Inference, not training alone, is now the whole game — every data pipeline, RL loop, and agent runtime ultimately cashes out as test-time compute, making inference efficiency the key strategic bottleneck for AI progress.

Jon Durbin argues inference efficiency has become the central strategic bottleneck in AI, since all upstream work — data, RL, and agents — ultimately resolves into test-time compute costs. ✦ AI generated

jon_durbin · Latent Space · 2026-07-07 · original ↗

Inference efficiency is increasingly the strategic bottleneck: @jon_durbin argued that inference, not training alone, is now “the whole game,” because every data pipeline, RL loop, and agent runtime ultimately cashes out as test-time compute. That perspective also showed up in lower-level kernel work: Chutes reported major speedups for MiniMax MSA and GatedDeltaNet-2.

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