Why China’s New AI Model Has Silicon Valley and Washington on Edge

A little more than a year-and-a-half after China‘s DeepSeek put fear in the hearts of U.S. AI companies, another Chinese model has been released that further narrows the gap between Chinese and the most advanced U.S. systems. Chinese startup Moonshot on Friday unveiled Kimi ​K3. It’s an open-weight model, meaning developers can access, study, and build on top of its architecture and users can download, run, and customize the underlying systems, unlike ​closed-source models most commonly used in the U.S. Moonshot claims Kimi K3 surpasses top systems from both OpenAI and Anthropic in some benchmarks (though it falls short of Claude Fable 5 and GPT 5.6 Sol on overall performance). Kimi K3 is the largest AI model to come from China to date, with 2.8 trillion parameters, a figure that refers to the size of its neural network. And AI experts are saying it could signal a tipping point in the industry. “Kimi K3 may be an important inflection point for AI,” wrote Gavin Baker, managing partner at investment firm Atreides Management, in a social-media post. “A world where there are only [two to three] dominant frontier labs with 90 percent inference margins is net negative for every other layer while being awesome for those [two to three] labs.” It took just a few hours for Kimi K3 to jump to the leader position in the Frontend Code Arena, a live evaluation and benchmarking platform that tests how well artificial intelligence models generate user-facing web applications. That marked the first time a Chinese model had taken that spot, which sounded alarms among tech and political leaders. Some say this bolsters the argument many AI evangelists have been making against guardrails on American AI companies. “This is concerning,” wrote David Sacks, the general partner and co-founder of Craft Ventures who previously served as the White House AI and crypto czar. “America is tying itself in knots: politicians and bureaucrats are banning new data centers, piling on state regulations, and pushing for new federal agencies to pre-approve frontier models. This is how you lose the AI race. The rest of the world won’t play by our rules if we bog ourselves down.” Kimi K3’s launch came less than a month after Anthropic’s Fable and Mythos models were withdrawn by the U.S. government due to security concerns. Seeking dominance Kimi K3 represents a threat to the U.S. attempts to secure dominance in the AI world. Federal officials have laid out an AI Action Plan, which calls for accelerating innovation, building massive domestic data center infrastructure, and leveraging advanced AI for national security. Aside from national security concerns, there are financial factors at play. Three major AI companies are in various stages of going public. SpaceX (which contains Elon Musk’s AI company) made its Wall Street debut just over a month ago, with the largest IPO in Wall Street’s history. (Shares have since fallen 22 percent from their first trade price and are currently below the IPO price.) OpenAI and Anthropic, meanwhile, have both filed confidentially with the Securities and Exchange Commission as well, though neither has announced a fixed date yet. (Anthropic is currently scheduling investor meetings and is said to be targeting a possible October IPO, while OpenAI is said to be considering waiting until next year.) There have also been whispers of the government taking an equity stake in AI companies, though nothing has been decided. Chinese AI firms, though, have been releasing new models at an increasingly faster pace. Last month, for instance, Chinese AI startup Z.ai released its new flagship model, GLM-5.2, whose benchmarks narrowly trailed closed-source models. That model operated at roughly 17 percent of the cost of closed U.S. models, raising fears that U.S. companies could use a Chinese-created model, rather than a domestic one. Analysts at the time believed China’s AI models were four- to six-months behind the top models in the U.S. but were forced to reevaluate those thoughts. Kimi K3 appears to narrow the gap further and heightens fears that China may not only be catching up with U.S. AI companies, but could one day surpass them. “Despite persistent hardware/compute capacity constraints in China, K3 demonstrates that pre-training scaling, paired with architectural innovation, can still deliver step-change gains for flagship Chinese models,” said Bank of America in a note led by Alex Liu. BY CHRIS MORRIS @MORRISATLARGE

Nvidia’s Jensen Huang Just Declared the ‘Next Frontier’ of AI—and It’s Not Chatbots

They may not have been delivered with the sonorous tones of Star Trek’s William Shatner or Patrick Stewart speaking over the show’s iconic intro, but when Nvidia’s CEO Jensen Huang talked about the “frontier” of AI this week, his words still carried significant weight. That’s because AI is seemingly everywhere and in everything at the moment, and Nvidia‘s chips are on a (much longer than five year) mission to power as much of the revolution as possible. Huang was technically unveiling a new AI model—which you may think is not really news in a time when new AI models are popping up like weeds through cracked pavement. But the new Cosmos 3 Edge model Nvidia revealed is something different. It’s a “world model.” Speaking in Japan, Huang’s comments were aimed partly at the nation where Nvidia is creating a coalition with some of the biggest tech manufacturing names, but they’re also a significant sign about where AI is going in the near future: into robots. “The next frontier of AI is in the physical world,” Huang said in a statement, adding that the coalition being created to tap physical AI is a “once-in-a-generation opportunity for Japan.” He noted that, in many ways, “Japan invented modern manufacturing,” and as he sees it, the country now has an “opportunity to reinvent it for the age of intelligent industries.” But Huang’s words will echo far beyond Japan’s borders. While OpenAI, Anthropic, and other players in the game release large language models to power their linguistic-centered AI tools, Cosmos 3 Edge is designed to help systems perceive and then navigate and manipulate physical environments in real time, CNBC reported. This kind of “world model” takes in very different types of data to the often text-centric material used in AI chatbot training, technically teaching the AI physical details about things in our human world. In a press release, Nvidia claims Cosmos “combines vision reasoning, world generation, and action prediction.” It’s all about understanding and generating “text, images, video, ambient sound and actions with leading physics accuracy.” The goal is to allow robots to maneuver in the real world and manipulate items they encounter. Think of a world model as a kind of enhancement of the AI that gives Teslas their real-time self-driving powers. Robots—be they humanoid or more purpose-designed, like welding bots in car factories—will need this kind of understanding if they’re to move into our workplaces and homes in a safe way, and if they’re to have the ability to take on different jobs they’re assigned in real time. This is where the Cosmos 3 Edge gets its name, because it’s designed to do “real-time inference at the edge.” This means that significant AI processing will happen inside robots, at the “edge” of the digital network they’re connected to, allowing the robot to, for example, predict which way something that it throws will go. Why should you care about this? Robotics company 1X recently revealed a spectacularly dexterous robot manipulator it had created for its humanoid bots. The new hand is capable of unsettlingly human-like movements, and is both delicate and precise enough to pick grapes from a bunch or to speedily manipulate a gaming controller. In its press release, 1X suggested that human-like hands were one of the last barriers to allowing robots to work in human workspaces, and that the only limit left was the sophistication of the AI code. Huang proclaiming physical AI as the “next frontier” his tech giant company is going to conquer is another giant sign that the robotic revolution may be arriving much sooner than you think. Remember, ChatGPT only really arrived in late 2022… and now sophisticated AI tools are everywhere online. If your company’s plan for the next couple of years doesn’t involve robots, that’s probably fine, but you may want to get your planning team thinking about how robots will be working for you inside five to 10 years. BY KIT EATON @KITEATON

Google’s Head of Search Shares 3 Rules Every Business Needs to Win in the AI Era

Google Search has undergone some serious changes that will likely have big implications for how businesses present themselves online—and how people find them. Google VP of Search Liz Reid says there are no quick tips to hack the system, but she does explain there are techniques that entrepreneurs and business-owners can use to optimize their digital footprint for AI search. Hint: it’s all about quality. When Reid took stage at the tech company’s annual developer’s conference in May, she spelled out her vision for a brand new world of Google Search. She touted the biggest change to Google’s iconic search bar in 25 years. That included baking AI into the search experience through conversational language search interface, contextual search history, and even search with video and images. She also teased agentic features that are still only slowly rolling out to select users. Given the fact that AI Mode rolled out in 2025, these changes are hardly the first to rock Google search in recent years. Entrepreneurs may be understandably confused about how best to optimize their online presence, as AI changes the game for keywords and SEO. Despite all these changes and perhaps a bit of confusion, there are still some best practices to keep in mind. Reid sat down with Inc. to share her advice for getting the best results out of Google search for your company. Go deep and niche Reid says the recent changes to Google Search have resulted in a higher overall volume of queries and a change in their nature. The incorporation of generative AI into Google Search means people are ditching keywords in favor of questions that are phrased the way they might actually ask them. They are getting specific, providing context, and asking follow-up questions. Because users are able to get more detailed about their questions and preferences, Reid says this new behavior offers a real opportunity for companies to seize upon what makes their product or expertise unique. She gives the example of searching for shoes, which in the era of keyword search may have weighted many shoes equally. Now users can specify that they want “eco and sustainable shoes,” made by a founder with certain specifications, Reid notes. “Now this opportunity for people to differentiate themselves and shine becomes much more possible than if you could have done it before with keywords,” she says. Of course, that means business owners have to work that individuality into content on their own websites, online storefronts, and social media. Make content with value Reid encourages entrepreneurs not only to identify what makes them unique, but create value around that. In e-commerce, that might mean uploading more detailed product information to Google’s Merchant Center, and including nuanced descriptions of products on a company’s website. For a service-based business, that can mean focusing on thought leadership through mediums like podcasts, articles, and blogs that dig deeper than surface level information people may already be able to find in a Google AI overview. “Sometimes you’ll have a bunch of sites in which they’re basically saying the same thing as 500 other sites. That’s not going to go,” she says. “People would rather go and see a site that doesn’t say this exact same thing as AI overviews, but now takes it down to a much deeper level.” And, of course, Reid says, content has to be interesting: “It’s not going to rank well if when people click on it, they hate it, and they leave right away. So the first thing you should figure out is, would anyone want to read it?” Be wary of GEO claims SEO, or search engine optimization, has quickly given way to what’s being called GEO, or generative engine optimization. And a number of businesses have already been built on the claim that they’ve unlocked the secret to GEO. Reid says entrepreneurs should be skeptical of such claims and check out Google’s own resource with best practices to help people optimize for generative AI. The site notes that SEO rules still apply, and offers more detail on what high quality content actually means. It also includes “myths” about AI search, including that websites must have machine readable .txt files, content written especially for AI, or specially structured data. “I would definitely encourage people to use the tips and to express some skepticism in what they’re reading from some folks, especially if there’s business incentives for them to claim they can solve all your problems,” she says. BY CHLOE AIELLO @CHLOBO_ILO

The Best Leaders Aren’t Replacing Employees With AI, They’re Empowering Them

For years, you’ve been told that artificial intelligence is coming for your job. Silicon Valley insisted automation would replace millions of workers. Now, the people who pushed that narrative are changing their message, and that shift could reshape how you think about technology at work. OpenAI chief executive Sam Altman and other prominent tech leaders are completely abandoning the doomsday narrative. They have realized that terrifying your workforce is a terrible way to scale an enterprise. When your employees constantly fear for their livelihoods, operational momentum stalls, engagement drops, and your best talent starts looking for the exit. To protect your company and accelerate your growth, you must completely flip your internal messaging. You need to frame these new tools as massive productivity multipliers that make your people indispensable instead of replaceable. Here are three critical leadership lessons to help guide your team through this technological transition. 1. Stop selling fear and start selling execution power. If you tell your staff that an algorithm can do their jobs faster and cheaper, you immediately destroy all psychological safety in your organization. Workers who feel threatened will actively resist adopting the expensive new tools you just purchased. Instead of framing automated software as a replacement for human talent, you must position it as an upgrade for human potential. Show your team exactly how automation eliminates their most tedious administrative tasks so they can focus on high level strategy and creative problem solving. According to recent Gallup workplace data, broader technology adoption among employees is strongly associated with having active managerial support that focuses on strategic integration. 2. Ditch top-down decrees for internal champions. You cannot force true innovation through an executive mandate. Many white collar workers remain deeply skeptical of the automated tools their employers roll out. If you send a company-wide email demanding immediate compliance with a new system, your team will only use the tool when you are actively watching them. People inherently resist mandates that are forced upon them. To build a truly innovative culture, your adoption strategy must rely on organic influence rather than executive pressure. Look closely at your team and identify the employees who are already experimenting with these tools on their own time. You must empower these enthusiastic early adopters to train and encourage their peers. When a highly skeptical employee sees a trusted colleague using new software to finish a project early and leave the office on time, they will eagerly ask for a tutorial. Peer influence will always be your most effective strategy for managing corporate change. 3. Protect human ownership of the final output. The goal of implementing automation is never to turn your employees into passive observers. If your team members become completely dependent on algorithms to think for them, the quality of your business output will rapidly decline. As a leader, you must remind your workforce that human judgment remains your ultimate competitive advantage. Encourage your team to question automated summaries, challenge algorithmic data, and inject their own unique perspectives into every project. True productivity happens when human creativity directs the technology, not the other way around. The ultimate leadership takeaway The companies that thrive in this new era will not be the ones with the most advanced algorithms. They will be the ones with the most empowered humans. Your primary job as an entrepreneur is to build a culture where technology serves your people. Stop predicting the end of human labor and start building a workplace where your team feels truly irreplaceable. EXPERT OPINION BY ASH KUMRA

AI Was Supposed to Save Companies Money. Instead, It’s Blowing Up Budgets in a Big Way

The refrain from executives amid the seemingly-continuous job cuts over the past few months has been a common one: AI can do the job at a lower cost than human workers. But a new report has issued a stark warning: That school of thought is wrong. Very wrong. A survey from KPMG finds business owners are aghast at their bills for AI, now that many AI companies have shifted to a usage-based model. The accounting firm spoke with 2,145 executives around the world, and one-third said they had a limited understanding of usage costs. AI companies used to charge corporate clients a flat rate, but as compute costs have increased, many major operators are switching to a different model to help control costs. That wasn’t factored into some executives’ decisions to go all-in on the technology. “AI is now as much a financial management priority as it is a technology one,” Rob Fisher, global head of advisory at KPMG, said in a statement. “The real risk isn’t investing in AI but doing so without cost visibility and an understanding of the economics of AI. Organizations that have visibility into their costs and maintain strong oversight are the ones translating AI investment into real, measurable value.” Making matters worse, the higher pricing model comes as many businesses are still figuring out how to use AI efficiently. Many did not realize, for instance, the need to build the capabilities required to forecast, monitor, and manage AI spending, the report says. There have been several examples of this in the past year. Uber blew through its entire 2026 AI budget in just four months. (The company has since set usage caps on various AI-powered tools used by its staff.) Another company, which remains unnamed, spent $500 million on AI in just one month, since its employees apparently had no limit on how many licenses they could use. AI companies acknowledge the rising prices but aren’t signaling things will change anytime soon. Last month, OpenAI CEO Sam Altman said in an interview: “People are really saying, ‘My company spent my entire 2026 budget in Q1. Can you make this more efficient?’” And since the start of the year, Altman continued, it went from being “an issue that never came up (people were totally happy with the amount they were spending) to, all of a sudden, a huge issue.” Part of the reason for that new urgency is the escalating cost of new models as AI companies battle for supremacy. Each new top-level release is “roughly twice as expensive per token as the one it replaced,” Arvind Jain, CEO of AI company Glean, told CNBC. Prioritizing people While there has been no slowdown in tech layoffs so far (though Gartner says half of those will be reversed by 2027), a growing number of executives say they’re focusing more on human-AI collaboration, utilizing the advantages of both, and choosing to upskill their remaining workforce. “By putting AI directly into the hands of their people, organizations are better positioned to translate adoption into real business value,” KPMG’s report says. Value is key, as just 7 percent of the executives surveyed said they were seeing a return on investment in AI. Nearly one-quarter of those executives, however, said they were facing pressure to prove the technology’s value to investors. Step one of that is getting a better handle on spending. Some 23 percent said they struggle with usage-based costs, and 42 percent said they only have partial visibility into AI spending. That’s making tools like monitoring dashboards, which track the cost of each employee’s AI usage, more common. And roughly half of the executives say cost reviews have become part of the AI approval process. Companies that take those steps, says KPMG, are five times more likely to report an established ROI. “We’re seeing a clear divide between organizations with leadership accountability at the top and those without,” said Steve Chase, KPMG’s global head of AI and digital innovation. BY CHRIS MORRIS @MORRISATLARGE

Research Says Leaders Are Overlooking 1 Simple Way to Accelerate AI Adoption

Almost every company is doing something with AI right now. Some are testing chatbots. Others are automating workflows, redesigning roles, or asking employees to “use AI more” without much direction. But embracing AI and scaling it well are two very different things. According to the 2025 McKinsey Global Survey on AI, only 38% have successfully begun scaling AI across their businesses. Enter the workforce readiness recession. Employees aren’t falling behind because they’re resistant to AI, but because they lack the confidence, clarity, and reinforcement to embrace it. As AI reshapes the workplace, leaders must become both advocates for change and trusted guides through it. According to Achievers Workforce Institute’s (AWI), leaders under pressure to demonstrate AI ROI may be overlooking one of the simplest ways to accelerate adoption: employee recognition. Recognition isn’t separate from an AI strategy—it’s a practical leadership tool for accelerating adoption. Stop readiness from falling behind AWI’s seventh annual State of Recognition Report finds that just 19% of workers feel confident using AI tools, and only 18% feel supported in adapting to AI. How can companies expect results when over 80% of employees haven’t been given the confidence or support to see where AI fits into their day-to-day work? “Those who create the conditions for employee change readiness will separate the winners from the losers, both in the race to realize AI’s potential and in building a great workplace culture,” said David Bator, Managing Director of AWI. “Employees aren’t going to wake up one day ready to do their best work with AI. Change on that scale is never automatic. Confidence is built brick by brick through everyday leadership behaviors. Leaders who embrace recognition will be the ones who create the confidence, trust, and advocacy needed for AI to scale across their businesses.” AWI’s research shows recognition is most effective when it reinforces learning, adaptability, and progress rather than perfection. Closing the recognition gap closes the readiness gap Leaders have long fallen short on recognition. The first thing AWI advises is to get right is frequency. Every employee should receive meaningful recognition at least monthly to feel supported through change, yet just 19% of workers say they are regularly recognized by their manager. Leaders need to make regular recognition a management requirement, not an afterthought. Then focus on one keyword: meaningful. In the AI era, meaningful recognition isn’t about celebrating AI for AI’s sake. It’s about recognizing the human capabilities behind successful AI adoption. If an employee uses AI to uncover new sales opportunities, don’t recognize the technology, but the creativity, initiative, and business impact behind its use. “A common misconception is that recognition does nothing beyond making people feel good,” added Bator. “While celebrating your people early and often is important, leaders should see appreciation as a change catalyst. When managers reinforce learning, adaptability, and responsible AI use, they recognize good work and help drive organizational progress as AI integrates into daily work.” AI raises the value of humane leadership There’s no denying it: AI is a force of disruption at work. But every major technology transformation has ultimately been about people. A great leader understands this and ensures employees experience change as something they can grow through, not something being done to them. Right now, there is a lot of ground to make up: just 18% of workers feel informed when changes affect their job, and only 23% say communication is clear during uncertainty. Employees are asking for clarity, coaching, and confidence, and leaders can’t delegate that responsibility to AI. Recognition is most powerful when it comes from another human being. In my book, Humane Leadership: Lead with Radical Love, Be a Kick-Ass Boss, I argue that humane leaders exhibit two essential qualities —trustworthiness and advocacy —that matter even more in the AI era. Leaders bring clarity to AI by using recognition to reinforce what good work and responsible behavior look like during rapid change. Done well, recognition helps employees trust themselves, trust their company, and understand what great work looks like in a workplace being reshaped by a technology we have never seen before. EXPERT OPINION BY MARCEL SCHWANTES, EXECUTIVE COACH, SPEAKER, AND AUTHOR @MARCELSCHWANTES

IBM’s CEO Has a Message for Founders: Treat AI as ‘Day Zero’

We’re fast approaching the fourth anniversary of the launch of ChatGPT, meaning artificial intelligence has been a part of the business conversation for quite some time now. Many companies have experimented with it or attempted slow roll-outs in select areas of their operations. But IBM CEO Arvind Krishna says the days of sticking your toes in the water are over. It’s time to jump in. The rollout of the technology, he says, should be treated as a “Day Zero” event, a chance to reset the competitive race among businesses. But to do that, your business needs to start implementing AI at scale. “It’s time to sit down and take it seriously,” Krishna said on the Masters of Scale podcast. “You’re not in the experimentation phase. Day Zero, the race is about to start. Put yourself in the blocks and start sprinting.” Krishna says he isn’t talking about incorporating AI in every aspect of your company or automating a large percentage of the workforce. Instead, he recommends fully embracing AI in some aspects of your business as a case study of sorts to help you better understand what it can do for you. From there, you can expand your use of AI. “Take three, four, five things—not 100—and learn how to do them at scale, because that’ll teach you how to get all your change management done,” he said. “How do you get your data organized? How do you really get people motivated to change a process? Do a few things at scale. Learn how to do that really well. Then do 10—and then give yourself the confidence to do the next 20.” Despite all the talk of AI, Krishna estimates that just 20 percent of businesses are utilizing it correctly. The rest, he says, are not getting a return on their investment or don’t quite know what to do with it. Incorporating AI might mean bringing on new staff in some cases. And while the instinct of some founders will be to search for an AI expert, Krishna says the smarter move is to find someone who understands the difference AI can make for your company. “Find that 20 or 30 percent who are motivated to say, ‘I want to learn a new way to do things,’” he said. “I think curiosity and willingness to adapt are more important.” When it comes to measuring the returns of AI on your business, that too is going to require a shift in mindset for business owners, Krishna said. Efficiencies and savings aren’t going to be immediate, he warned. In fact, there could be additional expenses. For the first six months to a year, he said, businesses will likely spend more than they save, as they dedicate engineers to implementation and pay for tokens. But as companies operate AI at scale for a use case, they learn how to implement the technology, making subsequent rollouts cheaper. IBM played its part in introducing the world to AI with Watson, which made headlines when it won on the TV show Jeopardy! But that awareness was also a wake-up call to other companies, which began to invest in AI very heavily while IBM did not, said Krishna. “As opposed to creating building blocks, we wanted to create solutions in verticals. That, I think, is a mistake, as technology shows,” he said. Today, the company isn’t trying to be OpenAI or Anthropic. Instead, it’s betting on AI orchestration—the coordination of multiple AI models into a single workflow. It’s also focused on Enterprise AI, providing businesses with tools to build, scale, and govern artificial intelligence. Lately, there has been growing consumer pushback to AI. One recent report from AI platform Parloa found that during automated customer-service calls 61 percent of respondents have screamed at automation to get routed to a human faster. A separate survey from WordPress VIP, which offers an enterprise version of the publishing platform, found that 60 percent of the people it polled found AI in a brand’s messaging to be a turnoff, not a feature. Meanwhile, some companies that went all-in on AI are starting to realize the real cost of the technology. Uber, for instance, exhausted its 2026 AI budget in just four months and was forced to cap employee use. And several companies that fired workers in favor of AI are bringing those employees back. Krishna argued that companies that don’t incorporate AI ultimately face even more potential problems. “The riskiest route is taking zero risk,” he said. “What happens in any business that takes no risk? It means you’re trying to extract profit—or what an economist would call rent—from what you already have. But that means you’re giving everybody else the opportunity to clone you or copy you, to innovate from the bottom, and pick off the most profitable parts of your business.” BY CHRIS MORRIS @MORRISATLARGE

Microsoft and LinkedIn Just Analyzed the Future of Work and AI. It All Points to 1 Key Skill Set

Algorithms can now write code, draft legal contracts, and generate entire marketing campaigns in seconds. As artificial intelligence automates increasingly complex work, it’s easy to assume technical expertise will become the defining trait of great leadership. The evidence points in the opposite direction. Recent data from Microsoft and LinkedIn reveals a fascinating reality. While AI is automating execution, leaders are aggressively prioritizing soft skills like emotional intelligence. As tools become more artificial, humans crave the authentic. The ultimate competitive moat is no longer technical execution. It is the ability to forge genuine human connection. If you are a founder or an executive, community building fueled by high emotional intelligence is the single most important leadership skill you must master. The isolation crisis A massive psychological shift is happening in the workplace. Gallup research confirms that employee stress remains at record highs, and loneliness is a massive factor. When you introduce generative models into your daily operations, your team members spend more time prompting machines and less time talking to each other. This creates a vacuum of trust. Humans are biologically wired for social connection. When people feel isolated, their brains enter a state of chronic stress. You can deploy the most advanced foundational models in the world, but if your team feels disconnected, your output will plummet. The smartest leaders recognize that their job is not to manage workflows. Their job is to manage energy and connection. The empathy premium When technical output becomes a commodity, what becomes scarce? The answer is human resonance. The American Psychological Association recently found that workers who are worried about artificial intelligence are significantly more likely to feel tense, stressed, and isolated. A machine can generate a flawless and sterile piece of text. A human brings vulnerability, shared struggle, and nuanced understanding. In a market flooded with synthetic perfection, people will pay a premium for authentic imperfection. The same principle applies to your internal culture. Your team doesn’t want a flawless manager who acts like an algorithm. They want a leader who understands their anxieties about the future of work. They want someone who can build a safe environment where it is acceptable to experiment, fail, and learn together. Empathy is the engine of psychological safety, and psychological safety is the engine of true innovation. Your blueprint for human connection How do you operationalize emotional intelligence and community building inside your company? It requires a deliberate approach to how you structure your daily operations. Optimize for unstructured connection. Don’t just schedule meetings for status updates—a machine can read a status update. Instead, create intentional spaces where your team can connect over shared interests, challenges, and ideas without a rigid agenda. Reward vulnerability over perfection. If you want your team to trust you, you must go first. Share your own challenges and uncertainties about navigating the new tech landscape. When leaders admit they don’t have all the answers, it gives the team permission to be honest and collaborative. Elevate human milestones. Algorithms don’t care about birthdays, work anniversaries, or personal triumphs. You must. Celebrate the unique human moments that machines cannot replicate. The future of leadership isn’t about competing with algorithms. It’s about doubling down on the things algorithms can’t do. Step away from the dashboard, look your team in the eye, and start building a culture rooted in genuine connection. EXPERT OPINION BY ASH KUMRA

AI is powering an economy in which many Americans are falling behind

At the Richmond Neighborhood Center in San Francisco, more than 200 people are on the waitlist for the food pantry. The center is just a couple of miles west of “AI Alley,” where a cluster of major AI companies take in billions of dollars in investments and pay out high salaries to employees — in turn making home prices and rent payments soar. San Francisco serves as a prime example of how the roaring AI industry is helping drive economic growth more broadly, but masking the economic inequality of lower-and-middle-income families. And San Francisco reflects the same patterns happening on a national scale: In the first three months of the year, the US economy overall grew at a solid 2.1% annualized rate, largely due to businesses ramping up AI-related investments, according to Commerce Department data. Yet consumer sentiment is languishing near record lows over wartime price spikes, and the bottom quarter of Americans on the income spectrum have seen the weakest wage growth of any other cohort this year, according to the Federal Reserve Bank of Atlanta. “The inequalities in the neighborhood have just grown and grown and grown,” Yves Xavier, community programs director at the Richmond Neighborhood Center, told CNN. “We can’t draw a direct line to AI’s impact and say ‘That’s exactly it’ because it’s been happening for a while, but it doesn’t exactly take a rocket scientist to see how that’s widening the inequalities in a city already dealing with those issues.” He added that demand for the nonprofit’s food pantry is up about 10% this year. ‘An economy of winners and losers’ The diverging fortunes of the poorest and wealthiest Americans has emerged as a key theme in the US economy, and experts say AI is playing a significant role. The billions poured into the AI industry have minted a cadre of handsomely paid workers in tech hubs across the country, including San Francisco, New York, Seattle, Los Angeles, San Jose and Washington, DC, according to a report by Oxford Economics. Those workers are part of the wealthiest 10% of Americans who are increasingly powering US economic growth with their spending, or as much as 62% of growth, according to Moody’s. “You’re seeing incredible concentrations of wealth as a result of AI for these new companies, their founders and their first employees,” said Manuel Pastor, director of the Equity Research Institute at the University of Southern California. “It’s exacerbating an economy of winners and losers.” The winners in today’s economy are clearly involved in the development and funding of AI, including early investors, experts told CNN. SpaceX debuted on Wall Street last month as the largest initial public offering on record. The AI and space exploration company is now worth more than $2.1 trillion, and investors widely expect it to be a windfall for Americans’ retirement accounts. AI stalwarts OpenAI and Anthropic, both headquartered in San Francisco, are also gearing up for their own IPOs, which would add trillions in new market value. And San Francisco companies comprise nearly two-thirds of worldwide AI funding, according to data firm Crunchbase. Those losing out are vast swaths of Americans, particularly recent college graduates who are struggling to find a job; low-income Americans who continue to rack up debt as they feel the sting of higher inflation; and even workers in creative industries, according to Pastor. “What people put on the internet or put into books is being privatized by these AI companies, making it more difficult for those same people to make money,” he said. “That’s happening to people who are authors, to people who are musicians, anyone who is a creative.” The AI hype is also skewing the health of Main Street businesses. “If you exclude AI, business investment would be actually falling, which is quite unprecedented outside of recessions,” said Maxime Darmet, senior economist at Allianz Trade. “The technology is powerful in propping up the economy, but at the same time, there’s a lot of spending being cut in more traditional areas.” Meanwhile, the gap between the broader AI-fueled economic growth and the lived reality for millions of Americans continues to widen. “The inequalities here are very, very stark,” Xavier said of San Francisco. “It’s been an issue for a long time, and I think it’s just continuing to be an issue.” By Bryan Mena

The AI Era Is Creating a New Trust Crisis at Work. Great Leaders Respond With 3 Simple Behaviors

Layoffs are back in the headlines. Across industries, companies are restructuring, reducing headcount, and redirecting resources toward AI initiatives and operational efficiency. For many leaders, the focus naturally turns to cutting costs, productivity targets, and reassuring investors. But in my experience coaching executives for more than two decades, that’s not where the biggest damage occurs. The real casualty after layoffs isn’t productivity or efficiency. It’s trust. And once trust is broken, the costs can linger long after employees are gone. What leaders often miss When layoffs occur, leaders tend to focus on the people leaving. But there’s another group leaders often overlook: the employees who stay. These employees are asking questions that rarely appear in engagement surveys. Am I next? Can I trust leadership? Does this company still care about people? Does any of this matter anymore? When those questions go unanswered, something dangerous happens. Employees stop giving their full discretionary effort. They become cautious, withhold ideas, and protect themselves. The organization may still function, but trust starts to break down. One of the biggest myths in leadership is that people lose trust because of difficult decisions. That’s rarely what I see. Employees can handle bad news. They can handle uncertainty. They can even handle layoffs. What they struggle to handle is silence. What employees want to see and hear from their leaders When leaders disappear after difficult decisions, employees fill in the blanks themselves. And human beings are remarkably good at creating worst-case scenarios. A few years ago, I worked with the CEO of a mid-sized company that had just completed a painful round of layoffs. The reductions were necessary, and to his credit, he handled the departures with empathy and respect. But once the layoffs were over, he assumed everyone wanted to move on. So the leadership team stopped talking about it. For months, employees heard almost nothing beyond routine business updates. No acknowledgment of what people had experienced. No discussion of the company’s direction. No opportunities to ask difficult questions. Within six months, the company lost several of its highest-performing employees—not because they feared another layoff, but because they no longer trusted leadership to be transparent. In exit interviews, one theme kept surfacing: “I felt like I was left to figure things out on my own.” The CEO later admitted something that stuck with me: “I thought silence would help people heal. Instead, it made them wonder what else we weren’t telling them.” That’s the thing about trust. If leaders don’t fill the communication vacuum, employees will. So, let’s say you’re a leader who wants to regain trust. That’s great. Your starting point? It’s to aways remember that trust isn’t built by protecting your people from reality; it’s built by helping your people understand reality. That’s why communication becomes even more important after layoffs than before them. Three behaviors that rebuild trust The best leaders I’ve worked with and coached consistently do three things after workforce reductions. 1. They communicate early and often Not every answer will be available. That’s okay. Employees don’t expect perfection. But they do expect honesty. Leaders who provide regular updates—even when those updates include uncertainty—create stability during unstable times. A simple message such as, “Here’s what we know, here’s what we don’t know, and here’s what we’re doing next,” can go a long way toward rebuilding confidence. 2. They acknowledge the human impact Too many leaders move immediately to business metrics after people’s livelihoods are destroyed by layoffs. Their colleagues and coworkers notice. So, before discussing strategy, be human and acknowledge loss. Recognize the contributions of those who left. Give employees permission to feel disappointment, concern, or grief. Human-centered leadership doesn’t avoid emotions in something as traumatic as a layoff. It recognizes them. 3. They create opportunities for dialogue As we have determined, the remaining employees will look to their leaders for answers. But not through company-wide announcements alone. That doesn’t build trust. Trust grows through conversations. Managers should be encouraged to ask questions like: “What concerns are you carrying right now?” “What do you need from me to be successful?” “How can I support you?” These conversations demonstrate something employees desperately need after disruption: evidence that leadership is listening. The leadership lesson Layoffs may be a business decision. But trust is, and always will be, a leadership decision. The organizations that emerge strongest from difficult periods are not necessarily the ones that cut costs most effectively. They’re the ones whose leaders understand that people are watching how decisions are made, how communication happens, and how employees are treated when things get hard. At the end of the day, employees don’t expect leaders to eliminate uncertainty. They expect leaders to help them navigate it. And that’s where trust begins. EXPERT OPINION BY MARCEL SCHWANTES, EXECUTIVE COACH, SPEAKER, AND AUTHOR @MARCELSCHWANTES

An Explosion of AI Slop Is Pushing People Offline and Back Into the Real World

There was a time, not so long ago, when the internet was a pretty fun—and useful—place. There was silliness aplenty, with sites like The Fish Doorbell. There were absolutely useless sites such as Zombo or The Useless Web that were still, somehow, fascinating. We came together watching iconic videos. And when there was a major news event, there was a wealth of coverage from both professional outlets and eyewitnesses. Today, though, people describe the internet with a word that would have seemed insane in those golden years: boring. It’s not that many of the oddities that made the internet so fun in the first place have vanished. It’s the things that have come since. AI slop and a perceived lack of creativity are turning more people away from the web and back towards the real world. A survey of 8,400 people across Europe, the U.S. and Latin America by ReverseLookup found that people are spending less leisure time online. Some 61 percent of the respondents said they want to spend more time in offline or local communities over the next year, while 44 percent said they are actively trying to reduce passive scrolling. It’s not that there’s less to do online. There’s more content today than ever. In just one second, an estimated six new websites go live, Redditors post 41 comments, Facebook users post more than 4,000 photos and there are 500 minutes of video uploaded to YouTube. The majority of that, though, is garbage. “For many users, [the] sense of discovery has weakened,” wrote ReverseLookup. “The internet has not become empty. It has become crowded with sameness.” When asked about the quality of online content today, 57 percent of the people surveyed said they now encounter more posts, images, captions, comments or articles that feel artificially generated or low-effort. And 49 percent said online spaces feel less original these days because of the continued spread of “synthetic content.” They’re bypassing AI and suspected-AI content, too. Some 42 percent said they have recently skipped or closed content because they suspected it was produced by AI instead of a person with something specific to say. They’re probably right. Earlier this month, Cloudflare reported the number of bots accessing websites outnumbered human web users for the first time. The trend has held, with 57.7 percent of web traffic coming from bots in the past seven days. That represents a turning point not only for web users, but for how businesses use the web to grow their business. “By 2030, the web as we know it will be dead,” says Rajiv Garg, a professor at Emory University’s Goizueta School of Business. “We’re moving from human-to-screen to machine-to-machine. It’s a total shift. … The value of local, unique data is about to skyrocket. The companies that win will be the ones holding the best raw ingredients for AI.” Of course, the internet isn’t going anywhere. It has woven itself into people’s lives and will remain essential for work, information, support, safety, and connection. But the growing abundance of slop and repetitive content is making more people think of the online world as less of a destination and more of a utility. They’ll still utilize it, but the fun factor that came with the internet less than 20 years ago has disappeared. And in its place is a more mundane and often divisive tundra. And that makes the real world a lot more interesting once again. “The offline revival is not a rejection of modern life,” wrote ReverseLookup. “It is a rejection of the parts of online life that have become predictable, performative and synthetic. Offline life is gaining value not because it is always more exciting, but because it is harder to mass-produce. For many people, the most interesting place left may be the one that does not ask them to scroll.” BY CHRIS MORRIS @MORRISATLARGE

Half of AI Job Cuts Will Be Reversed by 2027, Gartner Says. Here’s the Real Lesson

Half of the companies that cut workers for AI-related reasons will hire those roles back by 2027, according to Gartner. Forrester’s Predictions 2026 report had already documented the underlying cause: Fifty-five percent of employers who restructured for AI now regret the decision. The pattern points to a specific mistake. As I’ve explored before, the question of when to trust data versus judgment matters more than most executives acknowledge. The companies reversing course replaced jobs with AI that required human judgment and got information retrieval instead. Research into how AI is actually being used inside organizations shows that humans need to be in the loop when real judgment of tradeoffs is required. Don’t assume that because AI can access everything your people know, it can do everything people do. Those are two entirely different things. And that’s the leadership mistake underlying both the Gartner projection and the Forrester data. The Cost of Getting This Wrong Consider what it would mean to hire a surgeon who had only read surgery textbooks. The information is complete and the reading is thorough, yet the surgeon has never operated on anyone. You’d never hire that surgeon. But companies across industries made the equivalent decision when they replaced workers whose value came from having done the job under real pressure, thousands of times. Klarna ran this experiment at scale. In 2024, the Swedish fintech claimed its AI chatbot did the equivalent work of 700 customer service agents and projected tens of millions in savings. By May 2025, they publicly acknowledged that while automation takes on more of the high-volume, simpler queries, they still needed human agents equipped for complex, sensitive cases like fraud disputes, complex billing issues, and emotionally charged customer situations, a different profile than traditional outsourced support. They began directly hiring a small number of high-skilled humans into the customer service process to identify where the human touch brings the most value to customers. AI is indeed a transformative technology. People are scared it’s going to take their jobs. When jobs are lost to AI, it’s disruptive to the organization. But then to reverse course shortly thereafter, it creates a whipsaw effect that can have negative effects on the people who remain, and the culture. The Human Gaps in AI Technology AI can categorize problems and retrieve policies at speeds no human can match. Sitting with a frustrated customer, rebuilding trust after a systemic failure, and deciding in the moment that this person needs an exception are calls it has never made. The distance between those two categories is the same one that opened up when Klarna’s chatbot was given jobs that required having experienced something nuanced before and needing to draw on personal judgment about it. There’s a profound difference between reading about surgery a thousand times and having done surgery a thousand times. Same goes for customer service when it comes to upset customers. One produces knowledge, and the other requires judgment. Many organizations confuse the two. Three Things to Get Right The leaders closing this gap are deploying AI for what it’s built for and protecting the people who supply what it can’t access. Here’s what to do: Audit AI Capabilities. Ask honestly whether the roles you’ve automated require simple task execution, experience under pressure, or tradeoffs requiring judgment. Treat Experience as Infrastructure. The pattern recognition, institutional memory, and client trust carried by experienced workers are harder to rebuild than most leaders realize until those assets are gone. Design for Human-AI Teams. The most effective deployments use AI to process what’s routine and protect the people who handle what requires a depth of experience. What This Moment Is Really About Underneath the Gartner projection and the Forrester data is a deeper truth about what AI is and does. It’s the most powerful information system ever built, and information and judgment are different things entirely. AI has read everything. But it’s lived nothing, and providing it with “rules” that replace human judgment based on experience may not ever be feasible, or desired. The judgment behind a complex customer decision, a high-stakes negotiation, or a leadership call in the middle of a crisis comes from having navigated those situations before under real consequences. That lives in the kind of intelligence that only experience builds. EXPERT OPINION BY SOREN KAPLAN, WSJ BESTSELLING AUTHOR, KEYNOTE SPEAKER, AND LEADERSHIP STRATEGY ADVISOR

Why AI May Be the Best Thing to Happen to Creativity in Decades

There is a collective consensus, on my feeds at least, that AI is eroding creativity. But the people actually building with these tools in innovative ways are combating that messaging with their imagination. At Magnific’s annual Upscale Conference in San Francisco, they made that abundantly clear. I went into it curious, not knowing what to expect. I found a room full of people who weren’t replacing creativity with automation, but instead a gathering of entrepreneurs, filmmakers, and artists using these tools to make the most personal, ambitious work of their careers. The message to the doomsayers was consistent across every keynote: you’re not watching creativity die. You’re watching the gate come down. The system was always broken. Before diagnosing what AI is doing to the culture of creativity, it’s worth asking what it looked like before. Magnific co-founder Joaquín Cuenca had a clear answer. “The system, beautiful as it is, is kind of broken because so few people can enjoy working in that system,” he explained. Creative industries have always been denominated in years of training, proximity to the right institutions, and gatekeepers deciding whose vision was worth funding. Cuenca calls what’s emerging the “No-Collar Economy,” a third civilizational wave alongside the industrial and digital revolutions, except this one restructures who gets to participate in creative and entrepreneurial work entirely. “There are very, very few opportunities to really define history,” he said. “Very few breakthroughs that were so deep that didn’t change one job but changed one entire industry. It’s almost like you go from creating a company to creating an economy.” The entrepreneurial implication is enormous. When the cost of execution collapses, the only remaining differentiator is the specificity of what you have to say. Your story is your IP. Nobody on that stage understood this more viscerally than Momo Wang, animation director and founder of Bunny Galaxy. “When the tools are easy and cheap to access, nobody has to give up their dream anymore,” she reflected. “And when everybody has access to the same tool, the only thing that makes a difference is you. Every moment of your life, up and low, the happy ones, the painful ones, the embarrassing ones — every part of your life builds up your voice, your perspective, your story. And that’s something no tools can generate, and no prompts can replace.” History rhymes — if you’re listening Writer, director and head of AI and innovation at Echobend Pictures, Noah Wagner, titled his Upscale keynote Nothing Has Changed. He meant it as both provocation and thesis. “I keep thinking about the 1960s and 70s — the new Hollywood era — when the studio system was weakening and cheaper, more flexible production tools gave way to a new generation of exciting filmmakers like Scorsese, Coppola, Spielberg, Lucas, everyone with their own stories to tell,” he said. “History constantly rhymes if you listen for it.” In a follow-up conversation, Wagner pushed the idea further. “I try not to follow fads,” he shared. “I’m just old enough to have experienced some 20 and 30-year patterns where I’ve heard those rhymes.” Wagner read on the current moment isn’t fear but pattern recognition. The tools change, but the fundamentals don’t. “We can’t lose sight of the fact that we’re doing all of this for an audience of humans,” he encouraged. He went on to share that the meticulous human decision-making process about what to keep is where the art lives. “Intention is the difference between art and slop,” Wagner said. “In a world where there’s so much abundance, scarcity can be a superpower.” The person behind the prompt I told Cuenca that after the two days of keynotes, networking, and immersing myself in the vibrant community, the conference had made me look again at how we look at my own creativity through the lens of being a shadow artist, with instincts and references but always one step removed from making the thing itself. He didn’t flinch. “Your life is the most precious thing,” Cuenca concluded. “That’s the thing that separates artificial intelligence from human intelligence. We, as entities, live a set of experiences that are unique to us.” Then simply: “Your taste is your DNA.” Wagner, in our follow-up, put the uncertainty of this moment in perspective. “There’s a lot of uncertainty, but there’s a lot of possibility in that uncertainty,” he noted. The No-Collar Economy doesn’t promise ease, but it does provide unprecedented access and opportunity. The entrepreneurs who stay ahead will be those who understand that the tool is only as interesting as the person behind the prompt. EXPERT OPINION BY SOPHIE MEHARENNA, FOUNDER + NARRATIVE STRATEGIST, @WORDYSOPH

The Hidden AI Problem No One’s Talking About: It’s Destroying Customer Trust

AI is in nearly everything now. It’s easy to see some of the disruption this is causing, including the mass layoffs that continue to make the news. But there is an effect that AI is having in another area, too: consumer trust. As people see more AI in their everyday life, they may become less likely to immediately trust what they see, hear or read. Think about the last time you read a marketing message. Did you believe it at first sight? Or did you question things? More specifically, did you wonder if it was AI-generated? And if you did, when was the last time you thought that and ended up trusting what you were looking at? AI may be contributing to lower trust at first contact “Is this AI?” It’s a relevant question in 2026. It’s also a subtle yet important new part of the consumer filter. The simple act of asking whether something is AI, even if it isn’t, can reduce trust in what a person is seeing. The average person interacts with around 5,000 ads every day. That’s ten times the number of ads they had to sift through in the 1970s. Now, complicating this constant ad exposure is the fact that many of these ads are filled with AI-generated copy. They have photos, songs and videos that aren’t human — or at least aren’t fully human. This is because everything from visual clips to news stories to blog articles can easily be created and shared now at a fraction of the cost compared to traditional, human-made marketing assets. Complicating matters further, the multi-billion-dollar AI industry is working to make these AI look-alikes more sophisticated all the time. They are working to make AI-generated material more difficult for the general public to identify. This new reality raises the stakes for business owners, chief marketing officers and anyone trying to get a promotional message out there. Trust is no longer something companies can automatically count on simply by maintaining a strong product and good reviews. If you want a potential customer to notice you in the first place, you increasingly need to demonstrate value quickly. Trying to prove value in a trustless AI space AI is increasing the importance of trust. Companies need to think more carefully about how they can build trust with their target audiences. Traditionally, trust has come from some pretty basic activities. If you could maintain consistent brand messaging and be honest and transparent, over time, consumers would trust your brand. Now that AI can replicate many common marketing approaches, marketers need to be more deliberate about how they use their marketing assets to help build trust. One way to do this is by investing in resources that are less flashy and more substantial. BioStem Technologies is one example. The regenerative-medicine company openly addresses the scientific complexities behind its work. In fact, it has built entire pages on its website devoted to explaining the science behind its business philosophy. Other resource pages tackle deep, complex questions surrounding its products. Providing detailed information instead of relying primarily on broad marketing language signals to potential clients that the company has invested in its solutions. You can also demonstrate value that builds trust by showing your commitment to adhere to industry regulations. Companies already need to follow regulations. This shouldn’t be a back-room-only element of a business. Instead, companies should repurpose the effort they put into following regulations into their marketing, too. They can build consumer-friendly, customer-facing resources that are framed as a business code of conduct. These can share details about investments made to uphold ethical behavior or integrity in how a business operates day to day. Again, this can signal to clients that a business is not focused solely on revenue growth. There are real integrity boundaries in place. Creating accessible resources that demonstrate this investment without heavy legal jargon can help reassure customers and build trust alongside other marketing materials. Investing in trust in the AI era As consumers sift through a growing quantity of AI content, business leaders should recognize how valuable consumer trust has become. Marketing leaders must lean on less “thin” content and look for ways to build strong, substantial resources. These should go beyond marketing slogans and aim to demonstrate data-backed science and clearly defined company philosophies. If marketers can integrate these integrity-based elements into their strategies, they may be better positioned to build trust with customers as AI contributes to greater skepticism around content. EXPERT OPINION BY JOEL COMM, AUTHOR AND SPEAKER @JOELCOMM

20 Incredibly Useful Things You Didn’t Know Google’s Gemini AI Could Do

When we hear about Google’s Gemini AI engine these days, it’s almost always the result of some wildly ambitious and futuristic-sounding advancement. You don’t have to look far to find examples. Gemini, like other generative AI systems, is increasingly being positioned as an agent that can handle complex tasks for you, as we heard about throughout Google’s I/O conference keynote last week. They span everything from shopping and purchasing tickets to planning travel and even meandering around the web on your behalf. And, of course, there’s vibe-coding your own custom apps without needing to know a lick of code. That’s all well and good, but for most of us, it isn’t exactly the sort of stuff we’re relying on in day-to-day life. In reality, it’s Gemini’s more mundane and less marketing-worthy wizardry that’s likely to be most useful in an ordinary moment. And those are exactly the types of tricks that are underemphasized and go unnoticed—often because they’re off the beaten path and buried. So today, we’re going to skip over the standard superlatives and focus instead on the wow-worthy little gems lurking within Gemini that you don’t usually hear about and might otherwise never encounter. Check out the 20 truly useful Gemini abilities below and see how Google’s AI can actually help you. (Note that, Gemini, like all generative AI systems, can at times be inconsistent and may relay inaccurate info. The use cases I’m highlighting here generally minimize that risk and focus on more confined data sets and task-oriented missions that play to the technology’s strengths—but, as always, proceed with caution and approach all results with a critical eye. AI may be powerful, but the human touch around it very much still matters. And that part’s on you to provide.) 1. Act as your on-demand memory expansion Sometimes, the simplest feats really are the most valuable of all. The next time you find yourself facing some manner of random fact you need to remember—the name of someone’s partner or kids, the gate or door code at a particular place, the license plate on your vehicle or rental vehicle, or anything else imaginable—just tell Gemini: “Remember that Susan’s husband is named Carl.” “Remember that the gate code at Josh’s apartment is 8934.” And so on. Then, whenever you next need that nugget of info, all you’ve got to do is ask. 2. Set a timely reminder in no time Speaking of remembering, don’t forget that Gemini can also perform the simple but supremely useful task of helping you recall specific things at specific times—thanks to its native integration with the oft-forgotten Google Tasks service. No matter what device or interface you’re using, ask Gemini to remind you about anything at any date and time you want. It’ll set the reminder in Tasks and then pop up an alert when the right moment arrives. Just make sure you’ve got the Google Tasks app installed and set up on your phone—be it Android or iPhone—so you see the notification. 3. Help you find your way back anywhere One final reminder-related resource that’s worth tucking away in your memory bank—a two-parter: First, if you’re using the Gemini mobile app on a phone, make yourself a mental note that you can always ask Gemini the only slightly embarrassing question of “Where am I?” So long as you’ve allowed the app the proper location-sensing permissions, it should then tell you roughly where you are—with a city name and, depending on your whereabouts, also potentially the name of a specific business or address. Then, if it’s a place you want to remember for the future, ask Gemini to “remember that location as”—followed by whatever description you want (e.g., “remember that location as the best place to park in Westwood”). You can then ask Gemini for that info anytime down the road, and it’ll zap you right back to the spot you need. 4. Dig up details from a video You probably know that Gemini can summarize most any text you show it. One of its even more mind-blowing powers is its ability to summarize and analyze any video you feed into its metaphorical maw. Now, when you’re watching something for pleasure, this probably isn’t a power you’ll need. But when you encounter a video that you need to parse for purely informational purposes and you don’t feel like sitting through 22 minutes to get a shred of knowledge that’d take you 10 seconds to read, you can upload the video file or simply copy and paste its URL directly into Gemini—then tell Gemini to “summarize this video” or “give me a short bulleted summary of the high points.” If you’ve got something super-specific you’re seeking, you can also just ask Gemini about it: “What does this person say about battery life?” “Does the interview reveal anything about when the product will be released?” “What sort of screwdriver does this say to use for installation?” You get the idea. 5. Create your own personal podcast On the flip side of that last item, if you’ve got a dense document that you need to digest and you think you’d do better hearing it as a conversation, try uploading the doc into Gemini and asking it to “Generate a 10-minute conversational podcast between two experts discussing the findings.” You can get as nuanced as you want with your request, and Gemini should spit back out a personalized play-ready creation that’s ready for your aural consumption. 6. Skim over your emails Provided you’ve got Google’s Personal Intelligence option available and active, you can always ask Gemini to summarize your most recent incoming emails—or even get more specific. For example, you can ask it what the last email from your lawyer said, what your roofer quoted as the estimate for repairs, or anything else that might make sense for your inbox. 7. Find you a killer deal If you aren’t in a rush to make a purchase, try telling Gemini to monitor the price of a specific item and alert you if a certain kind of sale ever comes along. You can get as broad or as specific as you want with it: “Monitor the price of the Pixel 10 Pro and notify me if it goes on sale.” “Monitor the price of the Pixel 10 Pro on Amazon and notify me if it goes on sale.” “Monitor the price of the Pixel 10 Pro on Amazon and notify me if it drops below $900.” Your future self will thank you. 8. Create custom product comparisons All deal-seeking aside, Gemini can work wonders when it comes to comparing products and serving up exactly the info you need. Ask it to compare the battery life on two phone models you’re considering or to compare a series of specific refrigerators you see in a store and then tell you how they’re actually different—or even just to give you a table-style comparison of the most important differences across certain products from a purely practical perspective. 9. Decipher doctor-style handwriting Got a note that you can’t for the life of you read? Snap a pic of it and ask Gemini to decipher the writing. You’d be surprised how often it manages to interpret even the messiest script. 10. Act as your error-interpreting technician No matter the device or appliance, whenever you next encounter an error code that looks like gibberish, ask Gemini to help figure out what it means and how you can fix it. The more specific you can get, the better—telling it the manufacturer and model name of whatever’s giving you the error, for instance, or just showing it a picture—but even if you don’t know all the details, there’s a decent chance it’ll be able to point you in the right direction. 11. Serve as your handyman helper While we’re on the subject of repairs, you can show Gemini a photo of a random screw, connector, or component of any sort and ask what it’s called and where you can find a replacement—or anything else you might need to know. Whether you’re a seasoned repair pro or a befuddled homeowner with next to no handy knowledge, the answer it coughs back may be invaluable. 12. Parse an impossible document With the hopefully obvious caveat that you should absolutely consult with a lawyer for anything truly important and before making any consequential decisions, Gemini can be surprisingly helpful when it comes to going through dreadful-seeming documents filled with endless clauses and clusters of legalese. If nothing else, it can help you wrap your head around the info within and any sticking points you might want to mull over. For instance, I fed in an agreement for an upcoming bouncy-house rental for a kiddie (and, if I’m being fully honest, also adult) party we’re having in our backyard. Gemini identified a couple of potentially problematic and not-at-all-necessary sections that were easy enough to ask the vendor to remove. Similarly, I used it to compare a few vexingly similar insurance policies and translate the differences into real-world terms. For those sorts of scenarios or even as a pre-lawyer-meeting preparation, Gemini’s ability to ingest mountains of complex material and then identify and explain important points can be indispensable. 13. Become your manual magician Now that Google’s NotebookLM system is essentially integrated into Gemini, the feat I suggested in my recent collection of practical NotebookLM revelations can also apply to Gemini itself. That involves creating confined notebooks to hold specific manuals and then asking natural-language questions anytime there’s knowledge you need. I did this with the digital version of a manual for a recently acquired vehicle and was blown away by how much easier it became to find info simply by asking what a particular button does. The same strategy can work equally well with manuals for appliances, electronics, you name it. 14. Perform fast web fixes for you Human designers and developers are undeniably important when it comes to creating high-quality web work, but for those teensy little tweaks and frustrating fixes where you used to have to pester a professional, Gemini can now step in to help you come up with a correction—and help your coding-minded colleagues focus their time on higher-level concerns. Try showing Gemini a screenshot of a website you’re responsible for and then explaining what’s wrong or what you want to have changed, while providing any pertinent details about your setup—that you’re using WordPress with a particular theme, for instance—and see what it suggests. It can sometimes take several rounds of back-and-forth iteration, but if you’ve got the patience (and a solid staging site for low-risk experimentation), it can get you to the finish line much more easily than you’d expect. 15. Cook up some spreadsheet sorcery Speaking of coding chops, one area where specialized knowledge has traditionally been required is in the ever-confounding arena of spreadsheets. And while there’s certainly still a place for spreadsheet expertise, you can make your life a heck of a lot easier by letting Gemini guide you toward crafting complicated formulas. Just fire it up, explain what you want to have happen, and ask it to give you the formula you need—for Google Sheets, Microsoft Excel, or whatever specific program you’re using. Or, on the flip side, paste a formula you’ve found somewhere into Gemini and ask it to tell you what, exactly, it accomplishes. 16. Create custom Chrome extensions While full-fledged vibe coding can help you dream up all sorts of insanely customized complete programs, you might not be ready to take the plunge just yet. But you can dabble with the same sort of superpower and feel a sense of its addictive effects by asking Gemini to create a custom browser extension on your behalf. Unlike native applications, web-centric extensions require no compiling or separate steps beyond just taking a series of plain text files Gemini gives you and then plopping them into your browser. (It’ll even walk you through the exact process of doing that.) And with all the time you probably spend working on the web, you can accomplish some pretty spectacular stuff by doing that—like changing the appearance of web apps to better suit your preferences or giving yourself pop-up panels with simple tools you’ve never quite been able to find. It’s also incredibly fun and empowering to play around with—without requiring you to venture into exceptionally geeky waters. 17. Become your prompt-mastering guide Oftentimes, the biggest challenge with Gemini—or any AI chatbot—is figuring out the right approach for wording a request and getting the bot to do what you want. In an amusingly meta-twist, Gemini is actually quite good at advising you on the best way to phrase prompts for itself. The next time you’re struggling to get the service to do your bidding properly, consider asking it what the best prompt would be for the purpose you have in mind. It seems silly, but it often works astonishingly well. 18. Wear the hat of an AI detection agent In a similarly entertaining sense, Gemini is impressively effective at detecting images that were generated by Gemini—or another similar AI tool. It’s not foolproof, but it’s right up there with the best options we’ve got at the moment. If you’re ever trying to decide if an image is genuine or AI-generated, ask Gemini and see what it says. 19. Answer in whatever way you like Maybe you’re someone who prefers reading things in conversational paragraphs—or in short, succinct lists. Or maybe you like having a detailed, in-depth answer with a “TL;DR”-style summary at the start. Whatever the case may be, if you ask, Gemini shall oblige. Tell the service exactly how you prefer to have your info provided as part of your next prompt—or, if you want it to always follow a specific formula, tell it to always answer in that way, and it’ll adjust your account-wide preferences. You can also check any saved settings along those lines and modify them directly on the Gemini instructions page. 20. Transform into an entirely new personality Why stop at formatting? Gemini has the ability to completely adjust its personality and act in any way you want—again, either for a specific prompt or in a more generalized and ongoing sense. You could ask it to become a tough but supportive coach, for instance, or a lifelong friend who’s always brutally honest and direct with you. Or you could request it to take on the role of specific jobs, like a veteran software engineer, a travel agent, or a legal adviser—or even some combination of different identities. The possibilities are practically endless, and you never know what might resonate and prove to be useful until you try. By JR Raphael

AI Was Supposed to Replace Sales Teams. Here’s What’s Happening Instead

“Distribution is the new moat” is the hot new phrase in business circles. VCs are saying it. Consultants are saying it. Entire frameworks have been built around it. They’re right that distribution matters. They’re wrong about what distribution actually means. The popular argument goes something like this: AI has collapsed the cost of building software, so the only remaining advantage is to build an audience and get to those customers before a competitor. That’s the starting point. But it’s not a moat. Distribution is more than building an audience Anthropic, the mega-AI company behind Claude, has millions of social media followers and created one of the most viral products in history. Yet as of this writing, the most in-demand role at the company is… sales. You read that right. Anthropic is currently hiring more salespeople than engineers and product managers. The company that predicted AI would replace salespeople is now hiring hundreds of them. Here’s what Anthropic knows better than anyone: Building a viral product and massive audience is not the same as having a distribution moat. Real distribution muscle comes from the capacity to build relationships at scale, expand your footprint within organizations, and turn one-time customers into repeat business. In other words, real distribution muscle is built in the sales organization. The distribution moat is created through expansion and retention So, distribution is about landing new business? Yes, but that’s just the beginning. The real distribution moat starts to form when you have a system designed to retain and expand existing customers. Wasabi, a Boston-based cloud storage company, is a case study I’ve taught at Harvard Business School for years (full disclosure: I’m also on the board). They scaled from a few hundred thousand dollars in revenue to hundreds of millions. Their strategy? Good ol’ fashioned channel sales. Working with resellers is not sexy or on trend, but it’s one of the most durable distribution channels around. Resellers have relationships with the end users you want. You are creating essentially two layers of lock-in: One with the resellers and one with the resellers’ customers. Today, over 14,000 channel partners work hard to sell and expand Wasabi’s install base. But making this channel a success was not easy. Wasabi made two key changes: First, they aligned sales incentives to compensate their own salespeople for selling through channel partners. They created a special version of their product to encourage channel salespeople to sell Wasabi cloud storage over competing cloud and on premises storage. Channels sales reps started pushing Wasabi over Amazon cloud storage or EMC on premises storage. Second, they changed how they measured success. Onboarding channel partners is one thing. But could they actually sell the product? Wasabi decided on a KPI of “Time to Second Sale,” to measure and incentivize their top partners. Anyone can make one sale. The second sale is proof of a relationship. And relationships, not features, are what competitors can’t copy. Relationships—not distribution—are the real moat In the age of AI, everyone can build. But not everyone can sell, expand, and retain. Founders who treat distribution as audience-building are playing a different, shallower game than founders who treat it as relationship-building at scale. The moat isn’t how many people have heard of you. It’s how many people can’t imagine operating without you, because someone at your company took the time to understand their business, earn their trust, and keep showing up. AI can help you reach people faster. It cannot replace the human judgment, persistence, and relationship-building that turn a first sale into a second, a second into an expansion, and an expansion into infrastructure. Before you hire your next engineer, ask yourself: do you have the sales and customer success capacity to actually turn your distribution into a moat? BY LOU SHIPLEY, SENIOR LECTURER, HARVARD BUSINESS SCHOOL

Bots Now Outnumber Humans Online. Here’s What It Means for Your Business

It was only a matter of time before bots outnumbered humans on the internet, but many experts thought the flesh and blood majority would stand for a few more years. They were wrong—and the impact on business owners could be significant. New data from Cloudflare shows the number of bots accessing websites over the past seven days outnumbers human web users, with about 57 percent of web traffic coming from bots, who are busy browsing, querying, summarizing, shopping, researching, and scraping, increasingly via AI agents. “Welp, that happened faster than I predicted,” wrote Cloudflare CEO Matthew Prince in a social media post. “Thought it would be end of 2027, then early 2027, but agentic traffic [is] growing so fast that bots have now passed human traffic online for the first time in the internet’s history.” For business owners, that could mark the beginning of a new phase in how to handle business online. Instead of using the internet to attract human customers, it could be time to consider whether to structure your site to attract bots, say some experts. “Stop building for the human eye and start building for the machine mind,” says Rajiv Garg, a professor at Emory University’s Goizueta School of Business. “If an AI agent can’t read your data, you don’t exist.” The hurdle with that approach, though, is just as human visitors might either be customers or hackers looking for a weakness, not all bot traffic is the same. Some bots are malicious. Some are crawling for search engines. And some are sent from AI agents on behalf of potential customers. The challenge for businesses — and their tech teams — is figuring out which bots are which. “There is no protocol to verify whether an AI agent is acting on behalf of a real person, whether it has been authorized to perform its actions, or whether it is benign or hostile,” says Zach Meltzer, CEO and founder of Miami-based VeryAI, a ‘proof of reality’ platform designed to verify human identity and prevent AI-driven fraud. “Platforms cannot differentiate between a personal AI assistant booking a flight for its owner and a bot farm scraping data. The current workaround — forcing agents to impersonate humans via browser automation — is inefficient for legitimate agents and trivially bypassed by malicious ones.” There are also cost issues with bot traffic that business owners need to consider. Automated traffic can chew up bandwidth and impact analytics without generating any revenue. That could result in higher than expected bills, which could hurt the bottom line of companies that have smaller infrastructure budgets. While customer acquisition is expensive, human visitors are more likely to result in a sale, a subscription, or a viewing of content. Additionally, as bot visitors increase, it becomes more difficult for business owners to connect web traffic with customer demand. Garg suggests that the era of winning customers with creative web design is coming to a close, and future online iterations should move away from visual interfaces and more toward lean sites that emphasize behind-the-scenes data exchanges. “The internet is shifting from a destination [that] humans visit to an invisible infrastructure that AI agents navigate for us,” he says. “Your tech budget needs to shift from beautiful UIs to robust bot interfaces. Don’t build a better website. Build an MCP (Model Context Protocol) server that lets the AI ecosystem seamlessly transact with your business.” For instance, one SaaS company, Monday.com, has created an AI agent-only sign-up flow on its website. It employs a reverse CAPTCHA system that it says only AI can get through. That could increase your business’s chances of AI chatbots recommending your site to users, much like today’s search engines do. The shift from primarily human users to primarily bots is one experts have been predicting for quite some time. Automated traffic across the internet grew almost eight times faster than human activity in 2025, according to the 2026 State of AI Traffic report from cybersecurity firm Human Security. Cloudflare calls it the next phase of the internet’s evolution, but cautions that it will create challenges that current IT infrastructure and cybersecurity were not designed to handle. “IT leaders now face fundamental questions about trust, visibility, and control that traditional architectures can’t answer,” the company said in a blog post. “The organizations that recognize this shift — and redesign their infrastructure accordingly — will shape how the internet evolves. Those that don’t will find themselves constantly outmaneuvered.” BY CHRIS MORRIS @MORRISATLARGE

The Flaws in Mass Layoffs for AI Productivity Are Beyond Obvious Now

Just when I thought I was done calling out tech CEOs for horrible mass layoff decisions, one of those CEOs doubles down on the mass layoff rhetoric. So here’s what we’re gonna do. You know how, when your mom or dad tried to give you solid life advice that just didn’t stick, they ended up sitting you down and listing out the flaws in your reasoning one by one? Well, sit down, kid. And like I tell all my kids: Look, I’m gonna yell at all of you, but I’m really only yelling at one of you. You’ll quickly figure out if you’re the one I’m actually yelling at, but it’s not gonna hurt any of you to hear what I have to say. This Could Be Any Corporate Tech CEO My opening histrionics aside, I want to make it clear that I’m not really trying to smack anyone individually. What I’m about to criticize is not the work of a single misguided leader, it’s the culmination of a spreading misguided follow-on leadership strategy. I also want to apologize if any of this comes off as flaming the writer or the publication behind the article I’m going to use as an example, because it literally could have been any tech CEO speaking to any publication after cutting double-digit percentages of their workforce and being all super-pumped about the future. It is such a bad look, so good job exposing it. But I guess we’re all numb to it now, when we read things like: “What [tech company’s] mass layoff tells us about the future of work” I encourage you to read the article. Go ahead and give the author some respectful clicks, because they get it right at the end, with facts. But ultimately you don’t have to read it because I’m about to take apart the “fire-all-the-humans-and-replace-them-with-AI” strategy point by point. This Isn’t a New or Novel Strategy Let’s start at the top: “Zeb Evans, CEO of the collaboration software startup ClickUp, claims that this shift is imminent. Last Thursday, Evans announced on X that the company, which was last valued in 2021 at $4 billion, had laid off 22% of its workforce.” A couple of things are quickly evident. First, this appears to be the same “everybody’s gonna eventually do it” reasoning that Jack Dorsey used when Block laid off 4,000 people just a couple months ago. Second, it’s not a coincidence that the company was last valued in 2021 at $4 billion or that that might have been its peak. 2021 was the acceleration point of the Great Labor Arms Race, and Corporate Tech companies across the industry started hiring people ill-equipped for poorly-defined roles at salaries that would have broken the bank if money wasn’t so cheap—or even free, via automatically forgiven loans. But the pretzel logic that gets used here is what makes this follow-on strategy especially obtuse. When Cutting Costs Isn’t Cost-Cutting The CEO “characterized that reduction as not a cost-cutting measure, but rather a radical embrace of AI that will propel the company to the next level.” Quick question: When is cutting 22 percent of your labor costs not a cost-cutting measure? I’ll just let that one hang. I’m not a mean person, really. The Biggest Mistake a CEO Can Make “‘Most savings from this change will flow directly back into the people who stay. We’ll be introducing million-dollar salary bands. If you create outsized impact using AI, you’ll be paid outside of traditional bands,’ Evans wrote.” One of the best lessons a mentor ever gave me about leadership is: The worst mistake a leader can make is looking at a chart that goes up and to the right and believing that chart will always go up and to the right. It never works out that way, but the temptation to think it will is always there. Using my mentor’s advice, I have several questions: Is this CEO talking about paying a percentage of profits based on whatever metrics they invent to show “outsized impact created by using AI”? Does the CEO plan to keep paying that employee their $1-million salary when the “outsized impact created using AI” returns to the mean? Quick follow up: Does that keep going until it breaks the budget or is this just, like, an MLM thing? If not, and the CEO does the sensible thing that every other company in the history of companies has called “commission,” won’t that employee just hop to the next company when that company offers them a $1-million salary to do what they just did? And then finally, let’s do the cut-throat AI thing that serves as the reason for the 22 percent “savings” in human labor: Once that “outsized impact created by using AI” materializes, why do you still need that employee? Especially if you’re now paying them a million-dollar salary? Isn’t that more “savings” just waiting to be “saved”? Making a Fortune Babysitting That last question kind of introduces another question: What are we paying these employees a $1-million salary for? “ClickUp recently introduced roughly 3,000 internal AI agents to handle a wide range of complex tasks on behalf of its employees…. Instead of performing the work themselves, staff members are now expected to direct these agents and ultimately review the output to ensure it meets the company’s standards.” Are we planning on paying million-dollar salaries to babysit agents? Because the last year has shown us that’s not where the seven-and-eight figure salaries are going. The 100x Productivity Myth “Evans’s goal, according to his X post, is for AI to turbocharge ClickUp into a ‘100x org.’” I’ve been on the AI front for over 16 years, and I get called a “100x guy” or a “10x guy” a lot. I don’t know what that means, but it sounds cool so I just smile and say thank you and get back to the data. Actually, I do know what it means, in another context, because I’ve spent my entire career as an entrepreneur and/or consultant, and have worked with a vast array of venture capital and private equity firms and their strategies. One of those strategies, familiar to everyone, is to create ROI by what I’m going to dub “numerator-maxxing” (see, I can make up buzzwords too). The strategy starts out logical enough. The company that the firm is investing in is doing something very right. Their numerator—the value that the company is generating—is a lot higher than their denominator—the money and sweat effort and brainpower being put into the company. The firm believes that the company’s numerator is artificially low and is being constrained by a weak denominator. So the firm dumps a bunch of money into the denominator. That’s their bet. When this happens, the numerator almost always increases. Where it goes wrong is when, a couple years into it, the numerator has not increased by orders of magnitude to compensate for being weighted down by a heavy denominator. One hundred over 10 is a much bigger number than 1,000 over 1,000,000. Sorry for the math. So yeah. Machines are less weight in the denominator than humans, and you can also add exponentially more of them to the denominator without it getting much heavier. But what do they add to the freaking numerator? Where AI and agent productivity is concerned, no one—no one—is looking at the numerator. Well, no, wait. Gartner took a look. Vindication Isn’t What It Used to Be Like I said, the writer gets a lot right at the end, and does it without my cartoonish fist shaking. In response to the metric most commonly used to measure that “outsized AI impact”: “[C]ritics argue that “tokenmaxxing”—as this concept is known—is the wrong metric because it simply racks up AI expenses.” In response to the company’s incredibly circular claim that people who automate their jobs with AI will always have a job: “But if AI keeps taking over more tasks, ClickUp will eventually need fewer and fewer people.” However, the most damning truth came from a quick mention of a quiet study from Gartner on ROI from AI-as-labor-replacement, just three weeks ago, from which I wish the writer had pull-quoted: “Many CEOs turn to layoffs to demonstrate quick AI returns; however, this disposition is misplaced,” said Helen Poitevin, Distinguished VP Analyst at Gartner. “Workforce reductions may create budget room, but they do not create return. Organizations that improve ROI are not those that eliminate the need for people, but those that amplify them by aggressively investing more in skills, roles and operating models that allow humans to guide and scale autonomous systems.” Am I still Don Quixote for screaming about this for the last 16 years? In fact, I said the same thing yesterday when highlighting the unintended consequences of this misguided follow-on leadership strategy, and it still feels like the loud part being said far too quietly. It will always be the humans behind the tech that will make the tech successful—not the babysitters, not gamification, not agentmaxxing, tokenmaxxing, or numerator-maxxing. So if any of those leaders see this rhetoric and still believe these mass layoffs are about AI and not about mistakes made by leadership a few years ago in hiring the wrong people for roles that were never clearly defined at salaries that never should have been offered, I’m begging you, think twice before you agentmaxx. The flaws are now obvious and documented. There is nowhere left to hide. EXPERT OPINION BY JOE PROCOPIO, FOUNDER, JOEPROCOPIO.COM @JPROCO

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

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

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

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