If, in a plausible future, a film is made about “how the AI bubble burst,” Ed Zitron will undoubtedly be a central character. He fits the perfect outsider archetype: the eccentric loner who saw it all coming, shouting warnings from the sidelines that no one heeded. Just as Christian Bale played Michael Burry, the investor who predicted the 2008 financial crash in The Big Short, you can easily picture Robert Pattinson and Paul Mescal, for instance, vying to portray Zitron—the lively, colorfully abrasive, yet doggedly detail-oriented Brit who has become one of big tech’s most vocal critics.
This isn’t to say the AI bubble will necessarily burst, but amid a tidal wave of AI boosterism, Zitron’s blunt, brash skepticism has turned him into something of a cult figure. His tech newsletter, Where’s Your Ed At, now boasts over 80,000 subscribers; his weekly podcast, Better Offline, consistently ranks in the Top 20 on tech charts; he’s a regular dissenting voice in the media; and his subreddit has become a safe haven for AI skeptics, including those within the tech industry itself—one user describes him as “a lighthouse in a storm of insane hypercapitalist bullshit.”
Zitron first began examining generative AI in 2023, a year after OpenAI’s industry-shaking launch of ChatGPT. “The more I looked, the more confused I became,” he says. “Not only did large language models (LLMs) clearly fail to do what people were excited about, but they also had no path to achieving it. Nothing I found suggested this was a real business, let alone something that would supposedly change the world.”
He’s speaking via videocall from his Las Vegas office, wearing a red hoodie, surrounded by framed pop-culture prints and American sports memorabilia. And boy, can Zitron talk. As listeners of Better Offline know, the 39-year-old is a prodigious speaker—adept at extended monologues, delivering his viewpoint in accessible, often cheeky language, peppered with facts, statistics, analogies, and a fair share of expletives. His London accent only accentuates his role as a Silicon Valley contrarian—someone who drops his T’s when saying “datacentres.”
Explaining Zitron’s thesis on why generative AI is doomed to fail isn’t simple: last year, he laid it out in a 19,000-word essay. But it can be broken down into two interrelated parts: the actual efficacy of the technology and the financial architecture of the AI boom. In Zitron’s view, both foundations are shaky.
First, there’s the issue of generative AI delivering on its promises. Over the past few years, we’ve seen escalating prophecies of the technology upending work as we know it. For example, Dario Amodei, CEO of Anthropic—OpenAI’s closest rival—warned last May that AI could eliminate half of all entry-level white-collar jobs within five years. “The current generation of AI large language models will not be doing that,” Zitron states confidently. “My evidence is they’re basically the same as they were a year ago. They have the same efficacy. And every attempt to turn these into something that can actually do things autonomously has failed.” He argues that LLMs hallucinate and provide wrong answers, give different responses each time, and cannot truly learn, create, or perform many complex tasks. He even questions labeling this technology as “intelligence.”
“It’s intelligent in the same way a pair of dice are intelligent,” he says. “Large language models are transformer-based architectures that use large-scale probability to generate the next token. They do this at scale, so you might think, ‘Oh, it’s coming up with things.’ No, it has a large corpus of data and so many parameters…”Generative AI operates by drawing from existing data to produce outputs, nothing more. We wouldn’t consider an Excel formula intelligent, so we shouldn’t label generative AI as intelligent either.
Many disagree with this view, particularly regarding AI’s impact on jobs. Across industries like film, customer service, government, and tech, professionals report that AI tools allow them to accomplish the same tasks with fewer people. Even if it doesn’t eliminate half of all jobs, AI is poised to transform the workplace. A survey from last June indicated that entry-level positions in the UK had decreased by nearly a third since ChatGPT’s launch.
Zitron counters that “correlation does not equal causation,” citing reports that question or downplay the role of machine learning in job losses. For instance, a recent MIT report on the “state of AI in business in 2025” found that 95% of companies trying to integrate AI saw “zero return.” The report noted that most generative AI systems fail to retain feedback, adapt to context, or improve over time.
This leads to the second part of Zitron’s argument: the economics of the AI boom don’t add up. The level of investment pouring into AI is unprecedented. The “Magnificent Seven”—Alphabet (Google’s parent), Amazon, Apple, Meta, Microsoft (which owns 27% of OpenAI), Nvidia, and Tesla—now represent 34% of the S&P 500, an index that accounts for about half of the global market. As the leading producer of GPUs, the powerful chips essential for AI, Nvidia is essentially “printing money,” according to Zitron. Meanwhile, others are borrowing and spending billions they may never recoup.
While Silicon Valley startups have traditionally operated at a loss to gain market share and profit later, the current gap between supply and demand is alarmingly large. Building AI requires massive investment: a typical data center needs tens of thousands of GPUs, each costing over $50,000 (£37,000), plus software, networking, large facilities, and significant amounts of electricity and water. The estimated cost for 1GW of AI data center capacity is $35 billion (£26 billion). Consequently, only deep-pocketed “hyperscalers” like Google, Meta, Amazon, Microsoft, and Oracle can compete at this scale.
On the demand side, the outlook is less clear and far from certain. For example, OpenAI plans to spend $1.4 trillion (£1 trillion) on AI infrastructure over the next five years, yet its projected revenue for 2025 is only about $20 billion (£15.8 billion). Zitron points out that many deals between AI companies involve them essentially paying each other. Last September, Nvidia announced a $100 billion investment in OpenAI, which in turn will use the funds to buy Nvidia chips. Similar arrangements are common in the industry. Even “neocloud” companies like CoreWeave, Lambda, and Nebius, which build data centers and rent out GPU capacity, rely heavily on business from giants like Google, Microsoft, Amazon, and Nvidia. Zitron claims that without these hyperscalers, total AI compute revenue for 2025 would be less than a billion dollars.
As for profitability, ChatGPT now has an estimated 800 million users, but most don’t pay. Even for paying subscribers, the costs of connecting a user to…For an AI model like GPT, the cost of each user interaction can vary dramatically. A user might ask a simple question, or they might ask something that prompts the model to generate a complex response. As Zitron points out, there are no economies of scale here—every query requires “compute,” or computer processing, at the provider’s expense. “The more someone is a power user of these platforms, the more they’re going to cost you. This is almost the opposite of how Silicon Valley usually works.” And if the answer isn’t satisfactory and needs to be reworked, which happens often, “that’s more compute burned, without making you any extra money.” While AI models are constantly becoming cheaper and more advanced, this is only achieved by using even more computing power. “It’s like the price of gas going down a bit, but you have to drive an extra 250 miles to get somewhere. So this is really problematic—because it means there’s no point of profitability.”
A typical data center requires tens of thousands of GPUs.
Again, none of this guarantees that a major AI crash will happen, but “if I’m wrong, I don’t know how I’m wrong,” he says. “Every counterargument I’ve read to my work is mostly just wishful thinking that ‘the AI will get better.'”
Many have accused Zitron of having a grudge against big tech, but he denies this: “I have a problem with those who don’t want to talk about reality.” He certainly doesn’t avoid attention, but that’s not why he got into this, he explains. “I like writing. I like pulling things apart. I like solving puzzles. I guess I like being able to understand things. A lot of this is just me trying to explain it to myself, rather than to an audience.” He has no formal training in economics or computer science and has never worked in tech. “I’ve learned basically everything from the ground up.”
Zitron has always been drawn to technology, though. He says he has built 10 personal computers over his lifetime. It started when his father bought him a PC with a dial-up connection when he was 10. “So I was online from quite an early age. I immediately thought, ‘This is the future. I adore this. I love that I can talk to people and game with people.’ I was quite a solitary child. I didn’t have a lot of friends, but I made a lot of friends online.”
Growing up in Hammersmith, west London, Zitron describes his parents as loving and supportive. His father was a management consultant; his mother raised him and his three older siblings. But “secondary school was very bad for me, and that’s about as much as I’ll go into.” He has dyspraxia, a coordination disorder, and was diagnosed with ADHD in his 20s. “I think I failed every language and every science, and I didn’t do brilliantly at math,” he says. “But I’ve always been obsessive over the details.”
After studying media and communications at Aberystwyth University, he began writing for gaming magazines, but “I got to a point where I was miserable in London.” So he moved to New York in 2008 and started working in tech PR. He says he can’t imagine returning to the UK. He doesn’t discuss his personal life beyond mentioning he has a son, which is why he lives in Las Vegas. He doesn’t mind it there: “Everyone’s weird, so no one’s weird.” It has been reported that he has been married and divorced twice.
Zitron continues to work in tech PR, which seems at odds with his role as a tech critic—either like biting the hand that feeds him or a conflict of interest. He doesn’t see it that way. He says he doesn’t have AI clients or work with big tech, and only has a few clients now. The work has given him a network of contacts in the industry and possibly helped him market himself.In 2013, he published a book called This Is How You Pitch: How To Kick Ass in Your First Years of PR. However, he may not be working in PR much longer. “The media side of things is making up more of my income these days than I ever expected,” he says. He’s currently writing a new book, due out next year, titled Why Everything Stopped Working. “It’s kind of a dig into how the world got the way it did and how technology is everything now,” he explains, adding that only one chapter is about AI.
If Zitron has an axe to grind, it’s against neoliberal capitalism in general. “I don’t think people have taken seriously enough how bad the deregulation of financial markets by Thatcher and Reagan was. I don’t think people take seriously enough how bad it was not putting people in prison for the great financial crisis… I don’t think people have taken seriously the threat of growth-focused capitalism and growth at all costs.”
Rather than leading us to a utopian future, Zitron sees AI as the logical conclusion of neoliberalism. “The biggest thing we’ve learned from the large language model generation is how many people are excited to replace human beings, and how many people just don’t understand labor of any kind,” he says.
Zitron is no longer quite so alone in his views. He aligns with figures like Cory Doctorow, who has appeared on his podcast and whose “enshittification” thesis similarly argues that tech companies are now more driven by profit than by creating useful products. Meanwhile, other AI skeptics, such as cognitive scientist Gary Marcus, note that they have been making similar arguments as Zitron, but feel overlooked in his narrative. Regardless, backlash against AI is growing: local groups are opposing the construction of environmentally destructive data centers; consumers are resisting the insertion of AI into every possible product; creators are taking legal action against the industry’s use of their work without permission; and there is public outrage over social media harms, highlighted by incidents like Elon Musk’s Grok creating nonconsensual, borderline-deepfake pornography.
At the same time, speculation about an AI bubble is increasing. Warnings are now coming from everyone from the Bank of England to Microsoft CEO Satya Nadella. Investor Michael “Big Short” Burry says he is betting against Nvidia, and a recent New York Times op-ed speculated that OpenAI could run out of money within 18 months. Zitron thinks it might happen even sooner. He points out that big tech companies are about to report their annual earnings for 2025 and have been vague about their AI-specific revenues. “Why would they do that? Well, because they’re not very big. So this whole thing is—to use a phrase I hate—it is a vibe.” If something significant occurs, like Nvidia missing its targets, it could trigger a reassessment of the entire sector and possibly even a new global financial crisis. All those data centers might end up as empty shells. In the end, he jokes, we could be witnessing “the largest laser-tag arena construction of all time.”
Zitron insists he doesn’t enjoy being contrarian. “It isn’t fun being alone in an idea, which is actually why I think a lot of people are pro-AI, because it’s much easier to do that.”
He clarifies that he doesn’t hate technology or even AI itself. “I love technology, but I hate what the tech industry is doing… If you can’t critique this stuff without it being claimed that you don’t support the world or innovation, I think you realize we’re in this weird peasant economy where even wealthy, well-to-do famous people have to kneel at the feet of these companies. And these companies have done very little to make our lives better, all while making so much more money than we will ever have.”He just wants to tell it like it is. “It would be much easier to just write mythology and fan fiction about what AI could do. What I want to do is understand the truth.”
Frequently Asked Questions
FAQs on Ed Zitrons View Tech Cycles AIs Push to Replace Humans
Q1 Who is Ed Zitron and what does he write about
A Ed Zitron is a wellknown tech journalist and commentator He writes a popular newsletter called Wheres Your Ed At where he critically analyzes the tech industry its business culture and the oftenhyped promises of new technologies like AI
Q2 What does Zitron mean by techs cycles
A Hes referring to the repetitive pattern in the tech industry where a new technology is hailed as worldchanging attracts massive investment and hype but often fails to deliver on its grand promises leading to a period of disillusionment before the next big thing comes along
Q3 Whats his main point about AI and replacing humans
A Zitron argues that the current AI boom led by companies like OpenAI is less about creating useful tools and more about a deepseated desire within the tech industry and its investors to reduce costs by automating and replacing human labor often without regard for the societal consequences
Q4 Isnt the goal of AI to help people not replace them
A Zitron would say thats the marketed promise but the driving business incentive is efficiency and profit When a company says an AI can augment a worker the underlying goal is often to eventually have the AI do that job with fewer humans making it a replacement strategy in disguise
Q5 Can you give a realworld example of this replacement thinking
A A clear example is in customer service where companies heavily invest in AI chatbots with the explicit goal of handling millions of queries without human agents Another is in content creation where AI is pitched as a tool to generate marketing copy articles or images at a scale and cost that human writers or artists cant match
Q6 Whats wrong with automating boring or dangerous jobs
