Vision
Vision
The Boring Turn
A year ago, the enterprise AI conversation was about capability — whether agents could navigate websites without hallucinating. That conversation has moved on. The top budget line is now observability tooling. Over half of enterprises have a formal agent operations lead. The questions sound like infrastructure management because they are. And once something becomes operational, the difficulty changes character entirely. The organizational questions that emerge are ones most deployment playbooks aren't built to detect.
The Boring Turn
Ayear ago, the enterprise AI conversation was about capability — whether agents could navigate websites without hallucinating. That conversation has moved on. The top budget line is now observability tooling. Over half of enterprises have a formal agent operations lead. The questions sound like infrastructure management because they are. And once something becomes operational, the difficulty changes character entirely. The organizational questions that emerge are ones most deployment playbooks aren't built to detect.
Two Consequences
Where Does Judgment Come From Now?
Entry-level job postings have fallen 35 percent since early 2023. The tasks vanishing from junior roles—debugging, data reconciliation, document review—look like busywork from the outside. They were also how professionals learned what "normal" looks like in their field. The development pipeline hollowed out one rational automation decision at a time. Now the experiment in whether judgment can form without apprenticeship is already running, undesigned.
What the Six Percent Are Doing Differently
While the apprenticeship pipeline thins, a different question is surfacing at the organizational level: what does work look like when you design it around agent capabilities instead of bolting agents onto legacy processes? Six percent of leaders report progress on this. The other 94 percent are watching gains plateau. What separates the two groups is something more mundane than technology: someone asked what the work actually is before automating it.
The Desire Mismatch
Stanford's SALT Lab mapped 2,283 Y Combinator company descriptions against preference data from 1,500 workers spanning 104 occupations. Forty-one percent of company-task mappings landed where workers either resist delegation outright or see it as neither desirable nor feasible.
Workers want more human agency than the investment thesis assumes. The occupations most eager for automation account for just 1.26% of actual AI usage.
Infrastructure is hard to renegotiate once it's set. If the ecosystem is building around technical tractability rather than actual demand, the correction gets more expensive every quarter it compounds.
Further Reading
Past Articles
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