Why AI transformation stalls on the left side of the quality-velocity U-curve
hazelcough · x · 2026-11-12
Fast iteration eventually becomes a form of quality through more contact with reality — but fear of the mid-transition quality dip stops teams. The author argues this is why many organizations stall while transforming how they work with AI, and asks for reliable paths through, admitting early successes haven't yet yielded a playbook.
More from AGI Musings
- "Hard to tell a 92% model from a 99% model": the bottleneck is evals — chris_j_paxton · 2026-11-12
- A 4-bit sparse flop and a synaptic firing both cost ~30 femtojoules, but the brain has far more connections — MikePFrank · 2026-11-12
- Economist on AI plagiarism dispute: an innocent author would publish their Google Doc history — paulnovosad · 2026-11-12
- Researcher mourns loss of top open-weight advocate, fears regulatory capture — KevinKaichuang · 2026-11-12
- Radiologist tests Fable 5.2 vs experts: about a second-year resident's level — JHochderffer · 2026-11-12
- Developers predict AI model plateau: the real gains will come from inference optimization — bendee983 · 2026-11-12