Two ways to handle constant model releases: optimize current tasks and probe the frontier
bendee983 · x · 2026-09-08
GergelyOrosz sparked discussion by admitting zero excitement about the latest model releases ("Fable 5.1", "Astra"): existing models already do more than he can utilize, and he's still calibrating how much to trust them.
bendee983 responds that we should look in two directions at once:
- Optimize current work: you don't need the best model—just one that's good enough, fast and cheap, including open-weight options
- Find new things to do: keep pushing frontier models on near-impossible tasks and retest each generation, priming yourself for future applications
Takeaway: the bottleneck has shifted from model capability to knowing how to use it—strategy beats chasing releases.
More from AGI Musings
- Study of 26,811 Chinese Students: AI Boosts Homework Scores 18% but Cuts Exam Results 20% — soumitrashukla9 · 2026-09-08
- Kids Using AI Got 18% Better Homework Marks but Scored 20% Lower on Tests — korymath · 2026-09-08
- Gary Marcus Fires Back: 'Deep Learning Hitting a Wall' Was Right About Tools and Harnesses — GaryMarcus · 2026-09-08
- antirez: AI solving problems early helps humans understand them faster, not less — antirez · 2026-09-08
- Chatbots saying "I understand you" reveals little about real experience, researcher says — preslav_nakov · 2026-09-08
- Humans solved 1 of 7 Millennium Prize Problems in decades — then AI showed up — airkatakana · 2026-09-08