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