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