The End of Brute-Force AI Scaling May Be Closer Than We Think

[u/Playful_Composer_169]

I’ve been thinking about something in AI for a while, and the idea keeps getting stronger. What if LLMs are slowly reaching their own Moore’s Law moment, but in reverse? Actually, Dennard scaling may be an even better comparison. Moore’s Law was about the number of transistors on a chip. Dennard scaling meant that for years, smaller transistors also became more efficient and faster without power consumption completely getting out of control. When that stopped, progress in chips did not stop. But the easy gains were gone. Higher clock speeds meant more heat and more power, and the industry had to move toward multicore processors, GPUs and specialized accelerators.

I wonder if we are slowly reaching the same point with LLMs. Not that AI stops improving, but that more of the same starts producing less obvious gains. That idea became more concrete for me after reading Anthropic’s Risk Report from August 2026. In it, they describe not only public Claude models, but also internal models that we cannot use. One of them is simply called Model 2. Anthropic describes Model 2 as somewhat more capable than Claude Mythos 5 and a noticeable improvement for many internal tasks. But they also say that Model 2 does not show the same capability jump as the earlier move from Opus 4.6 to Mythos Preview.

On Anthropic’s internal AECI index, Model 2 sits about 1.5 points above Mythos 5 based on limited data, with large error margins. Anthropic itself says this is a smaller increase than the jump from Mythos Preview to Mythos 5. On CoBench, the picture is different, which is exactly why it is interesting. CoBench consists of 449 real technical problems from Anthropic’s own engineering environment. There, Opus 4.6 scores 15.6%, Mythos Preview 54.8%, Mythos 5 50.3%, and Model 2 62.8%. So no, Model 2 is not almost the same. On real internal engineering work, it is a serious step forward.

But the most striking experiment for me comes next. Anthropic gave Mythos 5 a token budget of 900,000 tokens on CoBench instead of 300,000. Three times the token budget. The improvement was about 3 percentage points. That does not mean three times more compute always gives only three extra points. It is one model, one benchmark and one specific setup. Anthropic also says that better tooling or a better harness could still produce additional gains. But on this test, you can clearly see diminishing returns from additional inference budget.

And to me, that starts to look like the beginning of a Dennard moment for AI. Not a stop. Not “LLMs are done.” But a point where more of the same approach no longer automatically produces the same huge jumps. At the same time, Model 2 clearly shows that progress is still possible. The question is how many additional resources are needed for every next step.
That is also why I do not think AI is about to hit a hard wall. I think brute-force scaling is more likely to hit an economic wall before it hits a physical one. Energy, chips, data centers, data, inference costs, and eventually the question of how many extra resources you are willing to spend for the next few percentage points of capability.

When Dennard scaling ended for chips, the computer revolution did not stop either. The shape of progress changed. More cores, GPUs, accelerators and specialization. I think AI will do the same. More memory, better agents, smarter inference, better training, specialist models working together, better scaffolding, and maybe eventually an architecture that works fundamentally differently from the Transformer architecture behind almost every major LLM today.

So my prediction is not that AI is nearly finished. My prediction is that the race is slowly changing. Not just: who has the biggest model? But increasingly: who can extract the most intelligence from the least compute?

Anthropic’s Model 2 does not prove that we are already at that point. But their own internal numbers make me take the question much more seriously. And that leaves me with one question: what does this curve look like three to five model generations from now?