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.”
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 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.
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 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 phenomenon of AI psychosis is not entirely new. During the dot-com boom of the late 1990s, executives similarly overestimated the power of the internet, leading to massive investments in unproven business models and subsequent layoffs when the bubble burst. In the early 2010s, the rise of cloud computing saw a similar pattern: companies rushed to migrate workloads without fully understanding the cost implications, leading to “cloud cost blowouts” that often resulted in restructuring. Now, with generative AI, the cycle repeats itself but with a new twist. CEOs are not just investing in technology; they are using it as a justification to reduce headcount dramatically, claiming AI will handle tasks that, in reality, require significant human oversight and judgment.
One of the most telling examples of this disconnect comes from the healthcare sector, where AI-powered diagnostic tools have been deployed in hospitals. While these systems can flag potential anomalies in medical imaging, they still require radiologists to verify findings. Yet some hospital administrators have pointed to AI as a reason to reduce the number of radiologists on staff, leading to increased burnout among the remaining doctors and higher rates of misdiagnosis. Similarly, in customer service, companies like Duolingo have cut a portion of their human translators and content creators, relying on AI-generated lessons. But users have reported a decline in quality, with stilted phrasing and cultural inaccuracies that require human intervention to correct.
The term “AI psychosis” itself reflects a broader cultural issue within the tech industry: a tendency to treat AI as a magical solution rather than a tool that works best in combination with human expertise. This is partly fueled by the venture capital ecosystem, which rewards founders and CEOs who promise exponential growth through AI. Investors like Marc Andreessen and Peter Thiel have famously argued that AI will disrupt every industry, creating immense value for those who adopt it first. As a result, CEOs feel pressure to demonstrate aggressive AI integration, even if the actual benefits are marginal. The ClickUp case is particularly instructive: by replacing a quarter of its workforce with AI agents, the company has made a bold statement, but the long-term impact on product quality, company culture, and customer satisfaction remains to be seen.
History suggests that such drastic moves may backfire. In 2018, a large financial institution attempted to automate its compliance department using rule-based AI, resulting in thousands of false positives that overwhelmed the remaining human auditors. The project was eventually scaled back, and the company rehired many of the workers it had laid off. Similarly, in the early days of robotic process automation (RPA), companies like IBM and Accenture found that many processes could not be fully automated without significant human oversight, leading to a hybrid model where bots and humans worked side by side. The current wave of generative AI is even more complex because it creates content that can appear plausible but is often wrong, as seen in legal cases where lawyers used ChatGPT to generate fake citations.
To avoid the pitfalls of AI psychosis, experts recommend a more measured approach. First, CEOs should invest time in understanding the granular details of their organizations’ workflows, rather than relying on high-level prototypes. Second, they should implement AI incrementally, measuring actual productivity gains before scaling. Third, they should resist the temptation to use AI as a pretext for layoffs, recognizing that human workers bring judgment, creativity, and contextual understanding that AI currently lacks. Finally, they should engage employees in the AI adoption process, using it to augment rather than replace their roles. This approach, sometimes called “human-in-the-loop,” has been successfully adopted by companies like IBM, which uses AI to assist developers but still relies on experienced engineers to review and approve code changes.
The stakes of getting this wrong are high. If the current wave of AI psychosis leads to widespread layoffs without corresponding productivity gains, the tech industry could face a talent crisis. Skilled workers who have been let go may be reluctant to return to companies that replaced them with AI, leading to a long-term brain drain. Moreover, the backlash from consumers and regulators could result in stricter oversight of AI deployment in the workplace. Already, the European Union has proposed the AI Act, which includes provisions for transparency and human oversight in high-risk applications. In the United States, the Biden administration has issued an executive order on AI safety that calls for responsible deployment. CEOs who ignore these trends may find themselves facing not only organizational chaos but also legal and reputational damage.
In conclusion, while AI offers tremendous potential, the current mindset among many tech CEOs is dangerously optimistic. The data simply does not support the idea that AI can replace large swaths of the workforce overnight. Instead, the most successful companies will be those that treat AI as a complement to human labor, not a substitute. As Aaron Levie put it, CEOs need to “come out the other side with an appreciation for both the upside and the real work.” Until that lesson is learned, the tech industry will continue to suffer from the delusional cycle of AI psychosis, laying off workers only to discover that the promised productivity gains remain elusive.
Source: TechCrunch News