alt Hacker NewsNot trying to discredit the premise but I do wonder how much this is from vertical scaling of model intelligence in weights vs the sheer amount of capital being thrown at training and horizontal scaling of agent swarms etc.
Anecdotally it seems like the majority of continued uplift comes from models doing more and exploring more of the search space, and we are getting better at giving them embarrassingly parallel problems to Hoover up with infinite token spend.
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The article's author didn't have direct access to Anthropic's infrastructure, but she references a report from colleagues who did (https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleratio...), where they say:
> Anthropic has made roughly two years of historical progress over the last year [...] We are confident that the acceleration was not due to an acceleration in the scaling of training compute.
Note that Anthropic has essentially heavily censored this report, and the authors themselves say "the public has to rely on METR’s judgment much more than would be ideal".
I grok that you're not trying to discredit anything, or anything like that but what are you trying to do, if I may ask?
What information are you trying to either ask for or share with us?
The original article is pretty clear, agent swarms and token spend are AI automating AI, and the RSP attribution condition specifically excludes your capital thrown at training concern?
Counterarguments to that premise:
1. Intelligence per dollar spent/token output seems to be increasing rapidly. Opus 4 was $15/75 per million input/output tokens when released. Opus 5.5, which is significantly better, is $4/15. Sure it cost more money upfront to train Opus 5.5, or the bigger model (Fable 5.5) which tuned it, but once it is reached, there are massive diffusive effects.
2. The capital equation is still favoring more spend. Anthropic's revenue is growing rapidly and they are profitable on inference. Huge upfront spend for realized gains later on is standard for R&D. As long as serving the models is profitable and generates more value than the energy/compute consumed
3. The search space is too large to assume that improvement in AI assisted research is just models dumbly trying every combination until something works. For every additional bit searched, the space doubles, so even a trivially small combinatorial exploration incurs prohibitively large costs. Intelligent searching which continuously prunes that space along dimensions of effectiveness is not a trivial skill and is among the emergent accoutrements of these smarter models.