8 Messy Weeks to an AI Startup: How a New Accelerator Pushed 2 Founders to Pivot Their Idea to Launch

San Francisco’s Chase Center is home to the Golden State Warriors, but for the last weekend of July, it filled with a different sort of hard-driving Californian: thousands of entrepreneurs who descended on the arena for “Startup School,” a two-day business-building summit hosted by the startup accelerator and tech investment giant Y Combinator. The conference, which featured speakers like Jensen Huang, Patrick Collison, and Alexandr Wang, embraced the same spectacle and scale you might expect from a pro sports tournament. Sam Altman walked out to hype-up music like he was rallying for a title fight; onlookers compared the vibe to a megachurch. Yet amid all the excess, some tech insiders saw worrying signs that Y Combinator’s meteoric growth has dulled the program’s edge. “The YC brand felt like it was designed to filter out status chasers…but you can feel the gravity of a status vortex slowly tearing down what made YC special,” wrote Scott Stevenson, co-founder of the legal tech platform Spellbook, in a post on X about the event. Arguing about whether YC has lost its mojo has become a long-standing tradition among tech types. In 2022, debate broke out on the popular tech forum Hacker News about whether the accelerator had become too risk-averse after it announced that most members of an incoming cohort had already secured outside funding. More recently, observers have bemoaned the program’s expansion from two to four batches a year as well as its zeitgeisty preoccupation with AI ventures. Data from Brookings indicates that accelerators grew dramatically in the years following Y Combinator’s 2005 launch. Traditional VC firms now run their own accelerator-style programs. A 2024 paper out of the University of Pennsylvania’s Wharton business school tracked 8,580 companies as they moved through 408 different accelerators across 176 countries, and estimated that 160 accelerators are operating in the United States alone, with 2,000 worldwide. Although accelerators have become big brands, you can still find scrappy, homebrewed ones in quieter corners of the tech ecosystem, run by smaller-scale investors with little more than pocket-change funding, their own business instincts and a lot of elbow grease. Consider HomeRuns, a New York-based micro-accelerator run by Karthik Senthil, a serial entrepreneur and founder of 806 Capital, that’s evocative of Y Combinator’s scrappier salad days. Inc. followed its inaugural class—two ambitious young entrepreneurs who’d joined the program with only the roughest sketches of a business pitch—as they scrambled to stake out their own little corner of the AI gold rush in just eight weeks. In the process, we caught a glimpse of what startup incubation looks like when stripped of all its pageantry and prestige: a series of false starts, dead ends, logistical headaches and pragmatic pivots, all endured in hopes that at some point, hunched over a laptop in a coffee shop or hotel lobby somewhere, there would come a hard-won victory that made all the hustling worth it. An accelerator with the heart of early YC Back in February, Senthil posted a simple request on X: He was looking for AI-savvy, New York-based founders who wanted help turning their “crazy idea” into a bona fide company. Better yet, he would give them some money to make it happen. “We’ll whiteboard, refine the thesis, ship a real v1, strategize on product + risks, and get in front of early believers,” wrote Senthil. He would offer $15,000 for a 2 percent stake, plus the option to later take 3 percent more for another $30,000 if he chose to double down. “The way I’m setting up this program harkens back to the days of super early YC,” Senthil said at the outset of the HomeRuns program. “YC started like this, and now they’re the credential that they hated. They espoused that it was sucky that to be cool you had to have a Harvard degree—but now most startups apply to YC to get the YC stamp.” Senthil, 43, was nearly a YC alum himself. After graduating from Carnegie Mellon with a computer engineering master’s degree in 2004, he took a job at Goldman Sachs. But after eight years, he was ready for something else, so he and a friend developed a music app and pitched it to Y Combinator. They got an interview, he says, but ultimately weren’t accepted. So Senthil took a job at a startup, then tried to launch his own venture, though it never took off. He wound up with a management job at Amazon, then launched another startup, this time in the crypto space. In the meantime, he’d begun making a series of small investments, which he formalized in 2025 with the establishment of his own venture firm, 806 Capital. Angel investing, he says, is a good way to lose money. But it’s also proven a rewarding way to get his hands dirty and meet founders, adds Senthil. HomeRuns, which he named in a nod to founders taking big swings, aims to put that philosophy into practice. Senthil says he ultimately received about 30 apps for his own accelerator program. By April, he’d chosen his favorite. The pitch: an economy of “one-human companies” John Amhanesi, 27, and Timilehin “Timi” Dayo-Kayode, 28, first met at a high school math competition in Nigeria, where they grew up. They later immigrated separately to the U.S. and continued to stay in touch as their lives diverged. They reconnected in New York City, where Dayo-Kayode was working for a venture accelerator affiliated with the Brooklyn Nets and organizing events for tech brands. At the same time, Amhanesi had enrolled in a tech management master’s program at Columbia. Last fall, Dayo-Kayode says, he proposed they capitalize on their shared history and start a business together. Not long after that, they saw Senthil’s post about HomeRuns. They pitched Senthil on an idea they were already working on: a recruiting tool that would micro-target coders with job opportunities based in part on their GitHub accounts. But over the course of Senthil’s vetting process, the investor pushed them to think bigger. Why cater to human job applicants when, as Dayo-Kayode puts it, AI is “going to use software at a much larger volume than humans ever have?” So Amhanesi and Dayo-Kayode instead locked in on something weirder and more ambitious: a marketplace that would facilitate transactions with humans on one side and autonomous AI “agents” on the other. Rather than the full automation of human labor that some people anticipate will result from the AI boom, they envisioned an economy of “one-human companies,” or OHCs, where a single human would oversee a workforce of AI bots. OHCs, they predicted, would sell specialized services, software, or information to other AI agents, creating a gig economy of humans looking to fill in the gaps in AI’s knowledge or abilities. Their proposed business, which they called Chekk, would offer a marketplace for those deals. “You could know something really unique that you think is valuable to an agent—something about the subway or the best way to think about geography in New York, any kind of niche—and you have some proprietary data set, or some proprietary algo, that can do that,” Senthil says, suggesting what a human might sell an AI via Chekk. “The bet is that agents are going to think very differently than humans and will have no qualms paying a cent, a dollar, whatever, for that information.” Or as Dayo-Kayode puts it: “We see a world where Chekk can become the index of all software that agents use.” Changing their business idea so early on, Dayo-Kayode says, was “a testament to the strength of conversations that we were having with [Senthil] during that interview process, and also speaks to the value that we feel that the program can provide.” It was a good mindset to have, since this would not be their last pivot. Who is our user base? Re-conceptualized as a human-to-AI marketplace, Chekk represented a strategic bet on where business opportunities might lie in the age of automation. It was, in other words, exactly the sort of big swing that Senthil had been looking for. He notified Amhanesi and Dayo-Kayode at the end of April that they’d made the cut. But at HomeRuns’ May 4 kick-off meeting, everyone’s focus was shorter-term and more pragmatic: setting goals and expectations for what the team hoped to achieve by the end of the program. “What’s the most plain, vanilla way that we should define success for Chekk?” asked Senthil. “Everything should flow from that.” “Humans and agents using software on Chekk, unprompted,” suggested Dayo-Kayode. To grow that user base, however, there would need to be something for sale on the marketplace that was worth showing up for. Dayo-Kayode and Amhanesi had run into a “cold start” problem: no one would come to Chekk if it had nothing cool for sale, but there wouldn’t be anything cool for sale until people showed up. Senthil proposed a solution: by scouring AI forums and reaching out directly to hobbyists, they’d figure out what sorts of tools would get early adopters excited about Chekk, then build those tools in-house and seed them on the platform. If Chekk could get AI enthusiasts to signal-boost the platform to their peers, he explained, “that’s a huge step, because then people are like, ‘Oh shit, this is cool.’” By the end of their opening meeting, the team had their marching orders: research Chekk’s core user base, figure out what sorts of software would get them excited, then build both that software and the platform over which to distribute it. Ideally, that pairing of tooling and delivery infrastructure would be so appealing to hardcore AI fans that they’d talk it up online, setting off a supply-and-demand flywheel and turning the Chekk marketplace into a flourishing ecosystem of human-to-AI commerce. “We’re trying to do stuff and see if it works” Three weeks later, as the co-founders sipped oat milk lattes in the second-floor cafe of Union Square’s W hotel, they were noodling over a new obstacle: their efforts at market research hadn’t yielded as many hits as they’d hoped. Coming out of their kick-off meeting, they’d taken a deep dive into the world of AI enthusiasts to figure out what might entice that community to start using Chekk. But getting people to talk had proven difficult, and they’d undershot their customer interview goal by about half. One conversation, however, had caught their attention: An AI power user who ran multiple different AI agents but was struggling to coordinate between them. “I literally have a Discord that I set up, and I just have my agents all in there,” the AI super-user had explained. “If something like this existed that was better, I would use it.” It was a promising market signal. If AI’s early adopters were deploying multiple agents, then Dayo-Kayode and Amhanesi could build the infrastructure for those bots to communicate with one another—a sort of AI group-chat tool they dubbed Agentspace. “We just want to run a go-to-market experiment, ship quickly, do stuff and see what happens—and then keep doing that until we find something that works,” Senthil explains. “Eventually there will be demand for agents to potentially do deeper interactions, i.e. transact with each other…but communication is a good gateway drug.” The transition from a marketplace to a messaging platform brought a host of new considerations. How, for instance, should new agents get onboarded to group chats? Would human authorization be required? How much access to a user’s personal info and accounts should a bot really have? “If I want to book a vacation with my girlfriend,” Dayo-Kayode noted during the team’s hotel summit, “it doesn’t really help us solve that problem unless my agent has access to my calendar, my bank account, and her agent has access to her calendar, knows when she’s free, maybe even her email. Can her agent even set up her ‘out of office’ message? Can her agent take days off through her company website?” As the trio volleyed between ideas, problems and solutions, the contours of what they wanted to build came into focus. Still, as enthusiastic as they seemed about this new direction, they cautioned that it was still just an experiment. “We’re trying to do stuff and see if it works,” says Amhanesi. If this “groupchat for agents” didn’t fit the bill, they could always change course once again. Pivoting to a traditional consumer product The HomeRuns team spent the next few weeks trying to track down and interview more prospective users, while simultaneously building out the platform’s technical capabilities. By the time they met on June 10 for a product demo, the Agentspace platform could onboard users’ outside agents, log them in a searchable directory and drop them into iMessage-esque chats. But technical solutions are only half the battle. What the team really needed to hone was their vision of when and why people would use Agentspace in the first place. “We’ve gotten to a point where we have this model that’s interoperable across any kind of agent framework,” explained Dayo-Kayode. “But the reality is then, like, okay—what are they being used to do?” To that end, they had about 10 people on a waitlist who they hoped would test out the preliminary product and provide feedback. “One of the things we think is super important is for us to talk to as many of them as we can, just to better understand their use case and better understand what they’re looking for,” Senthil says. “The part we want to avoid…is that we don’t have something that people find cool.” Yet by their June 25 meeting, they’d changed course yet again. “Conversion was lower than we were hoping” among the beta testers, Dayo-Kayode conceded, and some people had expressed security concerns, too. “There was some uncertainty about plugging their agent into this random platform that we said we built.” Those conversations, he added, had opened their eyes to how hard it would be to get people actually to use their product. Rather than cater to AI-savvy hobbyists, they wanted to reorient the product to be more of “a traditional consumer product,” Dayo-Kayode says, and fully embrace event scheduling as the core use-case. The platform would now focus on using agents to coordinate schedules between different users, allowing the AI to act as a personal assistant and handle the logistics of making plans. “Fourth of July, I’m asking my friends what they’re doing; my friends are asking me,” Dayo-Kayode explains. “We’re just going back and forth, and we think that maybe there’s an opportunity to solve that problem if we have agents that know people’s schedules. … It’s a new approach with similar infrastructure.” What had begun as an in-the-weeds play for AI enthusiasts was now undergoing yet another evolution, this time into an AI “wingman” that would automate the logistics of group hangs for the general population (while still drawing on the agent-to-agent communications tech the team had been developing). It was a conceptual pivot that reoriented the entire product along consumer-tech lines: the team began name-checking Partiful as a point of inspiration, and rechristened the platform Sidekick. The platform would now offer users a pre-packaged, out-of-the-box agent if they didn’t already have one, and let them engage with it via text message. “There’s an opportunity to build for people that aren’t power-users of agents,” Senthil says. “A lot of people are just like, ‘Hey, if you can use AI to make your life easier, why not?’” ‘Let’s always keep swinging bigger’ By July 9, HomeRuns was officially over, and the team reconvened at Inc.’s offices to debrief. They estimated that Sidekick, now targeted squarely at the mainstream, was about 80 percent ready; they’d be leaving the HomeRuns program with plans to finish fleshing it out while also doing more user research. They hope to pilot it on Columbia’s campus in the fall, and reach 1,000 users by the end of the year. “I was really excited for the guys to be like, ‘Hey, there’s a consumer angle here that’s a really big swing,’” Senthil remarked, looking back on the latest turn in their journey. “That was awesome, to see this progression where we went into the program with one idea, we evolved our view to something else—still in the same direction—and then we evolved our view again towards the end of the program. I think that was good to see this notion of, ‘Let’s always keep swinging bigger.’” “We’re happy about the problem space we’re tackling,” Dayo-Kayode agrees. “It’s not what we came into the program attempting to tackle, but it feels like the best fit for our backgrounds, what we’re excited by and interested in spending our time doing.” The program was a positive enough experience that he and Amhanesi are now hoping to bring Sidekick to another accelerator: possibly Elbow Grease, a 10-week, $300,000 program based out of lower Manhattan that would offer more money, higher stakes, and an opportunity to really dig into their newly-embraced schedule-coordinating market. “We know what we’re doing more so now, and we’ve also gone through this training ground of building together,” says Dayo-Kayode. “This was like a nice kindergarten for us before we move onto the next stage.” The co-founders say they’d like to raise a pre-seed round later this year, but for now, they’re focused on stretching out their capital runway for as long as possible. Senthil said Wednesday that he’s planning to exercise his option to make a second, larger investment in the company, but is waiting to see more data from Sidekick’s beta launch. (The founders plan to onboard 50 more early users starting Friday.) Meanwhile, the gears are already spinning on the next iteration of HomeRuns. Applications for the fall cohort are now open; it will run for 12 weeks, bringing it more in line with Y Combinator and other leading accelerators. Otherwise, Senthil says, it will maintain a lot of its distinguishing quirks: the one-company-at-a-time focus, the dual investment tranches, the emphasis on scrappy New York founders eager to take big swings. As Senthil gets his second cycle up and running, HomeRuns’ two newly minted alumni will keep chugging forward, now with a little bit more capital to burn and, perhaps more valuably, some hard-won business-building experience to draw on. Asked at the end of the program what his number-one piece of advice would be for founders participating in an accelerator of their own, Dayo-Kayode offered an insight you’d probably find in most MBA textbooks: the importance of being willing to change your mind. But after two months of watching him and Amhanesi make seemingly constant pivots, adjustments and tweaks, it felt earnest, not cliched. “Don’t be in a long-term relationship with your idea,” he encouraged. “We move very quickly when we learn new things, because we don’t spend any time mourning what we had before.” BY BRIAN CONTRERAS @_B_CONTRERAS_

The AI Boom’s Latest Winner: A Brand-New Startup That Just Landed a $10 Billion Deal With Anthropic

Things change fast in the AI world, and the market is evolving so quickly that brand-new companies are able to make a splash in the same way that more established brands can. Case in point: a Bloomberg report says AI giant Anthropic has invested $10 billion for access to computing capacity from a startup called Volta Infra Holdings Ltd. The startling factor here is that Volta was only established at the start of this year, and yet it’s suddenly a key player in the AI game. Though the deal has yet to be made public, Bloomberg learned that Anthropic’s contract centers around access to a data center managed by Volta. This is exactly Volta’s business—it’s all about supplying the behind-the-scenes tech that is powering the AI revolution. The company’s website says it thinks ”Compute should be a utility. Not a bottleneck” and promises it’s “developing, financing, and operating AI factories at global scale.” Not much else has been reported about the California-based startup, though the U.K.’s Times newspaper noted that its cofounder and CEO Ricard Boada Rafart, a Spaniard living in the U.K., set up the company with a single £1 of share capital. Anthropic has been trying to keep up with increasing demand for its AI products like its Claude chatbot, Bloomberg reported, and the deal is the latest maneuver by the large firm to gain access to computer power. Anthropic has also signed deals with other suppliers to gain access to powerful AI computer resources, including Elon Musk’s SpaceX. SpaceX absorbed Musk’s AI outfit XAI prior to its recent IPO, and has long term plans to put AI processing servers in orbit as part of its Starmind project. Volta, for its part, did not acknowledge Anthropic’s interest to Bloomberg. But in a LinkedIn post, Boada acknowledged a “$10 billion strategic partnership with a leading AI lab to develop an AI Factory in Europe, generating approximately $1.7 billion of annual revenue for Volta.” He also explained that the company’s belief is simply that “AI infrastructure needs both technology and institutional capital. One without the other isn’t enough.” Bloomberg says the Volta deal is in partnership with a bitcoin mining firm, Norway’s Bitdeer Technologies Group. Bitdeer’s data center is said to be equipped with Nvidia’s new Vera Rubin AI chips, and Bloomberg notes this maneuver is part of a trend where tech firms pivot away from cryptocurrency mining and toward the AI market. Nvidia’s newest AI chips were also recently at the center of another AI infrastructure deal, where the chipmaker itself invested $5 billion in another small AI startup, Safe Superintelligence. Separate reports say that Volta has also raised $300 million in a funding round led by new investors, including well-known VC firm Andreessen Horowitz LLC and also Nvidia. This funding means the seven month-old startup is valued at a staggering $2.4 billion. Depending on how Anthropic’s rumored investment is structured, this valuation could even skyrocket. Anthropic declined to comment to Inc. Volta directed Inc. to a press release that confirmed the existence of a $10 billion deal but did not add any additional information beyond that reported previously. The deal is a reminder that AI really is the hottest tech game playing out right now. Volta achieved a multi-billion dollar valuation incredibly quickly, simply by solving an important middle-layer infrastructure issue for a leading AI supplier. If your company is looking for new fields to venture into, it would seem that the right moves in AI tech can lead to big wins. There’s a little more to think about here, however. When you buy an AI service from a supplier like Anthropic or OpenAI, you may assume that your data and chatbot queries are handled by that firm alone. The fact is that this is not true for many AI suppliers, and in reality your data could be being shepherded around the globe to wherever the supplier can find computer power at affordable prices. For most of your AI needs, this is not going to be an issue. But for some firms, this could represent a potential compliance complication. Meanwhile, the fact that third party AI compute suppliers are involved should be another reminder that AI models are not necessarily secure, and extra links in the supply chain may increase the risk of data leaking out. This kind of third-party supplier issue has stung other tech firms in the past. Cyber risk management firm FortifyData is keeping a running total of notable leaks this year, and it warns: “Third-party data breaches are no longer rare. They’re becoming the new normal.” Lastly, the news could prompt savvy CEOs to beef-up AI security and safety training for their workforce. Too many users trust AI models implicitly, and you may be surprised at how many of your workers are riskily exposing your own company data by typing it into chatbots. BY KIT EATON @KITEATON

ChatGPT’s Latest Upgrade Isn’t Just a New Model. It’s What Free Users Can Do Without Limits

OpenAI, aware of the ever-increasing role ChatGPT is playing in people’s lives, has tweaked something that will benefit millions on a daily basis: it’s upgraded the default version of the chatbot for free users. And while the new improved GPT-5.6 Luna model will be great, the most important benefit is another new feature: unlimited text-based chats for free users. This could dramatically open up how people use ChatGPT. In its blog post announcing the changes, OpenAI pointed out that there are still a few constraints in place for free unlimited access. While you can query the chatbot using text-based questions as much as you like, and receive the resulting text-based answers, if you’re asking the chatbot to analyze a file, or input an image or voice as a query, there are still limits for free users. Similarly, limits remain if you’re asking the AI to generate an image, and a few other higher-power features. It’s worth keeping in mind that the complex computer power needed to process a user’s chatbot query burns through resources that cost OpenAI money. Giving users free unlimited access to the core AI features is thus a risk for the AI maker, because it will consume a variable percentage of the available resources. It’s a smart move to control this risk by limiting how free users can use the models. Meanwhile, the unlimited access may encourage people to use the AI in more meaningful ways, and more frequently, which will certainly tempt a share of users on the free tier to pay to upgrade their membership so they can access the AI’s full capabilities. Unlimited queries alone would be a great perk for free tier users, but OpenAI is also upgrading the default AI model to its GPT-5.6 Luna system, which it promises is a major improvement because responses containing factual errors are rarer for this system. In fact, the AI maker says that responses with errors are about 62 percent less common than the previous model (GPT-5.5 Instant). Users who pay for the Go tier, costing $8 a month, will gain these upgrades too, along with allowances for more messages with tools, more uploads, more image creation and more voice chats without busting through limits. Go users will also get free unlimited text chats. And both Go and free tiers will get a button that instructs the tool to think more deeply about answering (which may take more time). The higher Plus and Pro tiers (starting at $20 a month and $100 a month, respectively) actually get a better version of the default model, GPT-5.6 Sol. OpenAI says this tool is better at delivering tightly-focused answers with less unnecessary formatting. It also has a more level tone across multiple types of conversation, and it’ll make fewer mistakes because it’s better at accessing source material to look up details. What’s the practical upshot of these upgrades? The whole world may breathe a sigh of relief at the upgrade to GPT-5.6 Luna. In its press push for these improvements, OpenAI explained that one billion people use ChatGPT every week. An email from an OpenAI spokesperson noted that the Luna upgrade also brings “major improvements in factuality to the majority of people who use ChatGPT.” Given the carefree way that many people use this tool for generating content they share online, on social media or for work purposes, the model having an improved grip on facts may lead to less misinformation from hundreds of millions of people. Fewer erroneous answers also will benefit businesses that let their staff use ChatGPT in the workplace. As does unlimited access to text-based queries: for companies on a tighter budget, this could even turbo-charge how you use AI to polish processes and free up worker time for meaningful tasks. Companies who pay for their workers’ ChatGPT access will get still more powerful tools, and an improved experience which has clear benefits. The changes also remind us that things change quickly in the AI world, and companies that are comfortable with using the tech need to pivot quickly to keep up with the fast-evolving tools. AI-savvy CEOs will also see these tweaks to the market-leading AI as a reminder that they need to upgrade their staff training on how to best use AI, along with guidelines on which company data is and isn’t appropriate to share as part of their unlimited queries. BY KIT EATON @KITEATON

With Just 5 Words, OpenAI’s President Admitted the Problem With the New ChatGPT App

Confused about the new ChatGPT desktop app? OpenAI president Greg Brockman thinks it’s messy, too. Brockman addressed the app’s unintuitive user interface in a wide-ranging interview with tech journalist Joanna Stern, released on YouTube this Wednesday. Stern pointed out that the app was “kind of a mess,” and Brockman echoed her, saying, “it’s kind of a mess, we agree.” OpenAI launched a do-it-all app on July 9, transforming what was formerly the ChatGPT app into an everything app, merging it with their standalone coding agent app, Codex, and introducing a “Work” mode alongside “Chat” and “Codex.” The app formerly known as “ChatGPT” became “ChatGPT Classic,” which only has the chat mode. At launch, users struggled to understand the difference between the Chat and Work options, and were frustrated by how unintuitive it was to switch between the different modes. “I am so confused right now,” wrote tech writer and investor M.G. Siegler in a post titled “The ChatGPT ‘Super App’ Sort of Super Sucks.” The next day, Codex engineering lead Thibault Sottiaux acknowledged user complaints in an X thread, and on July 16, OpenAI updated the desktop app with a clearer way to switch between Chat, Work and Codex. But navigation is still not intuitive. The app now has a “Chat/Work” toggle at the top of the app, but to select the Codex mode for coding work, users have to navigate to a drop-down menu on the left sidebar to choose between “ChatGPT” and “Codex.” The default mode is also not consistent across the desktop and web apps. The desktop app defaults to “Work” but the web interface defaults to “Chat,” while “Work” on the web is only available for Pro plans. “I’m almost afraid to ask but… in the web interface, what on earth is the difference between chatgpt ‘work’ and ‘chat’ mode?” posted Lucas Beyer, Meta researcher and ex-OpenAI employee, on X. “Asked ChatGPT Work to summarize it for you (and agree the UI should tell you this directly and you shouldn’t have to come here to ask),” replied Codex engineering lead Thibault Sottiaux. “ChatGPT Work is optimized for work!” Sottiaux and OpenAI describe Work as intended for longer, multi-step tasks where “agents” plan and carry out tasks more autonomously, such as generating research reports. Work follows Codex usage structure, so can draw down more tokens than Chat. OpenAI product engineering lead Akshay Nathan also said this week that ChatGPT tries to push users to the “Work” mode if they are creating a spreadsheet. In his interview with Stern, Brockman said he expects they will phase out the Work tab by the end of the year. “We were hoping to land with zero tabs,” he admitted in the interview. “But I definitely think that by end of year there should be no work tab. You know, that this will be something that will just seamlessly mold into ChatGPT.” In the meantime, don’t ask Brockman to call it a super-app. “We actually don’t really use the word super-app,” he told Stern. “I regret that [it] has become the term of art,” he said with a laugh. BY JULIE LEE

Nvidia Just Bet $5 Billion on an AI Startup Most People Have Never Heard Of. Here’s Why

Nvidia, the world’s leading AI chipmaker, just announced it’s investing $5 billion dollars—a giant chunk of money by any reasonable measure—into a smallish AI startup that most people probably haven’t heard of. It’s an outfit called Safe Superintelligence, SSI for short, based in Palo Alto, California and Tel Aviv, Israel. The investment could nevertheless be hugely important for a number of reasons relating to the future of AI tech. Here’s what’s going on. What is Safe Superintelligence? SSI was founded only in mid-2024, and its co-founder and CEO is Ilya Sutskever, formerly OpenAI’s chief technology officer. Sutskever created a fuss when he left OpenAI because he’d been intimately involved in bringing ChatGPT to the world, and was leading the AI’s alignment team. This part of OpenAI was critical, especially at the time, because it had one main job: making sure that as OpenAI’s products got more sophisticated they aligned with humankind’s needs, rather than evolving into a threat. Sutskever’s departure led to some serious questions about OpenAI’s good intentions when developing future AI models—a debate that’s still bubbling away today, as OpenAI’s Sam Altman stirs the pot by announcing he feels we’re already in a powerful singularity moment. Safe Superintelligence is actually about more than just building AI chatbots like Siri or ChatGPT. The clue’s in the name: it’s not just about intelligence—it’s trying to make “superintelligence.” This, experts have long suggested, is an AI capable of out-thinking even the most agile human mind, in pretty much any area you can imagine. The company’s own website says this may be “the most important technical problem of our time” and it “is our mission, our name, and our entire product roadmap, because it is our sole focus.” Rather than developing ever-smarter AIs and then bolting on safety and security, SSI is developing AI and safety “in tandem, as technical problems to be solved through revolutionary engineering and scientific breakthroughs.” News like the recent international ban of Anthropic’s Mythos 5 model by the U.S. government for national security reasons, and the fact that an OpenAI model went rogue, escaped and then attacked a rival’s AI systems with a hack, show how important safe AI development is becoming. For superintelligent AIs, safety is clearly going to be even more important. SSI has been in and out of the headlines since its founding because although it’s raised sizable investments (topping $3 billion in total by late 2025,) it has yet to release any kind of meaningful product. The new $5 billion from Nvidia, its biggest financial injection yet, has resurfaced many questions about the company. Why does the investment matter? The mammoth investment is a sign that Nvidia, which by many accounts makes the world’s leading AI chips, is interested in a safe future for AI, and that it’s thinking beyond the current AI chatbot paradigm and has an eye on the next generation of superintelligent systems. As part of the deal, SSI will also gain access to Nvidia’s Vera ​Rubin chips. These are next-generation chips that are even more sophisticated than the company’s Blackwell chips, which stirred plenty of media attention when they were revealed in 2024. For certain key processes needed to make AI calculations, the new chips may be between two and five times faster than the Blackwell chips. This kind of raw power should speed the development of SSI’s AI systems significantly. In a press release, Nvidia also revealed the investment and technology deal goes both ways. It noted that the “two companies will also collaborate on the technical advancement of NVIDIA’s current and future compute platforms, leveraging SSI’s unique insights into the future of AI.” This implies that SSI has made powerful and useful progress that Nvidia executives have seen, and they want to make the most of SSI’s developments to keep their own company at the cutting edge of AI. The release quotes Nvidia CEO Jensen Huang, who praised Sutsever’s expertise and “fundamental breakthroughs at the foundation of modern AI.” He went on to say he was “excited to see what new breakthroughs SSI will discover powered by our Vera Rubin platform.” Why should you care about this? The news won’t directly impact the way you use AI in your own company. But it may remind you that current-generation AI tech isn’t inherently safe or trustworthy. Use these tools in the wrong way, and you could make a disastrous financial decision because of false information an AI told you, for example; or, a third party AI company may leak sensitive data your own staff have shared with an AI tool (as just happened with Anthropic’s Claude model). But the idea of superintelligent AI tools should be a part of your long term business plans. If you’re finding today’s AI tools helpful in building your business, you shouldn’t sleep on the innovative AI models that the future promises. Constantly checking to see if AI matches your needs could become a new business norm. BY KIT EATON @KITEATON

AI Has Freed Interns From Grunt Work. Now Companies Have a New Problem

Internships have long been considered a valuable steppingstone toward future full-time jobs, often because many companies hire people they previously mentored. But a number of recent workplace trends—notably increasing business deployment of AI tools—are creating new challenges for many younger apprentices as they seek to effectively learn the corporate ropes. Frequently, those changes are leaving interns with little or even none of the administrative grunt work they’d been traditionally given, and which AI applications have now automated. Even if that sounds like a good thing, it can strand many trainees in what business consultancy Korn Ferry called “a new kind of boredom.” In an effort to respond, companies are now assigning novice workers weightier workplace responsibilities they haven’t been trained to assume and are often terrified to take on. Consider it a feast-or-famine scenario for post-AI workplace apprenticeships. Under it, the workflow management and decision-making skills interns previously learned through repetitive and boring administrative tasks are being swapped for far more consequential roles usually reserved for more seasoned employees. “Thanks to AI, the drudgery that typically accompanies an internship—data entry, report drafting, meeting tracking—is being replaced by a different kind of cognitive burden that requires more fact-checking than judgment or critical-thinking,” a Korn Ferry blog post on the change said. “Companies expect [interns] to arrive fully proficient in AI so that they can spend more time developing their problem-solving and other human skills.” Unmentored interns The problem with this shift isn’t merely the bigger risks and higher costs of trainees stumbling while making decisions previously reserved for experienced staff. Interns may also lack the ongoing supervision they’d previously benefitted from, in part because of the effects of now common remote working arrangements or the manager-depleting effects of hierarchy “flattening” strategies. Indeed, a survey by the National Associations of Colleges and Employers (NACE) found nearly 20 percent of 2025 interns said they didn’t have a mentor, “which is a key anchor point to and sounding board for an organization.” Having a mentor is also critical in preventing trainees from being too terrified to make the decisions they’ve been assigned, or making costly errors after plucking up that courage. Alternatively, in many cases where workplace mentors were either never or infrequently present, novice workers were told to carry out work using the same AI bots that had already automated the administrative tasks earlier generations fulfilled. A far duller form of that came with younger trainees being instructed to monitor output of those apps—essentially an updated form of earlier grunt work that now keeps watch for slop the tech produces. Why should the shifting skills and experiences interns are learning be of concern to employers? According to Korn Ferry, a big reason is that companies frequently recruit the same young people they’ve helped train. Its post said last year that fully 63 percent of former interns received a full-time job offer from either employers they apprenticed with, or other companies that appreciated those training efforts. Fully 89 percent of businesses say they rely those programs on to identify future full-time employees, with NACE data showing nearly half of internship veterans received at least one employment offer before graduating from college this year. Internship programs need a big AI update Meanwhile, the size of the talent pipeline is huge, with an estimated 3 to 4 million people interning for U.S. companies each year. That flow of trainees is becoming an even bigger recruitment focus for businesses, now that AI has eliminated many traditional entry-level jobs for college graduates don’t have similar hands-on experience. But as important as internship programs have become, employers need to revise and refocus them in order for both youthful participants, and the employers themselves, to continue getting maximum benefits from them. With apps and AI replacing the administrative tasks trainees previously performed, businesses must find a middle ground between the contrasting alternatives many have adopted in the post-AI era. That means enriching roles situated somewhere between vetting chatbot output for slop, and the often terrifying responsibility of making decisions typically reserved for managers. “Firms are going to have to be more deliberate about teaching and coaching the skills they actually want interns to develop,” Mark Royal, a Korn Ferry senior client partner who specializes in employee engagement, said in the blog post. That means employers should focus less on the immediate, pragmatic concerns about assigning interns whatever work remains available that AI hasn’t taken over yet. Instead, they need to aim for the longer, strategic objective of identifying the tasks, experiences, and roles that will shape the kind of young workers they’d be eager to hire as employees. “Internships aren’t really about the output,” said Jerry Collier, leader of Korn Ferry’s EMEA assessment and succession practice. “They’re supposed to be about developing people.” BY BRUCE CRUMLEY @BRUCEC_INC

Apple Finally Fixed Siri—and It May Be Your New Favorite AI Tool

Apple’s Siri is the OG personal AI assistant. It debuted so long ago that the entire class of these voice-activated systems has been renamed “chatbots.” Siri, which launched as an integrated service on Apple devices in 2011, was useful for basic hands-free stuff—it could set timers, add calendar entries for business meetings, tell you awful jokes, even play music on request. But despite being improved gradually through the years, it remained limited. When ChatGPT arrived in 2022, Siri looked outdated overnight. Apple promised a dramatic revamp of Siri in 2024, but blundered through the process, earning Apple some rare media bruises when it was slammed for its failure to deliver. But now the new Siri—also known as Siri AI—has finally arrived, ready for public consumption. And boy, was it worth the wait. For many tech-wary execs, it may even become the AI that you interact with most often. What can Siri AI do? Available on newer iPhones, Macs, and even Apple Watches, Siri is now a true chatbot like many others, but it makes the most of being integrated into Apple’s devices in ways that rivals like ChatGPT or Claude can’t beat. Apple’s own blurb explains that new Siri will be a “truly helpful AI that’s centered around you and your needs” because it’s “integrated into your apps, grounded in your context, and private at every step.” It works across lots of different aspects of your phone or computer, so you can ask Siri to show you photos from a finance meeting in Berlin in one request, text your partner in the next, and finally ask it to create a summary of a document so you can quickly give your PR team some guidance. There’s no need to leap between apps manually—you can just activate Siri and make these kinds of requests whenever you need. Reviewers have pointed out that though Siri is now comparable to market-leading AI chatbots like ChatGPT, it has some quirks. It may come across as a little more serious-sounding than ChatGPT, and you can’t interrupt its replies in the way that you may be used to with OpenAI’s product. You probably won’t rely on this general-purpose chatbot to write lots of high-end code for you either. Specialized AIs are still going to be better for developers. Why should you use Siri more now? Because Siri can now process natural language requests, you can task it with more sophisticated duties, including useful prompts like “set up a regular Monday meeting with Miriam and add it to the calendar.” You should also be able to ask it the kind of “why does X work the way it does?” questions that you may have asked of other AIs if you need to quickly learn something new. And because it’s built into Apple’s devices, you don’t even have to touch the keyboard or screen to get the chatbot’s attention—the classic “hey, Siri” request still works. That means the AI is just a second away anytime you need an answer or a quick digital task completed. Essentially, Apple’s made Siri a hundred times more useful than it was before, both for home tasks and many workplace-centric duties. Siri and privacy This is where Apple’s devotion to user privacy may pay off. We’ve all read reports of how chatbots can leak sensitive information that users type into it, and noted the horrifying percentage of workers who blithely share confidential company secrets like financial reports with these third-party companies, not caring a bit about the legal ramifications. Companies like OpenAI and Anthropic also use your prompts to train their future models, and you may have let them do this without even being aware. Apple promises Siri won’t do any of that. Much of the new Siri service works on your devices, so nothing gets sent off to a distant digital cloud. And when your AI queries do need this extra compute power, Apple handles it so that the cloud data isn’t kept or associated with you. Siri is “aware of your personal information without collecting your personal information,” Apple promises. For many executives, this will be a key selling point. And for some companies, Apple’s privacy rules may allow use of Siri AI on company hardware in ways that have been banned or restricted for employees until now. The reviews are good Because Apple also places a premium on making its services simple and easy to use, MacWorld suggested that the new Siri is like “ChatGPT for people who hate AI.” The reviewer noted it was “dependable for quick questions, digging up personal data, and controlling your iPhone,” remarking that it was a little more simplified than more established chatbots. A reviewer on tech news site The Verge explained that “Siri AI is already changing the way I use my iPhone,” noting that “it’s practically stopped me from opening my browser for most things, since it’s easier, faster, and more enjoyable” to engage with Siri, either by voice or by swiping down on the phone’s screen and typing in a query. Even many commenters on Reddit, often a critical and snarky bunch, have been largely positive in a thread about the new Siri. One said: “It’s amazing. For everyday queries I haven’t touched the ChatGPT app in days.” Another wryly referenced some of the supply, trust, and pricing woes affecting rival chatbot makers by remarking: “Apple is the only company not in an AI crisis.” Important things to remember about the new Siri While Siri is now publicly available, it’s only as part of a beta OS release for Apple’s devices—and not all older devices can power the advanced services the new iOS and Mac software offers. You’ll also have to put up with the occasional bug while it’s still in testing. And while Siri AI is free, to access some of the more powerful cloud compute features you’ll need to subscribe to Apple’s services. There’s also a waiting list to activate Siri AI, but in my case, the waiting period was less than half an hour. Interested in trying out the beta release on one of your devices? Go to Settings on your iPhone, iPad, or Mac, then General, and under the “Software Update” heading, find the options list and select “iOS 27 Public Beta” for your iPhone, “iPadOS 27 Public Beta” for your iPad, and “MacOS 27 Golden Gate Public Beta” for your Mac. BY KIT EATON @KITEATON

A New Harvard Study Reveals Why Some Young Workers Turn AI Into Results—and Others Don’t

When people talk about AI and entry-level jobs, they usually fret about the threat the tech embodies to workers just starting to climb the career ladder. But while these youngsters are noted for being tech-savvy, and are often relied on for their skills as the first “digitally native” generation, many are reportedly deeply skeptical about AI. When you look around your workplace, you’ll see some who are excelling at leveraging it to make their job faster or better, while plenty more are struggling. New research in the Harvard Business Review may help you get a handle on this and bring all your young workers up to the same speed. The new study centered on why some junior staff seem to do so well when using AI to further a company’s goals, and yet others don’t. It turns out the best differentiator between young workers who excel at using AI and those who don’t isn’t the individual AI skills the workers have. Instead it’s more about how each worker tackles their tasks that matters. This runs counter to all sorts of narratives about AI literacy levels and what specialties young workers bring from their higher education. Some young workers, for example, have the AI skills but fail to use them in a way that delivers value for their employer. Others can appear competent, because they know how to rely on AI’s core systems to get to a deliverable answer, but they’re actually hiding their foundational business knowledge weaknesses. Meanwhile, here’s what sets successful young AI users apart: According to the study, the best users combine their “strong domain knowledge, critical thinking, and AI literacy” into every step of using AI to complete a task, including initial queries, shaping the outputs, and then delivering the required product. It’s a holistic skill, rather than a collection of shallow abilities. The business researchers note that it makes perfect sense for business leaders to prioritize hiring entry-level workers for their critical thinking skills and AI literacy. But because their study found “employees with similar levels of foundational skills and knowledge can generate dramatically different outcomes when working with the same AI agent,” savvy companies may rethink this policy. The problem is that being AI-savvy isn’t the same as knowing how to use an AI tool to solve a tricky business problem. Here’s what you can do The study suggests that company leaders keen to get all their young staff using AI in the most productive way possible should worry less about what their workers know, and instead train them in a way that emphasizes “how employees frame problems, interrogate AI outputs, refine results, and integrate insights into decisions.” Essentially, instead of taking it on trust that your Gen-Z and Millennial staff know what they’re doing, you need to actually teach them best practices. This involves leading them step-by-step through prompting an AI with first thoughts, evaluating its replies, tweaking what they’ve asked it, and incorporating the output into meaningful products. Then, when you give them an AI tool to use, it should translate into more meaningful impacts for your company. There’s another piece of advice: The researchers say companies should treat AI learning as “an ongoing development model rather than a one-time training intervention.” This probably holds for training older employees, too. Personalizing this training to plug up an individual’s skills gaps is an even better idea. And to really nail the solution, companies could actually embed AI learning into the tasks and decisions that workers face each day. Why all this matters AI boosters promote AI as being able to save companies time, effort and money—no matter what individual AI tools are designed to do, they all have this overall goal. These promises are why this tech is ubiquitous. But you can’t just “fire and forget” AI, nor can you assume that Gen-Z workers will just immediately master the tech, in the same way you wouldn’t trust them to run your PR office just because they’re already proficient with using TikTok or Instagram for fun. You need to train your young workers on an ongoing basis, ensuring that you work out where they’re strong and weak on knowing how to use AI in an impactful way, and plugging gaps through learning. The same goes for older staff too, especially if you’re hoping that the young Gen-Z whippersnappers will help drag your Gen-X workers into the AI era. BY KIT EATON @KITEATON

AI is making your life more expensive. Here’s how

AI is raising prices for Americans – and not just electricity bills. Major technological innovations carry the potential to transform economies by creating opportunities, jobs and even new industries while supercharging productivity and growth. However, that promise of longer-term gains often is preceded by short- and medium-term pain. In the case of artificial intelligence, that has included job losses, slower wage growth, widening wealth inequity and, especially in recent months, higher inflation. Recent data shows that the gargantuan interest and investment in AI adoption (estimated to be around $750 billion for this year alone) have pushed a variety of prices higher, lifting overall inflation in the process. “The higher inflation means that households must spend just over $375 more to purchase the same goods and services as they did this time last year due to AI’s inflationary impact,” Mark Zandi, chief economist at Moody’s Analytics, wrote in an email to CNN. The good news: AI is still just a minor contributor (an estimated 0.2 percentage points) of overall inflation, and the impacts are currently limited to a handful of categories. But the not-so-good news: AI is pushing inflation higher and further compounding longstanding affordability concerns in the process. Plus, these price pressures aren’t expected to go away anytime soon, and they very well could broaden. That potential dynamic has Federal Reserve officials, including the central bank’s new chairman, on alert. Here’s a look at where AI has already shown up in inflation and where it could crop up next. Electricity AI data centers can have voracious appetites for energy (notably electricity and water), and the rapid expansion of these monoliths threaten to strain grids and drive prices up further. That’s largely because demand is outrunning supply. Data center facilities can be built or expanded at double or triple the pace of new electricity generations systems needed to serve them, PJM Interconnection, America’s largest grid operator, noted recently. Combine those needs with retiring coal plants, increased electrification needs, extreme weather and aging infrastructure, and it further widens the gap between supply and demand. “Data centers are demanding huge amounts of power, and that’s tending to crowd out the electricity available to distribute to residents; it’s also led to wholesale electricity prices being bid up, because data centers are willing to pay the price that providers ask them for, and that ends up also raising the prices for residential electricity costs,” Pooja Sriram, US economist at Barclays, told CNN. US Consumer Price Index data shows that residential electricity prices rose about twice as fast in 2025 as compared to the average seen in years prior, she noted. And through the first five months of this year, electricity prices were climbing even faster than in 2025, Bureau of Labor Statistics data shows. “I think that is one of the clearest imprints of AI data center demand driving up residential electricity costs,” she said. Electricity prices unexpectedly fell 1% in June but continue to outpace overall inflation and are up 4% from a year ago, the latest CPI data shows. Memory chips The data centers’ appetites, however, aren’t fully sated with power alone. The massive buildouts also led to a surge in demand for memory chips, which has benefited manufacturers handsomely, Sriram said. “The issue is not just the demand; the issue is the supply side for those memory chips has been very constrained,” she said. The trillions of dollars chasing AI-related components now have storage and memory suppliers prioritizing their wafer-manufacturing capabilities toward high-bandwidth and high-speed (and highly profitable) memory products commanded by data centers, she said. “What that does has basically diverted (the production of) the memory chips that you need for consumer products toward very specific high-performance memory chips that the data centers need,” she said. The pricing pressures of these and other components have been most evident at the producer level. The Producer Price Index chart semiconductor and other electronic component manufacturing industry looks like a hockey stick. As of June, that category’s wholesale prices were up 26% from a year ago – a stark shift from June 2025 when prices were falling 0.8% on an annual basis, PPI data shows. “Why this matters for the end consumer is, at the end of the day, our laptops, computers, iPhones, iPads all have some sort of memory chip embedded in that hardware, and those chips have become quite expensive,” she said. Computer hardware and software Late last month, Apple hiked the prices for some of its most popular products by roughly 20%. In a statement, the company noted that AI data centers created an “extraordinary surge” in demand for memory and storage. Sony upped the price of its PlayStation console earlier this year, and Microsoft last month raised the price of its Xbox consoles by about 25% for similar reasons. “The entire consumer electronics industry is struggling with the current components crisis, but the effects are particularly hard on consoles,” Microsoft wrote in a statement. Computers and related hardware have typically been a highly deflationary product category in the CPI: Because of technological advancements, consumers can get more bang for their buck. (For example, a $1,500 computer in 2025 was likely more powerful than a $1,500 year-ago model, and as such, the BLS treats this as a price drop) For the first half of 2026, however, computers and related products have experienced price inflation, BLS data shows. “We’re in the early innings of these consumer price pressures and the pass-through from higher producer prices, higher import prices and greater demand, especially for AI-led investment,” said Gregory Daco, chief economist at EY-Parthenon. Adding AI features in business applications also affects the price of software. For example, Microsoft raised personal Office 365 prices by 43% in February (30% for a family plan) after keeping them steady for a decade. The new feature: Copilot, Microsoft’s new AI tool. Construction costs, wages Data centers also are impacting the supply of other key construction inputs such as copper and electrical wiring, as well as workers. “If you’re looking for data that was conclusive (about AI’s effect on the economy and inflation), albeit a bit more subtle, you would look and see whether wages in construction were going up more than wages in the rest of the economy,” said Thierry Wizman, Global FX and rates strategist at Macquarie Group. “Because if in fact there is upward pressure, straining resources of the economy because of the AI data center buildout, you would see it in wages as well – specifically in the wages of labor that would be working these projects,” he added. So far, the available national-level wage data is showing a “robust divergence” between the construction sector and the aggregate, he noted. Regional data could prove even more telling, he said, noting the importance of tracking construction wages in areas with a high concentration of data centers. (That will take more patience, however, as that more localized data is lagged due to collection and modeling needs). “We’re having a problem with housing in the country these days; people talk about it as being unaffordable,” Wizman said. “It could be the case that the fact wages in construction have been rising a lot is putting upward pressure on houses as well.” By Alicia Wallace

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