If models are so great, why not let them tackle real problems? Racing for low-hanging fruit signals doubt
JMannhart · x · 2026-09-09
A sharp take on AI lab culture: if the models are so capable, why not let them work on genuinely useful problems instead of racing to scoop each other on low-hanging fruit?
- The author concedes the low-hanging fruit is incredibly attractive in the current racing game
- But argues that focusing this narrowly makes labs look unconfident in their own tech
- His analogy: the less confident you are of finding your own Millennium Prize Problem (or something equally impressive but more useful), the more you'll grind out incremental wins; true confidence in the tech would remove the incentive to race
A pointed commentary on the tension between publish-fast competition and long-term research in AI.
Related event: Critics Say AI Labs Chase Low-Hanging Fruit Instead of Hard Problems(2 posts)→
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