Anthropic suspends all access to Mythos model after US government bans foreign nationals use

AI company Anthropic has disabled customer access to its most capable systems after the US government ordered it to suspend all use by foreign nationals, Anthropic said in a statement Friday evening. The move is the latest in a series of adverse Trump administration actions targeting the company. The broad directive to Anthropic’s Mythos 5 and Fable 5 models is one of the furthest-reaching actions the government has taken in response to the advanced capabilities of an AI model. Anthropic said the US government gave it the directive, citing “national security” issues. The company said the government didn’t provide specific details about the national security concerns, though it believed the government had “become aware” of a method of “jailbreaking” Fable 5, or getting around its internal safety guardrails. “We reviewed a demonstration of this specific technique being used to identify a small number of previously known, minor vulnerabilities,” Anthropic said in its statement. “These vulnerabilities all appear relatively simple, and we have found that other publicly-available models are able to discover them as well without requiring a bypass.” Anthropic said it had instituted several safeguards for its newest models to “greatly reduce the likelihood” that they are “misused for tasks related to cybersecurity,” noting they’ve received complaints from users about those guardrails being too strict. Anthropic also noted it has worked with the US government to “red team” Fable’s safeguards and that no model is completely resistant to any jailbreak. Anthropic said that while they are complying with the directive and removing access to the models for everyone, “we disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people.” “If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers,” the company added. The restriction means that many foreign nationals working for Anthropic will not be able to touch those models. The Commerce Department, which issued the restriction, did not immediately respond to a request for comment. Axios reported the government’s directive would require Anthropic to obtain a license “for the export, re-export or domestic transfer of those Anthropic models.” Anthropic’s newest model, Mythos, has spooked the US government and Wall Street with its capabilities, which experts say can exploit cybersecurity vulnerabilities at an unprecedented pace. The model was seen as so capable, Anthropic initially limited its release to a group of key partners in order “to secure the world’s most critical software.” Anthropic released Fable 5 last week as a version of Mythos that is safe for general use. The model also helped spark the Trump administration’s recent executive order on AI, which asks companies to voluntarily share new models deemed to have advanced cyber capabilities with the government up to 30 days before providing access to other partners. One source with knowledge of early discussions of the executive order said the idea of banning foreign nationals from working on such models had been floated for that order, but the idea never made it into a draft. The government has had a complicated relationship with Anthropic. Earlier this year, the Trump administration blacklisted the company, declaring it a “supply chain risk” in military dealings over Anthropic’s insistence that the Pentagon include certain safety guardrails for the government’s use of AI in warfare. Anthropic sued the government over the designation as “unprecedented and unlawful” and notched at least one early win in the ongoing case. Despite President Donald Trump’s directive at the time of the designation for all of the federal government to cease working with Anthropic products, the White House has stayed in close touch with the company, and some parts of the federal government have found a workaround to continue accessing Anthropic’s models, especially after the release of Mythos. Anthropic was also deeply involved in helping draft the latest executive order, sources familiar with the situation told CNN, and its executives had been invited to the White House for a signing ceremony that was ultimately canceled at the last minute. By Hadas Gold

AI Is Wreaking Havoc at Starbucks and Pizza Hut. Social Media Is Having a Field Day

AI woes are coming for the food service industry, and social media can’t help but celebrate. This week, both Starbucks and Pizza Hut made headlines for negative news about their internal applications of artificial intelligence. At Starbucks, an inventory tool got the chop after making frequent counting mistakes, while at Pizza Hut, a delivery tool drove a franchisee to file a lawsuit. Social media users are saying the two stories may point to a larger trend—that for the first time in the AI era, more companies will pull away from AI than embrace it. Starbucks walks back an AI tool On Monday, Starbucks told employees it was retiring an inventory-counting tool powered by AI after the technology led to inaccurate counts and mislabeled products. “Starting today, Automated Counting will be retired,” said an internal company newsletter verified by Reuters. “Beverage components and milk will now be counted the same way you count other inventory categories in your coffeehouse.” In a statement to Fast Company, a spokesperson for Starbucks explained that the company’s choice to axe its Automated Counting tool is in line with its larger AI strategy, which is based on trial and error. “We test ideas in our coffeehouses, listen closely to partner feedback, and make changes to deliver a better, more consistent experience.” Starbucks’s move to ditch one AI tool doesn’t mean the company is forgoing the technology entirely. The company is still investing in internal AI applications, including an AI assistant for baristas called Green Dot Assist and an AI-powered order-sequencing system called Smart Queue. The brand is also experimenting with an integrated Starbucks app within ChatGPT. Pizza Hut’s delivery system backfires Where Starbucks’s choice to nix its AI tool came from the top down, the anti-AI sentiment at Pizza Hut started with a disgruntled franchisee. In a lawsuit filed on May 6, franchisee Chaac Pizza Northeast, which operates more than 100 Pizza Hut locations, alleged that the company forced it to adopt an AI tool called Dragontail, which inadvertently pushed average wait times from under 30 minutes to over 45 minutes in more than half of all orders. The complaint explained that the issue wasn’t with Dragontail itself, but with the information the tool provided to DoorDash drivers. Dragontail is meant to optimize food delivery by giving delivery drivers real-time updates on order preparations and timing. But according to the lawsuit, its implementation in 2024 caused “cascading operational breakdowns and customer dissatisfaction,” resulting in more than an estimated $100 million in lost business and enterprise value. Reportedly, once DoorDash drivers could see the real-time status of multiple orders through Dragontail, they would wait inside restaurants until multiple orders were ready, meaning some orders were being held for up to 15 minutes after they were ready for delivery. Because Chaac Pizza Northeast relies on DoorDash for all of its deliveries, the franchisee alleged that the forced change to its delivery model had a major impact on its sales. At its New York City locations, Chaac said its sales swung from positive 10.19% to negative 9.78% after implementing Dragontail. “With the intention to improve efficiency and service to the customer, Dragontail did the exact opposite,” the lawsuit stated. “It caused significant delays and pummeled consumer satisfaction.” Pizza Hut has not responded to Fast Company’s request for comment. Social media sees a trend With the stories from Starbucks and Pizza Hut breaking in quick succession, social media users are drawing connections between the two food service chains’ AI troubles. “Over the next 1-2 years we’re going to start hearing more reports about companies pulling back from AI than adopting AI, and markets aren’t ready,” one X user theorized. “The AI bubble might burst quicker than I thought,” echoed another. “You’re going to be hearing a lot more about forced AI integration and what a disaster it is for businesses and consumers,” a third user agreed. Other users pointed out that all of these problems could have been avoided if tasks hadn’t incorporated AI in the first place. “To err is human,” one person quipped, “but to really screw things up, you need a computer.” By Jude Cramer

Instagram’s AI Support Bot Made a Costly Mistake. It’s a Warning for Every Company

Sometime last weekend, hackers asked Meta’s AI support bot for access to someone else’s Instagram account. The bot said yes. The method, reported first by 404 Media, was almost insultingly straightforward: instruct the chatbot to add a new contact email to a victim’s account, confirm the change with a code sent to that email, then use it to reset the password. In some cases, all that was also needed was a VPN to spoof the victim’s location. Among the accounts taken over: the former White House page from Barack Obamaʼs presidency, cosmetic chain Sephora, and the chief master sergeant of the U.S. Space Force. While this hack was obviously a security flaw, most businesses should consider that this blunder also reveals a management error. The front door is now automated AI customer service is everywhere. According to MarketsandMarkets, the global AI customer service market was valued at $12.06 billion in 2024 and is projected to reach $47.82 billion by 2030, growing at a compound annual growth rate of around 25.8 percent. The economics are hard to argue with: AI scales cheaply, doesn’t require shift management, and doesn’t call in sick. AI has been shown to cut response times by 37 percent and reduce operational costs by 35 percent. Companies have moved quickly. Banks use it for dispute resolution, airlines use it for rebooking, telecoms route complaints through it, and e-commerce platforms use it to handle returns. Around 80 percent of companies are either currently using or planning to adopt AI-powered chatbots for customer service by 2025. Meta is not unusual for deploying AI support at scale. When Meta launched its AI support assistant in March, it was presented as a way to help users reset passwords, regain account access, and report problematic content. These are not trivial tasks. Account recovery involves identity, security, and trust—the kind of territory that, not long ago, companies deliberately kept behind a human review process because the consequences of getting it wrong were considered too serious to automate away. Meta has also reassigned thousands of employees into AI-related roles and used AI to automate risk assessments of updates, safety features, and content moderation—all of which are tasks that previously required human review. Therefore, the support bot exploit arrived in an organization that had been replacing human judgment with automated processes across multiple functions simultaneously. Authority without judgement The exploit worked not because the AI behaved unpredictably, but because the AI behaved more or less as it was designed. A user asked it to update a contact email, and it updated the contact email. The system executed a process it was authorized to execute, but in doing so, handed a hacker the keys. This distinction matters enormously for anyone responsible for deploying AI systems. Authority can be automated, but judgment is a different problem entirely. A junior employee in a similar situation might also make a bad call. Humans fail too, but organizations know how to supervise, train, audit, and escalate human decisions. Real people typically offer something an AI system can’t: the ability to notice that something feels off. In this case, hesitation at an unusual request, the instinct to double-check account history, and the recognition that a request arriving via VPN from an unfamiliar location (asking to change contact details on a high-profile account), warrants a pause that could have prevented disaster. Gartner forecasted that 20 to 30 percent of businesses will replace human customer service agents with AI by 2026. The efficiency case for doing so is well established, but what’s less well examined is the category of decisions that look like processes but aren’t. Account recovery looks like a process, but embedded within that process is a judgment call: Is this request legitimate? Humans often make that call poorly too. The difference is that when a human makes it poorly, the failure is legible. There’s accountability. There’s someone to fire. Victims of the Instagram account takeovers told 404 Media there was no way to escalate their problem and speak to an actual human. Historically, high-stakes support requests — account recovery, fraud disputes, identity verification — were escalated to human agents because the potential for harm was too high to leave it to a scripted response. The escalation path was a precaution for the edge cases that automated systems weren’t designed to handle. Research published in the Journal of Consumer Research found that customers evaluate service provided by bots less favorably than identical service provided by humans, partly because they perceive automation as the firm cutting costs at the customer’s expense. That perception hardens quickly when customers discover there’s no human available at all. The harder problem The lesson here isn’t that companies shouldn’t deploy AI in customer service, but the quality of the decisions being made around where AI gets authority and what happens when it exercises that authority incorrectly. The questions that matter aren’t technical; they’re structural. What decisions can AI make autonomously, and which ones require a human to sign off? What triggers an escalation, and who owns it? When the AI gets something wrong — not if, but when — can a customer reach someone who can fix it? Gartner concluded in mid-2025 that half of the organizations expecting to significantly reduce customer service headcount because of AI would abandon those plans by 2027, and that 95 percent of service leaders planned to retain human agents in a digital-first but not digital-only model. The companies that arrived at that conclusion before Meta’s incident are in a better position than the ones who’ll arrive at it after. The Instagram story will probably be remembered as an AI security vulnerability, and will likely get fixed as one. As of June 3, it reportedly still hadn’t been patched. As companies race to automate customer interactions in pursuit of lower costs and faster resolution times, they’re discovering that it’s far easier to reduce the cost of executing a decision than it is to replace the judgment behind it. With better training, retrieval-augmented verification, anomaly detection for suspicious requests, and well-designed human-in-the-loop escalation, AI systems may be able to take on increasingly sophisticated judgment-like tasks over time. But deciding which requests deserve more scrutiny than the process allows remains, stubbornly, a human problem for now. AI can execute authority at scale, but the question that the Instagram incident forces into the open is a simpler one: When it gets things wrong, who’s responsible? Right now, the answer at too many companies seems to be nobody. BY CONNOR JEWISS

How to AI-proof your job

Meta. Nike. Intuit. UPS. It seems every day a new company announces layoffs, often citing AI as the cause. AI is already reducing US monthly payroll growth by roughly 16,000 jobs in the past year, according to a recent Goldman Sachs report. Knowledge workers face the sharpest exposure, as their output is exactly what AI replicates best, at superhuman speed, around the clock. “The most valuable jobs, the ones that we tell people to go to school for – software engineer, finance professional, accountant, lawyer – a lot of these cognitive professions, those are the ones that are the most vulnerable… to AI automation,” David Shrier, professor of AI & Innovation at Imperial College London, told CNN. But humans will always be needed in some form – and there are steps you can take to increase the chances you’ll protect your own job. Audit your role Before you can future-proof your career, you need a clear-eyed view of what you do in your job. Think of jobs as a “collection of tasks we switch between, often many times a day,” Oded Nov, a professor of technology management at New York University, told CNN. Consider which of those tasks are the most repeatable, rule-based computer tasks, like processing expense reports, which takes raw data and converts it into a different form. The more predictable a function, the more vulnerable it is to automation. The CEO of Cloudflare wrote in an opinion piece for the Wall Street Journal that he recently laid off 20% of his work force, focusing on “measurers”: middle management and those who work on audits, operations, compliance, etc. “AI isn’t coming for builders or sellers, but it is coming for measurers,” he wrote. “Tireless, independent, efficient and available, AI systems can now measure an organization with a level of objective detail and precision that was previously impossible even for the best employees.” Some jobs, such as those in hospitality, healthcare and skilled trades, still need someone physically present to do much of the work. Robotics is at least a decade away from replacing those roles. Invest in skills that are structurally hard to automate After your self-audit, focus on skills that are not repeatable, predictable and rule based. In addition to physical duties, AI is not yet as good at handling tasks that require emotional and social awareness, such as “understanding organizational culture or group dynamics,” Nov said. AI tends to be recursive, rather than inventive or creative. “AI is bad at creativity, but it’s surprisingly good at elaborating on creative prompts,” Shrier said. “But you still need the human to come up with the idea and guide the AI to do something interesting.” Invest in those skills. If part of your job involves sales and convincing people to sign a contract, focus on the interpersonal skills that help you build trust with clients. Customers might go to AI to research, but they usually still want to deal with a human being when making big purchases. Embrace AI and learn how to make it work for you AI will soon become a pervasive part of our lives, just like the internet. Get familiar with the major AI systems – ask various AI chatbots about your job and how they can help make your work more efficient, then give their suggestions a try. Play around with new coding tools that help you create your own app and website without needing to write your own code. But chatbots aren’t what makes AI so useful in the workplace, it’s AI agents, or programs that run automatically, make decisions and take actions autonomously. You can learn how to make your own, and a chatbot can help. Try the prompt: “I want to learn how to make an AI agent. Walk me through the steps to create an agent that can do [specific task]”. “In some ways it’s never been a better time to be an entrepreneur, because if you can think of it, you can make it,” Shrier said. “There are people making robust enterprise-grade software that is built off of a prompt in plain English.” Humans can’t be entirely replaced Even roles that include a lot of AI-achievable tasks will still need humans in some way. AI has particularly affected the coding industry. But Anthropic employees still edit and review code even if they’re not writing it, CEO Dario Amodei said at the World Economic Forum in January. “In best-case scenarios, the more mundane tasks that are part of people’s jobs today will be handed over to AI, while the more interesting and rewarding tasks will be done by humans, probably with some support of AI,” Nov said. “It’s very likely, if history is a guide, that new jobs, consisting of new collections of tasks, will be created.” By Hadas Gold

AI ‘voice cloning’ scams are on the rise. Here’s how to protect yourself

A California mom says she was scammed out of thousands of dollars this month after receiving a call that sounded like her daughter in distress. She now suspects it was an artificial intelligence-generated hoax. She’s one of many who have been targeted by so-called “voice cloning” scams as AI tools allow anyone to create a convincing replica of someone’s voice with only a few seconds of real audio. Americans lost more than $893 million to AI-related scams last year, including voice cloning attacks along with AI-generated phishing emails, romance scams and other hoaxes, according to the Federal Bureau of Investigation. Scammers can mimic anyone from family members and friends to coworkers or professional services workers. Banks including the United Kingdom’s Starling and the Commonwealth Bank of Australia have warned customers to watch out for voice cloning scams. Experts say AI voice replicas have gotten so realistic that most people can no longer reliably distinguish them from real human voices. “For the everyday person, it is just not fair to expect them to be able to spot this stuff,” said Henry Ajder, an expert on AI-generated media who consults for governments and companies. “I struggle with it. Most people do.” How do AI voice scams work? Scammers can create an AI replica of someone’s voice using a short recording of their speech — often pulled from social media or an earlier scam call that was surreptitiously recorded. Social media can also provide a trove of information about family members and close friends who could be targeted. Fraudsters will typically make it sound like the loved one they’re mimicking is in distress, purportedly having been kidnapped or in jail. Then they’ll urgently demand money in exchange for their loved one’s release. “There was no time to think,” Gary Schildhorn, a Philadelphia attorney who was targeted by an AI voice scam mimicking his son, told CNN last year. “It was all, ‘I have to react to help my son. He’s in trouble.’” In some cases, the AI voice may be more than just a single recording. Sophisticated attackers could use text-to-speech tools or “voice skinning,” which manipulate a scammer’s voice so they sound like the person they’re imitating in real time. Those techniques facilitate back-and-forth conversations between the target and the AI clone voice, potentially making the scam more convincing, Ajder said. Hackers can also make it appear as if a call is coming from a known number through a tactic known as caller ID spoofing — so you can’t necessarily trust that a call that appears to be coming from your mom is indeed her. How to avoid falling victim to AI voice scams Strange pauses or vocal fluctuations were previously considered red flags that a caller’s voice might be AI-generated. But those signals may no longer be present now that AI has advanced. Instead of trying to determine whether a voice is authentic, look for other general scam warning signs, Hany Farid, a professor at UC Berkeley and chief science officer at GetReal Security, told CNN last year. Is the person on the other end giving a deadline or introducing a sense of urgency? Are they encouraging you not to tell anyone else what’s happening? Are they asking you to move large sums of money in unusual ways? Those are the types of questions experts say to keep in mind. Targets who receive these types of calls should try contacting their loved one through other means, such as via a text message, calling them on another person’s phone or reaching out to someone who would know where they are. Deborah Del Mastro, the California mom recently targeted, told ABC7 News that she called her daughter only after sending money to the scammers. Her daughter answered right away and was at work. Families or coworkers can also establish a precautionary “code word” that can be used to verify each other’s identity. It should be a word or phrase that only a small group of people know and isn’t discoverable online. “Ultimately, if you suspect that something might not be right, it is much better to have your mum or your brother or your friend laugh at you for thinking that they’re a robot,” Ajder said, “than it is to potentially be running to an ATM.” By Clare Duffy

Nobel Prize-Winner Demis Hassabis Says AI Job Cuts Are Dumb. Research Agrees

Across America, graduating college students are booing commencement speakers who mention AI, and communities are battling data centers. A backlash against AI is clearly underway. It’s not hard to see why. With their talk of a jobs apocalypse and general creepiness, plenty of AI CEOs these days don’t seem to realize they often sound like Hollywood supervillains. But there is one notable exception — Demis Hassabis. If you’re not already familiar with him, Hassabis is the head of Google-owned DeepMind. That seems like a position with supervillain potential. But he also won a Nobel Prize in chemistry for his work using AI to solve one of biology’s most vexing problems — predicting the shapes of proteins — and then releasing the solution to the world’s scientists for free. Rather than using AI to replace workers, cheat on everything, or flood the world with slop, Hassabis enthuses about its potential to help cure disease and engineer energy abundance. He’s soft-spoken and bespectacled rather than square-jawed and cape bedecked, and he positions himself as AI’s science-minded good guy. His latest public stance is likely to add to this reputation. As Wired recently reported, he called the recent mania for AI-fueled job cuts basically just dumb. Why AI-driven layoffs are dumb Speaking at Google’s I/O event, Hassabis explained that DeepMind doesn’t look at AI as a way to eliminate jobs and cut costs. Instead, he sees the technology as a way to help businesses dream bigger and do more. “Perhaps there is an ulterior motive for putting those messages out; raising money or whatever,” Hassabis said of other firms slashing headcount supposedly because of AI. “From my point of view, from DeepMind and Google’s point of view, if engineers are becoming three or four times more productive, then we just [want to] do three or four times more stuff.” Sure, companies can use potential productivity gains of AI to trim headcount and pad profits for the benefit of investors. But there’s no shortage of other ways they could deploy the resources freed up by handing many tasks over to the bots. “I have a million ideas, from lab drug discovery to game design,” Hassabis continued. “I’d love to have some free engineers to go and do those kinds of things.” Companies trimming tech talent may be suffering from “a lack of imagination—and a lack of understanding of what’s really going to happen,” he believes. Research shows that layoffs usually backfire Is Hassabis right that companies chasing a short-term boost through AI job cuts may come to regret the decision? We’ll all have to wait for a definitive answer to that question. But there are good reasons to think he might have a point. First, research shows that layoffs in general are often a stupid idea. Of course, they save money. Sometimes they’re genuinely unavoidable (though that mostly doesn’t apply to the mostly super profitable firms doing them now). But multiple studies show that a short-term bump in performance is usually followed by a longer-term decline. Stanford’s Jeffrey Pfeffer summed up the research this way: “Layoffs often do not cut costs. … Layoffs often do not increase stock prices, in part because layoffs can signal that a company is having difficulty. Layoffs do not increase productivity. Layoffs do not solve what is often the underlying problem, which is often an ineffective strategy, a loss of market share, or too little revenue. Layoffs are basically a bad decision.” That’s not even taking into account the human cost to workers. Other research has found that being laid off increases a person’s risk of suicide by two and a half times and increases mortality by 15 to 20 percent over the following 20 years. So why do layoffs? As Hassabis suggests, some firms may be peakcocking for Wall Street. Others may genuinely need to free up funds for a strategic shift. But mostly, Pfeffer insists, “layoffs are the result of imitative behavior. These companies … are doing it because other companies are doing it.” An AI-specific reason layoffs are dumb That’s the general case against layoffs, and it’s pretty strong. But there is another argument against AI-driven layoffs specifically. On the MIT Sloan Management Review website, consultant and author Andrew Winston points out that when firms use AI as an excuse to trim headcount, they also diminish their pipeline of talent. They may save a few dollars. They may also find themselves short of skills and talent when they need them in the future. You can ignore this problem, but it might come back to bite you. As the case of climate change proves, asking CEOs to forego a quick performance bump for longevity and resilience later may be borderline hopeless. Winston continues to point out this inconvenient truth about AI anyway. “I’m asking companies to accept potential (short-term) competitive disadvantages on the basis of uncertain future benefits and collective responsibility. That’s a hard sell,” he acknowledges. “But I have a sneaking suspicion that we will look back at early 2026 and kind of wish we had just stopped.” Don’t get caught up in AI layoff FOMO All of which is a warning to other bosses looking on at tech company layoffs with FOMO. When some of the biggest names in business are all slashing jobs, you might feel like you’re missing a trick if you don’t follow suit. But both Nobel-winner Demis Hassabis and a whole bunch of research suggest leaders should take a breath and really consider whether they want to get swept up in AI job-cut mania. Cutting costs has obvious benefits for your bottom line. But what profit-making possibilities will you not pursue because of those job losses? How will the departure of their colleagues affect the state of mind and productivity of those left behind? Will you likely regret the move later when you find your team is burned out and your bench of experienced talent is incredibly thin? Before you cut jobs, take a minute and consider whether Hassabis is right. Doing more things because of AI could be a smarter choice than employing fewer people. EXPERT OPINION BY JESSICA STILLMAN, CONTRIBUTOR, @ENTRYLEVELREBEL

AI May Replace 80 Percent of Skills. This Last 20 Percent Will Make You Irreplaceable

I work in front of a screen. And I’ve been thinking about how AI will change my work. What does it even mean for my future? It’s completely normal to wonder about this. Most people are convinced artificial intelligence is a threat to their careers. But what they are forgetting is the human value they bring to their work. Aaron Levie, CEO of the enterprise cloud company Box, recently pointed out that when people watch AI at work, they are most likely seeing it take over the first 80 percent of a task—the heavy lifting of repetitive processing. The last 20 percent is where you come in. Your domain expertise, judgment, and relationships. That is what makes you irreplaceable. AI can finally give you the space to add human value at work. “The extra 20 percent, it turns out, is all the value creation of that profession. All the expertise and domain knowledge is in that last 20 percent, not the text that got generated,” Levie said in an interview with Casey Newton of Platformer, the online publication about tech and democracy. I couldn’t agree more. Your judgment is valuable Take the work of a lawyer. Junior associates spend most of their week reading precedents, looking for case connections, and summarizing legal statements. That’s the 80 percent of the work. The long, tedious, trainable, reproducible task. No client hires a lawyer just for that. They expect them to make a better and more persuasive case for them to win. To convince the judge. To save the dying deal. The 20 percent only you can do. The practical human value. AI work feels like completion, but it’s not. Not even close. It’s good at execution, but the meaning and context are all up to you. The career anxiety you feel about AI is normal, but it may be misdirected. When people say “AI is taking my job,” they usually mean it’s taking their tasks. Writing code, analyzing long documents, and doing the research. The first pass. And yes, super-intelligent machines are coming for those. If you built your professional identity entirely by executing tasks, that’s hard reckoning. The good news is, your knowledge from doing and experience is still relevant. All of that makes your judgment valuable. AI cannot replicate that. Domain expertise under pressure must count for something. A cybersecurity engineer knows exactly what steps to take when an attack is live. Making that call in real time with incomplete information changes their approach. Data doesn’t always give a clear answer. You have to decide anyway. Who bears that decision? Not the AI. It can notice the patterns. But it’s the engineer who must come up with a specific solution to solve the problem. Most companies are limited by execution capacity. They can’t pursue every good idea because they don’t have the people to execute them all. When AI takes care of the execution, the constraint becomes the quality of the ideas. The clarity of human judgment. And the client relationships they can’t afford to lose. If you still want to hold onto the 80 percent, you’re running in the wrong direction. You can’t compete with AI on speed. Focus on honing your quality of judgment. The value and usefulness only a human perspective can fill. Your clients don’t buy your services just for the deliverables; they buy the peace of mind too. And they also like to work with people with a better reputation. Trust is not a digital asset. In the future of work, the world will reward the 20 percent more. The calculator case When calculators became universal, they didn’t make mathematicians obsolete. They just took over repetitive math, which freed mathematicians to spend more time on quality and better mathematical thinking. The profession evolved upward. The entry-level work disappeared. The high-level work expanded. AI is doing the same thing to knowledge work. At scale. Only it’s happening everywhere, in every field, all at once. Build the kind of expertise that requires better judgment. And relationship capital that compounds over time. Develop your specific point of view at work. What AI can’t do is replace your original perspective earned through engagement with practical problems over time. Your distinct angle on your field, built from specific experience, failures, and observations is what matters. It’s the experience that makes your presence valuable in the room. The threat from AI is understandable. The pace of change is insane. Some jobs and skills are becoming less valuable. Don’t stay terrified. Fear takes away your ability to think clearly. It makes us hold onto the routine tasks we feel safe doing. But routine tasks are exactly what the machines want. If you’ve been doing meaningful work for any serious amount of time, you’ve accumulated things AI cannot access. AI is taking the parts of your job you probably didn’t love that much anyway. Even that requires your input. If you feed AI the wrong ideas, it will give you a brilliant, highly optimized wrong answer. The rarest skill right now is the ability to diagnose the actual problem before rushing to fix it. You are still needed for work that requires your specific experience. Don’t underestimate what you’ve already built. You have what it takes to survive AI. By Thomas Oppong FAST COMPANY

Apple’s Siri Update Could Include a Major AI Privacy Twist

More rumors about Siri’s big makeover are leaking ahead of Apple’s annual developer conference—and one big change could have a lot to do with data privacy and security. Apple is expected to launch a standalone app for its embattled AI assistant, Siri, which will operate as a chatbot-like interface, similar to Anthropic’s Claude or OpenAI’s ChatGPT. Users are expected to be able to type or speak requests. Although the app will be able to store conversations to mine for contextual information for future requests, Apple is expected to differentiate itself from its competitors by allowing users to auto-delete their conversations, according to a report from Bloomberg. This is much like the way Apple allows users to auto-delete their text messages, which works so well, Bloomberg notes, that it has invited complaints over government officials using the feature to delete their messaging histories. Apple also plans to establish stricter guidelines around what information does hang around in the system and how long it can be kept. Competitors typically allow users to toggle on temporary incognito modes that prevent conversations from being used to train AI models. It’s worth noting that Apple has brought in Google’s Gemini and cloud infrastructure to keep the Siri update functioning and on schedule—after costly delays. Apple first introduced its Apple Intelligence as a mix of on-device and cloud-based computing that it billed as similar to iPhone security but in the cloud. Called Private Cloud Compute, it was expected to operate on Apple’s own servers and chips. Bloomberg reports that Apple will still call its system “Private Cloud Compute,” but the mechanics of how it might operate aren’t clear, given the Google integration. The two-year delay around the roll out of an updated Siri has proved to be a stain on Apple’s recent track record, as well as an expense, because of a recent $250 million settlement on a class-action suit that alleged false advertising. But if Apple is successful in relaunching Siri with an emphasis on privacy, it could justify to consumers the longer wait time, especially as concerns gather about what people are sacrificing in the name of cutting-edge AI features. Apple’s Worldwide Developers Conference will kick off on June 8. The tech giant is expected to announce iOS 27, as well as software for Mac computers and iPads. And the graphic for the conference, featuring glowing lettering, hints at a new look and functionality for Siri. BY CHLOE AIELLO @CHLOBO_ILO

They Won a Prestigious Writing Prize. Then These Key Giveaways Sparked Allegations of AI

A London-based literary competition is facing major scrutiny after three of five winners have been accused of using AI—partly or wholly—to write their prize-winning stories. The 2026 Commonwealth Short Story Prize selected one winner each from five regions that span Africa, Asia, Canada and Europe, the Caribbean, and the Pacific. Following publication of the winning entries in literary magazine Granta, online sleuths called foul. The Caribbean regional winner, Jamir Nazir, was praised for the “lyrical precision and haunting atmosphere” of his short story “The Serpent in the Grove,” as well as “the confidence and restraint of its voice,” according to a post on social media platform X by Commonwealth Foundation Creatives. But internet denizens allege that the very same voice that won the prize may not be human at all. Nabeel S. Qureshi, an AI marketing entrepreneur and a former visiting scholar at the Mercatus Center at George Mason University, flagged certain signs he said were AI tells such as “‘Not X, not Y, but Z’ sentences,” as well as the use of the word “‘hum,'” in a post on X. He concluded by writing: “A major milestone for AI, at any rate …” Following the allegations, Wired ran the text of “The Serpent in the Grove” through AI detection tool Pangram, which the publication notes has consistently outperformed other similar tools. It determined that the text was 100 percent AI-generated. It’s worth noting that no AI-detection tools are totally accurate. “The Serpent in the Grove” isn’t the only story under scrutiny. Pangram determined that “The Bastion’s Shadow” by John Edward DeMicoli, the winner from Malta, was also fully AI-generated and that “Mehendi Nights” by Sharon Aruparayil, the winner from India, was partially written using AI. Holly Ann Miller’s “Second Skin,” and Lisa-Anne Julien’s “Me and Ma’am,” however, were ruled “fully human-written” by Pangram, according to Wired. The regional winners were chosen from 7,806 entries, which the Commonwealth Foundation noted on its site is the “second highest number in the Prize’s history.” A final winner from among the five will be announced on June 30. Following the allegations, the Commonwealth Foundation released a statement on its website, acknowledging the challenges generative AI poses to literary and creative work. The statement also noted that the foundation’s judging process is robust, but judges do not currently use AI checkers in any stage. “We are aware of allegations and discussion regarding generative AI and our Short Story Prize. We take these claims seriously and are committed to responding to them with care and transparency,” the statement reads. “When they submit stories to the Prize, writers accept our entry rules and guidelines. These include confirming that their submission is their own original work. All shortlisted writers have personally stated that no AI was used and, upon further consultation, the Foundation has confirmed this.” The foundation also noted that until a reliable tool emerges with which the organization can screen unpublished work for AI, the prize competition “must operate on the principle of trust.” As always, Redditers had much to say about the subject, some assuming AI guilt, others questioning the accuracy of the AI checking tools, and many picking at the quality of the stories more broadly. “This…doesn’t surprise me given the state of contemporary literary prose. It honestly just reads like bog-standard ‘MFA voice,’” one Redditor wrote of Nazir’s story. A recent report from digital marketing agency Graphite found that since the debut of ChatGPT in 2022, there has been a meteoric rise of AI-generated content on the internet. The number of articles written by AI now equals that of human-written content, although the overall share seems to have plateaued. Axios reported at the time that the quality of AI-generated writing has meaningfully improved, not to mention that it can be difficult to determine what constitutes AI writing. Whereas some content is mostly or entirely AI-generated, some writers use AI tools throughout the process of drafting and editing. BY CHLOE AIELLO @CHLOBO_ILO

AI is changing the internet forever. Here’s how

There’s a simple reason Google is making sweeping changes to its iconic, decades-old search engine: users are making complicated requests. “People are asking much longer and harder questions that no longer have a clear response anywhere on the internet,” said Robby Stein, vice president of product for Google Search. Stein spoke to CNN about a new feature that lets Google generate custom visuals, interactive graphics and even mini-apps running on Google’s search page in response to queries by piecing together sources from across the web. It’s one of many updates the internet giant announced at its annual conference this week. The most valuable real estate on the internet is evolving to reflect the new ways people find information online, the latest example of how artificial intelligence is changing the internet across search, social media, online shopping and more. People are starting to use longer, more specific search terms instead of succinct generic keywords, according to Google, and are increasingly beginning their searches in apps like ChatGPT, experts say. Fake, AI-generated influencers are causing a stir on social media. And people are increasingly using AI to compare and buy products. It’s getting impossible to avoid using the internet without somehow encountering AI, despite growing anxiety about the tech and its impact on jobs, safety and the environment. “After a while, it just becomes part of the way you live,” said Joseph Turow, a University of Pennsylvania media professor who will soon be releasing a book about AI’s impact on internet advertising. ChatGPT ‘trained’ people to search differently Google says its search box is getting its biggest upgrade in 25 years. The new search field expands to fit more text and makes it easier to add other media to a search — like photos, files and Chrome browser tabs. The goal is to shrink the number of steps for a user to complete a search, according to Stein. That includes tasks like performing a search based on a photo or switching to Google’s AI Mode before asking a follow-up question. Searches that involve questions based on snapping a photo or circling something on a phone screen are growing 60%, year-over-year, he said. Searches in AI Mode, or the version of Google tailored for back-and-forth interactions, have more than doubled every quarter since they launched a year ago, and AI Mode queries are triple the length of a regular search on average. Data from SEO and marketing firm Semrush indicates some people are starting to search Google the way they type to ChatGPT. Searches containing 11 words or more increased from 3.27% to 5.37%, and conversational queries jumped from 5% to 20%, while keyword-style searches decreased. Yet the median query still contains just three words, suggesting that most people still search the old-fashioned way. Robert Langenback, president of SEO marketing agency Eight Oh Two Marketing, said he’s observed people typing in more searches that range from three to five or five to 10 words instead of two to three words. That started before ChatGPT’s arrival in late 2022, although it’s ramped up significantly since then. “(AI has) really almost trained people how to search differently,” he said. People generally use a mix of AI apps like ChatGPT and Google. More than 20% of ChatGPT referral traffic goes to Google, Semrush found after analyzing 1 billion lines of US clickstream data, or “trails” of user activity across the web. Google is typically used for direct questions or transactions, while ChatGPT is used for summarizing information, making comparisons and drafting materials, Semrush said in an email to CNN. “There’s a lot of just, ‘I’m trying to find something and help me get to it right away,’ that is the bulk of the queries that have gone into Google over time,” said Leigh McKenzie, director of organic visibility at Semrush. The rise of AI influencers AI’s reach extends far beyond search. Take Aitana Lopez’s Instagram profile. Online she looks like any other social media influencer, photos showing her posing at glitzy events, hitting the gym and sharing beauty tips to nearly 400,000 followers. But she’s not real. Lopez is one of the most prominent AI-generated characters to rise to internet stardom, along with Lil’ Miquela, Lu do Magalu and Granny Spills. Nearly 80% of marketers have increased spending on creator content that uses generative AI in the last 12 months, according to social agency Billion Dollar Boy. There are even awards celebrating the best AI-generated internet personalities. AI personalities are appealing to brands because they’re typically cheaper than high-profile human influencers and can morph to fit specific campaigns, said Turow. Tech giants want to make AI an even bigger part of social media. Meta is integrating its Muse Spark model into apps like WhatsApp, Instagram and Facebook and is testing side chats with its AI assistant in group conversations. On Tuesday, Google announced Gemini Omni, a new AI model that people can use to generate realistic avatars of themselves. The race to own online shopping Traffic to US retail sites from AI services grew 393% year-over-year in the first three months of 2026, according to Adobe, with Meta, Amazon, Google and OpenAI all introducing AI shopping tools. Google this week introduced a new “universal” shopping cart that allows users to add items from different retailers across the web. Amazon recently folded its Rufus shopping assistant into a new tool called Alexa for Shopping, which incorporates the AI helper into the online retailer’s search bar so shoppers can ask it to compare products and pricing history, among other things. But even as AI directly answers shoppers’ questions at the top of Google, Stein says there’s still a need for quality websites created and maintained by humans. Google says it still send billions of clicks to websites every day, although Pew Research data last year found that Google users are less likely to click links when viewing an AI summarized answer. Langenback says that while his clients are seeing less traffic, the traffic they are getting is leading to higher engagement — completing a purchase, booking an appointment or requesting a quote. “You just have to be ready to adapt, because (search) could look a lot different six months or a year from now,” he said. By Lisa Eadicicco

Google is making its biggest change to the search bar in years

To get ahead in the new internet age, Google wants to help you google less. The company on Tuesday revealed a flurry of AI-powered features for its search engine, AI assistant Gemini and other services. It’s part of Google’s latest effort to revamp its decades-old business model to fit the era of artificial intelligence. Among those updates is a new version of the search bar that can crawl the web on a user’s behalf and a new mode in Gemini that can work autonomously over periods of time. The changes bring Google’s search engine closer to the likes of its biggest competitors today: Anthropic and OpenAI, whose sophisticated AI models have taken over some of the duties of search tools and web browsers. Revamped search Google for years has been moving away from delivering a list of blue links in response to search queries. But the refreshed search engine, which runs on the company’s new Gemini 3.5 Flash model, represents what may be its biggest shift yet toward AI and away from traditional search. The new search field expands to accommodate longer queries that are more conversational, aligning with the way one might type or talk into Gemini or ChatGPT. Users will be able to create “agents” in Google’s search engine that can track or research topics on their own. Google says it’s useful for tasks that require tracking and monitoring announcements and listings over time, like apartment hunting or new apparel releases. One can, for example, enter a query like “Keep me updated when any of my favorite athletes announce sneaker collabs or signature drops” to prompt Google to monitor announcements from notable athletes and brands, the company cited as an example in a press release. Google will also now generate custom visuals and mini apps in response to certain requests, such as creating a fitness tracker that incorporates a person’s location, weather data and apps connected to their Google account. A new Spark Since it launched the AI-powered Gemini, Google has struggled to differentiate the assistant from its main search engine. Spark, a new mode within Gemini that can work on tasks in the background, is its latest attempt to change that. Spark will be able to work on recurring long-term tasks like monitoring credit card statements and email inboxes for important updates and creating summaries or to-do lists. It can also reference content across certain apps, like compiling notes from Google Docs, Gmail and Slides, and the company says more third-party apps will be supported in the future. The company is also adding Spark to the Gemini app on Mac computers so that it can work with local files, and users will be able to monitor what their agent from their phones through a new feature called Android Halo. The agent will stay active even when the person’s laptop is closed or their phone is locked, Google says. The focus on autonomous features seems like a direct response to OpenClaw, the buzzy AI agent that made waves in Silicon Valley earlier this year for its ability to run programs and commands without constant prompting from the user. Building AGI Google has been pursuing AI agents for years, although use cases have mostly focused on specific tasks like shopping or email management and haven’t taken off with consumers broadly. That’s largely because the technology simply hasn’t been reliable enough. “I think there’s this uncanny valley where the models aren’t yet good enough, so you can’t trust them fully, and so you aren’t really sure what you can and cannot do,” Tulsee Doshi, senior director of product management at Google DeepMind, told CNN. Google hopes the updates will bring it closer to its big-picture goal of developing artificial general intelligence: a theoretical stage of AI in which the technology becomes as intelligent as a human at broad range of subjects. OpenAI, Meta and others are racing to be the first to get there. But AI will have to get better at updating its own intelligence before AGI is possible, said Koray Kavukcuoglu, chief technology officer at Google’s DeepMind AI lab and the company’s chief AI architect. “Right now, our models (have) some sort of capability in doing that, but you can imagine that they’re a little bit static in time,” he told CNN ahead of Google’s conference to announce the updates. DeepMind DeepMind is at the center of the company’s AI strategy and has become one of its biggest assets in the AI race. It’s Google’s “secret weapon in the AI wars” according to Dave McCarthy, an analyst covering cloud and infrastructure services for market research firm The International Data Corporation. Most tech companies don’t have massive consumer reach and direct access to a research lab and cloud systems. “Google is the only company that I can think of that actually has a play in every one of those areas,” McCarthy said. Yet Anthropic and OpenAI are largely perceived as being ahead of Google in AI business products; Anthropic has been releasing new models and AI agents for coding, finance and other office work at a rapid clip this year. Anthropic accounted for 34.4% of paid AI business subscriptions in the US in April while OpenAI accounted for 32.3% and Google’s share was just 4.5%. That’s according to finance platform Ramp, which analyzed contract and transaction data with AI companies from more than 50,000 American businesses. AI is also causing concerns over the future of jobs, safety and the impact of data center construction on local communities and the environment. Half of American adults say the increased use of AI in everyday life makes them feel more concerned than excited, according to Pew Research. But Google, like many companies, is staking its future on the technology. Gemini now has more than 900 million active users, and the company expects to spend about $180 to $190 billion this year on expenses related to AI infrastructure and chips, Alphabet CEO Sundar Pichai said in a press briefing ahead of the conference. And the technology will undoubtedly continue to move quicky. Varun Mohan, a director at Google DeepMind who works on Google’s Antigravity AI coding product, said they ship a new release “close to every day” for internal developers. “We’re open to the fact that we are going to need to make changes very quickly, because otherwise we are going to have a product that is old for our users,” he said. “And we’ll be doing our users a disservice if we just hold on to our ideals of what the product is today.” By Lisa Eadicicco

AI Tools Are Rewriting Business Security, and Not in a Good Way

AI is completely rewriting the script on how founders run their businesses. As founders implement more AI tools into their workflows, they need to understand the security of their AI software supply chain. Only recently, deployment platform Vercel suffered a massive security breach as the result of an employee connecting a third-party AI tool to their corporate Google account. Revolutionizing your business operations isn’t going to do much good if sensitive data is compromised. Before you roll out AI-powered tools, you must consider how they affect the entire software supply chain. What are the risks of an under-managed AI software supply chain? Currently, enterprise enthusiasm for AI adoption seems to be outpacing companies’ ability to enact meaningful security measures. According to a report by cloud and AI security solutions provider Wiz, while 87% of security professionals are using some type of AI service, only 13% have an AI-specific posture management security strategy. Twenty percent aren’t implementing any type of AI security strategy. Another 25% admit they don’t know which AI services are currently being used in their organization. The lack of information and oversight creates major challenges for founders. Reports have found that as many as 80% of workers use unvetted and unapproved AI tools on the job. This isn’t just among lower-level employees. Senior managers and executives often have even higher rates of unapproved AI usage. The problem? Unvetted AI tools often use open-source components which can house major security flaws. The flow of information to and from micro-services, LLMs and database servers can be difficult to track, with the potential for serious connections and permissions vulnerabilities. The Vercel breach exposed a huge amount of database credentials, API keys and third-party integrations. This happened simply because an AI tool was given permission to read software environment variables. In some cases, cyberattackers, who insert false or misleading information into the training data, intentionally “poison” public machine learning models. This can make the AI malfunction in ways that trigger it to provide wrong answers, leak sensitive information or behave in a biased way, even when the model seems to be functioning normally. As agentic AI becomes more widely used, the risks grow exponentially. Agentic AI’s capabilities to carry out complex series of tasks without oversight can be a boon for time-strapped founders. It also allows AI agents to be used for increasingly sophisticated and devastating attacks if they are compromised. Minimize the risks, and maximize the results. For founders, the same risks that exist from a “standard” cyberattack also exist within the AI software supply chain, but at scale — potential regulatory, legal and financial accountability, significant downtime, and lost trust. All of those become even greater risks when founders don’t do their due diligence on their entire AI software supply chain. So how do you minimize the risks for your organization? Start by comprehensively vetting tools your organization uses. Even basic steps such as reviewing terms of use and understanding how an AI tool may use data you feed into its system can help reduce risk. For businesses operating in privacy-focused industries, tools should also meet all relevant regulations. You should also carefully vet the developers behind these AI tools. Ask yourself: Has your AI developers regularly updated AI tools, especially for security? Does the developer provide testing and validation results? Are they transparent with how your data is used or stored? What is their reputation like? Even if a given AI system is deemed safe unto itself, it’s important to map out the downstream connections it has with your apps and servers. This ensures that you are managing all relevant identities and workflows safely. The best AI supply chain security strategies take all of these components into account. Securing AI workflows and access When you integrate AI into your stack, you should also adopt many of the same security practices you use with human employees. Zero trust governance, with strict access and authentication controls, can ensure that AI tools only access the information they need to perform their critical functions. Finally, you need to establish clear AI policies and make sure your entire team follows them, on the user’s end as well as the developer’s end. The 2025 Verizon Data Breach Investigations Report found that roughly 60% of breaches had a human element, usually employee error. Ongoing training regarding safe AI use will hopefully keep your team from using unauthorized AI tools that could compromise your systems. Do you know where your AI comes from? As AI adoption accelerates, ensuring it doesn’t compromise your security becomes an increasingly high-stakes game for founders. It’s great to unlock exciting automations and boost productivity. But you need to reduce risk wherever possible. A proactive and informed approach to your AI software supply chain will help you avoid becoming another cautionary tale. EXPERT OPINION BY HEATHER WILDE RENZE @HEATHRIEL

He Spent 18 Years as a Software Engineer. AI Replaced Him in Weeks—and Exposed the Reskilling Myth

For nearly two decades, David was the guy you called when the system crashed at 3 a.m. With 18 years of experience as a senior software engineer, he had seen the industry move from physical servers to the cloud, surviving bubble bursts and economic downturns. He assumed his deep expertise was his ultimate insurance policy. Then the email arrived. It was the standard “restructuring” notice that has become the soundtrack of the modern tech sector. But this time, the chairs weren’t just being rearranged. David’s entire department was being dismantled to make room for a new team of AI specialists. David wasn’t worried at first. He had spent his career learning new languages and frameworks. He bought the books, took the online courses, and prepared to “reskill” into the AI-driven future that every LinkedIn influencer and CEO was shouting about. The reality, however, was a cold shower. After applying to over 100 jobs, the feedback was consistently the same. Hiring managers told him his skills were obsolete. The very experience he viewed as an asset was now being treated as a liability, a relic of a pre-generative era. Today, David doesn’t spend his nights debugging code. He spends them working the night shift as a waiter at McDonald’s. His story is not an outlier. It exposes the reskilling lie. The myth of the easy pivot We have been told a comforting story about the AI revolution. The narrative suggests that while some jobs will disappear, a vast ocean of new roles will open up for those willing to learn. It sounds logical in a keynote presentation, but it falls apart on the ground. Midcareer professionals are being sold a bill of goods. The idea that a 45-year-old engineer with a mortgage and family can simply take a six-week boot camp and compete with 22-year-old AI natives is a fantasy. It ignores the structural ageism and economic realities of the hiring market. Companies aren’t looking for veterans who have “reskilled.” They are looking for specialists who have lived and breathed neural networks for their entire academic lives. The “pivot” is more of a leap across a widening chasm, and for many, the landing isn’t there. Part of the problem is they haven’t yet grasped the disruption confidence cycle that takes place every time a new technology comes along and changes the game. When experience becomes a liability In the traditional business world, 18 years of experience commands a premium. It represents depth of knowledge and a history of successful projects. In the AI-first world, that same history is often viewed as “legacy baggage.” Hiring managers are increasingly biased toward candidates who don’t have “pre-AI” habits. They want people who think in prompts, not in procedural logic. This creates a trap for midcareer professionals who find themselves overqualified for entry-level AI roles but “underskilled” for senior ones. The result is a talent graveyard. Brilliant minds are being discarded not because they can’t learn, but because the corporate machine doesn’t want to pay for the transition time. It is cheaper to hire a specialist than to wait for a veteran to adapt. The leadership failure This is not just a technology problem. It is a leadership failure. Executives are prioritizing short-term AI integration over long-term talent retention. By replacing veteran staff with specialized newcomers, they are hollowing out the institutional memory of their organizations. When a senior engineer leaves, they take more than just their coding skills. They take the knowledge of why certain decisions were made five years ago. They take the understanding of the client’s deep-seated needs. AI cannot replicate that context yet. Leaders who buy into the replace-and-reskill narrative are often surprised by the problems it creates. The new AI systems might be faster, but the loss of human oversight leads to hallucinations that can cost millions. Replacing a seasoned pro with a prompt engineer is a gamble that rarely pays off the way the spreadsheets predict. Seeking alternative paths For those caught in this transition, the traditional job market can feel like a closed door. This is why many are looking toward independent tools and solo ventures to reclaim their agency. Instead of waiting for a hiring manager to validate their AI skills, they are building their own workflows. Some have turned to platforms that simplify complex industries such as travel planning, financial management, legal paperwork, real estate, health care navigation, online education, and small-business operations. It allows them to leverage their organizational skills without needing a corporate badge to prove their worth. The goal for many is no longer about finding a new desk in a glass building. It is about creating a career moat that AI cannot easily bridge. This requires a move from being a worker to being a builder, regardless of the industry. The hidden cost of displacement The human cost of this displacement is staggering. When a senior professional is forced into low-wage service work, it isn’t just a loss of income. It is a loss of identity and social utility. The psychological toll of being told you are “obsolete” after two decades of high-level performance is profound. This creates a ripple effect throughout the economy. Families lose their stability, and the community loses the tax base of high-earning professionals. The “reskilling” narrative acts as a convenient shield for companies to avoid the moral and economic responsibility of their hiring decisions. Redefining the promise If the reskilling lie is to be corrected, the conversation must change. We need to stop telling people that a pivot is easy and start talking about how to protect and adapt existing expertise. We need policies that incentivize companies to retain and transition their veteran staff. True reskilling requires time, investment, and a willingness to value experience. It isn’t a weekend workshop. It is a collaborative process between the individual and the organization. Without that partnership, the “pivot” will continue to be a cliff for many. For the Davids of the world, the McDonald’s shift isn’t a failure of effort. It is a failure of a system that promised a ladder and then pulled it away once he reached the middle. The AI era doesn’t have to be a zero-sum game between veterans and newcomers. But as long as we pretend that “learning to code” or “learning to prompt” is a magic bullet for a 45-year-old, we are setting millions up for disappointment. Leaders must be honest about the limitations of reskilling. They must acknowledge that experience still matters, even in an automated world. Until that happens, the night shift at McDonald’s will continue to be the unintended destination for some of our brightest minds. EXPERT OPINION BY JOEL COMM, AUTHOR AND SPEAKER @JOELCOMM

These 40 Jobs May Be Replaced by AI. These 40 Probably Won’t

A new study measuring the use of generative artificial intelligence in different professions has just gone public, and its main message to people working in some fields is harsh. It suggests translators, historians, text writers, sales representatives, and customer service agents might want to consider new careers as pile driver or dredge operators, railroad track layers, hardwood floor sanders, or maids — if, that is, they want to lower the threat of AI apps pushing them out of their current jobs. Why should anyone heed yet another of the myriad, sometimes conflicting reports in AI’s potential impacts on jobs? Because the researchers behind the new findings really know what they’re talking about. They all work for tech giant Microsoft, which is developing Copilot and related AI apps examined in the study. And those tools, the authors say, risk putting ticket agents and telemarketers out of work far sooner than orderlies and paving equipment operators. The Microsoft study comes as debate continues about the employment threats AI may pose to millions of people in clerical, administrative, communications, marketing, and other jobs. Executives of several tech companies, including AI developer Anthropic CEO Dario Amodei, have alternatively cheered or warned about bots automating a wide range of work tasks, potentially eliminating up to half of all white-collar and entry-level jobs in the process. Other business leaders, notably serial entrepreneur Mark Cuban, believe the tech will generate even more new positions than it erases by assuming a lot of repetitive drudge work. Microsoft’s new research doesn’t offer an opinion on the quantitative consequences on employment that AI will ultimately have. But it does provide clear indication of which 40 jobs are already using apps most frequently — and the contrasting 40 professions reflecting the tech’s lowest levels of penetration. The full text of their findings and the two rankings are available here. These 40 jobs are most threatened by AI Interpreters and Translators Historians Passenger Attendants Sales Representatives of Services Writers and Authors Customer Service Representatives CNC Tool Programmers Telephone Operators Ticket Agents and Travel Clerks Broadcast Announcers and Radio DJs Brokerage Clerks Farm and Home Management Educators Telemarketers Concierges Political Scientists News Analysts, Reporters, Journalists Mathematicians Technical Writers Proofreaders and Copy Markers Hosts and Hostesses Editors Business Teachers, Postsecondary Public Relations Specialists Demonstrators and Product Promoters Advertising Sales Agents New Accounts Clerks Statistical Assistants Counter and Rental Clerks Data Scientists Personal Financial Advisers Archivists Economics Teachers, Postsecondary Web Developers Management Analysts Geographers Models Market Research Analysts Public Safety Telecommunicators Switchboard Operators Library Science Teachers These 40 jobs are least threatened by AI Phlebotomists Nursing Assistants Hazardous Materials Removal Workers Helpers–Painters, Plasterers Embalmers Plant and System Operators, All Other Oral and Maxillofacial Surgeons Automotive Glass Installers and Repairers Ship Engineers Tire Repairers and Changers Prosthodontists Helpers–Production Workers Highway Maintenance Workers Medical Equipment Preparers Packaging and Filling Machine Operators Machine Feeders and Offbearers Dishwashers Cement Masons and Concrete Finishers Supervisors of Firefighters Industrial Truck and Tractor Operators Ophthalmic Medical Technicians Massage Therapists Surgical Assistants Tire Builders Helpers–Roofers Gas Compressor and Gas Pumping Station Operators Roofers Roustabouts, Oil and Gas Maids and Housekeeping Cleaners Paving, Surfacing, and Tamping Equipment Operators Logging Equipment Operators Motorboat Operators Orderlies Floor Sanders and Finishers Pile Driver Operators Rail-Track Laying and Maintenance Equipment Operators Foundry Mold and Coremakers Water Treatment Plant and System Operators Bridge and Lock Tenders Dredge Operators The results were obtained by analyzing 200,000 “conversations between users and Microsoft Bing Copilot.” Researchers then matched those with “measurements of task success and scope of impact, [to] compute an AI applicability score for each occupation.” The jobs with the highest use rates tended to be office positions or other work communicating data or thoughts for specific business purposes. “We find the highest AI applicability scores for knowledge work occupation groups such as computer and mathematical, and office and administrative support, as well as occupations such as sales whose work activities involve providing and communicating information,” the study says. “Additionally, we characterize the types of work activities performed most successfully, how wage and education correlate with AI applicability, and how real-world usage compares to predictions of occupational AI impact.” The upshot of that is data showing the work of ticketing agents, proofreaders, and PR specialists is already being automated at far higher levels than labor provided by housepainters and plasterers, embalmers, ship engineers, and phlebotomists — the technicians who draw blood for medical tests. But despite the study establishing a de facto ranking of the jobs threatened the most — and least — by AI, its authors ultimately waffle a bit on just how big the tech’s impact on overall employment and workplace stability might be. That’s probably not surprising, given they all work for the same Microsoft employer whose business future will largely depend on successfully developing and selling those work-automating apps to other companies. And some of the study’s disclaimers suggest underlining AI’s potential for possibly eviscerating current employee counts wasn’t considered the best messaging for broadening the appeal of apps to prospective customers. “It is tempting to conclude that occupations with high overlap will experience job loss,” they write in one of those hedges on AI’s likely impact on employment. “This would be a mistake, as our data do not include the downstream business impacts of new technology, which are very hard to predict.” On the one hand, authors do specify that AI tools are being used most often for communications tasks like language interpretation, emailing, and composing marketing materials. But on the other, they hasten to add it isn’t clear apps are also being asked to assume the complete array of tasks those workers perform on the job — or whether they’d even be capable of doing that. The researchers similarly note the different objectives employees studied had in using bots. Some of those workers asked apps to entirely handle and complete certain job tasks. But in many other cases, people queried AI assistants about the most effective ways to fulfill work duties themselves, retaining their own value to employers. Meantime, the study’s authors also seem to balance between the higher productivity objectives — and potentially decreased labor costs — that some employers hope AI will provide and employees’ contrasting fears about their job security. Those diverging focuses, the authors say, won’t generate the zero-sum results many warn of — at least not necessarily. “For example, if AI makes software developers 50 percent more productive, companies could raise their ambitions and hire more developers as they are now getting more output per developer, or hire fewer developers because they can get the same amount done with fewer of them,” they say. “Our data is only about AI usage and we have no data on the downstream impacts of that usage, so we only weigh in on the automation versus augmentation question by separately measuring the tasks that AI performs and assists.” But the study also makes it clear that the jobs least likely to be disrupted initially by increased or dominant use of AI are those involving some mixture of manual activity, use of machines, and interaction with people. That combination leaves nursing assistants, hazardous waste removers, car windshield installers, and medical equipment preparers among the professions with the lowest level of app penetration. But even there, the authors create some wiggle room for eventual employment outcomes. They note their research is based on use of AI that Microsoft developed from large language models (LLMs). More focused apps tailored to individual professions — possibly paired with robotic machines — might still leave many manual jobs vulnerable to the tech’s influence in the future. “Note that our measurement is purely about LLMs,” the authors note. “Other applications of AI could certainly affect occupations involving operating and monitoring machinery, such as truck driving.” Meaning, any historians or brokerage clerks feeling fearful about their work after reading the study might want to rethink any plans about rushing into careers as a roustabout or packaging machine operator. Because those professions, too, may come under the growing influence of specialized AI apps in the not too distant future. BY BRUCE CRUMLEY @BRUCEC_INC

Stop Letting ChatGPT and Other AI Chatbots Train on Your Data. Here’s Why—and How

When you interact with a chatbot, there’s a good chance that everything you say, and every prompt you give, isn’t just used to generate replies to your queries. Nearly every chatbot company on the planet also uses the information you provide to train its AI models. This can leave your privacy—and even your employer’s confidential information—exposed. But you can mitigate these privacy risks by telling chatbots not to use your data for training. Here’s how. What is AI chatbot training? In order for a chatbot to provide knowledgeable and (hopefully) accurate answers, the underlying large language model (LLM) that powers it needs to assimilate a massive amount of information, which it then uses to help answer your questions. This process of information assimilation is known as “training.” The more information an LLM trains on, the more intelligent the LLM, ostensibly, gets. LLMs acquire training data from numerous sources, including public websites, social media platforms, encyclopedias, video-sharing sites like YouTube, and, unfortunately, sometimes even without permission from authors, novelists, artists, musicians, and other creatives. But LLMs also get their training data from you, too. Every time you enter a prompt to give a chatbot information, that information is likely being used by the AI company to further train its models. And that can leave your privacy severely exposed. Why you shouldn’t let AI chatbots train on your data It’s generally a good idea not to allow LLMs to train on your data, especially if, in your interactions with a chatbot, you share a lot of sensitive information about yourself. If you talk to a chatbot about your physical or mental health, your finances, or your relationships, you should know that that data is, by default, usually used by the AI company to further train its LLM, which means your most intimate thoughts, worries, and concerns are becoming part of the model. AI companies say they anonymize the information you provide before using it to train their models—but you really just have to take them at their word. Even if they do anonymize your information, that doesn’t mean a bad actor in the future couldn’t use some technique to link all the prompts about a particular health, relationship, legal, or financial issue back to you. And if you are using an AI chatbot for work, you could be exposing your employer to legal and regulatory risks if the data you feed it contains confidential user or client information. Even if it doesn’t, you could inadvertently give away your employer’s corporate secrets, such as proprietary code or sales data. The chatbot may give you the answers you’re searching for, but it will also use all the data you give it to further train its models—and retain that data as part of itself. How to prevent AI chatbots from training on your data All this means that it’s a very good idea to prohibit a chatbot from training on your data. Doing so will not hinder the quality of the results the chatbot provides to you, but it will ensure, as best as possible, that the data you provide to it won’t be permanently absorbed into the bot’s underlying LLM. The good news is that most reputable chatbots—including the four most popular ones: OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, and Perplexity AI’s Perplexity—now offer ways you can opt out of having your data used for training. Here’s how to tell the big four chatbots to stop training on your data: ChatGPT: Select your profile to access the chatbot’s settings. Select Data Controls. Select “Improve the model for everyone.” Toggle the “Improve the model for everyone” switch off. Gemini: Go to the Gemini Apps Activity settings page. Select the button that says “On.” From the pop-up, select “Turn off.” Select “Got it” in the confirmation box that appears. Claude: Select your profile to access the chatbot’s settings. Select the privacy menu. Toggle the “Help improve Claude” switch off. Perplexity: Select your profile to access the chatbot’s settings. Select the Preferences menu. Toggle the “AI data retention” switch off. Once you’ve done this, none of the big four AI giants should be able to use the prompts and other information you give their chatbots to further train their LLMs. However, since these firms haven’t provided independent auditors with access to their systems, you have to take the companies’ word that they will stop using your data to train their models. Also note that even if AI companies agree not to use your data to train their models, they may retain information from your chats and other information you provide for legal or regulatory purposes for a set period of time. And even with these anti-training orders in place, it’s still a good idea to thoroughly (and correctly) redact sensitive information from any documents before you upload them to an AI chatbot. To get even more privacy when interacting with popular chatbots, consider using proxies like Apple Intelligence on the iPhone or DuckDuckGo’s Duck.ai, which can help better obscure your digital footprint from AI giants. By Michael Grothaus

AI isn’t actually ‘taking’ your job. Here’s what’s happening instead

AI probably won’t take your job anytime soon. At least not all of it. Concerns about artificial intelligence replacing human workers have simmered over the past year as companies slash headcounts, AI models grow more capable of office work and businesses integrate AI more deeply into their operations. AI was the top reason companies cited for job cuts in April for the second month in a row, the executive outplacement firm Challenger, Gray & Christmas said Thursday. “The anxiety around AI at work is real—from fears of job loss to the pressure to keep up with rapidly evolving technology,” Microsoft wrote in a report about how AI is changing jobs released last week. But the reality of AI in the workplace isn’t so black-and-white, experts say. Companies are using AI to automate certain parts of jobs rather than replace entire positions. Business leaders are figuring out what AI can and can’t do, recalibrating existing jobs around responsibilities that can only be done by a human. And thousands of jobs have been cut in the process, with web infrastructure company Cloudflare and cryptocurrency firm Coinbase among the latest to announce staff cuts. “It’s very few jobs that are actually entirely automated away by the current AI and robotics technology that’s out there,” said Alexis Krivkovich, a senior partner at McKinsey & Company who helps lead the company’s People and Organizational Performance Practice. AI is technically capable of automating 57% of work-related activities, Krivkovich said, citing McKinsey research. But that percentage is spread across “pieces and parts” of various jobs and responsibilities across an organization. Nitin Seth, the cofounder of digital services and consulting firm Incedo, claims his company helps clients boost productivity using AI by at least 20% to 25% without reducing staff at the same scale. That’s because AI only handles certain parts of different roles. “You can’t take one quarter of Lisa, one quarter of Jessica, one quarter of Nitin and one quarter of somebody else and make it one person,” Seth said. The fear that AI will take jobs has disrupted the tech industry the most. Software engineers have increasingly embraced the tech to help write code, with 90% of tech workers using AI in their jobs, according to a September survey from Google’s research arm. Stack Overflow, a popular question-and-answer forum for developers, found that 84% of respondents either use AI tools in the software development process or plan to. But a software engineer’s job involves much more than just coding: It entails reviewing the code, designing systems, troubleshooting problems and deciding what to build. Companies may adjust job titles to reflect that, says Boris Cherny, head of Claude Code at Anthropic. “I think by the end of the year, we’re going to start to see the idea of software engineering go away,” he told CNN in March. He thinks the term “builder” might be a more fitting title as the job expands, and writing lines of code becomes a smaller part of it. Sujata Sridharan, who most recently worked at the fintech firm Bolt and has spent roughly a decade as a software engineer, is one of the many engineers living through that transition. Although she uses AI, her work still requires problem solving and critical thinking, she told CNN over email. The difference is that the execution now involves a mix of writing code and prompting AI. “With AI being used more and more, the skills that are actually required on the job have shifted to, are you able to recognize what is the right code quality? Are you able to problem solve?” she said. That’s not to say AI isn’t contributing to job losses; it just likely isn’t taking over full roles. AI has been cited in more than 49,000 job cuts so far this year, the report from Challenger, Gray & Christmas said. Block, the financial tech company behind Square and Cash App, laid off 40% of its staff this year because AI has allowed it to do more with smaller teams. Coinbase is reducing its staff by about 14% in part because AI is enabling engineers to “ship in days what used to take a team weeks,” its CEO said Tuesday. And Cloudflare said the way the company operates has completely changed, adding that its AI use has increased by more than 600% in the last three months alone. It’s possible there will be “some job disruption on the horizon,” according to Dan Priest, PwC’s US chief AI officer. Still, he said he isn’t seeing mass layoffs at most companies and whole categories of jobs aren’t currently at risk. Most companies haven’t yet adjusted its employee metrics and incentives to fit with how AI is changing work, Microsoft said in its report, which surveyed 20,000 workers using AI across 10 countries. Instead, many are simply grappling with which skills are needed from human workers. And the tech landscape may keep changing as AI models evolve and potentially take on more office tasks. For example, Anthropic on Tuesday announced new AI agents built for financial work, like building pitchbooks and crafting credit memos. “It starts at the bottom, and it keeps going up,” said Umesh Ramakrishnan, cofounder and chief strategy officer at executive search firm Kingsley Gate. “And I don’t know where it stops.” By Lisa Eadicicco

The Coasean Singularity: Why AI Is Ending the Org Chart as We Know It

In 1931, a 20-year-old commerce student at the London School of Economics received a modest traveling scholarship and boarded a ship to the United States. His name was Ronald Coase. He had worn leg braces as a child. Economics had found him almost by accident, in a seminar where a professor unpacked Adam Smith’s theory of the invisible hand. “It was a revelation,” Coase would remember decades later. He had planned to study law. Instead, armed with letters of introduction from a Bank of England contact, he spent a year visiting Ford plants, General Motors factories, and a long list of American businesses, puzzling over a question: Why do firms exist? The question came to him while watching how real industries organized themselves. Some activities stayed inside the company. Others were contracted out. So if markets are as efficient as Adam Smith suggested, why build firms at all? Why hierarchies? Why middle managers? Why conference rooms, reporting lines, and org charts? Coase wrote up his answer in an essay published in November 1937, when he was 26. He titled it “The Nature of the Firm.” The idea was simple: Markets are expensive to use. Friction is everywhere. Finding a spinner takes time. Negotiating the price for every yard of yarn takes effort. Writing a contract for every afternoon of work takes a lawyer. These are transaction costs. Many of them are replaced inside the firm by something blunter and cheaper: authority. A manager tells an employee what to do. The firm expands until the cost of organizing one more transaction internally equals the cost of carrying it out through the market. That is the boundary of the firm. The essay was short. Dry, even. Coase published it in Economica. Fifty-four years later, in 1991, it helped earn him the Nobel Prize in economics. For close to a century, the theory has held. It explained why companies got big during the industrial age (coordination was expensive, so internalize everything), why they got flatter during the internet age (email and ERP systems reduced internal coordination costs), and why outsourcing boomed in the 2000s (India’s IT services made market transactions cheap enough to push work outside the firm). Now, in 2026, Claude and other AI agents are pushing Coase’s logic toward its limit. On the supply side, AI is collapsing coordination costs in ways the internet never could. An agent can draft a contract, source a supplier, negotiate terms, and monitor delivery. That persistent friction of the past is finally dissolving. On the demand side, the comfortable middle of the market is thinning. The place where Applebee’s fed families, where Gap clothed the masses, and where standard software packages served nearly everyone has been hollowing out. What remains is a barbell: the ultra-cheap and algorithmic on one end, the hyper-personal and curated on the other. AI did not create these forces. It is accelerating both at once. Driving coordination costs lower on the supply side. Splintering expectations further on the demand side. And the result is rewiring the firm and rewriting our jobs. Firms will not disappear. Jobs will not disappear. But in a world moving toward near-zero transaction costs, both are being remade. This is what it looks like when Coase meets Claude. I call this the Coasean Singularity. The Death of Normal In 2007, you walked into a theater and bought a ticket. No research. No TikTok. You picked a movie because it started at 7:30 p.m. and your date liked George Clooney. That was enough. Michael Clayton cost $21 million. It was nominated for Best Picture. It was a movie for adults, made by a studio, distributed widely. People who sold insurance watched it. People who taught high school watched it. People who were on their second date and just picked something watched it. That movie does not get made today. What replaced it is a barbell. One end is the $200 million superhero franchise. The other end is the $7 million A24 film about grief and mushrooms. The middle is gone. Those boutique masterpieces are smarter. They reach exactly those who already know what they’re looking for, thanks to Instagram and TikTok. The middle of every major consumer market is collapsing. Films, restaurants, music, retail, careers. What is replacing it is a barbell. Enormous weight on each end. Nothing in the center. Walk into a strip mall and you can see it. In May 2024, Red Lobster filed for bankruptcy with roughly $300 million of debt. James Berke, 23, a server in New Jersey, woke up Monday to the place padlocked. The parking lot was full, and the doors were locked, and nobody had said a word. “They made us work Mother’s Day to get that quick buck, and then they closed us.” Freezers were carted out. Booths were auctioned off. Lobster tanks were drained. More than 130 locations closed. Red Lobster wasn’t alone. TGI Fridays shuttered 185 locations. Denny’s closed up to 150. Applebee’s trimmed 47. Hooters, Buca di Beppo, and dozens of restaurant chains filed for bankruptcy in 2025 alone. But Sweetgreen’s shredded kale and wild rice bowl is doing fine. The taco truck is doing fine. The premium survived. The cheap survived. The place where a janitor and an accountant once sat in the same room and ordered from the same menu is what’s disappearing. And no one seems to think that is a problem, which tells you exactly who the replacements were built for. They are more interesting; they serve fewer people. The cultural monoculture vanished somewhere around the Game of Thrones finale in May 2019. That was the last time 19.3 million people watched the same thing on the same night. Nothing since has come close. Algorithmic feeds now create individual reality tunnels. TikTok spins new ones every few weeks. Shein adds six thousand new styles a day. Trends flash, peak, and vanish. That’s how consumers stopped wanting “good enough for people like me.” They want good enough for me. You can see that inside your own company, too. Microsoft and LinkedIn found that 75 percent of knowledge workers already use AI at work. And 78 percent of AI users are bringing their own tools, because the company-issued version feels generic. Bosses call this shadow IT and treat it as a compliance problem. But these were not rogue employees. They were the diligent ones. The ones who stayed late. They just wanted tools that worked the way they worked. The market has ceased to be a mass. Demand is distributed, specific, shifting, and impatient. People want software, clothes, meals, careers shaped to their exact workflow, their exact body, their exact mood. Companies have taken notice. Pernod Ricard now tracks how AI models talk about its whisky brands and uses those insights to refine its marketing and creative. Instacart built a ChatGPT plugin that lets you add groceries to your shopping cart mid‑conversation, and has since made it a full in‑ChatGPT flow. These are real adaptations. They are also mostly interface moves. They will not be enough. When you get AI to highlight Ballantine’s as affordable in real time, that does not help you launch a limited-edition single malt in seventy-two hours because a TikTok bartender in Seoul just made peat smoke go viral. Serving those ephemeral demands requires a supply structure that can spin up fast, go narrow, and dissolve when the moment passes. Most firms still plan in annual cycles, manufacture in big batches, and launch on timelines measured in quarters. Which brings us back to the 20-year-old in leg braces who sailed to America in 1931. The Supply Side: When Coordination Gets Cheap To understand what is happening on the supply side, I spoke with Sangeet Paul Choudary, author of Reshuffle. I asked him what most companies get wrong about AI. Everyone starts with tasks, he said. A lawyer drafts contracts faster. A marketer generates copy faster. A developer writes code faster. Fine. But that is still the same workflow. The deep shift, he argues, comes from cutting the effort it takes to turn one team’s output into another team’s input. The meeting on your calendar because engineering and marketing speak different languages. The email chain because the CRM does not talk to the ERP. In Coase’s language, those frictions are transaction costs. And AI’s biggest economic payoff comes from driving those transaction costs down to nearly zero. When AI agents can negotiate terms, monitor quality, interpret documents, and reconcile outputs, firms can distribute work more widely, without losing the grip they once needed hierarchy to provide. What does that look like in practice? It starts with what Choudary calls the atomic unit of work: the smallest piece of productive activity that can be separated and then assigned. Take fashion. For Zara, the atomic unit was the collection: a coherent batch of designs that had to fit the brand, be manufacturable, and move fast enough to catch a trend. That required tightly coupled human judgment across design, sourcing, production, and logistics. So Zara built a supply chain around speed and coordination, with production close to Spain and fresh inventory moving out of La Coruña twice a week. Then Shein kept shrinking the unit of work. A collection became a set of fragments. Then the fragments got smaller still, until what remained was a single design decision. “The designer still exists,” Choudary told me. “But their creative bandwidth has been compressed to almost nothing.” Work that once sat with a designer who could hold manufacturing constraints, brand identity, and seasonal timing in mind can now be split into narrow prompts: Reference these three images. Draw a collar. Rework the cuff. Try a different hemline. Human judgment still matters. But it shows up less. More of the intelligence now sits in the system that parcels out the work, recombines the pieces, and decides what deserves to scale. In a cramped Guangzhou workshop, workers would hunch over sewing machines under hard fluorescent light, finish a piece, slide it into a turquoise bag, and toss it onto the pile. The system reacts to each signal as it arrives, from an Instagram click to an Amazon sale. That helps explain why Gap or Under Armour struggle. They are still organized around the older unit of work: long cycles, big production runs, broad seasonal catalogs. Their unit is still too large. You can see it in software, too. Adobe treated the file as the basic unit of design work. You work on the file. You pass the file around. Figma treated the element, the text block, the shared object, as the unit. Suddenly everyone is on the same canvas. Version reconciliation fades. Governance stops being something imposed after the fact and becomes part of the workflow itself. By the time Adobe recognized the threat, it was serious enough that the company attempted a $20 billion acquisition. Regulators blocked it. So the pattern is consistent. The unit of work gets smaller. The pieces become more modular. Coordination gets cheaper. And once that happens, more of what once required a firm begins to look like something that can be orchestrated across a network instead. Where Supply Meets Demand The convergence matters because it changes the minimum viable size of a firm. When coordination gets cheap and demand gets weird, more businesses can exist at smaller scale. Think YouTube channels versus TV shows. It took 150 people to make a TV show. Now 5 to 10 people with AI tools can run a wildly successful channel. The same explosion is coming to software. A software company used to require 10,000 customers, 50 employees, and $1 to $5 million in capital. But AI is threatening to drop those requirements to 500 customers, 2 people, and minimal funding. That is why Wall Street repriced software companies so much. In early February 2026, software and services stocks shed roughly $830 billion in market value over six trading days. Salesforce, Adobe, ServiceNow all get hammered. The market realized that software barriers built on workflow lock-in will dissolve if AI agents can coordinate across tools without anyone agreeing on a standard. None of this means large companies disappear. They never do. When e-commerce arrived, Sears collapsed, but Amazon rose in its place. The form changes. The scale persists. So what does a big company look like after the Coasean Singularity? How Large Organizations Survive (When They Do) The honest answer is that most won’t in their current form. But there is an alternative, and it has a proof of concept. It is called Haier, the world’s No. 1 major-appliances brand for 17 years running. In December 1984, a 35-year-old named Zhang Ruimin was sent to run a failing refrigerator factory in Qingdao. His predecessors had all quit. Annual turnover was 3.48 million yuan. The factory was losing 1.47 million. Workers urinated on the floor. A few months later, a customer returned a faulty refrigerator. Zhang checked the inventory and found 76 defective units, about a fifth of the stock. He lined them up on the factory floor, handed workers sledgehammers, and told them to smash every one. Then he picked up a sledgehammer himself and brought it down on a refrigerator door. Each unit was worth four years’ wages. Some workers wept as they swung. That sledgehammer now sits in Haier Group’s headquarters museum, beside the refrigerators and washing machines that came after it. But the sledgehammer was only the beginning. Over the next two decades, Zhang pulled apart the hierarchy itself, reorganizing more than 80,000 employees into over 4,000 self-managing micro-enterprises. Each one operates like a startup. It owns its own profit and loss. It makes key decisions. It stays tied to a specific user need. Employees do not collect a fixed salary simply for showing up. Instead, their income rises or falls with the value they create for customers. A strong micro-enterprise earns its place and stays; a weak one does not. Zhang saw the traditional enterprise as a ship. One captain, one direction, everyone on board. Fine for calm seas. But the seas were not calm. What he wanted was a rainforest: diverse, distributed, self-organizing, resilient because no single failure could bring the whole system down. So he broke the ship apart and let the pieces find their own currents. One micro-enterprise leader said, “My wife used to complain that I didn’t come home from work until after 9:00 at night. But now she is very patient and proud about the hours I keep, because she knows I am building my own company and working for the benefit of our own family.” He paused. “And I am making my own decisions, not acting on the decisions someone else has made.” When Haier acquired GE Appliances in 2016, GE’s market share in home appliances had languished around 2 percent for four years. Under the micro-enterprise model, it surged tenfold. Revenue more than doubled. Small teams swarmed opportunities the way a traditional hierarchy never could. So why isn’t this just fragmentation? Why isn’t it 4,000 independent contractors with a logo? Because when Haier operates as a platform, a kind of internal market with a shared brand and balance sheet, it does what a contract cannot. It provides accountability. It provides trust. It provides a brand, which in a world of infinite AI-generated noise is one of the few filters customers actually rely on. And it provides the capital to absorb failure. “The task is not to turn Haier’s internal staff into entrepreneurs, but rather to attract all the entrepreneurs in society onto our platform,” Zhang says. Most micro-enterprise bets lose. Only an entity with sufficient scale can sustain the portfolio of experiments required to find the ones that win. The Question That Matters I went back to Coase because I needed an anchor. Transaction costs are falling. The unit of work is shrinking. The middle of the market is thinning. An economy dominated by fragmented supply, splintered demand, and AI agents handling the coordination in between does not resemble anything most of us were trained to navigate. All the while, the forces driving us toward the Coasean Singularity are too theoretically fundamental and too empirically visible to be a mirage. My nephew is fourteen years old, living in New Zealand. When I think about the economy he will inherit, I do not think about which AI tool will be dominant. By the time he enters the workforce, “company” might mean something his father would not recognize. A temporary coordination pattern. A portfolio of bets. And “career” means portfolio. If the micro-enterprise economy works only for the relentless, for the people who can treat career volatility as a lifestyle brand, then our economy is also a tournament. And tournaments produce a few winners and a lot of wreckage. The urgent question is how to position yourself. Still, the defining question is whether we can build institutions and shared norms strong enough to make this new economy socially durable and make it possible to live a decent life. We have not yet passed the Coasean Singularity. Pay close attention to who is shaping the rules, and find a way into the conversation. EXPERT OPINION BY HOWARD YU @HOWARDHYU