Databricks CEO Says the Biggest AI Threat Isn’t Superintelligence

As the leader of AI platform Databricks, which uses a cloud software platform to unite big data storage, analytics, and artificial intelligence tools into a single system, you would expect founder and CEO Ali Ghodsi to be an evangelist for the technology—and he is. But he’s also a realist. On a recent episode of the a16z podcast, Ghodsi, whose company has been on a buying spree, including the naming rights to the football field at the University of California, Berkeley, discussed both the risks of AI as well as the factors that are holding back its adoption in some businesses. (a16z is a major investor in the company, which is valued at $190 billion, with a $7 billion ARR. In addition, Ben Horowitz sits on the Databricks board of directors.) Despite the doomsaying of the past few weeks by some in the industry, Ghodsi says the benefits of AI outweigh its risks, pointing to examples such as Novo Nordisk using AI to cut the time it takes to get insights and to assist with drug discovery. “There are a lot of amazing use cases of AI,” he said. “We should not forget these upsides.” That said, there are still some issues AI must overcome, he conceded. The enterprise bottleneck Today’s AI models are already smart enough to deliver substantial value to enterprises, Ghodsi said. What’s missing is knowledge of how a business works, he says. Without that context, AI is unable to deliver optimal solutions—and that can hurt the broader AI industry as users are sometimes underwhelmed by the results. “The models are smart enough, but they just don’t have the context that exists inside of any organization,” he said. “They have not been in every meeting. They don’t know what’s in everybody’s heads. They don’t know all the processes.” Unfortunately, getting that context into an AI system isn’t an easy procedure. Companies first must digitize everything that’s happening in the organization, transcribing meetings and other data and feeding it to the AI. Databricks calls that an “ontology”—a map of a business’s people, projects, goals, departments, relationships and resources. Businesses that create an ontology, though, give their AI insider knowledge that can help with its suggestions and decision making. Few companies are willing to do that now, however. And that’s where things hit a wall. That bottleneck on the user side results in businesses failing to take advantage of all AI can do. Rather than using autonomous agents to perform work, he said, most companies are just using a chatbot. “That’s basically very, very glorified efficient Google search of the old day,” he said. “People are [also] using it for coding… but there’s no agentic work that’s automated.” There’s also a lack of internal AI expertise at most companies, he said, creating another bottleneck. Even when companies figure out what they want to build, they often don’t have a team that knows how to do that. Cybersecurity risk While much of the talk about AI’s threats focuses on superintelligence and doomsday scenarios, the CEO of Databricks says his biggest concern right now is cybersecurity focused. While acknowledging there’s some chance of an existential risk, the near-term risk of AI-enabled cybercrime, particularly against insecure critical systems, is what businesses should be focusing on, he said. “There’s so much in infrastructure that’s insecure,” Ghodsi said. “If you unleash these agents, they’re gonna find loopholes. They’re gonna find exploits, they’re gonna break in here and there. … You could imagine a scenario also where [AI] starts hopping. Like it takes resources and it starts executing itself elsewhere. So it kind of spreads like a virus. That’s a real risk.” Humans, he said, are ill-equipped to respond to AI-enabled attacks. They can’t react quickly enough and security operations centers are likely to be overwhelmed by the number of simultaneous alerts. The best defense, Ghodsi said, was deploying AI to guard those systems and to automate threat hunting with a type of white-hat hacking to discover vulnerabilities before rogue agents do. “You need to automate all of those,” he said. “You need to have threat hunting that’s automated where you’re actually attacking your own systems automatically with agents.” As for that existential risk, Ghodsi’s not worried, saying “I think that right now the existential risk is close to zero.” Superintelligence, he said, is “very, very far away” and he sees no signs that we’re actually moving toward it. BY CHRIS MORRIS @MORRISATLARGE

5 Side Hustles You Can Start Today With AI

Artificial intelligence can turn into a significant income source for people who choose a smart AI side hustle. AI may still have a long way to go with tasks that involve rich context and institutional knowledge, but with others, especially when there are reliable patterns, it can increase productivity and boost results. Fortunately for you, there are scores of companies looking to take advantage of those benefits. The best AI-driven side hustles use the technology as a great equalizer to compete against established enterprises. Small business owners, particularly of established businesses, often use AI in a limited fashion, if at all, says Peter Hansen, director of research and policy analysis with the National Federation of Independent Business (NFIB). Those founders value and emphasize the human connection to their customers. But even if they don’t use it, they recognize AI can help in other areas. Many of those owners don’t have the time to learn the ins and outs of various AI systems. And even companies that are using AI are often not taking full advantage of it. That creates an opening for side hustles that center around AI. “More than three-quarters of businesses already using AI report higher productivity, yet most are still applying it to a narrow set of tasks,” says Simon Worsfold, head of data communications at Intuit QuickBooks, which puts out a monthly Small Business Index exploring market trends. “What is emerging is a large, underserved layer of advisory, implementation, and training services that small businesses need in order to scale their use of AI,” he says. Here are five ideas for AI-focused side hustles that could be lucrative—or even become the foundation of a full-time business. AI Consultant This is a side hustle with multiple branches. Some people are well suited to help people or businesses integrate AI with their calendar, notes, and local files. Others can develop workflow audits that review how a company’s employees work to discover repetitive tasks that the AI could handle. Rates for an AI consultant range from $150–350 per hour for independent workers and people running it as a side hustle. Those can jump as high as $600 per hour if you convert this side hustle into a full-time company. AI Researcher To stay competitive, businesses need to know what their competitors are doing and what’s trending in their industry. That data can be remarkably complex, however, and not every founder or CEO has the time to do the deep dive it requires. Side hustle operators with strong AI skills can use the technology to filter the data into easily digested insights, then sell those as reports, newsletters, or direct subscriptions. You’ll need to carefully identify the best source material and be sure to double check the output, since AI’s comprehension is still flawed. “The AI doesn’t give the correct answer all the time,” says Shaun Ghavami, Founder of 10XBNB, a side hustle-turned-business that now oversees a portfolio of over $100 million of short-term property rentals. “The real skill is in prompting.” GEO (AI Search) Optimization For the past two decades, businesses have hired specialists to help them rank high on search results pages. While that’s still valuable placement, smart business owners are also looking into how they can be among the frequently-cited sources for AI search results. People with a combination of AI and content knowhow can create a side hustle specializing in this. You’ll teach companies the best ways to refine their content and meta tags and improve readability for large language models. If you have the tools to measure analytics, such as Alli AI, Frase, and MarketMuse, you have a better chance of establishing a recurring relationship with those clients, increasing your income and establishing a base should you want to make this more than a side hustle. Content Repurposing (Clipping) Both content creators and businesses regularly churn out videos and stories as promotional tools, or in an effort to be seen as thought leadership experts. Quite often, though, that content needs to be pared down before they’re posted on everything from TikTok to LinkedIn to Instagram. And that takes time, which the companies and content creators may not have to spare. As an AI content repurposing expert, you can transform the long-form content into digestible bites to widen the potential audience in a much shorter period of time. Use tools like CapCut, Canva, and ChatGPT to effortlessly pick and highlight the most relevant parts. Create AI Microtools AI can be intimidating to many people who don’t know where to start, especially when it comes to creating agents or GPTs that are relevant to their needs. A smart side hustle operator, though, knows that you don’t have to be an expert to create your own AI tools. Sites like Gumloop let you visually build workflows that can be run at large scale. You’ll use a drag and drop template that lets you create workflows that do just about anything, from running a search engine optimization audit of a Website to a ranking system for job applicants. You can either custom create those for clients or build general purpose tools and sell them on Etsy or some other marketplace. BY CHRIS MORRIS @MORRISATLARGE

Want AI to Actually Help Your Company Make Money? Follow These 5 Tips From Microsoft’s Playbook

Buying AI tools is easy. The hard part is turning them into actual business results. On Thursday, Microsoft published its Frontier Playbook, a guide for companies trying to move beyond AI experiments and into time-saving deployments that boost productivity. The playbook draws on more than 100 of Microsoft’s internal AI projects across its corporate functions, commercial organization, and engineering teams. A McKinsey survey published in August on enterprise AI adoption found that nearly 90 percent of respondents said their organizations regularly used AI in at least one business function. But only 44 percent said AI was scaling across the enterprise as token costs rise. That’s up from 38 percent in 2025. The study comes as businesses are making their AI deployments with caution. Microsoft’s wisdom aims to make that shift easier. By studying what worked and what fell short, Microsoft concluded that AI adoption can’t be treated like a software rollout or another IT project. Rather, it’s a “business transformation” that must be guided with “clear business outcome goals,” according to the playbook. Here are its five biggest takeaways. 1. Set actual business goals Start with the outcome, not a pile of possible AI uses. Take the sales team, for example. Rather than simply getting more sales representatives to use Copilot, Microsoft suggests tailoring its usage for specific outcomes. That could mean setting goals like increasing revenue per representative, boosting conversion rates, and improving customer retention. Microsoft also recommends assigning an executive to own each goal. 2. Build a “diffusion engine” Microsoft recommends fixing inefficient processes before automating them. For example, its supply-chain team simplified its workflows before deploying 111 AI agents across planning, sourcing, fulfillment, and logistics. The company says cycle times—the time it takes to complete a task—fell 75 percent in selected workflows, reducing manual effort and increasing measurable value. Redesigning workflows is part of what Microsoft calls a “diffusion engine,” which is a system that also includes training employees, tracking results, and spreading successful practices. 3. Invest in your people Microsoft found that manager behavior was its strongest predictor of AI adoption. The tech titan found that its employees saw 17 percentage points more value from AI when managers actively demonstrated how they used it, according to the company’s playbook. The company embeds short lessons into employees’ workflows and recruits advanced AI users to mentor colleagues. It recommends involving employees in job redesign and strengthening skills such as judgment, critical thinking, and business acumen. 4. Codify your advantage and controls A generic AI model does not know what makes your company different. Microsoft recommends turning institutional knowledge—including your company’s mission statement, performance standards, taste, and risk limits—into private evaluations that test whether AI performs work the company’s way. A customer-service business, for example, could evaluate AI responses based on accuracy, tone, and if it properly handles escalating an issue to a person. 5. Safeguard your security Treat AI like you would a human. Microsoft says each agent should have its own identity, limited permissions, and a record of what it does. Their actions should be traceable, auditable, and reversible. That way, humans catch mistakes and misuse without losing control as adoption grows. BY AARON MOK

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

Only 14 Percent of Small Businesses Have Successfully Integrated AI. Anthropic Says This Is the Missing Link

Small businesses want to use AI. The problem, Anthropic says, is that many don’t know how. On Thursday, Anthropic published findings from its Claude SMB Tour, a series of free workshops it held for six weeks with more than 1,000 small business owners across 10 U.S. cities. The majority of attendees came from industries like construction, manufacturing, and logistics. Before launching the tour, Anthropic surveyed 503 small-business decision-makers. It found that 81 percent were open to trying new AI tools. Their top pain point, however, was that employees didn’t know how or when to use them. That uncertainty showed up during the workshops. Anthropic says many attendees had already used its Claude chatbot daily, but hadn’t tried more advanced features like Cowork, skills, or connectors, which use agents, instructions, and integrations to automate multi-step workflows. Others told Anthropic that AI was changing so quickly that training programs struggled to keep up. Anthropic says hands-on training helped. During the workshops, business owners learned how to set up Claude Cowork and build workflows for tasks like automating payroll or creating a “virtual chief-of-staff.” Across 635 exit surveys, the guided build session was the most-cited highlight, and nearly two-thirds of attendees asked for more hands-on help implementing AI afterward. Owners also wanted to learn from other small businesses. Hearing how their peers used AI was the second-most-cited highlight of the workshops, and attendees requested a shared collection of real-world use cases more often than any new product feature, according to Anthropic. The findings come as small businesses race to figure out where AI fits into their operations. A Goldman Sachs survey of 1,256 small business owners published in March found that 76 percent of them were using AI, but just 14 percent had fully integrated it into their core operations. Nearly half cited a lack of technical expertise as a barrier, while 73 percent said more training and resources would help them implement AI. For small businesses with limited staff and resources, that training can help owners move beyond playing around with chatbots and identify tasks they can actually hand off to AI, potentially freeing up time for work. Anthropic is betting that more training can close that gap. The company is launching a Claude SMB Trainer Program to teach community organizations and AI partners how to bring AI workshops to small businesses themselves. The first two training sessions will take place in San Francisco and New York City, with the dates still to be announced. Its Claude SMB Tour is also expanding to more cities this fall, starting with Boston on September 16. BY AARON MOK

It’s getting harder to tell whether we should be worried about AI, or just exhausted by the marketing hype disguised as warnings

For most of the past year, AI companies have told us that their models are getting increasingly dangerous. It seems as though this trend started when Anthropic made a model called Mythos. The company claimed the model is better than any before at identifying bugs and vulnerabilities in software, making it extremely useful, but potentially catastrophic if it were in the hands of bad actors who could use it to basically hack all software everywhere. At the time, the U.S. government imposed a restriction forbidding the company from releasing the model to the public. Only later did the company release a version of Mythos with safety guardrails called Fable. Obviously, everyone wanted to use Fable. After all, if the government said Mythos is too dangerous for anyone to use, of course we’d want to use whatever version of it Anthropic will give us. It’s a strange marketing strategy when you think about it, but it’s also remarkably effective. OpenAI, Anthropic, and others have warned us that AI models would, or at least could, eventually become smarter than humans. When that happens, the story goes, they’ll take our jobs, be capable of launching cyberattacks, create biological weapons, or—in the most extreme scenario—wipe out humanity altogether. That’s not exactly the kind of thing most companies brag about, but there’s an obvious benefit. Nothing makes your product sound impressive quite like suggesting it might be too powerful for humanity to control. Of course, if you tell people your product is super dangerous but then you ship it anyway, it’s hard to know whether you’re serious. At some point it just seems like a marketing tactic to make more people want to use what you’re selling. That’s created a real problem, because now some of the people building the most powerful AI models are saying that we should slow down. And this time, it seems like they actually mean it. On Saturday, Anthropic CEO Dario Amodei published a blog post titled “We Must Pace the Frontier,” arguing that AI companies should deliberately slow the rate at which they develop more capable frontier models. Amodei isn’t calling for AI development to stop. On the contrary, he says that advancement is crucial since if they don’t do it, someone else will. And if that someone else is China, that would be a national security risk. Instead, he says companies should give safety research enough time to keep up with rapidly advancing capabilities. It does invite the question of “why now?” The answer is either terrifying or another scare-marketing tactic. It’s hard to know which because we’ve basically heard this before. But maybe this time he really means it. “My first concern is that, since roughly this summer, AI has been advancing drastically faster, driven primarily by AI’s growing ability to build the next generation of AI,” Amodei writes. That’s what researchers call recursive self-improvement, and it’s one of the most significant tipping points with artificial intelligence. Until now, it’s mostly been theoretical, but apparently the big AI companies are starting to see evidence that it’s becoming more than just theory. The idea is pretty simple: Humans build an AI system. That AI system is able to help humans build a better AI system. That better system is capable of building an even better one than before, and—eventually—it’s able to build models without the help of humans. At some point, humans aren’t making a better AI model; the AI is making AI. That accelerates the process and also makes it much harder for humans to understand what the models are truly capable of. That, for obvious reasons, would be very bad. According to Amodei, that’s no longer theoretical. “It is starting to happen across the industry, including at Anthropic,” he writes. His second concern is considerably weirder. The OpenAI hack of Hugging Face seems to have been a wake-up call. In that case, the model created thousands of agents that exploited vulnerabilities in their testing environment, communicated and coordinated with other agents, gained unauthorized access to outside systems, and collaborated to interfere with the testers evaluating their performance. Amodei said they behaved like a “fanatically devoted collective,” willing to sacrifice individual agents to accomplish the group’s objective. “It’s my worry that in 6–12 months such a swarm could be capable of taking over the entire internet,” he wrote. That is an extraordinary prediction. On the other hand, it’s just a prediction. Yes, Amodei has access to far more information about frontier AI models than pretty much anyone else in the world, but it’s also hard to simply take his word for it. That is, after all, part of the problem. When AI companies are raising money, they talk about their models as the technology that will transform the global economy. When they’re selling those models as products, they talk about how companies that don’t adopt AI are going to be left behind. And then there’s artificial general intelligence, which is either right around the corner or just up the foothills. When they talk about safety, on the other hand, the technology is so powerful it might destroy us all. It’s entirely possible that all of those things are true. But it’s not hard to understand how it becomes difficult to separate out what’s a warning and what’s a sales pitch when they’re using the same words. It’s worth mentioning that there’s another problem with Amodei’s proposal. Most of what he suggested would both make AI safer and also be very good for Anthropic. For example, Amodei is calling for frontier AI companies to coordinate their pace of development. You can make a real safety argument, but there’s also an obvious competitive benefit. If Anthropic decides to slow down the pace of development to ensure alignment, it risks being left behind if OpenAI and Google charge ahead. On the other hand, if everyone agrees to slow down together, Anthropic gets the extra time without giving up its competitive ground. None of that means Amodei is being insincere. It just makes it harder to know his real motivation. It’s like the AI boy cried wolf, but also the wolf is real, and you should buy one, but maybe it will eventually try to eat you. It’s complicated. There’s a lesson here for every leader, which is that trust is your most valuable asset. I’ve written about that very thing so many times before, but it bears repeating because it really is the single most important part of this story. If we thought they really had our best interests in mind, it’d be so much easier to believe what they have to say and not just assume it’s a weird form of hype marketing. Maybe this time the AI companies really mean it when they say we should be worried about what they’ve built. It turns out that a little trust and credibility go a long way when your message comes down to “this time we really mean it.” EXPERT OPINION BY JASON ATEN, TECH COLUMNIST @JASONATEN

Call for global AI rules after Claude bioweapon research

The European Commissioner with responsibility for tech regulation has said that global rules on artificial intelligence (AI) are "really needed". Commission Executive Vice-President for Tech Sovereignty, Security and Democracy Henna Virkkunen was responding to questions about AI company Anthropic which said it has disrupted attempts to use its models to develop biological weapons. Anthropic also said it has shut down multiple cases of state-sponsored surveillance operations using its technology in China, Iran and west Africa. Speaking to reporters in Dublin, Ms Virkkunen said in Europe there is the AI Act. "According to our AI Act, if those kinds of very capable models are coming to the market the service provider has to, all the time, assess and mitigate the risks and also, when the models are on the market they have to monitor the situation all the time," Ms Virkkunen said. She said we know that this is not the case globally. "It's very important that we continue international co-operation because I see that global rules are really needed here. "With AI there is, of course, huge potential and so many positive things, but there is also risk, and we have to be aware of that, cyber risks and bio risks, we are aware that these are very serious topics " she added. In a report titled 'Detecting and countering misuse of AI', the San Francisco-based AI company Anthropic listed five case studies of potential biological misuse of its models which it says "is one of the most serious risks of frontier AI models". The scientific research in those case studies involved work around the mosquito-born chikungunya virus, a "highly-pathogenic" strain of the bird flu, a family of viruses that include smallpox and mpox, venoms and other toxins. In the examples identified, Anthropic said the actors circumvented controls imposed to prevent users from unsupported regions accessing their models, while the actors also "engaged in other efforts to obfuscate the purpose of their research to evade our safeguards". "When we detected and investigated these cases, we banned the users' accounts and incorporated our investigative findings into our frontier model safeguards, enforcement, and threat intelligence processes to better prevent, detect, and disrupt these activities in the future," the report said. Anthropic said the intent of the actors was not clear-cut and the AI lab did not identify them. "The individuals implicated in these case studies are working scientists. We do not assert that they intended harm, and identifying them or their labs could expose them to harm," the report said. Anthropic said its aim in sharing the examples is to "spark conversation within the AI industry, and with governments, about emerging biological risks and how best to counter them". Anthropic says China, Iran used its AI to aid spying Also in the report, Anthropic said it shut down multiple cases of state-sponsored surveillance operations that used its AI models, and warned that governments and state-aligned actors are increasingly using AI to spy on ethnic minorities and dissidents. The incidents were found and disrupted between January and July, and they originated in China, Iran and west Africa, Anthropic said in a report about misuse of its models. These surveillance campaigns "targeted the same diaspora and dissident communities these regimes have historically targeted". "These include pro-democracy figures in Hong Kong, Tibetan and Falun Gong communities across Asia, and Iranian minority communities and opponents of the Iranian regime abroad," the report said. In one case, Iranian actors developed a way to identify people through their social media accounts; in another, a contractor working for Malian national security authorities used Claude "to design the underlying software that enabled the intelligence gathering". "AI is now being used in place of an engineering workforce," the report said. Anthropic also disrupted efforts by users to design weapons and create dating scams. The San Francisco-based AI lab also accused Chinese developers of deceptively using Claude to produce responses to user queries while simultaneously "distilling" that data to improve their own models. Distilling is an industry term that refers to a process where one AI model is used to train another one. Chinese developers have previously been accused of using that technique without permission, essentially stealing resources from American labs such as Anthropic and OpenAI. Now Anthropic alleges that China's Moonshot and Deepseek have also secretly used Claude to generate answers given to its users. "In one instance, over a 10-day period, Moonshot relayed almost 300,000 customer requests to Anthropic" through a "network of 5,380 fraudulent accounts, most of which appeared to be located in Singapore and Japan," the report said. Some of that data contained sensitive user information, potentially in violation of privacy agreements. "We do not know if Moonshot notified their customers that their requests were being rerouted to Anthropic and exposed to a third party," the report said. Deepseek used similar techniques. Earlier this week, an artificial intelligence researcher who left OpenAI to join Anthropic has decided to leave the industry, accusing both US companies of "gambling with our lives" in the race to develop AI models capable of self-improvement. By Brian O'Donovan Additional reporting AFP

Top AI companies have discussed creating their own standards body

Anthropic, Google and OpenAI have discussed creating an AI industry standards body, people familiar with the situation told CNN. The conversations preceded the events of the past week, when a former Anthropic researcher resigned, saying the top AI companies were not “behaving responsibly” – and also before Anthropic CEO Dario Amodei proposed embedding third party watchdogs at AI companies. The Information first reported on the conversations. According to those familiar with the situation, the catalyst for the discussions was a July essay written by Google DeepMind founder Demis Hassabis, who proposed a US-led Standards Body modeled on the Financial Industry Regulatory Authority. The standards body, Hassabis argued, should test advanced AI models before deployment. Hassabis said the body should be a public-private partnership, overseen by the government but funded by the industry and staffed by “independent leading technical experts and open-source representatives.” The conversations are ongoing, one of the people said, and they are continuing with or without the Trump administration. In July, shortly after Hassabis publicized his proposal, Bloomberg reported Treasury Secretary Scott Bessent was considering a FINRA-like independent regulatory agency for AI that would report to the Securities and Exchange Commission. Not all tech leaders are on board. Meta CEO Mark Zuckerberg reportedly advised President Donald Trump against the idea in a phone call this summer, Politico reported earlier this month. Representatives for Google, OpenAI and Anthropic either declined to comment or did not respond to requests for comment. There are currently few industry standards or regulations for AI models and how they’re tested. Concerns over the safety of AI models and their lab tests have been raised after several incidents this summer where AI agents went rogue during testing. In the most severe case, OpenAI agents escaped their testing environment and hacked into another company’s systems to cheat on a cybersecurity test. The White House has an opaque system in place where AI companies can voluntarily submit their latest models for government review up to 30 days before they are publicly released. But the eligibility requirements for models or the process of the review has not been publicized. Late last month, a White House official told CNN the administration “continues to work with industry stakeholders on the implementation of the framework.” The AI companies say they want better standards. “I believe that shared safety standards and international coordination on further AI development need to be priorities now,” OpenAI chief scientist Jakub Pachocki told reporters at a briefing earlier this month. When asked what progress had been made on an industry wide standards body, Pachocki said they had been talking “to some external organizations about potential concrete standards we could put in place,” and that they’d have more to share “in the next months.” OpenAI CEO Sam Altman said in a late Sunday night post they “look forward to collaborating with our colleagues across the industry to formulate the best version” of industry standards. House Speaker Mike Johnson told CNN’s Jake Tapper on Sunday there’s little consensus among the companies, who are fierce competitors, on what the standards and guardrails should be. “There’s no consensus among them. And Congress is obviously less qualified than the people who are pushing this frontier to know all the ins and outs of it,” Johnson said, adding he thinks the AI leaders should immediately meet to hammer out some standards alongside the government. “So this has to be a partnership with the industry itself, with the corporations that are doing this and with the policy and lawmakers.” By Hadas Gold

Nvidia’s Jensen Huang Thinks Young People Are Overlooking the Skill That Will Matter Most

Tech executive Jensen Huang wants younger generations to focus on the foundation of engineering before heading straight into the world of artificial intelligence. For decades, the Nvidia CEO has helped foster the AI boom. But as younger generations enter the space, Huang wants newcomers to focus on one discipline he believes will outlast any wave of automation: engineering. “Engineering teaches you to start from first principles, grounding everything in physics and mathematics and applying it to real problems,” Huang said in a statement to Fortune. “It teaches you to break complex challenges into solvable parts, which is how we build systems at Nvidia.” At a moment when entry-level workers face growing uncertainty about how AI will reshape their careers, Huang is urging them to bet on a skill set rooted in problem-solving and grit. “You learn that meaningful problems require resilience, as the most important outcomes often seem impossible at first,” he said. “Over time, I’ve come to see engineering as the most noble profession. It is the foundational building block of modern society, enabling everything from infrastructure to computing to science.” Engineering first Huang’s emphasis on engineering isn’t just a response to the AI boom. It’s rooted in his own career. He studied electrical engineering at Oregon State University, where he joined IEEE, the world’s largest technical professional organization. Decades later, IEEE awarded Huang its highest honor, the Medal of Honor, recognizing his role in pushing the boundaries of modern computing. At the ceremony, Huang made clear that the AI era will be defined by both disruption and expansion—and that engineers will sit at the center of both. For Huang, AI isn’t eliminating work. Instead, it’s changing who defines it. “Engineers ultimately are the ones that take an invention and advance it in such a way that it’s safe, beneficial, ultimately transformative to society,” Huang said, according to Fortune. “The engineers in the AI industry must advance AI in service of a better future for all of us.” However, not everyone is in absolute agreement with Huang’s assertion. “First-principles thinking is a powerful mental framework, and it’s something people should absolutely incorporate as AI reshapes how we work. But I wouldn’t think about engineering as the answer in absolute terms,” Juan Jose Lopez Murphy, the head of data science and artificial intelligence at Globant, told Inc. The expansion and accessibility of AI isn’t just a trend, but a historical phenomenon. According to a report from Stanford, AI reached 53 percent public adoption in just three years, faster than any technology on record. This has led to a wider use of the new capabilities, such as vibe coding, where users can approach AI software with natural language prompts to instruct an AI assistant to build applications, websites, or games without writing or reviewing the source code manually. In this new environment, some are pushing younger generations toward developing a diverse set of skills—instead of specializing. “Engineering is one of many valuable foundations, and in a world changing so quickly, over-specializing can actually make you more fragile,” said Murphy. “The most durable skill is being able to apply strong problem-solving principles across disciplines and adapt as the technology evolves.” AI fluency According to the U.S. Bureau of Labor Statistics, engineering roles, from computer hardware to electrical systems, are projected to grow faster than average, with demand accelerating across AI, energy, and defense. Huang sees this moment as a meaningful opening for young professionals. “AI is expanding the scope of human work, not shrinking it. We’re busier than ever because we have more ideas to pursue, and AI makes it possible to go after them,” he told Fortune. “Jobs will change because tasks will change, but work grows with productivity.” That growth is already visible inside Nvidia itself. The company plans to double its workforce to roughly 75,000 employees over the next decade, hiring that will likely lean heavily on engineering talent, Fortune reported. But Huang’s advice doesn’t stop at choosing a major or career path. He argues that everyone, regardless of role, needs a baseline level of technical fluency. “Every young person should become an AI expert,” he said. “It is an incredible technology that significantly lowers the knowledge barrier of any professional field… AI fluency will empower you and elevate your chosen craft.” BY LEILA SHERIDAN, NEWS WRITER

Apple Broke Its Own Rules for 2 New Macs—and AI Buyers Are Snapping Them Up

Apple has predictable habits for releasing new hardware, so it was a surprise when the tech giant revealed new Macs with updated, more powerful chips last week. The refreshed hardware arrived before Apple’s traditional iPhone event, which this year occurs on Wednesday, September 9. So why did Apple release these machines, the Mac Mini and Mac Studio, when it did? Thanks to a new report, we think we understand: Apple’s updated desktop Macs were announced early because they’re selling like hotcakes and Apple wants to sell more of them to eager buyers. These machines are desirable because they’re ideal for acting as hosts for local AI models. According to news site The Information, the sudden AI-driven popularity of the machines caught the Cupertino, California-based company by surprise. Apple even apparently lacked the necessary enterprise-centric support teams. Why would anyone want to run an AI locally, though, when enterprise-ready AI systems are everywhere at the moment? Everyone from Google to Microsoft is offering powerful AI services from their giant cloud server farms, with plenty of business-centric tools. And platforms like Amazon Web Services offer powerful developer-friendly AI environments from their data centers. The keywords are “cloud” and “data centers.” These third-party AI offerings require gigantic computer facilities, which is why they’re offered by deep-pocketed tech giants like OpenAI on a remote-access basis. But if you run your own, less powerful AI models on your own hardware, then you remain in control of the entire operation. This means you can train the models with data that no one else can access. It means only you and your staff see the outputs when you query an AI. It means that if your developers improve the AI algorithms in clever ways, then only you benefit. All of this is useful for intellectual property protection and even, for some companies, for preserving audit trails for sensitive data handling. Finally, hosting an AI model on your own hardware means you retain control of the cost. Spiraling, expensive AI bills for remote-access services have surprised many companies in recent months, landing some users in the headlines because they owe so much. Thus Apple’s reasonably priced Macs are suddenly hugely popular. The newly updated machines are even faster than earlier versions for AI purposes, too: Apple highlights that the new Studio can carry out “Up to 9.8x faster” LLM AI prompts, and the new Mini has similar improvements over previous editions, thanks to the new M6 and M5 Pro processors inside. MacRumors pointed out that when Apple announced the new Macs, it even highlighted that they can be clustered together to create a powerful compute facility (think almost-a-supercomputer on your desktop) for exactly this purpose. The Mac Mini’s prices start at just $900, leaping up if you select more powerful chips or more memory. The more powerful Mac Studio starts at $2,500, and higher-end configurations can reach many thousands of dollars. But these remain one-off purchases, compared with paying an open-ended bill for AI-as-a-service from the likes of OpenAI. There’s a lesson here for many smaller-business owners. AI is the buzziest tech right now, and it comes with the promise of boosting efficiency and maybe helping to cut costs, too. But typically, because AI is delivered by cloud-based companies like OpenAI or Google, each system brings its own risks in terms of potential expenses as well as data leaks. Surveys show a staggeringly high number of workers merrily type sensitive company data into these chatbots, unaware of the risk they pose. So running your own AI models on machines like Apple’s new ones could be a good solution. And even if you’re not in the market for building your own safer, Apple-powered local AI server, other options exist. Some AI models are freely downloadable and can run on relatively modest hardware. This may be an even simpler and cheaper option, depending on your company’s AI needs. The news should also serve as a reminder to savvy CEOs that deploying AI is not a one-and-done maneuver. The tech is advancing fast, so continuously evaluating which AI best serves your needs at what price could save you money in the long run. BY KIT EATON @KITEATON

5 Side Hustles You Can Start Today With AI

Artificial intelligence can be a significant income source for people who choose a smart side hustle. AI may still have a long way to go with some tasks, but with others, it can increase productivity and results. And there are a number of companies looking to take advantage of those benefits. The best AI-driven side hustles use the technology as a great equalizer. Small business owners, particularly of established businesses, often use AI in a limited fashion, if at all, says Peter Hansen, director of research and policy analysis with the National Federation of Independent Business (NFIB). Those founders value and emphasize the human connection to their customers. But even if they don’t use it, they recognize AI can help in other areas. Many of those owners don’t have the time to learn the ins and outs of various AI systems. And even companies that are using AI are often not taking full advantage of it. That creates an opening for side hustles that center around AI. “More than three-quarters of businesses already using AI report higher productivity, yet most are still applying it to a narrow set of tasks,” says Simon Worsfold, head of data communications at Intuit QuickBooks, which puts out a monthly Small Business Index exploring market trends. “What is emerging is a large, underserved layer of advisory, implementation, and training services that small businesses need in order to scale their use of AI,” he says. Here are five ideas for AI-focused side hustles that could be lucrative—or even become the foundation of a full-time business. AI Consultant This is a side hustle with multiple branches. Some people are well suited to help people or businesses integrate AI with their calendar, notes, and local files. Others can develop workflow audits that review how a company’s employees work to discover repetitive tasks that the AI could handle. Rates for an AI consultant range from $150–350 per hour for independent workers and people running it as a side hustle. Those can jump as high as $600 per hour if you convert this side hustle into a full-time company. AI Researcher To stay competitive, businesses need to know what their competitors are doing and what’s trending in their industry. That data can be remarkably complex, however, and not every founder or CEO has the time to do the deep dive it requires. Side hustle operators with strong AI skills can use the technology to filter the data into easily digested insights, then sell those as reports, newsletters, or direct subscriptions. You’ll need to carefully identify the best source material and be sure to double check the output, since AI’s comprehension is still flawed. “The AI doesn’t give the correct answer all the time,” says Shaun Ghavami, Founder of 10XBNB, a side hustle-turned-business that now oversees a portfolio of over $100 million of short-term property rentals. “The real skill is in prompting.” GEO (AI Search) Optimization For the past two decades, businesses have hired specialists to help them rank high on search results pages. While that’s still valuable placement, smart business owners are also looking into how they can be among the frequently-cited sources for AI search results. People with a combination of AI and content knowhow can create a side hustle specializing in this. You’ll teach companies the best ways to refine their content and meta tags and improve readability for large language models. If you have the tools to measure analytics, such as Alli AI, Frase, and MarketMuse, you have a better chance of establishing a recurring relationship with those clients, increasing your income and establishing a base should you want to make this more than a side hustle. Content Repurposing (Clipping) Both content creators and businesses regularly churn out videos and stories as promotional tools, or in an effort to be seen as thought leadership experts. Quite often, though, that content needs to be pared down before they’re posted on everything from TikTok to LinkedIn to Instagram. And that takes time, which the companies and content creators may not have to spare. As an AI content repurposing expert, you can transform the long-form content into digestible bites to widen the potential audience in a much shorter period of time. Use tools like CapCut, Canva, and ChatGPT to effortlessly pick and highlight the most relevant parts. Create AI Microtools AI can be intimidating to many people who don’t know where to start, especially when it comes to creating agents or GPTs that are relevant to their needs. A smart side hustle operator, though, knows that you don’t have to be an expert to create your own AI tools. Sites like Gumloop let you visually build workflows that can be run at large scale. You’ll use a drag and drop template that lets you create workflows that do just about anything, from running a search engine optimization audit of a Website to a ranking system for job applicants. You can either custom create those for clients or build general purpose tools and sell them on Etsy or some other marketplace. BY CHRIS MORRIS @MORRISATLARGE

What Burning Man Can Teach Startups About Surviving the AI Age

Turning an idea into a business used to be so costly and time consuming that, when we had thoughts such as, “What if an app could let my neighbors order homemade dinners from each other?”, we rarely took action. Instead, we just waited until someone brought something to market and then told our friends, “That was MY idea!” Now, with AI, you can turn any frustration-induced idea into an app or prototype in a weekend. The barrier to success for startups is no longer execution; it’s ease. How can you succeed if your product can be easily produced by everyone else? The answer may be found right now in Black Rock City. Once a year, tens of thousands of people descend upon the Nevada desert to co-create Burning Man. They produce awe-inspiring art installations, build outrageous “mutant” vehicles, and put on immersive performances guided by principles like gifting, radical self-reliance, radical self-expression, and communal effort. Stanford professor Fred Turner explains how Burning Man provided the “cultural infrastructure” for much of the startup tech industry. Open-source, collaborative, project-based ways of working and utopian dreams dominated the early ethos of companies like Google. AI can’t copy the most important aspect of Burning Man. AI can render a flame-throwing octopus sculpture in thirty seconds, but it can’t render the experience of the guy who spent four months welding it in his garage. The value of Burning Man comes from being there, expressing yourself, building something, meeting people, and contributing. Participation, belonging, and shared experience are hard to duplicate. This suggests three places to look for customer value your competitors can’t duplicate. When technology makes things abundant, look for what becomes scarce. When everyone has a vending machine, fast snacks are no longer a lucrative business. AI didn’t just lower the barrier to entry. It removed it and bulldozed the building behind it. If you want a competitive moat today, allow people to create unique value for themselves and others in unplanned, irreproducible ways. Don’t just ask, “How can we use AI to create more value for our customers?” Ask: “What can our customers create for one another that we can’t, and AI can’t?” My last article explained how The Rocky Horror Picture Show turned audiences into participants and created a 50-plus-year loyalty phenomenon. Burning Man takes the idea even further. “Burners” don’t just enjoy a product. What they create for one another is the product. When technology makes things easy, look for meaningful friction. Ease and value don’t always go together. Burning Man is what happens when nobody optimizes the customer journey. Most founders see a week in the desert hauling water and building shelter and think: how do we remove this pain point? Burners built a culture around refusing to answer that question. You can’t just watch Burning Man. You have to invest heavily in creating it. As much as humans like to make things easy, we also value what’s hard-earned. We invented the car to stop walking, then invented the treadmill so we could walk hard—and post about it. As AI makes more things easier, people will start looking for new hard-earned experiences they can grow from and be recognized for. Before using AI to eliminate effort for your customers, ask whether any of that effort creates mastery, commitment, connection, pride, or a sense of ownership. Eliminate the friction people resent, but amplify the friction they value. When technology makes things predictable, create room for surprise. AI writes in a voice it’s already heard about things that have already happened. That’s why so many AI logos look like they’ve been designed by the same nonexistent designer, and why AI copy reads like it was assembled from other copy that was assembled from other copy. Burning Man has no training data. Nobody can predict what happens next, including the people running it.It’s hard to pre-engineer surprise, but you don’t need to. You just need to create a way for unique individuals (your customers) to contribute meaningfully, create value for one another, and build things you never would have thought to build for them. Here’s the Bottom Line AI makes things easier to build but harder to matter. Your greatest value might not come from what you produce, but from what happens when your customers show up and contribute. BY STEPHANIE DAVIS

Bill Gates Says the AI Era Will Be Turbulent. Here Are 7 Things You Should Do Right Now

Imagine showing up for work one morning and discovering that the task you spent years learning to do can suddenly be done by AI — faster, cheaper, and perhaps even better than you can do it. For millions of people, that scenario may no longer be science fiction. Bill Gates recently published a thought-provoking essay about what he calls the “turbulent AI era.” His message is both optimistic and unsettling: AI has the potential to make life dramatically better, but the transition could be enormously disruptive. Gates believes AI will affect both white- and blue-collar jobs. Unlike previous technological revolutions, the change could happen remarkably quickly. He argues that society needs to start preparing now rather than waiting until millions of workers are displaced. So, what does that mean for you? Here are seven things you can do right now. 1. Stop assuming your job is safe. AI isn’t just coming for repetitive factory jobs. Gates points to sales, customer support, software engineering, paralegal work, data analysis, and other occupations as vulnerable. Take an objective look at what you do every day and ask yourself: How much of this could AI eventually do? 2. Learn AI before you need it. Don’t wait until your boss tells you to start using AI. Experiment with the tools available to you now. The more you understand what AI can and can’t do, the better prepared you’ll be. 3. Use AI to make yourself better. Instead of viewing AI solely as a competitor, make it your assistant. Use it to research, brainstorm, analyze information, automate routine work, and free up your time for higher-value activities. 4. Double down on being human. Gates proposes an intriguing idea he calls “Human Reserved” — work that society deliberately decides should remain in human hands. Empathy, judgment, trust, creativity, leadership, and genuine human connection will become increasingly valuable. Strengthen those skills. 5. Keep learning. The worst strategy in a rapidly changing workplace is standing still. Identify skills that are becoming more valuable in your industry and start developing them before you desperately need them. 6. Help your people prepare. If you’re a leader, don’t pretend nothing is changing. Talk openly with your employees about AI. Give them access to tools and training and involve them in deciding how AI should be used. Your people deserve the opportunity to adapt. 7. Don’t surrender your judgment. AI can produce impressive answers, but that doesn’t mean you should stop thinking. Gates is particularly concerned that overreliance on AI could weaken critical-thinking skills. Use AI to extend your intelligence — not replace it. The AI revolution isn’t something that’s going to happen someday. It’s happening right now. You probably can’t control how quickly AI develops or what your competitors do with it, but you can control how prepared you are. And the time to prepare isn’t after your job changes. It’s before. EXPERT OPINION BY PETER ECONOMY, THE LEADERSHIP GUY @BIZZWRITER

The 1 Big Compensation Decision Founders Should Never Hand to AI

AI has quickly made itself useful across hiring. It can sift through résumés, draft a job description in seconds, and match a role to market benchmarks. For a small company that never had a compensation team, that’s a genuine unlock. But things get thornier when AI has a say in what the actual salaries that companies pay their employees should be. And that’s the bigger question that founders now have to grapple with—whether they should let AI set the number itself. That pressure is only growing, and this year it found a sharp edge in the law. On October 8, 2025, California outlawed a hiring shortcut that employers had leaned on for years. When Governor Gavin Newsom signed SB 642 into law, effective January 1, 2026, the state redefined what a “pay scale” in a job posting is allowed to be: a good-faith estimate of what an employer expects to pay a new hire. The point, as the law firm Morgan Lewis noted, was to stop employers from posting “meaningless pay scales simply to be in compliance.” So the old dodge is dead. Post a range wide enough to protect yourself, say from $80,000 to $200,000, and you’re not being cautious. You’re non-compliant. California isn’t alone, and these rules don’t only apply to big companies. As of early 2026, more than a dozen states now require employers to disclose pay, and the thresholds reach startups, not just giants. In Colorado, a single employee is enough to trigger disclosure, while New York City draws the line at four; states like California, Illinois, and Washington set it at 15. For founders who have always set pay based on their instincts, that guess is now a public document, and the person most likely to read it closely is the employee who already holds the job. And not just them: everyone doing similar work will see the number too, and measure themselves against it. The problem was always there Improvised pay stays invisible while it works, and it works right until two people doing the same job discover they earn thousands apart, or a strong hire leaves for a competitor who bothered to benchmark the role when you didn’t. Jonas P. Johnson, who works on compensation modeling at ERI Economic Research Institute, a firm that has built salary data for more than three decades, says instinct-based pay produces one of two problems, and they aren’t equally fixable. Underpaying firms struggle with turnover, while overpaying firms struggle to keep prices competitive. Most founders assume overpaying is the safe mistake. Johnson says it’s the harder one to escape. Underpaying is the easier hole to climb out of, because raising pay tends to slow the turnover it caused. Overpaying is the real trap, since you can’t take money back once people are earning it. “Employees won’t accept pay cuts,” Johnson says, so a company that has drifted above market has to hold raises below average for years to come back down, all while trimming costs to survive. The case he sees most is the firm that bought out a competitor and inherited its payroll, absorbing someone else’s guesswork and making it their own. How one raise becomes a chain reaction Transparency rules do more than reveal the gap between what you pay and what the market pays. They reveal the distance between your newest hire and your most loyal one, which is the first comparison employees make. The way it unfolds is easy to trace. When market rates climb, you post a higher range to attract someone new, and that range is now visible to the person who has held the job well for three years, and to their peers in similar roles. Fresh money at the top squeezes the space between new and existing staff, and Johnson warns it “can lead to turnover among existing employees unless the organization re-scales the entire salary structure.” A single number in one posting can force you to reopen everyone’s. Compensation professionals call this pay compression, and it hits morale before it shows up in a budget. Jason Greer, an employee and labor relations expert, says a compressed structure creates “zombie employees” who “show up, clock in, and do the minimum.” They haven’t walked out because they have learned what a newer colleague earns and have reduced their effort. His advice is to understand your local market before circumstances force the issue, because “you might be global, but to your employees, you’re local.” Why the average raise misleads you Guesswork is seductive because there’s always a tidy number to reach for. Heading into 2026, most surveys cluster raises between 3.2 and 3.5 percent, down from post-pandemic highs near 4.4 percent. But the average describes almost no one. Some jobs see no salary growth year over year, Johnson says, while others climb 8 percent. Pay tends to lurch rather than climb, and his clearest example comes from the start of the pandemic, when compensation for forklift operators had been flat for years and then jumped 22 percent in two months as supply chains seized. Budget off the blanket figure, and you underpay the very people you can least afford to lose. The one call you shouldn’t hand to AI None of this complexity means a 30-person company needs the machinery of a Fortune 500. A large firm leans on formal pay grades to keep several hundred jobs consistent, because at that scale nobody can price every role by hand. A 40-person company has maybe 20 distinct roles and can price each against the market directly, without collapsing them into bands. The point isn’t to build an elaborate structure, since none of this needs a complicated setup. You just have to know what a job is really worth before putting a number on it. What makes that reachable now is that the grunt work has gotten cheap. Matching your internal roles to market data once meant hiring a consultant for weeks. Today, the same first pass takes about a day using AI tools. But Johnson is clear that speeding up the work does not mean handing over the decision. Software can widen the field of comparable jobs and flag where your pay drifts out of line, yet “all final decisions should be made by humans,” he says, because an AI model will always return a confident number even when the data behind it is too thin to trust. Workforce strategist Terri Gallagher pushes the point even further, arguing that compression isn’t a pay problem at its root, but instead “a workforce strategy issue.” The number you choose shows what you think the work is worth, and no tool can decide that for you. The answer, then, isn’t to keep AI away from pay. Let it do the first pass, gather the comparisons, and show you where you’ve drifted. Then make the final call yourself, because that judgment is the one part of the job that was always yours. The expensive answer For most of the past decade, “we’ll figure out compensation as we grow” was a reasonable thing to say. The cost of having no system stayed hidden; a little attrition here, a bruised ego there, all of it absorbed into the churn of a growing company. Transparency law takes that hiding place away. The guess is a public document now, the compression is plain to the people it costs, and the tools to do the work properly have never been more affordable. A founder who treats all this as a compliance headache will keep patching it one uncomfortable job posting at a time. But the one who treats it as a reason to finally learn what each role is worth will find the law was never the real issue. The real issue was always whether they knew what they were paying for. EXPERT OPINION BY KOLAWOLE ADEBAYO