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.

What is AI psychosis?

Levie posted on X that “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.” He added that CEOs “play with AI,” develop a prototype, or generate a contract, 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. 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.

The irony of AI advocates

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.”

Real-world examples of AI-driven layoffs

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.

What the research says

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.

Historical parallels

The current AI frenzy echoes earlier tech booms. During the dot-com bubble, executives across industries believed the internet would immediately transform every business, leading to massive investment in unprofitable ventures. Similarly, the early days of cloud computing saw companies migrating workloads without understanding the cost implications, resulting in runaway spending. In both cases, the gap between executive enthusiasm and operational reality created significant inefficiencies before the market corrected.

AI psychosis may follow a similar pattern. The key difference today is the speed at which decisions are made. With generative AI tools becoming widely available in 2023-2024, CEOs have been bombarded with promises of 10x productivity gains. Venture capital has poured $29 billion into AI startups in 2025 alone, according to PitchBook, fueling a narrative that companies must adopt AI or risk irrelevance. This urgency pushes executives to make drastic moves, such as replacing human staff with AI agents, before the technology has been rigorously tested at scale.

The organizational bottleneck

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.

Beyond the productivity paradox, there are practical concerns about AI reliability. Users of large language models frequently encounter hallucinations — confident statements that are factually wrong. In a business context, an AI agent misreading a contract could lead to costly legal disputes. The MIT researchers found that even with advanced models, error rates in tasks like data extraction and summarization remain high, often requiring human verification. This undermines the argument that AI can fully replace human workers in the near term.

The response from the tech community has been mixed. Some CEOs have criticized Levie’s characterization, arguing that AI is indeed transformative and that skeptics are holding back progress. Others have quietly adopted AI tools cautiously, keeping most of their staff while experimenting with automation. The diversity of approaches reflects the uncertainty surrounding AI’s actual impact. What is clear is that the current wave of layoffs attributed to AI is unprecedented, and the industry lacks best practices for integrating AI into the workforce without causing disruption.

Regulatory bodies are also beginning to take notice. The European Union’s AI Act, which came into partial effect in 2025, imposes requirements on companies using AI in high-risk areas, including employment decisions. In the United States, the Federal Trade Commission has issued warnings about AI-related claims that could be considered deceptive. This regulatory scrutiny adds another layer of complexity for CEOs making bold claims about AI-driven transformation.

Ultimately, Levie’s concept of AI psychosis serves as a cautionary tale. The technology has immense potential, but it is not yet at the point where it can replace the judgment, contextual understanding, and iterative problem-solving that human workers provide. CEOs who rush to adopt AI without understanding its limitations risk not only wasting resources but also demoralizing their teams and eroding trust in leadership. As the industry navigates this period of rapid change, the advice from Box’s founder may prove prescient: use AI extensively, but maintain a healthy respect for the last mile of work that remains stubbornly human.

Source: TechCrunch News