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. “So when they play with AI, they see the happy path results, often not considering the next 10 or 20 things that have to happen to get that value.”
Levie, who has built a cloud content management platform serving over 100,000 organizations, has been a vocal proponent of AI adoption. But his warning strikes at the heart of a problem that is reshaping the tech workforce. 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. Yet these top-level executives are not the people who review code, discover bugs, or identify calls to hallucinated libraries before software is deployed. They are not responsible for training AI models on a company’s idiosyncratic contract terms, nor do they spend days combing through contracts to find sneaky terms. In other words, CEOs don’t understand processes well enough to know what truly can and cannot be automated.
The disconnect has led to a wave of layoffs that has become the defining feature of the 2026 tech landscape. In just the first five months of 2026, the tech industry has 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.
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 was not 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 does not 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 are not 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 who 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.
The term “AI psychosis” itself is a play on the exaggerated optimism that often accompanies new technologies. In the past, analogies were drawn to the dot-com bubble, where CEOs overestimated the power of the internet, leading to massive spending and eventual collapse. Today, the stakes are higher because AI is being applied to core operational tasks, from customer support to software engineering. Companies like Klarna, which replaced hundreds of customer service agents with an AI chatbot, reported initial cost savings but later faced backlash over quality and data privacy concerns. Similarly, a financial services firm that used AI to generate legal contracts ended up with flawed documents that required significant human rework.
Levie’s advice for CEOs is 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.” He emphasizes that the “last mile” of work—the fine details, the edge cases, the integration challenges—is where the true value is generated, and it is also where AI most often fails. For example, an AI model might draft a contract clause that looks perfect but references a law that does not exist, or it may write code that compiles but introduces a security vulnerability. Only experienced engineers and domain experts can catch these issues.
The phenomenon is not limited to startups. Large enterprises are also under pressure to show AI results to investors. Amazon, which has deployed AI extensively in its warehouses and cloud services, laid off 27,000 employees in 2023–2024, though the company attributed many of the cuts to over-hiring during the pandemic. Similarly, Google and Microsoft have restructured teams to prioritize AI, leading to thousands of job cuts in non-AI divisions. In each case, executives cited the need to focus on AI, yet the tangible productivity gains remain elusive. A study by the MIT-IBM Watson AI Lab found that only 13% of organizations that adopted AI reported a significant financial return within two years.
What makes the current situation unique is that the CEO’s belief in AI is not entirely irrational. AI agents can now generate passable first drafts, summarize documents, and automate simple decision-making. The problem arises when that first draft is mistaken for a final product. In software development, a prototype is a long way from a production system. In legal work, a generated contract still needs human validation. And in customer service, an AI can handle common queries but fails when the context is nuanced or emotional. The gap between prototype and production is the source of “AI psychosis.”
History will likely judge this era by the number of companies that successfully bridged that gap versus those that fell into the trap of assuming AI could replace human judgment entirely. As Levie noted, the upside of AI is real, but so is the real work required to realize it. The most prudent executives are those who invest in understanding the technology deeply, rather than outsourcing that understanding to hype and hope.
Source: TechCrunch News