Big Tech CEOs AI Psychosis Is A Total Disaster

Añadido: 2026-06-05

[00:00:00]Aaron Levy is the founder and CEO of Box. He has 2.7 million followers on X, where he posts almost exclusively about how exciting AI is. He actively invests in AI startups. He writes blogs with titles like headless software is the future. He is by any reasonable measure one of the most enthusiastic AI advocates in the technology industry.

[00:00:23]And last week he publicly diagnosed his fellow CEOs with a condition he called AI psychosis. His exact words were the following. 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 they would play with a demo for example. They watch it generate a contract or write some code. They see the happy path. The happy path, for anyone unfamiliar with the term, is the version of the product where nothing goes wrong ever. It is also the version that has never once survived contact with a real user. And then they make the leap to believing agents can do the work without ever encountering the bugs, the hallucinated libraries, the hours of human review that separate a demo from a deployed product. When the most pro- AAI CEO in the room is the one diagnosing the others with psychosis, the condition might be more advanced than anyone is comfortable admitting. My name is L. I have a PhD in computer science and I analyze AI developments to understand what's actually happening beneath all of this hype. In this video, I'm going to walk through what tech CEOs are actually saying about AI and the decisions they're making on the back of it. Then I'm going to show you what the research actually says about AI and productivity because the gap between these two is extraordinary right now. After that, I want to connect this to something I covered recently about how AI psychosis works at the individual level because the mechanism turns out to be more similar than you'd expect. And along the way, I propose we play a game in this video. I'm going to show you some real quotes from real tech CEOs and I want you to tell me AI psychosis or just eccentric. Pause the video, drop your verdicts in the comments. We're going to find out together. Levy's diagnosis is very specific, and it's worth taking seriously, precisely because of who's making it. He is not an AI skeptic having a bad day on social media. He is literally a CEO who builds AI products, backs AI companies, and genuinely believes in the technologies long-term potential. So, he's not saying that AI is useless. His argument is that the people making the biggest decisions about AI deployment are the people least equipped to evaluate what AI can actually do because they're too far from the work itself. A CEO watches a demo.

[00:02:40]The demo generates the contract. The CEO thinks agents can handle contracts. What the CEO doesn't see is the legal team that would need to review every single clause. The edge cases the model cannot handle the liability implications of a hallucinated term. the 3 hours of human work that sits between impressive demo and deployable output. Levy's advice to CEOs is to use AI a ton, not to watch demos, but to actually push through the full workflow and come out the other side with an appreciation for both the upside and the real work. But this problem is older than AI. Much much older. If a human surrounds themselves with only people who agree and never challenge, their decisionmaking degrades over time. This is one of the most documented phenomena in organizational psychology, right? The yesmen problem, the emperor's new clothes, every leadership failure case study in history. Basically, the Bay of Pigs invasion happened in part because Kennedy's advisers were too afraid to voice disscent. Enron collapsed inside a culture where questioning the strategy was treated as disloyalty. Every leadership failure case study in history follows the same script. the Bay of Pigs, Enron, every group project you've ever been in where nobody wanted to say the idea was bad. The pattern repeats across decades, industries, and levels of intelligence. When the information environment filters out disscent, the quality of decisions collapses. What AI has done is industrialize this phenomenon. Large language models have a well doumented syphensy problem. They are trained to generate responses that align with the user's thinking rather than challenge it necessarily. But sick of fancy is not just an AI problem. It is a leadership problem. Fundamentally, a CEO who only hires people who agree with them ends up making worse decisions in the end. A person who only talks to a chatbot that agrees with them ends up kind of losing touch with reality. The mechanism is basically identical. The absence of friction degrades judgment regardless of how intelligent the decision maker is. I covered this at the individual level in my video on AI psychosis. basically how chatbots can trigger genuine breaks from reality in vulnerable people through social substitution, confirmatory bias, and blurred reality testing. The CEOs in this industry may not necessarily be experiencing clinical psychosis, but they don't really need to be. They just need to be operating inside an information environment that never provides the friction that might correct a bad assumption before it becomes a worse decision. A boardroom full of people who won't challenge you. A stock market that rewards headcount reduction.

[00:05:17]And a demo culture that only shows the happy path. That sounds like a feedback loop. No. And nobody's immune to feedback loops. Not individuals, not billionaires. Literally nobody. So, let's play that game that I mentioned.

[00:05:29]I'm going to show you some real quotes from real tech CEOs. Your job is basically simple. Is it AI psychosis or just an eccentric person? Pause the video after each one. Drop your answer in the comments below. First up, Mark Benov, CEO of Salesforce, a company worth $248 billion, on a podcast last year, said the following. I've reduced it from 9,000 heads to about 5,000 because I need less heads. 4,000 people.

[00:05:55]And then he added, I don't think it's dystopian at all. This is reality, at least for me. The phrase, at least for me, is doing a lot of heavy lifting in that sentence, I think, given that it is not doing any heavy lifting for the 4,000 people who no longer work there.

[00:06:09]So AI psychosis or just an eccentric guy? Second, Zeb Evans, CEO of ClickUp, valued at $4 billion. Last month, he fired 22% of his workforce, which is roughly 290 people, and deployed 3,000 AI agents to replace them. He swore it wasn't about cost cutting. He promised milliondoll salary bands for whoever remained. He called it an 100x org. One employee, Andy Kabaso, now manages 37 AI agents. 37? I mean, is that a job title or just a small animal shelter where none of the animals do what you tell them and several of them kind of hallucinate? Evans then declared, "Nearly every company will make changes like these. The ones that do it proactively will define what comes next." Evan also divided ClickUp's future workforce into three groups: builders, system managers, and frontliners. The 290 people he just fired were presumably a secret fourth category. What do you think? AI psychosis or just another eccentric guy.

[00:07:12]Third, Yansen Hong, CEO of Nvidia, the company that supplies the hardware powering practically all of this basically. His take on what AI agents will do to workers was they will harass and micromanage you, which is a selling point only if you have never been harassed or micromanaged. But here we are. He separately told workers they were confusing your jobs with the tools you use to do it. So AI psychosis or just another eccentric person. Fourth, Daria Amade, CEO of Anthropic, the company behind Claude, told Axio that up to half of all entry-level white collar jobs will dissolve within 5 years and unemployment could skyrocket to 10 to 20%. He said, I'm quoting, "We as the producers of this technology have a duty and an obligation to be honest about what is coming." So AI psychosis or just eccentric or and this is the tricky part possibly the only honest person in the room. Fifth Sam Alman CEO of OpenAI over the past 3 years he has said that AI will probably replace most of the jobs people do today that entire job categories will be totally totally gone.

[00:08:19]His words and that those affected will find all sorts of new things to do. He is now walking those statements back, as we discovered in a separate video analysis that I'm going to link below, suggesting the impact may actually be less traumatic than he originally predicted. So, either the CEO of the most prominent AI company in the world was wrong when he said jobs were finished, or he's wrong now when he's saying they're not. One of these two alts is suffering from something. AI psychosis or just eccentric. What's striking about this collection of quotes is not necessarily that they're individually unreasonable. Some of them might even turn out to be correct in the long run. What is striking, however, is the confidence. Every one of these statements is delivered with absolute certainty about the future of work by people who, as Levy points out, are the furthest from the actual work. Not one of them hedged in any way. Not one of them said, "The research is mixed. I'm not really sure." not one acknowledge that the evidence base for what they're claiming is at best incomplete as we do on this channel. By the way, this is what AI psychosis looks like in practice. Not delusion in the clinical sense, just kind of like a profound structurally reinforced absence of doubt. Here is where it gets even more interesting because what these CEOs believe about AI and what the research actually shows are two very different things. A metaanalysis published in October 2025 in UC Berkeley's California Management Review, drawing on recent meta analysis and systematic review across the field found, I'm quoting, no robust relationship between AI adoption and aggregate productivity gain. The productivity gains that CEOs are citing to justify firing tens of thousands of people do not reliably show up in the aggregate data. The nuances within that finding are worth understanding. So, let's have a look at that for a moment.

[00:10:09]AI productivity gains are highly context dependent. They vary significantly by user skill level and task complexity. A randomized control trial with 5,000 agents at a US tech support desk delivered a 35% throughput lift for bottom quartile workers, but almost no gain for experienced staff. And this is the finding that should be on every CEO's desk. I think human AI collaboration often underperforms either agent working independently except in creative tasks. Adding AI to a skilled worker can actually make the output worse in some cases, which is not the kind of thing you want to discover after you've already fired the skilled workers. Research published in March 2026 by the National Bureau of Economic Research did find that AI adoption improved productivity. But it also identified what it called a productivity paradox. Perceived productivity gains are consistently larger than measured productivity gains. Basically, CEOs think that AI is doing more than it is.

[00:11:11]The feeling of productivity is outpacing the reality, which if you think about it is a very polite way of saying people are kind of deluding themselves. AI [clears throat] psychosis. Anyone?

[00:11:22]Researchers at MIT tested thousands of AI agents on real tasks and concluded that at the current rate of improvement, models will be able to complete most text related tasks with success rates of 80 to 95% by 2029 at a minimally sufficient quality level. So not expert quality, not human quality, just base competence within 3 years. The agents that CEOs are deploying today to replace workers are by the academyy's best estimate years away from doing the work at the minimum acceptable standard. And Gardner found that approximately 80% of organizations deploying autonomous technology had job cuts, but the cuts did not translate into meaningful financial returns. Companies are firing people. The AI is not necessarily always delivering the promised savings and the cycle continues because the belief persists even when the evidence doesn't necessarily support it. If that sounds familiar, it really should. That's exactly what a feedback loop without friction produces. The Harvard Business Review added one more wrinkle to this story. When everyone in an organization is using AI to produce more output, the bottleneck simply shifts to the executives who have to authorize, review, and quality check everything the AI generated. The productivity gain at the individual level becomes an organizational log jam at the management level. You have not solved a problem.

[00:12:47]You've just relocated the problem upwards directly onto the desks of people who were least involved in the actual work to begin with. By the way, if you're finding these videos useful, the best way to support the channel is through Kofi or a channel membership.

[00:13:00]Both help keep everything free and available for everyone. Links are down below. Now, the part of the story where I want to be careful because these are real numbers representing real people.

[00:13:10]In the first 5 months of 2026, over 122,000 tech workers lost their jobs.

[00:13:16]That is a 33% increase over the same period in 2025. The total since 2020 now approaches 900,000 people and the pace is accelerating, not slowing, which is arguably the most concerning part of all. The industry is on track for a fullear total, approaching 370,000 people, close to the post-pandemic record of 430,000 set back in 2023. The companies doing the cutting are not struggling. Meta cut 8,000 roles while reporting 56.3 billion in quarterly revenue, up 33% year-over-year and 26.8 billion in net income. Oracle eliminated up to 30,000 positions targeting legacy database administrators and on premises support teams. Amazon cut 30,000 since October, which is about 10% of its corporate and tech workforce. While AWS posted its fastest growth in 13 quarters, Microsoft offered voluntary retirement to 8,750 US employees. These are very profitable companies, posting record numbers, cutting from strength. In March alone, Atlassian cut 1,600 10% of its workforce explicitly to self-fund AI while reporting 26% cloud revenue growth.

[00:14:33]Record performance, record cuts simultaneously. And [snorts] where is this money going? Into AI infrastructure. Google, Amazon, Meta, and Microsoft are expected to spend a combined 700 billion dollars in AI capex just in 2026, up 77% from the previous year. Meta's annual AI infrastructure budget now runs four to five times its entire human compensation bill. Whether or not that's a good idea, I've covered in a separate video. I'm going to link it down below. The people however are being replaced by the infrastructure and the infrastructure is being funded by replacing the people. That sounds like a circle to me. Whether it is a virtuous or vicious circle depends entirely on whether the AI actually does what the CEOs believe it does. And according to the research we just reviewed in this video, it doesn't. Not yet. Not at scale, not reliably. Stanford HI data shows software developer employment for workers under 26 fell nearly 20% since 2024. Young engineers, which are the exact people who were supposed to benefit most from the AI economy, are being hit the hardest. Median time to hire in the Bay Area stretch from 38 days to 67 days. A January 2026 Mercer poll found that 40% of employees are concerned about job loss due to AI, up from 28% in 2024. And the National Bureau of Economic Research working paper found that 44% of SFOs in US companies plan AI related job cuts in 2026, projecting roughly 502,000 roles eliminated by this year's end. That would be a nine-fold increase from the 55,000 AI related layoffs reported back in 2025. And Bloomberg data suggests roughly half of AI attributed layoffs will result in the same roles being rehired offshore or at lower salaries, which makes a meaningful portion of this a labor repricing story, not a labor reduction story. Some of this is genuine AI belief. Some of it is AI washing, which means using AI as a convenient explanation or an excuse for cost cutting that would happen regardless of whether a single model had been deployed or not. As analyst Ed Zitram put it, companies overhire during the pandemic and are now using AI to justify efficiency plays that are really about luring investors by appearing lean. The truth, as it usually is, involves multiple seemingly contradictory things happening simultaneously.

[00:17:03]I want to come back to where I started because I think Levy's diagnosis is more precise than even he may have intended.

[00:17:09]AI psychosis, the clinical kind affecting individuals, works through three mechanisms I covered in my previous video. Social substitution, confirmatory bias, and blurred reality testing. The chatbot becomes the only voice. The chatbot never challenges you.

[00:17:25]And the line between your thoughts and external reality dissolves because the AI reflects your own thinking back at you with the authority of an independent source. CEO AI psychosis works through structurally identical mechanisms. The demo becomes the only evidence. The board, the market, and the consultants never challenge the thesis and the line between what AI can actually do and what the CEO believes it can do dissolves because every signal in their environment confirms the belief. The stocks go up when you announce layoffs.

[00:17:57]The press covers your 100x org vision.

[00:18:00]The AS startup founders pitching you for investment are not going to tell you your strategy is wrong. The friction that might correct that assumption before it becomes a catastrophic decision simply does not exist. And nobody is immune to this, by the way.

[00:18:13]Not vulnerable individuals using chatbots at 2 a.m. and not billionaires making headcount decisions in boardrooms. The protective factor is the same in both cases. Welcome challenge.

[00:18:24]seek disagreement. Make sure the voices in your environment include ones that tell you things you don't really want to hear. That is true for a person managing their mental health is true for a CEO managing a company. And it's true for an industry spending $700 billion on a technology that the research says is not yet doing what they believe it's doing.

[00:18:45]While $142,000 people pay the price for that belief. And I want to be clear about something. I am not saying AI has no value. I use it every day. I know what it can do. The technology is generally powerful and there are deployments where it delivers real measurable improvements. What I am saying is that the gap between what the research demonstrates and what CEOs are claiming is wide enough to fire 142,000 people through. And the people making those decisions are operating in environments specifically structured to never tell them they might be wrong.

[00:19:17]Environments that look a great deal like the feedback loops that produce clinical psychosis in individuals. The only difference is scale. When an individual loses the ability to reality test, one person sadly suffers. When a CEO loses it, thousands do. The diagnosis is in.

[00:19:34]The question is whether the patient is interested in a second opinion. But what happens when AI deployment goes wrong outside the boardroom? When it's not jobs at stake, but public safety, civil liberties, and the infrastructure of everyday life. I covered that in my video on AI powered surveillance camera.

[00:19:50]The cities are now literally covering with bin bags because they cannot figure out how to turn them off. The pattern is the same. The consequences are different. That's the video that I would watch next. Thanks so much for watching this one. Subscribe and I'll see you on the next