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.
Levie coined the term “AI psychosis” in a series of posts on X (formerly Twitter) in late May 2026. He wrote: “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.” According to Levie, 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. 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.
The Rise 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.
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.”
What Researchers Say About AI Agents
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 Background of Aaron Levie and the Term
Aaron Levie co-founded Box in 2005 as a college student and built it into a publicly traded cloud content management company. Over the years, he has been a vocal commentator on tech trends. His “AI psychosis” term gained traction because it resonated with many engineers and middle managers who see their bosses making decisions based on superficial demos. Levie’s advice to 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.”
But in the current climate, many CEOs seem to skip the learning phase. The pressure to adopt AI is immense from investors, boards, and the media. A CEO who doesn’t talk about AI risks being seen as outdated. This creates a perverse incentive to overstate AI’s capabilities and to cut headcount prematurely to signal efficiency.
Industry-Wide Implications
The phenomenon is not limited to startups. Large tech companies such as Google, Microsoft, and Amazon have also laid off thousands while investing billions in AI. In some cases, these layoffs are framed as restructuring to focus on AI. However, the disconnect persists: AI models still require human oversight, data labeling, and customization. The “last mile” problem Levie describes — the detailed, messy work of integrating AI into real workflows — is often invisible to the C-suite.
Consider the example of a CEO who uses an AI tool to generate a contract. The tool might produce a passable first draft, but it cannot understand the nuances of the company’s legal history, negotiate terms, or handle exceptions. Yet the CEO may assume that entire legal departments can be replaced. This has been observed in sales, customer support, content creation, and software engineering. The myth of “full automation” persists despite abundant evidence to the contrary.
In software engineering, for instance, AI coding assistants like GitHub Copilot can speed up routine tasks but still require senior engineers to review and debug code. Studies show that while developers feel more productive, the actual output quality often suffers from subtle bugs that are hard to catch. A 2025 study by Stanford researchers found that AI-generated code introduced security vulnerabilities at a higher rate than human-written code. Yet CEOs see the time saved on boilerplate and declare victory.
The same pattern appears in customer support. AI chatbots can handle common queries, but complex issues require escalation. When companies replace support teams entirely with AI, customer satisfaction plummets. A notable case was a fintech startup that laid off its entire support staff in early 2026, only to rehire half of them two months later after a surge in complaints.
Historical Context: Parallels to Cloud and Dot-Com Eras
The current AI psychosis has parallels in previous tech cycles. During the cloud computing boom of the early 2010s, many companies moved workloads to the cloud without understanding the complexity, leading to runaway costs. Similarly, during the dot-com bubble, executives invested heavily in websites without clear business models. In both cases, the technology was real and transformative, but the hype led to waste and correction.
AI today is undeniably powerful, but its impact is incremental rather than revolutionary. The MIT researchers’ prediction that agents will reach 80-95% success on text tasks by 2029 suggests that true automation is still years away. Until then, companies that cut too many people risk losing the very expertise needed to make AI work effectively.
Moreover, the layoffs create a talent drain that could harm innovation. Laid-off engineers often leave the industry or join competitors, and the remaining employees suffer from low morale and burnout. A 2026 survey by Blind showed that 68% of tech workers believe their company’s AI strategy is “poorly planned,” and 45% are actively looking for new jobs. This instability could slow the very progress CEOs hope to accelerate.
In summary, the term “AI psychosis” captures a dangerous trend in tech leadership. As Levie and researchers warn, the gap between executive perception and ground reality must be bridged through deeper engagement with the technology. Otherwise, the industry risks a wave of organizational chaos, wasted resources, and human cost. The data from Layoffs.fyi, MIT, NBER, and Harvard Business Review all point to the same conclusion: AI is a tool, not a magic wand, and CEOs who treat it as such are likely to cause more harm than good.
Source: TechCrunch News