The Missing Junior Loop, in Anthropic’s own hiring data

Once you see that verifying AI’s work is the bottleneck, and that checking runs on tacit knowledge, you can predict the hiring shift before seeing the data.

We did, back in February.

Anthropic’s junior share of technical hires: 27% to 9%.

Output is priced in tokens. Verification is priced in human hours.

Anthropic estimates that reading a week of Claude output would take 424 hours per person! A week has 168.

That’s the verification constraint. And reading is the easy part! x.com

Junior employment grew more slowly where Claude handled more complex tasks.

Our model’s Missing Junior Loop: firms need top expertise while automating the work that produces it.

They’re hiring from a stock they’re doing less to replenish.

Fix one: flight simulators for work.

The “weights” in a junior’s head are too close to the model’s. Same textbooks, same blind spots.

Use out of distribution drills and expert feedback to get juniors to the frontier faster. Pilots train for failure. Juniors should too.

Fix two is the superpower.

The models that ate junior tasks also collapsed the cost of learning and experimentation.

Explore more fields. Test more ideas. Discover what you’re unusually good at, then use AI to do more with it.

Superpowers, if you deliberately use them. x.com

These dynamics are visible inside @Anthropic than in aggregate labor data. Why? Diffusion.

The lab is the extreme case of adoption. The broader economy still has to reorganize around AI, as with other general purpose technologies.

That lag is our window to act! x.com

Some Simple Economics of AGI, with @wu_jane & @xianghui90. February 2026.

Two cost curves. The Measurability Gap. The Missing Junior Loop. And ways out.

Paper: arxiv.org Report: anthropic.com

And while it doesn’t cite the paper…the charts do. 😅

Originally published as a thread on X.