4 AI Labs Built the Same System Without Talking to Each Other (And Nobody's Discussing Why)

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What's really happening with AI capabilities at work — and why the "jagged AI" frame is now obsolete?

The common story is that AI is brilliant at some things and broken at others — but the reality is that jaggedness was never about intelligence; it was about how we were deploying it.

In this video, I share the inside scoop on why AI agents in proper harnesses are smoothing the capability frontier for real work:

Why the jagged AI frontier was always a deployment problem
How multi-agent coordination unlocks long-horizon knowledge work
What Cursor's math breakthrough reveals about AI generalization
Where meta-skills like sniff-checking become your competitive edge

The organizations and individuals who learn to decompose work, delegate to AI agents, and verify outputs will extend their leverage — those who don't will find the shift happening to them anyway.

Chapters
0:00 The jagged AI assumption we all got wrong
1:45 Why single-turn AI mimics a 30-second analyst with no notes
3:20 Inference computing and the first signs of smoothing
5:10 Why jaggedness was never about intelligence
7:00 The Cursor math breakthrough that changed everything
10:15 How the planner-worker-judge architecture works
13:00 Four companies independently built the same structure
15:30 The real cost of multi-agent systems (and why it's worth it)
17:45 Two tiers of domain verifiability at work
20:30 What sniff-checking means for every knowledge worker
23:00 Meta-skills are the new execution skills
25:10 What this means for your organization right now
27:00 The surface is smooth — what to do about it

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