Scaling Hit a Wall. Three Labs Found What's Next.
Article summary
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The AI industry spent five years and hundreds of billions of dollars on a single hypothesis: make the model bigger, feed it more data, and capability will follow. That hypothesis is now running out of road. But the conversation about what comes next has been unfocused — vague gestures toward "test-time compute" and "post-training" and "agentic workflows." None of those address the structural problem. 📖 Read the full version with charts and embedded sources on ComputeLeap → Three independent…
1Key Takeaways
- The AI industry spent five years and hundreds of billions of dollars on a single hypothesis: make the model bigger, feed it more data, and capability will follow.
- That hypothesis is now running out of road.
- But the conversation about what comes next has been unfocused — vague gestures toward "test-time compute" and "post-training" and "agentic workflows." None of those address the structural problem.
- 📖 Read the full version with charts and embedded sources on ComputeLeap → Three independent….
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that the AI industry spent five years and hundreds of billions of dollars on a single hypothesis: make the model bigger, feed it more data, and capability will follow.
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