The root problem: At every company, there is always more work that
could be done, but there is
not always more work that would
increase profits.
Existing corporate command and control has optimized for people control, because people cost money and performed work. Control their assignments, and you control costs and what's worked on.
Widespread unmetered AI turns this on its head, because suddenly each employee is directing their own work and the AI spend that comes with it.
F.ex. Bob in accounting may think it's a brilliant idea to rebuild Lotus 1-2-3.
That may help Bob, but 10x'ing Bob's spreadsheet output doesn't change the company's profitability, because it wasn't a limiting factor. It was to Bob, but not to the company's revenue generators.
HN didn't like this article when it hit, but I thought it made a good point that corporate C2 is going to be the first thing AI adoption breaks: https://www.forbes.com/sites/jasonwingard/2026/04/23/vibe-co...
Increasing AI spend without profitability improvements is a symptom that C2 is failing (or was insufficient to begin with).
Seen through a charitable "CEOs know what the fuck they're doing" lens, the preemptive layoffs are about forcing AI efficiency gains in areas CEOs expect them: instead of allowing those departments' remaining employees to build their own apps, they're forced to deploy AI to cover for their missing 3 team members.
Unfortunately, the layoffs were executed before there were solid results about which departments could benefit from AI use (and without a plan for continuity of institutional knowledge), so... we'll see.