Claude’s New Watermarks Will Follow Text Even After It’s Copied. The Era of Secret AI Use May Be Ending

Using AI can be empowering, but it can also spark controversy when people try to pass off AI-generated content as actually being human-made. The European Union is wary of the implications of deepfakes and other AI-fashioned material, so a new law mandates that AI-generated content be clearly labeled as such. Reacting to this legal change, Anthropic just announced that it’s adding markers to text written by Claude, starting with the latest generation model and extending, soon, to earlier versions of the chatbot; the change applies, for now, to users inside the EU. It sounds like a subtle shift, but it may have large, long-term implications and affect businesses across the globe. In a blog post announcing the news, Anthropic noted that from now on, in the E.U., “new models will mark AI-generated content” with “machine-readable marking.” This means any text you generate with Claude, for personal or business use, will “carry embedded watermarks” and any generated files will “include digitally signed provenance metadata where supported.” The company explains that it’s conscious that as “AI-generated content becomes commonplace, greater transparency and signals about where content comes from can give people useful context about the information they consume.” So while its move to watermark content is following E.U. law, it’s really a transparency push that’ll help consumers and perhaps the public in general. As well as marking any generated files with relevant metadata, Anthropic notes that “when a supported Claude model generates text, it weaves an imperceptible watermark directly into the text itself.” It’s invisible, so “you won’t see it,” and the company stresses it “doesn’t change the meaning, quality, or readability of Claude’s response.” To ensure that people don’t try to circumvent these protections, Anthropic explains that since these invisible marks are part of the text, they’ll “travel with the text when it’s copied and pasted elsewhere, and may persist through some editing.” Anthropic’s new watermarks will change how businesses worldwide use Claude—and how transparent they are about it.

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_