just as massive we went from no agentic capabilities to fully agentic, 10-100x for a 10-100x increase in compute usage.

GPT-5 vs GPT-6 was atleast a 10x (upto 100x) increase in compute, but the difference is not that clear sure people say that one is significantly better than the other, but GPT-5.5 was good enough compared to where we are today.

Even on benchmarks compared to GPT-5.2 Codex we have almost 2x'ed the performance on say artificial analysis (53 vs 29). This is absurd. 10-100x increase in compute for 2-5x performance improvement is a massive gap.

[1]: https://bitterlesson.ai/ [2]: https://artificialanalysis.ai/?models=gpt-6-astra%2Cgpt-6-lu...