Current AI techniques are four to six orders of magnitude away from optimal in data efficiency and test-time compute efficiency.

Added to korrents 29 Aug 2026 · How quotes work · Something wrong? Tell us

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• 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

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