AI Intelligence Explosion May Arrive as a Daily Software Update
imjustnewatai · x · 2026-08-08
Based on Dwarkesh's predictions for continual learning, the author deduces an AI evolution flywheel: if millions of AI workers pool their daily successes and failures back into the base model, every copy starts from a higher baseline the next day.
This creates a loop of "better model → more users → harder work → more real-world experience → better model," forming an ultimate technological moat. A competitor could launch a smarter model on day one and still lose to a system that has absorbed months of real-world work.
It also changes the business model: AI labs might subsidize or give away their best models in exchange for learning from user workflows, making switching costs incredibly high.
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