Tech CEOs are apparently suffering from AI psychosis

Recorded: May 27, 2026, 5 p.m.

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Tech CEOs are apparently suffering from AI psychosis | TechCrunch

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Startups

Tech CEOs are apparently suffering from AI psychosis

Julie Bort

5:30 AM PDT · May 27, 2026

There is a certain wildness in the tech industry these days that both mimics previous eras of large changes, like cloud computing (runaway costs in the early days), and is like nothing we’ve ever seen before (record revenues accompanied by mass layoffs).
One possible explanation: tech executives, especially CEOs, are collectively suffering from delusions of AI grandeur. And at least one tech CEO has said as much 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.
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.
But these top-level executives aren’t the people who have to review code, discover bugs, and identify calls to hallucinated libraries before software is deployed. They aren’t responsible for training AI models on a company’s idiosyncratic contract terms, nor do they have to spend days combing through contracts to find sneaky terms, as Levie indicates.
In other words, Levie’s theory posits, CEOs don’t really understand processes well enough to know what really can and can’t be automated. But that lack of knowledge doesn’t stop them from acting on their beliefs.
It’s important to note that Levie is not an AI hater. Quite the opposite. He mostly posts AI positivity on X to his 2.7 million followers, writing blogs titled, “Headless software is the future” on how software built for AI agents is the way forward. He also puts his money where his mouth is, backing AI startups as an active angel investor.

So what are CEOs to do instead? Levie advises CEOs to use AI “a ton” to really see what it can and can’t do, “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 that there are CEOs out there attempting to do just that, but right now, they seem to be in the minority.
In only the first five months of 2026, the tech industry has already had nearly as many layoffs as in all of 2025: 115,430 people have been fired from 152 tech companies so far in 2026, compared to 124,636 people let go by 275 companies in 2025, according to industry layoff tracker Layoffs.fyi.

And the bulk of companies have pointed to AI as a reason for cutting these jobs. Many argue that the biggest tech companies are AI washing, or crediting AI productivity gains in the past or future, when other business decisions and metrics are really driving the cuts.
Still, some of these stories are surprising. Zeb Evans, the CEO of project management and productivity software startup ClickUp, proudly declared on X that he had laid off almost a quarter of his employees — 22% — after rolling out about 3,000 AI agents to do internal work.
Evans swore this wasn’t done to reduce costs. Instead, he wants a workforce composed of people who run AI agents and spend their days quickly reviewing the agents’ work. He believes this will create a “100x org,” as he calls it.
While AI can be a very useful tool, the data on AI and productivity doesn’t support such assumptions. By miles.
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 improved productivity, but noted “a productivity paradox, in which perceived productivity gains are larger than measured productivity gains.”
After creating thousands of agents to work on tasks, researchers at MIT concluded that agents just aren’t doing human-quality work yet in many cases. They predict at the current rate of LLM improvement, 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 perform at base competence on most tasks in about three years. These researchers believe agents will need another few years to outperform humans.

Meanwhile, research published in the Harvard Business Review showed that when everyone is using AI to produce more stuff, the bottleneck simply shifts to executives. Their work awaits the people that must authorize all the stuff everyone is producing. If everyone is empowered to act, then from what OpenAI experienced last year, we can tell that things may get out of control.
Are CEOs ready for that? If not, the most certain outcome of the ongoing CEO AI psychosis will simply be organizational chaos.

Topics

AI, ai psychosis, Box, Layoffs, Startups, tech layoffs, Venture

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

Julie Bort

Venture Editor

Julie Bort is the Startups/Venture Desk editor for TechCrunch.

You can contact or verify outreach from Julie by emailing julie.bort@techcrunch.com or via @Julie188 on X.

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Tech executives, particularly CEOs, are perceived to be experiencing a state of delusion regarding artificial intelligence, referred to as AI psychosis. This phenomenon is linked to the fact that these leaders are sufficiently removed from the operational "last mile" of work necessary to generate actual value through AI applications. As Aaron Levie suggested, executives often engage with AI by developing prototypes or generating contracts, then leap to the assumption that autonomous agents can handle the actual execution. This occurs because CEOs do not possess the detailed knowledge required to fully understand which processes can and cannot be automated, yet they act decisively based on their current beliefs. It is important to note that Levie is not an opponent of AI; rather, he advocates for its use, supporting AI startups and promoting concepts like headless software, suggesting the issue lies more in the disconnect between executive vision and operational reality rather than an inherent dislike of the technology.

The broader context of the tech industry reflects this tension, characterized by volatility such as record revenues juxtaposed with mass layoffs. While many attribute these cuts to the rise of AI, some perspectives suggest that AI is not the primary driver of these economic shifts. A meta analysis from the California Management Review found no robust correlation between AI adoption and aggregate productivity gains, and research from the National Bureau of Economic Research highlighted a productivity paradox where perceived gains exceed measured ones. Furthermore, studies indicate that while AI adoption can improve productivity, there is a paradox in the observed gains. Research from MIT also posits that agents are not yet capable of performing human-quality work, predicting that models will achieve baseline competence in most text-related tasks by 2029, though they may require more time to surpass human capabilities.

A significant operational implication arises when widespread AI usage shifts the organizational bottleneck. When employees are empowered to use AI to generate output, the responsibility for authorizing that output shifts upward to executives. If this shift is not managed effectively, the result could be organizational chaos. This dynamic was illustrated by Zeb Evans, CEO of ClickUp, who implemented approximately three thousand AI agents for internal tasks, aiming to create a "100x org" composed of people who manage these agents, rather than focusing on cost reduction. However, the overall data does not strongly support the assumption that AI adoption inherently leads to massive productivity increases. The core dilemma facing tech leadership is whether they are adequately prepared to manage the organizational complexities and accountability shifts that emerge when relying on tools whose internal mechanics and limitations they do not fully comprehend.