Anthropic just dropped a labor market report with a chart you need to see. It maps what AI could theoretically do across every major occupation against what it's actually doing. The gap is enormous. In computing and math, AI could theoretically handle 94% of tasks. Observed usage? 33%. Legal hits nearly 90% theoretical — real-world usage barely clears 20%.

This week on In The Loop, I break down why. Some of it is a people problem: adoption looks more like a cliff than a curve, with a tiny fraction of users actually pushing AI to its limits. Some of it is structural — enterprise contracts, legacy systems, and slow procurement cycles. And some of it is the technology itself. Reliability — not capability — is the real bottleneck right now.

This isn't pessimism. It's a realistic read on how long transformation actually takes.

⏭️ Episode Highlights

(01:15) – Anthropic's labor market report and the chart that tells the real story

(03:45) – Theoretical vs. observed AI usage across occupations

(07:20) – The adoption cliff: who's actually using AI at full capacity

(09:45) – Enterprise slowdown, legacy systems, and integration complexity

(11:55) – The supply issue

(12:55) – The reliability gap

(19:30) – The computer age parallel — and why patience might be the right call

🔗 Links & Resources

Anthropic's Labor Report:

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Jack HoughtonLinkedIn - https://www.linkedin.com/in/jack-houghton1/TikTok - @jackschats

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