NOTES / 2026.06.11 · 6-post thread · 19 likes
The Constraint on Mastery Is No Longer Access
Finding out what you're good at used to require the right mentor, firm, or zip code. AI collapses all three into a chat window — what's left is finding the domain where your learning rate is steepest.
FIG. 01 — FROM THE ORIGINAL THREAD
For most of history, finding out what you’re good at required the right mentor, firm or zip code. AI collapses all three into the same chat window. The constraint on mastery is no longer access. It’s finding the domain where your learning rate is steepest—and building.
The dark version is the “Missing Junior Loop”: AI automates the entry-level reps that used to build expertise. But the same models generate unlimited, personalized training/practice. The tech that severed the apprenticeship can rebuild it on demand with higher fidelity.
When a rep costs ~zero in any domain, you can run a parallel search and find rapidly where you improve fastest (and what you enjoy the most!).
Innate talent discovery stops being a lottery ticket and becomes a science. Something you can measure in weeks.
Why it matters: as execution is commoditized, the scarce asset is deep expertise that can steer and verify machine output. The meta-skill of the decade is accelerated mastery — building expertise faster than the market rate.
We unpack what AI-native individuals will do to stay on the frontier: accelerated mastery, synthetic practice, moving up the intent-and-underwriting stack in Section 8 of “Some Simple Economics of AGI” (w/ @wu_jane @xianghui90): arxiv.org
@wu_jane @xianghui90 Since the paper is over 100 pages, you can feed the MD file directly to your favorite LLM: catalini.com
Originally published as a thread on X.