Scaling laws are still working, but the models people actually use run a few months behind the maximum capability Google can deliver, because the biggest model is too slow and expensive to serve.

Added to korrents 6 Sept 2026 · How quotes work · Something wrong? Tell us

Where this collides

• In tension with

  Future AI, say in fifteen years, will not be built on the LLM stack; it will have to move to symbolic learning.

  Pichai reports that scaling laws are still working and that the constraint is serving cost rather than capability. Chollet expects the whole stack to be replaced by symbolic learning within fifteen years. A law that is still working and a stack that has to be abandoned can coexist only if the working law runs out well before the fifteen years are up.

Shared subject first, then similar wording. They may agree or disagree.

• A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. NBNoam Brown26 Jun 2026scaling laws
• A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. DPDylan Patel13 Mar 2026scaling laws
• After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. JHJensen Huang23 Mar 2026scaling laws
• AI scaling laws require exponentially more compute and resources to produce only linear gains in model intelligence. NLNathan Lambert19 Sept 2026scaling laws
• As models keep scaling up, modularity will become essential to how they are developed. SRSebastian Ruder23 Feb 2023scaling laws

On the map

Open the map on its own page