Tech CEOs exhibit signs of AI psychosis

There's a certain rawness in today's tech industry that echoes earlier periods of major shifts—like the early days of cloud computing with its runaway costs—yet also feels unprecedented, with record revenues happening right alongside mass layoffs.

A circulating theory tries to explain this paradox: Tech executives, especially CEOs, are collectively suffering from AI-induced delusions of grandeur. At least one CEO has admitted it openly: 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.

CEOs "play with AI," build a prototype, or generate a contract—using Levie's examples—and then jump to the conclusion that AI agents can handle the real work.

But these top-level executives aren't the ones who review code, find bugs, or spot calls to hallucinated libraries before deployment. They don't train AI models on their company's unique contract terms, nor do they spend days combing through contracts to uncover hidden clauses, as Levie points out.

In short, Levie's theory suggests that CEOs lack deep enough understanding of processes to know what truly can and cannot be automated. Yet that ignorance doesn't stop them from acting on their beliefs.

It's worth noting that Levie isn't anti-AI. Quite the opposite. He mostly posts AI-positive content on X to his 2.7 million followers and writes blogs like "Headless software is the future," arguing that software built for AI agents is the way forward. He also backs his words with action, investing actively in AI startups as an angel investor.

So what should CEOs do instead? Levie advises them to use AI "a ton" to truly understand its capabilities and limitations—"and come out the other side with an appreciation for both the upside and the real work."

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.

So when they play with AI, they see the happy path results, often not considering the next 10 or 20 things that have… https://t.co/ne5mvJ4Rgx

— Aaron Levie (@levie) May 24, 2026

I have enough faith in humanity to believe some CEOs are genuinely trying to do just that—but right now, they seem to be in the minority.

In the first five months of 2026 alone, the tech industry has already seen nearly as many layoffs as in all of 2025: 115,430 people have been let go from 152 tech companies so far this year, compared with 124,636 people laid off by 275 companies in 2025, according to industry tracker Layoffs.fyi.

And the majority of those companies have cited AI as a reason for the cuts. Many argue that the biggest tech firms are AI-washing—crediting AI productivity gains (past or future) when other business decisions and metrics are actually driving the reductions.

Still, some stories stand out. Zeb Evans, CEO of project management and productivity software startup ClickUp, proudly declared on X that he laid off nearly a quarter of his workforce—22%—after rolling out about 3,000 AI agents to handle internal tasks.

Evans insisted the move wasn't about cutting costs. Instead, he envisions a workforce made up of people who manage AI agents and spend their days quickly reviewing the agents' output. He believes this will create what he calls a "100x org."

While AI can be a powerful tool, the data on AI and productivity doesn't support such assumptions—by a wide margin.

A meta-analysis of other research, published in October in UC Berkeley's California Management Review, found "no robust relationship between AI adoption and aggregate productivity gain."

Research published in March by the National Bureau of Economic Research did conclude that AI adoption improves productivity, but noted "a productivity paradox, in which perceived productivity gains are larger than measured productivity gains."

After deploying thousands of agents on various tasks, researchers at MIT concluded that agents still don't produce human-quality work in many cases. At the current rate of LLM improvement, they predict models will "be able to complete most text-related tasks with success rates of, on average, 80%–95% by 2029 at a minimally sufficient quality level."

In other words, AI is on track to reach basic competency on most tasks in about three years. These researchers believe agents will need several more years to outperform humans.

Meanwhile, research published in the Harvard Business Review showed that when everyone uses AI to produce more output, the bottleneck simply shifts to executives. Their work still requires people to approve everything being produced. If everyone is empowered to act, then based on what OpenAI experienced last year, things can quickly spiral out of control.

Are CEOs ready for that? If not, the most likely outcome of the ongoing CEO AI psychosis is simply organizational chaos.