This Single ChatGPT Prompt Can Do Hours of Market Research in Minutes—Here’s How

Market research can be a slow, fragmented, and difficult process, often involving tedious internet searches, questionable data sources, and time-consuming manual synthesis. This makes it a great candidate for some assistance from AI. What’s more, an update to a popular feature on ChatGPT has made it even better at doing this kind of work. Imagine that you have a potential business idea but still need to validate how viable it actually is, identify primary competitors in your market, and develop an ideal customer persona. Instead of spending hours collating data, explains Dan McCarthy, an associate professor of marketing at the University of Maryland, you can use Deep Research, a ChatGPT feature that directs an AI agent to develop a comprehensive, well-cited report on any topic. Last week, OpenAI upgraded Deep Research with some new abilities. The feature now runs on GPT-5.2, one of the company’s most recent models (previously it ran on a much older o3 model), and can now prioritize specific websites in its search process. Deep Research is available for all paid ChatGPT users. Here’s how to use it to get some thorough market research done quickly. Step 1: Get your prompt right To test out how this feature could help with market research, I pretended that I wanted to start a digital transformation firm based in Denver with a focus on upgrading bars with mobile, bar-to-table ordering capabilities. All I needed to do in order to get started was click the plus button next to the text box, select More, then Deep Research, and enter a prompt. This prompt will determine the information that ChatGPT prioritizes in its search, so it helps to be verbose. If you need help developing a lengthy prompt, try using ChatGPT to help write it. McCarthy, who uses AI tools extensively, says that an easy way to develop a comprehensive prompt is to activate the chatbot’s voice mode and simply have a conversation with it. Once you’ve explained what you want, McCarthy says, you can ask ChatGPT, “Given all this that I’m telling you, what do you think would be the best thing that I should even be asking you?” That should help clear up any blind spots you might’ve missed. According to McCarthy, this method should produce a solid prompt that you can give to the Deep Research agent. When I asked ChatGPT to help expand my prompt, the platform generated a 673-word result. This prompt (which you can view here) defined the agent as a market research analyst and gave it objectives to determine the business idea’s viability, map out the competition, and define my ideal customer’s persona. Additionally, it provided details on the scope of the research, and information for how the agent should format its report. I also used ChatGPT to develop a list of specific websites for the Deep Research agent to prioritize in its search. Step 2: Start the research I entered my ChatGPT-created prompt, selected the Deep Research feature, and pressed return. Before getting to work, the agent broke down its objectives into the following bullet points: Collect primary vendor docs and pricing pages starting with user-preferred sites. Survey industry, local Denver sources, and hospitality reports for market context. Compile POS integration lists, local competitors, and implementation partners in Denver. Analyze demand, model ROI scenarios, and estimate Denver bar counts and adoption rates. Draft recommendations, ICP personas, GTM plan, and cite sources with confidence ratings. Over the next 21 minutes, the agent searched through hundreds of web pages. It found liquor license databases, census information, and data regarding competitors in Denver’s hospitality-focused digital transformation market. It compiled all this information into a multi-section report. Step 3: Read the report That report (which you can view here) ended up being roughly 4,000 words. It included an overview of the market, identified customer pain points, and listed out my potential competitors. The report also included recommendations for how to position my business, strategies to break into the Denver hospitality scene, and even identified a small business that would likely be my direct competitor: a Denver-based POS integrator called Megabite. ChatGPT found that while my business idea had potential, it wouldn’t fully meet the needs of Denver-based bar owners, who have reported that bar-to-table ordering can actually lead to fewer sales and tips. Instead, the report suggested, I should consider a system that can sit on top of popular POS in which diners don’t need to pay for every new drink they order, and can instead open a digital tab. What the expert thinks of the result McCarthy told me he was impressed by the report that Deep Research produced. In particular, he was pleasantly surprised by the agent’s cleverness in using liquor licenses to get a sense of the market size, and its thoughtfulness in calling out disruption to bar culture as a potential blocker to the business. But the report wasn’t perfect. McCarthy said much of what was included was unnecessary or needlessly complex. An easy prompt to fix this? “Just tell it, ‘Explain it to me like I’m an idiot.’” McCarthy adds, “I do that all the time.” He says that a solid market research report should also answer questions regarding the scope of adoption and how often repeat purchasing is expected. McCarthy also says that users should direct the Deep Research agent to be very upfront about the data it attempted to get but couldn’t. Many websites block AI agents from engaging with their content to prevent data scraping, which can hinder the research process. By telling your agent to list out the sites that it couldn’t access, you can manually obtain that data and add it to the analysis. Our bar-to-table digital transformation firm will have to remain a pipe dream for now, but it’s clear that AI has made the process of taking an idea from zero to one easier and faster than ever. If you have an idea for a new business or are planning on an expansion or pivot in your current business, consider giving Deep Research a spin. It might unearth something that makes you think in a different way. BY BEN SHERRY @BENLUCASSHERRY

Business Owners Have a New Security Problem: AI Agents With Keys to Company Secrets

While the OpenAI rogue agent that hacked Hugging Face has become a poster child for the latest AI threat, it was hardly alone. Days later, Anthropic announced that its AI models also went rogue, hacking external organizations. And a few days after that, Meta announced that one of its agents had also accessed the internet and hacked a third-party service. The incidents have raised concerns about whether AI can be controlled by its creators and what will happen as the technology continues to get smarter. Dylan Ayrey, co-founder and CEO of open-source security software company Truffle Security, and Feross Aboukhadijeh, founder and CEO of security infrastructure firm Socket, recently joined the a16z podcast to discuss what these attacks signal and how AI is in the process of entering a new era. That could mean developers and business owners have to rethink system security. Here are some of the top takeaways from the discussion. The barrier for hacking is a lot lower While the AI rogue agents haven’t invented any new hacking techniques, they have made existing methods much more accessible. “Everyone needs to worry about these models making it materially easier to hack into things,” said Ayrey. “The bar previously [for hacking] was just subject matter expertise—and now the models have the subject matter expertise.” Put another way: Wannabe hackers today simply have to ask the model, which has been trained to hack into things, to do it for them. And that puts businesses and individuals at greater risk. Low hanging fruit is the best target The AI models are goal oriented, Ayrey said. Their objective is to fulfill a request and they’ll use any cybersecurity technique they need to in order to achieve their objective. That’s not unlike human hackers, in a way. Hacker collectives generally look for targets that have unpatched vulnerabilities, as it saves time and effort. AI is similarly lazy when it comes to breaching a system, only the technology has a more extensive toolset than a typical human hacker. “They will do the path of least resistance to accomplish the task,” Ayrey said. “And that includes drawing on their cybersecurity expertise.” One way that attackers, including AI models, can find that low hanging fruit, said Aboukhadijeh, was by exploiting the trust developers have in software, rather than launching an attack on an individual system. “Get developers to install that, and then you could use the access stolen from those developers as they install it to self-propagate the worm,” he said. It’s not emergent behavior The behavior that we’re seeing from these AI models isn’t an emergent intelligence, said Ayrey. It’s a flaw in how they were trained. “If a lab tells you that this is an emergent super intelligence behavior, they’re just lying to you,” he said. The AI hacking risk isn’t limited to machine-driven attacks, either. Human hackers can exploit AI tools that developers use, rather than traditional malware, to end-run a system. That will bypass much of the EDR (Endpoint Detection and Response) tooling security systems have in place, since the AI has permissions to access data. There’s a growing need for better authentication and patching Aboukhadijeh brought up pending change in npm, the digital toolkit programmers use to build software. In January 2027, it will no longer run or publish updates automatically. A human will need to approve them, which is meant to stop hackers from sneaking malware into systems. “It’s going to be super disruptive, … but I think it’s the right call,” he said. Ayrey added that the accelerating speed of vulnerability discovery via AI makes current patching processes inadequate. Engineering teams can’t be depended on to make a complicated upgrade every time a vulnerability appears. “The frontier models are causing kind of a massive reduction in the time between the vulnerability discovery and vulnerability exploitation,” he said. “What we need to start thinking about is: How do we patch more quickly?” Protecting agents is the new Wild West AI agents, as the technology advances, are going to potentially have access to a large number of credentials. While that has some conveniences, it also presents a growing risk. And the industry isn’t quite sure what to do about it yet. As a result, the security problems that IT departments face is about to expand from just shielding human users and their credentials to protecting those AI agents and the secrets they have access to. “The way agents interact with secrets right now is a Wild West unsolved problem that we’re working very hard to solve,” said Ayrey. BY CHRIS MORRIS @MORRISATLARGE

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