A theory doing the rounds attempts to explain the phenomenon: Tech executives, especially CEOs, are collectively suffering from delusions of grandeur thanks to AI. And at least one tech CEO has said so out loud: Box founder Aaron Levie. “CEOs are uniquely prone to AI psychosis because they’re sufficiently distant from the last mile of work that still has to happen to generate most value with AI,” Levie wrote on X . TechCrunch AI is the main source layer for now, and the rest should be read as a signal that is still widening. Changes like this often look small on screen while shifting product habits and day-to-day operating workflows much faster than expected.
What is happening now
A theory doing the rounds attempts to explain the phenomenon: Tech executives, especially CEOs, are collectively suffering from delusions of grandeur thanks to AI. TechCrunch AI form the main source layer behind the core facts in this piece. This is still a developing thread, so the useful part is knowing which source signals are hardening and which ones still need caution. In software, the upgrades worth caring about are the ones that make workflows cleaner, reduce mistakes, and remove the need for extra tools.
Where the sources line up
TechCrunch AI is the main source layer for now, and the rest should be read as a signal that is still widening. And at least one tech CEO has said so out loud: Box founder Aaron Levie. TechCrunch AI form the main source layer behind the core facts in this piece. In software, the upgrades worth caring about are the ones that make workflows cleaner, reduce mistakes, and remove the need for extra tools. The people who feel the value first are often operators, editors, creators, and teams stitching multiple apps into one daily workflow.
The details worth keeping
“CEOs are uniquely prone to AI psychosis because they’re sufficiently distant from the last mile of work that still has to happen to generate most value with AI,” Levie wrote on X . Changes like this often look small on screen while shifting product habits and day-to-day operating workflows much faster than expected. The people who feel the value first are often operators, editors, creators, and teams stitching multiple apps into one daily workflow. The next step is to see whether the current signals harden into a durable change or fade as a short-lived experiment.
Why this matters most
The signal is strong enough to deserve attention, but it still needs to be read as something developing rather than fully settled. With 1 source layers on the table, the part worth reading most closely is where firm facts meet the market's early reaction. CEOs “play with AI,” develop a prototype, or generate a contract, to use Levie’s examples, and then make the leap to believing agents can do the work.
What to watch next
The next thing to watch is rollout speed, regional limits, and whether the update really changes day-to-day habits. Patrick Tech Media will keep checking rollout speed, user reaction, and how TechCrunch AI update the next pieces. From 1 early signals, the piece keeps 1 references that are useful for locking the main details in place. That is why the useful reading move is not to stop at the headline, but to compare the promise, the workflow change, and the likely cost before deciding anything.