Tech CEOs backtrack on AI job apocalypse as hype meets reality and investment questions mount

Friday essay: despite the AI hype, some experts warn of a bubble - what happens if it pops?

In the last few years, the hype around artificial intelligence has become stratospheric. Riding a wave of venture capital, tech leaders promised us AI would revolutionise work, boost productivity and lead to incredible new breakthroughs. OpenAI, the creator of ChatGPT, set a new record when it attained US$110 billion in investments several months ago - and its CEO, Sam Altman, recently claimed Australia could become a "data capital of the world." Sky-high promises have been accompanied by sky-high investment in data centres, the sprawling server farms that power the training, execution, and maintenance of these models. A monstrous new hyperscale facility proposed for Sydney's west - 1 gigawatt across 52 hectares - would rank among the world's biggest. It will join 162 existing centres and 90 in the works across Australia, which is projected to be the world's third largest data centre market by the early 2030s. But if AI backers are all in, public sentiment is far more mixed. A new study ranked Australia equal lowest on the scale of global AI sentiment, with 81% supporting stronger rules for how organisations use AI and 68% worried about losing control over decisions made by AI on their behalf. Grassroots movements against AI are growing. Last month, a "Stop the Slop" event challenging the Sydney data centre was relocated to a larger venue due to high interest. It joins other campaigns like StopAI and PauseAI that aim to slow down data centre development, ask how AI is impacting jobs and the environment, and consider more equitable and sustainable alternatives. And in the last few months, videos have begun surfacing of students at commencement ceremonies booing speakers like former Google chief executive Eric Schmidt, who speak in rapturous tones about "standing on the edge of technological transformation" and how AI will touch "every profession", "every classroom", and "every relationship". Faith in these monumental claims - and the monumentally expensive infrastructure they rely on - is slipping. What is the AI business model? AI's financial costs are astronomical. As tech critic Ed Zitron has shown over and over again, the major players are burning billions to keep models running, while lucrative profits remain tantalisingly out of reach. Some enterprises now spend more on rapidly rising token costs, the per-use cost of a model, than human workers. Even by cynical economic standards, the numbers don't add up. What exactly is the AI business model? Where is the killer app that will deliver genuine value and see millions of individuals or thousands of corporates pay costly subscription fees? "We have no idea how we may one day generate revenue," admitted OpenAI CEO Sam Altman in 2019, "once we build a generally intelligent system, we can ask it to figure out a way to generate an investment return." While the landscape has certainly shifted since then, use cases and revenue remain murky. Hard evidence of AI's contribution - rather than the vacuous claims of pitch decks and industry keynotes - remains largely elusive. A recent survey of 6,000 senior business executives across the United States, United Kingdom, Germany and Australia found positive perceptions but a disappointing reality: around 90% of firms said AI has had no impact on employment or productivity over the past three years. Another study, from MIT last year, found that 95% of generative AI pilots failed to deliver tangible financial value to the organisation, so were abandoned. If the upsides are unclear, the negatives are increasingly apparent. Politically, generative AI provides the perfect weapon to "flood the zone" with misleading or outright false content, muddying the informational waters and amplifying division. Is Netanyahu alive or dead? AI fakes make it harder and harder to tell. Socially, AI companions and models, gaining enormous trust with users via long-term conversations, have been cited in a growing series of court cases around suicides and mass shootings. A lawsuit filed this year described ChatGPT as an intimate and persuasive "suicide coach" who convinced a man in Colorado to end his own life. And environmentally, the turn to the far higher computation that AI requires means massive impacts as data centres demand more power and more water, creating hundreds of millions of tonnes of CO² emissions. If the 41 planned data centres in Sydney are built, they will directly use 15-20% of Sydney's water supply within a decade, predicts environmental accounting associate professor Michael Vardon. Even if its social, environmental and political fallout is dismissed, AI hype and investment misses what is happening on the technical level. Models in the last decade became "smarter" essentially by training on larger and larger data sets. But this paradigm yields diminishing returns. Yann LeCun, former chief AI scientist at Meta, has warned that the correlation-based "learning" of models is both inefficient and insufficient when compared to human learning. Models require trillions of tokens to train. Even then, they reproduce patterns without deeper understanding, while children learn in a generalised manner from a handful of examples. "Training is waning" is the new mantra, notes one Silicon Valley insider, as the brute force approach to foundational models gets left behind. It's far from clear whether massive models, and the massive data centres that underpin them, will even be needed. Where does this leave us? The possibility of the AI bubble bursting has shifted from a niche pocket of tech critics to mainstream policy wonks. "It's time to start asking not whether there will be an AI crash, but what we should do today so that we are best prepared to respond to one tomorrow," wrote two commentators in TIME magazine earlier this year. What will this look like? Any answer here would include speculation. And yet we can garner some insights from previous bubble bursts, from tech development trends, and by extrapolating from the socio-cultural fallout we've already witnessed. Let's step through each. Another dot-com bubble First, we can compare the AI bubble with the dot-com bubble of the late 1990s. Indeed, investment leaders - including The Big Short's Michael Burry, who famously anticipated the collapse of the subprime mortgage market - are already seeing disturbing parallels between the two. Burry warns that venture capitalists are funding "loss-mak[ing] companies like never before in history". As this suggests, the investments in this current AI bubble dwarf its dot-com analogue. If this bubble follows the blueprint of the last, we should expect to see massive layoffs in personnel and liquidations of AI startups with no discernible revenue. Of course, like the first bubble, the deletion of a company doesn't mean the technologies themselves disappear. Indeed, in the orthodox economic canon, the dot-com bubble was a "baptism of fire": a painful but necessary rebirth. The trivial players, buoyed by "irrational" valuations, disappeared, but the network infrastructure they helped expand was the foundation for the truly innovative tech products to come. Part of this "soft pop" future is almost certainly correct: the infrastructure will persist, even if underused. AI will continue being baked into a multitude of products, testing the market. And tech titans, sitting on data hoards and advertising monopolies, will march on. As scrutiny is increased, belt-tightening will occur. Companies will distil their product offerings, quietly begin limiting token use, and raise their subscription prices - all moves we're already seeing play out. But the larger question is whether tech companies - now just as then - actually contribute in meaningful ways to our broader world, or even merely our economies. As one Nobel-prize-winning economist famously quipped in the 1980s: "you can see the computer age everywhere except in the productivity statistics." More recent analyses of contemporary technologies have echoed this finding, suggesting the internet has little impact on economic growth. If this is the case for AI - as the numbers, the lack of products and even the rhetoric of its chief pundits suggests - then we have a social question, not just a financial one. What price are we paying for a technology that fails to deliver even on its own terms? Small is beautiful Second, tech development is moving away from the "bigger is better" mantra. Models are becoming much smaller and more efficient. The push is from the cloud to the so-called "edge": the far more mobile and low-powered devices, like your phone, where data is actually created and used. And there's a push to move the focus from "capture it all" quantity to quality, with targeted or carefully curated data. Some of this is a welcome -- and long-needed -- shift. A deluge of critical AI research in the last few years has extensively documented the major issues with bias in foundational models. In a not-so-shocking twist, indiscriminate training on a massive archive of social material with almost no oversight creates models that reproduce significant harms. To take just two well known examples: AI models discriminate based on race and gender, while AI-generated images consistently privilege white people over people of colour. Given these issues, the slower and more careful construction of models actually tailored to their communities and attuned to their language, needs, and desires can only be beneficial. Some languages, for example Indigenous languages with strong oral traditions, are considered "low-resource", or underrepresented, with much less material in standard training sets. Switch away from English, and see the accuracy of your response plummet. Future developers might work closely with communities to create their own archive of material that better reflects their ideas and beliefs. Here we start to see a meaningful idea of data sovereignty, where groups maintain control over their models and the data that underpins them, slowly disconnecting from corporate cloud regimes. Of course, if the "small and mobile is beautiful" approach attains real traction, this will mean today's massive investment in highly centralised data centres is the wrong move. What will happen to this massively overbuilt - and, we anticipate - soon underused infrastructure? In an ironic twist, dead shopping malls have been converted into data centres in the last two years to satisfy demand - yet these data centres might themselves become empty shells, physical reminders of an obsolete vision. Post-AI pathologies Third, AI cannot be stuffed back into Pandora's box. Even if AI development takes another path, the socio-cultural, political and environmental fallout of a post-AI world will continue - or even become exacerbated. In education, researchers warn that students who constantly turn to generative AI models exhibit a kind of "doom loop" of dependence: offloaded thinking gradually causes atrophy in critical thinking and reasoning. "When kids use generative AI that tells them what the answer is [...] they are not thinking for themselves," state the authors of a Brookings Institution study. They're not learning to parse truth from fiction. They're not learning to understand what makes a good argument. They're not learning about different perspectives in the world because they're actually not engaging in the material. In politics, cutting-edge image and video models make it increasingly difficult to parse fact from fiction. Gravity glitches and six-fingered hands are gone; new generative models like Nano Banana boast physically-aware rendering. Models can now produce photo-realistic news reports, for instance, that seem to show Ukraine president Zelensky surrendering. The result is a growing pervasiveness of the "liar's dividend", where muddied lines mean even genuine material is doubted or dismissed as being synthetic. The ability of evidence to document atrocity and persuade the public is undermined, with each side accusing the other of fabricating media. In the environmental sphere, the AI-driven boom in data centre construction will have long-term impacts. While society has begun to lower carbon emissions via electrification and renewables, AI's voracious demands threaten to reverse this progress. Sustainable generative AI is a fallacy. "AI datacenters are single-handedly leading to a major reversal in climate progress globally," declared tech critic Karen Hao, citing a recent UN report. From the extraction of rare-earth minerals to the burning of dirty diesel as backup, the strain on local power grids, and the siphoning of millions of gallons of freshwater in a warming world -- the damaging effects of AI supply chain capitalism - will be felt by the ecosystems and generations to come. Rage against the machine "I'm here to tell you the mission of your generation is to destroy AI," Daily Show comedian Ronny Chieng told Harvard graduates recently, to approving cheers -- a far cry from the boos and anger that met AI evangelists advocates at similar ceremonies. One strand of rising anti-AI sentiment is directed at data centres. A report found that US$64 billion of data projects have now been blocked or delayed amid local opposition. In one sense, of course, these wins are localised and limited: the "cloud" means data centres elsewhere can still run AI. But to see them as distractions from the bigger anti-tech battle is to miss the point. As tech critic Astra Taylor and community organiser Saul Levin argue, This brewing populist resistance isn't just about limiting local development - it represents a critical new front in the fight against tech-enabled authoritarianism. Where else can people push back on job-eating algorithms, distorting deep fakes, and autonomous drone strikes? These protests and campaigns signal a gulf between the current AI vision -- "tokenmaxxing" in an "AI everywhere" world -- and the desires of everyday individuals. Of course, this disparity alone doesn't signal the death of the AI boom dream: history is full of examples of elites rolling out exploitative technologies that run roughshod over the wishes of the people. But combined with other economic, social and environmental factors, these pushbacks begin to destabilise Big Tech's future-on-rails. There are other possibilities -- slower, smaller, more convivial, more sustainable -- for technologies that contribute to our lives, our society and our world.

The CEOs are No Longer (Publicly) Threatening to Replace Humans With AI

Since ChatGPT came onto the scene in late 2022, there has been a singular message from tech executives to the broader workforce regarding the new technology: it is coming for your job. They have insisted, sometimes in the language of a flowery, futuristic utopia and sometimes as a straight-up threat, that artificial intelligence will result in a complete upheaval in the economy, wipe out entire categories of jobs, and fundamentally change the human relationship with work. But over the last few months, a switch has flipped. Suddenly, the messaging around AI has gone from "meet your replacement" to "meet your new coworker, who is definitely not here to watch you work and eventually push you out!" The language has changed from warning to what feels like pacification -- and it comes at a seemingly strange moment. By most accounts, it seems like the AI companies won. The frontier model makers, including Anthropic and OpenAI, have filed to go public. Even xAI, Elon Musk's also-ran of an AI firm best known for allegedly mass-generating child porn, got tucked into SpaceX and turned into the biggest IPO in history. "I'm delighted to ⁠be wrong about this" The mood has significantly shifted in the executive suite. Take Mustafa Suleyman, the CEO of Microsoft AI, brought in to help the company wean itself off the teat of the OpenAI cash cow before it goes dry. In 2024, while speaking to the billionaire crowd at Davos, he said AI models "are fundamentally labor-replacing tools." Earlier this year, he got more specific on what labor. "White-collar work, where you're sitting down at a computer, either being a lawyer or an accountant or a project manager or a marketing person, most of those tasks will be fully automated by an AI within the next 12 to 18 months," he said in an interview with the Financial Times. But well before we reached the full automation timeline, Suleyman started walking back his position. "I said 'tasks,'" Suleyman told The Verge earlier this month. "So that does not mean jobs... Jobs and roles are the broader category, and tasks are the components of that." Instead, he now believes AI will make the tedium of work easier. "Sending an email, having a conversation with a colleague, putting together a PowerPoint -- sub-tasks will increasingly become digitized, automated, and we can basically generate more and more of them. That does not necessarily mean that the role goes away at all," he explained. Now, let's set aside the fact that in Suleyman's dream future, workers will be having more meetings and letting their AI equivalent chat with their colleagues -- a nightmare for people who are actually doing that work. The general thrust of the messaging is now "You're not being replaced, you're being augmented." He's far from the only one who has changed his tune in this way. Nvidia CEO Jensen Huang has also been shifting the narrative that he spins. In 2025, the head of the world's biggest chipmaker said programming AI is basically like "programming a person," which is definitely how people like to be talked about. To that end, it seemed Huang saw the goal of AI to fundamentally replace an entire class of workers with AI models capable of doing what they do. At the time, Huang said, "It is our job to create computing technology such that nobody has to program. And that the programming language is human, everybody in the world is now a programmer. This is the miracle of artificial intelligence." Now, Huang is warning against anyone suggesting that a particular type of career will ever go away. "This is one of the concerns that I have about the doomers describing the end of work and killing of jobs. If we discourage people from being software engineers, we're going to run out of software engineers," he said during an appearance on the Dwarkesh Podcast. He even pointed to an example of where this type of thinking has done real damage: "The same prediction happened ten years ago. Some of the doomers were telling people, 'Whatever you do, don't be a radiologist.' You might hear some of those videos still on the web saying radiology is going to be the first career to go and the world is not going to need any more radiologists. Guess what we're short of? Radiologists." Huang almost certainly wouldn't call himself a doomer, but what he was saying in 2025 doesn't feel fundamentally different than what he's warning against now. It seems, if nothing else, that Huang has caught onto a vibe that has been emanating out of those offices where workers are constantly being told their replacements are coming: fear. He acknowledged that there's a real risk of that resonating within the next generation, who are already not feeling great about their future prospects. "If we scare everybody out of radiology so nobody wants to be a radiologist because computer vision is completely free and no AI is going to do a worse job than a radiologist, we misunderstand the difference between a job and a task. The job of a radiologist is patient care. The task is to read a scan," he said. A similar shift has happened over in the c-suite at OpenAI. Back in 2025, Punchbowl News reported that Chris Lehane, OpenAI's vice president of global affairs, said it was important to "really be straight up" about "cohorts that potentially get displaced." Basically, Lehane was calling for transparency about whose careers were about to be in jeopardy due to the expansion of AI. Lehane's CEO, Sam Altman, has perhaps been among the most explicit in how he talked about the potential for job loss. Way, way back in 2015, when OpenAI was just a glint in his eye, Altman was telling people, "My job is to help people destroy jobs." In 2023, he told The Atlantic, "Jobs are definitely going to go away, full stop." When he said that, he even put it in the context that the more hopeful among his peers were lying to people: "A lot of people working on AI pretend that it's only going to be good, it's only going to be a supplement," he said before dropping the "jobs will be lost" hammer. Yet here we are in 2026, and the prognosis has changed. "I'm delighted to ⁠be wrong about this, I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened," he told Reuters last month. Lehane, meanwhile, is running a new message up the flagpole, chastizing the people who dared threaten the livelihoods of people in a very "we're all trying to find the guy who did this" fashion. "Some of the conversation out there is not necessarily responsible," he told the San Francisco Standard. "And when you put some of those thoughts and ideas out there, they do have consequences. ... This is really serious shit." He even went on to say that, "Our job at OpenAI and in the AI space -- and we need to do a much better job -- is to explain to people why ... this is going to be really good for them, for their families, and for society writ large." Losing popularity There seems to be a tell in that comment as to what is going on and why the message has changed so drastically. AI is not popular, and it is not popular because people are afraid that it will take their jobs. More than half of Americans are concerned AI will put someone in their household out of work, according to a recent poll conducted by Reuters and Ipsos. NBC found that just 26% of people view AI positively, with nearly double that holding a negative view of the technology. Unpopularity is one thing, and not necessarily enough to actually stop corporations from plowing forward. Labor unions remain weak nationally, and there is little by way of worker protection to prevent such a push. But citizens have found surprising success in fighting against the construction of data centers across the country, which potentially hinders the buildout these companies need in order to keep the cash flowing. As long as they are still in the growth and investment stage, they can keep burning through cash to put up these facilities with the promise, however improbable it may be, that they'll eventually create enough computing power to achieve artificial general intelligence. But the political roadblock of people pushing back against their big boxes of processors seems to be something of a signal that they are losing the PR war. Just relax, and stop fighting Hence, the shifting message is to stop fighting because this is going to be good for you. Amazon founder Jeff Bezos has tried his hand at this pitch in recent weeks, suggesting workers should "be so happy" about the introduction of AI because of how much easier it'll make their lives, claiming the technology will "elevate all of these people." Brad Smith, President of Microsoft, has gotten in on the act, too. In a blog post meant to respond to the resounding number of graduate classes that have booed the very mention of AI during commencement speeches, Smith tried his hand at guiding the incoming members of the working class to a new view of the technology. "Constant change has taught you how to adapt quickly. As AI reshapes how we work, you don't need to unlearn decades of habits the way some of us do. You are better equipped to move forward," he wrote. "Technology will change, but you can stand firmly and speak loudly for values that are timeless. Agency. Ambition. Dignity. All fulfilled through work and technology that gives us purpose." Within Smith's pitch is a bit of seemingly unintentional transparency into the thought process of corporate America. At one point, he writes in what certainly feels like a begrudging manner that the reaction from students makes it clear that "People will insist on having a say in deciding when and how AI is used." Now, it seems, the goal of these executives has become convincing people that they have been heard, that they are valued, and that they are anything but replaceable cogs who will get removed the moment it becomes cheaper to hand the task over to an AI agent. Meta's approach to informing its workers about how it is using AI might be the most instructive for others across all industries, as they are increasingly told AI tools are just here to make their lives easier. Earlier this year, it was revealed that the company was logging its employees' activity, recording how they do their work in order to train AI models on how to accomplish the same tasks. In a leaked audio, CEO Mark Zuckerberg reportedly said that Meta is using its employees for this because they are "really smart people." That's a nice compliment to give people who will soon be shown the door.

'I'm delighted to be wrong about this' -- Sam Altman says one of his biggest fears about AI hasn't come true

The OpenAI CEO now believes people may be harder to replace with AI than expected OpenAI CEO Sam Altman has done something few Silicon Valley bosses ever do, admit he is wrong. Speaking virtually at a Commonwealth Bank of Australia conference in Sydney in May, Altman confessed that one of his biggest concerns about AI simply has not played out the way he expected. For someone whose job often involves predicting the future, it was a surprisingly candid moment. "I'm delighted to be wrong about this. I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened," Altman said. "I now think I understand more about why it hasn't, and I'm obviously grateful, but that is an area where my intuitions were just off." Altman explained that OpenAI had been "roughly right" about many of the technological predictions it made when ChatGPT launched. AI has become more capable at an astonishing pace. What he appears to have misjudged was how those capabilities would translate into changes in everyday employment. Personal AI experiments Notably, Altman concluded he had been wrong after an experiment in which he let AI handle some of his own communications. He didn't need a labor market research report to see that it wasn't up to snuff. He used AI to answer Slack messages and emails, each labeled as coming from "Sam's AI" rather than from him directly. But Altman found himself pulling back from the experiment almost immediately. The reason had little to do with the quality of the responses. Rather, Altman simply didn't want to give up interacting with people to an AI model, no matter how efficient. "We really do care about our interactions with people and this thing, which is a huge amount of my time, is not something that I can imagine myself outsourcing to an AI anytime soon," Altman said. The experience appears to have shifted Altman's thinking about employment more broadly. Jobs often look simple when reduced to a list of tasks. In reality, many roles involve trust, relationships, judgment, and personal interactions that are difficult to capture in a spreadsheet. Human jobs None of this means Altman suddenly believes AI will leave the workforce untouched. OpenAI continues to release increasingly powerful models, and businesses continue searching for ways to use them more effectively. But the actual disruption of employment will be less catastrophic, according to Altman. Discussions about AI often treat jobs as collections of tasks that could be swapped out with the right AI prompt, but reality appears messier. Companies may automate parts of jobs long before they eliminate entire positions. "It really, in both positive and negative ways, updated me to thinking that the jobs picture is likely to be very different than we thought. I don't think we're going to have the kind of jobs apocalypse that some of the companies in our space advocate or talk about." That distinction matters because it helps explain why the labor market has not experienced the immediate shock that many observers expected. AI has certainly changed a lot of research and enterprise projects. But most organizations still need people to make decisions, manage relationships, and take responsibility when things go wrong. Altman's more positive view of AI on job prospects doesn't mean there's no problem with how the technology is being deployed. But people who might look to Altman for insight into AI might feel a little better, even if it's just him saying AI will have a muddled influence and not act as a straight assassin of careers. Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds.

AI absolutism is breaking our brains. The apocalyptic future we're being sold isn't inevitable

Nor is the dreamy promise that this tech will unlock boundless potential and productivity Everything we hear about artificial intelligence is conflicting, and hearing about it feels inescapable. AI is terrible. AI is wonderful. It will break the world. It will transform the future. It's essential to embrace it. It's a moral imperative to abstain from using it. Already, AI is projected to generate nearly unfathomable amounts of revenue. In the last quarter of 2025, it represented nearly 60% of the growth in the US economy. Already, pundits and economists wring their hands about what calamity will befall us if and when the AI bubble bursts. Since ChatGPT, the first of the large language models, was released in late 2022, more than half a million workers in the tech industry alone have lost their jobs. Any mention of AI tends to be accompanied by warnings that deeper jobs cuts across many more industries are coming for us all. Jensen Huang, CEO of chip giant Nvidia, said in 2025: "Every job will be affected, and immediately. It is unquestionable. You're not going to lose your job to an AI, but you're going to lose your job to someone who uses AI." In January, Anthropic CEO Dario Amodei predicted: "AI isn't a substitute for specific human jobs but rather a general labor substitute for humans." Increasingly, people young and old flock to a new gold rush in Silicon Valley to toil away on AI-fueled startups. Many of them are driven less by idealistic enthusiasm and more by the dread of missing a ticket for the last train to wealth - and getting stuck forever in the "permanent underclass" that, with any luck, they themselves will create. What all these divergently apocalyptic ideas hold in common is their AI absolutism - a way of seeing AI as a godlike force that will either hasten a golden age of productivity and innovation, or will doom humanity. It mirrors the political polarization of our era and even the zealotry found in religious fanaticism. This is by design. Contradictory as they may be, all these arguments and anxieties fit neatly into the overarching message of the people building this technology: AI's dominance is inevitable. Get on board or you will be left behind. The robber barons of our age stand to profit wildly from not only enthusiasm about their star product, but also, the terror of it. "If you want to justify this enormous valuation in your IPO, you need to point to the revenue stream that you're going to generate in the future," said Suresh Naidu, a professor at Columbia University's department of economics. "You just need to make it look like you have something that can eat all the work on the planet, so that an investor will think: 'Oh wow, I don't want to miss out on this thing.'" Naidu isn't refuting claims that AI will cut into jobs or upend certain industries. He called the technology "transformative" and said that he uses it every day in his work as a researcher and academic. It's just that when he zooms out and puts AI and all its attendant promises and warnings in historical context, he sees a lot of hype. There is no control group Anil Dash, the former CEO of the startup Glitch, who's been writing about tech for decades, is also unconvinced that the AI we're being sold will do all the things tech CEOs are predicting it will do. "Any technology that you invest like a trillion dollars into is going to be able to do a lot of things, good or bad. [AI is] a leap forward. I don't think we've ever had a machine learning system that can do as many things as this one does," he said. But "there's so much noise that it's hard to tell what the domains of applicability are." Coding is an exception, he said. It's easier to test an AI model's coding output because it will clearly work, or it won't. Many other applications for the tech are much more subjective and therefore less prone to immediate job replacement. That's why the tech industry has made the deepest job cuts so far - though, amid layoffs at companies such as Amazon, Meta and Block, reports from employees have emerged saying the AI productivity gains their bosses trumpet are overblown. Even the role AI is playing in those layoffs and reductions to entry-level positions isn't entirely clear. Martin Beraja, a professor at UC Berkeley Haas School of Business who studies technological innovation and business cycles, said recent studies that have drawn connections between the release of ChatGPT and a decline in entry-level software jobs are "problematic". There was "a buildup of jobs in [tech] coming out of the pandemic, and once ... consumption patterns moved away from online to the real world again, now we had too many people working in the industry that we didn't really need", Beraja said. Some of the biggest and most loudly pro-AI players in tech have arrived at similar conclusions as AI critics. Venture capitalist Marc Andreessen proclaimed in March that overstaffed companies are using AI as a "silver-bullet excuse" to clean house. In May, OpenAI CEO Sam Altman retreated on some of his prior claims of massive job replacement by AI, saying: "I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened." And if AI's worst-case scenario for tech jobs plays out - which would indeed be very bad for many people - that's still nowhere near the apocalyptic future of labor that many fear. "Is it, in fact, going to destroy all of the jobs?" Naidu asked. "I'm not convinced. Even take software. Software is only about 4 to 6% of GDP. So it's a lot, but it's not like the whole economy can be replaced by Claude Code." Convincing people that AI will replace human workers in droves is a clever marketing tactic. Not only does it stoke rabid investor speculation, but it distracts from a more realistic application of AI for the global workforce, stretching far beyond the borders of the tech industry: using AI to surveil and micromanage employees to squeeze yet more productivity out of them, all the while pressuring them to feel grateful that they have any kind of work at all. Gig workers, the people who pick you up in Ubers and deliver your food on platforms like DoorDash, have already been the guinea pigs for this kind of algorithmic management, and labor experts predict it will spread. It can feel like we're living in an experiment when it comes to the rise of AI. Naidu would like us to update that framing. "An experiment implies a control group of something that's not affected. There's no control group here," he said. Remember there are alternatives The version of AI that we're being sold doesn't have to be the version we buy. Nor does it need to be the story we believe in. This isn't an argument for an abstinence-only relationship with AI, something that has too much in common with evangelical Christianity's unrealistic stance on premarital sex. Anyone with common sense can see how those kinds of ascetic codes play out in reality. It's happening already with AI. "AI is just another technology Americans don't like but can't stop using," the Washington Post's Shira Ovide wrote earlier this year, referring to the polarized divide between polling that shows how much they distrust the tech and numbers of rapid user growth in the past year. Instead, this is an argument for moderation. Beraja, the UC Berkeley professor, said there's too much focus on AI as a job replacement technology. Outside a few industries like tech, he said studies show that the most effective ways for people and companies to use AI is not to replace workers, but to learn more, and learn faster. "Where I think we have to get to is, there can be alternatives," said Dash. "What we can imagine is, rather than the ChatGPT killer, a lot of different little AIs from little responsible players." A few are already quietly cropping up, harkening back to earlier and more optimistic days in the internet's history, and offering a glimpse of what could be possible if people took AI into their own hands. And for the industries and jobs that AI is upending, upheaval may open the way for a resurgence in worker power as white-collar workers begin to see the appeal of solidarity, whether with colleagues in their office or workers in the blue-collar world. After all, the Industrial Revolution, an earlier time of great technological transformation that strangely mirrors our current moment, was a key catalyst for the labor movement - even if its wins took time.