# OpenAI Copied a Startup in One Week, Then Turned ChatGPT Into an App Store - Platform Watch - Week of October 2, 2026 > Platform Watch for the week of September 25 to October 1, 2026. Podcast synthesis on OpenAI cloning Jev's Decisions API in about a week at DevDay and turning ChatGPT into an app store, Anthropic ending volume discounts, what the leaked Anthropic S-1 says about startup dependence, the debate over cheap and open models, Higgsfield's model-routing margin math, and the legal AI fight over Harvey and Legora. ## Platform Watch ### Week of October 2, 2026: OpenAI Copied a Startup in One Week, Then Turned ChatGPT Into an App Store --- *At its developer conference this week, OpenAI shipped its own version of a hot startup's product, built in about seven days. It also offered near-flagship intelligence at one-fifth the price and invited every app to live inside ChatGPT. The same week, Anthropic stopped giving some customers discounts. A leaked IPO filing showed how much the labs depend on startups paying for their tokens. And a $1 billion-a-year video startup explained exactly why it steers as much work as it can onto cheaper models. The lesson: being clever on top of a lab's model is a head start, not a moat.* On September 26, three of the sharpest people in software sat down on The a16z Show and talked excitedly about a new startup called **Jev**. Jev does one thing. Most AI models are built to write paragraphs. Jev's model reads text, looks at a short list of options you give it, and picks one. It also says how confident it is. That is very fast and very cheap, and it is exactly what ordinary software needs when it wants to ask an AI a yes-or-no or multiple-choice question. Steven Sinofsky, who spent decades at Microsoft, loved it. He also used a word every founder building on a platform should know: *"There's this famous expression in the Apple community called Sherlocking, where Apple looks around and the things from the outside world become features and people complain. But that's sort of how the innovation works."* (The a16z Show, "Aaron Levie, Steven Sinofsky & Martin Casado: How Do You Secure a World of AI Agents?," September 26, 2026) Three days later, OpenAI Sherlocked Jev. ## This Week's Platform Move: OpenAI's DevDay, a clone in a week, and an app store inside ChatGPT On September 29, at its DevDay developer conference, OpenAI launched a **Decisions API**. It gives a fast answer chosen from a set list, with a confidence score. That is Jev's product. Latent Space recorded a podcast at DevDay with OpenAI's API team. They were remarkably open about how fast the clone came together: - **It started about a week before launch.** "Firstly, huge props to Diogo and the Jev team for really inspiring a whole segment in the market... Our users are hitting us up. But also, like, our internal teams are like, we need a much faster classification system." An engineer from the infrastructure team "built a prototype, it like works, and now we're just like hill climbing on latency." The API lead said the whole thing "started a week ago." - **It wasn't on the plan a month ago.** Host swyx: "4 weeks ago, this was not on the [roadmap]." OpenAI: "No, not at all. This is like Jev-inspired." - **They didn't need a new model.** OpenAI is "building this purely on top of the same Luna weights that we have." Luna is OpenAI's small, cheap model. In other words, the lab took a model it already had, constrained the output, tuned its servers for speed, and shipped. Swyx noted it is priced the same as Luna. - **The lab gets extras for free.** "As a benefit, you have vision. They don't have vision." OpenAI: "We get it for free with Luna." - **Everyone else is cloning too.** Swyx said there had been "about 100" Jev clones "in the last 2 weeks." He called OpenAI "officially the first Frontier Lab to clone and adopt this." (Latent Space, "Why Dwarkesh is Wrong about Computer Use + How OpenAI shipped its Jev competitor in 1 Week," September 30, 2026) The hosts were fair about where Jev might still win. Copying the *interface* is easy: "Everyone can achieve the API. It's actually pretty trivial." What's hard is the speed, the accuracy, and confidence scores that actually mean something. OpenAI admitted its first version was "zero-shotting this on top of Luna," and that real calibration may need "a future model release." **Why investors cared so much.** On 20VC the same week, the panel had treated Jev as one of the hottest deals going. Rory O'Driscoll did the math on its market. Roughly **$100 billion** a year is spent on AI right now. He estimated about **20%** of that is the kind of work Jev does, which makes **$20 billion**. If Jev does the same work five times cheaper, that leaves about **$4 billion** of revenue to capture, "by literally taking money that's already being spent and saving 80 cents on the dollar." Jason Lemkin joked he'd put "30% of the fund" into it (20VC, "Instinct Raises $1B at $10B Valuation | AMD Buys Fei-Fei Li's World Labs for $8.2B | Meta Poaches MongoDB's CEO…," October 1, 2026). That's the problem in one line. When your pitch is "we save customers 80% versus the lab," the lab can go after the same savings with a model it already owns. ### The rest of DevDay: everything moves inside ChatGPT The clone made the best headline. The bigger strategic move was OpenAI's plan to make ChatGPT the place where software gets found and used. Tech Brew Ride Home, quoting TechCrunch, summed it up: *"Combined, the company's announcements pointed toward a bigger plan: a disruption of the traditional app store model. Taken together, today's announcements turn ChatGPT itself into the place where software can be discovered, launched, and used by people and agents alike."* (Tech Brew Ride Home, "Dippin Dots," September 30, 2026) What that means in practice: - **ChatGPT will suggest apps mid-conversation.** When it spots that an app could help, it offers it, and the user connects and uses it right inside ChatGPT. ChatGPT now has **1.2 billion weekly users**. - **"Sign in with ChatGPT" brings the user's AI allowance along.** People can spend the AI usage they already pay OpenAI for inside third-party apps. That's convenient for users. It also means OpenAI sits between your app and your customer's wallet. - **16 launch partners**, including **Cognition's Devin**, Notion, Vercel and OpenClaw. - **GPT-6.1 Sol** at **$2 per million input tokens and $10 per million output tokens**. (A token is a small chunk of text, about three-quarters of a word. It's how AI usage is billed.) OpenAI says Sol nearly matches its flagship Astra on coding and professional work at **one-fifth of Astra's price**. OpenAI's computer-use lead put it at "a seventh of the cost if you're looking at computer use specifically." OpenAI also said it cut the price of Luna by "like 80%," mostly through efficiency gains in how it runs the model. - **An Agents API with computer use.** Developers can now build on the same "use a computer like a person" ability that powers Codex and ChatGPT. OpenAI said it is building "a bunch of first-party products at OpenAI built fully on top of it," including a meetings tool and Codex security features. A meetings plugin that takes notes and writes summaries launched the same day, which will feel familiar to every AI note-taker startup. - **Dots**, a premium personal agent running on Astra. Each Dot gets "its own Linux virtual computer in the cloud." - **New pricing tiers.** OpenAI reopened its **$200 a month Pro** plan but halved the usage that money buys. It added a **$500 a month** tier with access to "Ultrafast," up to **8 times faster** output in Codex. - **ChatGPT inside Slack and Microsoft Teams**, plus shared team "Spaces." **Why this matters for startups.** Two ideas run through DevDay, and they pull against each other. First, OpenAI wants you building on it. Cheaper Sol, an Agents API with computer use, and a distribution channel with 1.2 billion users are real gifts. Second, OpenAI is watching what you build. The Decisions API went from a startup's launch to a lab feature in about a month. The meeting notes app went straight into ChatGPT. And the new app store means that for many apps, the customer relationship runs through OpenAI's chat window, not your website. Sam Altman put the strategy plainly to Bloomberg: "Our belief is that if we continue to drive the quality up and the price down and really lead the parade of frontier at every point on that curve, people will use our tools in tremendous ways" (Bloomberg Talks, "OpenAI CEO Sam Altman Talks IPO Planning, DevDay," September 29, 2026). "Every point on that curve" is the part to notice. OpenAI isn't only competing at the top. It wants to be the cheap option too. ## Anthropic gets tougher on price, and customers start to leave While OpenAI cut prices, Anthropic went the other way on terms. On The Information's TITV, reporter Laura Bratton explained that Anthropic is **ending discounts once customers hit their usage caps**. Those discounts were "around **15% off** of listed model prices." That matters because, per her colleague Kevin McLaughlin's reporting, "more than **100 firms** spent over **$10 million** each with Anthropic in the 12 months through June," and "more than **1,000 firms** spent over a million each." The early reaction: - **CodeRabbit**, the AI code-review startup, is switching to OpenAI. - **Replit** "shifted to OpenAI publicly in kind of a substantial way this summer, citing model costs," despite doing "a deeper integration with Anthropic and Claude" earlier in the summer. Bratton gave two readings. One is financial pressure ahead of Anthropic's IPO, given its huge computing commitments. The other is "pricing power and the fact that, you know, they don't necessarily have to keep offering these discounts." Meanwhile, "basically every company that is not Anthropic [is] really aggressively trying to discount" (The Information's TITV, "Inside Oura's IPO Postpone, Anthropic's AI Discount Cuts, Anthropic Launches Claude Sonnet 5.5," September 29, 2026). **Watch the bill, not the price list.** Anthropic's new **Claude Sonnet 5.5** shows why the price per token can be misleading. - Rayan Krishnan of the benchmarking startup Vals AI said Sonnet 5.5 ranked **#2 on its benchmark, behind only Opus 5.5**, even though it is meant to be a mid-tier model. But "it is quite token-hungry. So in expectation, we find it actually performs double the cost of its predecessor, Sonnet 5" (The Information's TITV, September 29, 2026). - The AI Daily Brief cited Artificial Analysis: **$7.60 per task**, "27% more expensive than Opus 5.5, and more than twice as expensive as GPT-6 Astra" on its benchmark run. Anthropic says real-world tasks come out 30% cheaper, and lower effort settings cut the cost by two-thirds. The YouTuber Theo called it "an incredible model that you probably shouldn't use" for most standalone work (The AI Daily Brief, "Gemini 4 Argon, Sonnet 5.5 and What Matters with AI Models," October 1, 2026). **Google is back, but you can't use it yet.** The same AI Daily Brief episode covered **Gemini 4 Argon**. It is tied for third on Artificial Analysis's index and costs **$1.99 per task**, versus **$3.26 for Astra**. But that's with a 50% launch discount, and Google is holding the model back from the public for cybersecurity reasons. OpenAI is also holding back **Astra 6.1** for safety reasons. For startups, the newest top model may now come with a delay, a waitlist or a "trusted access" program, while the cheaper versions ship right away. ## The leaked IPO filing: startups are the labs' biggest customers Several podcasts dug into Anthropic's leaked IPO filing (an S-1 is the document a company files before going public). The numbers matter to founders because a lot of that revenue comes from founders. - **The basic numbers.** Ed Zitron on Monetary Matters: Anthropic "spent **$2.75 to make a dollar**" in 2025, versus his estimate of $2.60 for OpenAI. "Anthropic lost **$8 billion** and **$4.6 billion** of revenue." - **Resellers.** "About **47%** of Anthropic's revenue came through sales from Google and Amazon reselling their models." Both of those companies also sell competing models. - **Concentration.** Zitron: "**80%** of OpenAI and Anthropic's revenue comes from **1%** of their customers who are predominantly AI startups." Prof G Markets added that **two customers make up 25%** of Anthropic's revenue and **six make up 60%** (Prof G Markets, "Anthropic's Financials Revealed: The Losses Are Stunning," September 30, 2026). - **Commitments.** **$518 billion** in computing commitments, **$413 billion** of them non-cancellable. - **Run-rate skepticism.** Zitron is an open critic of the AI industry, and he called "run rate" figures (a short period of revenue multiplied out to a year) "useless" without a definition. He passed along a claim from a newsletter that Anthropic's **$65 billion** July run rate was one day's revenue multiplied by 365. Treat that as one critic's claim, not established fact. (Monetary Matters with Jack Farley, "Ed Zitron on Anthropic's IPO (S-1), AI Debt, and Counterparty Risk," October 1, 2026) **Why it matters for startups.** If AI startups provide a big share of lab revenue, and those startups are moving their work to cheaper and open models (see below), the labs have to make up that revenue somewhere. On All-In, Chamath Palihapitiya drew that conclusion directly: *"The first version of the AI trade was relatively simple, which is you're just selling tokens, you're wrapping the tokens, you're passing it through, and there was enough disparity that there was value. I think that that's going away... It'll force OpenAI and Ant[hropic] to go up the stack, there is no choice... that's why they will have to go and do cyber, they'll have to go and do law, they'll have to go and do customer support, all these things that we weren't sure whether they were going to compete."* (All-In, "Anthropic IPO at Risk, Meta's Muse Pop, Token Prices Fall, Open Source Gains Share, Alignment Fails," September 26, 2026) His view on where the advantage sits now: models "are converging and clustering... within margin of error... And the edge is in the harness that you use to wrap the model." (A harness is the software around a model that gives it tools, memory and a workspace so it can actually do a job.) ## Cheap vs. frontier: has the shift to open models peaked? The fight over how much work moves to cheaper models got louder, with real disagreement. **The case that the shift is huge:** - David Friedberg on All-In: "In the last 12 weeks alone, token use has flipped from 80-20 closed versus open to 80-20 open versus closed... In the history of all technology markets, we've never seen anything like this." He also noted that if you run some open models yourself, "the cost on some of these is less than 10 cents for a million tokens." - SAP CEO Christian Klein on Big Technology described the process inside a big software company. SAP tests every agent it ships against multiple models and keeps switching: "Many agents we are building in the meantime have seen the fifth model because we are always optimizing the outcome versus the tokens." When Alex Kantrowitz said cheaper models tend to be "10% of the price for 80% of the performance," Klein agreed: "Exactly." His reason it matters: "It doesn't help you if some of your employees is getting 20% more productive, if at the same time, the cost is going 30% more up" (Big Technology Podcast, "SAP CEO: AI Won't Kill Software, But It Will Change Your Job, With Christian Klein," September 30, 2026). - Jack Altman on 20VC said the best bet of the past 18 months was "just keep buying inference," meaning companies that run models for others: "Modal, Baseten, Fireworks, FAL... It's just all worked." He pointed to **Modal tripling to a $15 billion valuation** and **Baseten in talks at $26 billion**. His explanation: "intelligence saturation." Once a model files your tax return correctly, "throwing more intelligence at that problem doesn't do you any good" (20VC, October 1, 2026). **The case that it has peaked:** - Jason Lemkin on the same 20VC episode: "I think we've reached peak open weights." He gave two reasons. First, "Anthropic and OpenAI are just deciding what they want to price their non-max frontier models... There's no reason they can't be as cost competitive as they want to be." Second, customer anxiety. He had just come back from Salesforce's Dreamforce conference: "No one wants to run on open-source models there. At least Chinese, China-based. Nobody." He also cited OpenAI saying it had hit **$70 billion** in enterprise. - David Sacks on All-In: OpenAI and Anthropic are "a stable duopoly for frontier intelligence," and a meaningful slice of the market "will pay this huge premium for true frontier intelligence," such as a hedge fund that "can't take the risk that your competitor has a better model." - Rory O'Driscoll offered a simple rule for who wins: "Compute share probably proxies to your token share, probably proxies with a little adjustment to your revenue share." **What it means for startups.** Both sides agree on one thing that matters most to founders: **the price of "good enough" AI is falling fast, and labs will compete at that level too.** OpenAI's cheaper Sol and its 80% cut to Luna are the labs answering open models directly. If your margin depends on being the cheap option, a lab can take that away whenever it chooses to. ## The clearest unit-economics lesson of the week: Higgsfield at $1 billion The most useful numbers came from Alex Mashrabov, founder of the AI video company Higgsfield, on 20VC. Higgsfield just crossed **$1 billion in annualized revenue**. He said it took **18 months** to go from $1 million to $1 billion, versus 24 months for Cursor. He was clear about the method: the last four weeks of revenue multiplied by 13, counting only live revenue. **The margin math:** *"The margin on own models and open weights models is over 80%... And for closed source models, it's probably between 20% and 30%."* That's the whole platform-risk story in two numbers. Every dollar of work sent to a lab's closed model keeps 20 to 30 cents. Every dollar sent to its own or open models keeps more than 80 cents. So the company built its business around choosing: "We choose which model we can use... in over than 40% cases." He calls it "tokenomics." **His prediction about the labs:** *"Fundamentally, I think they are going to completely demolish all their prosumer subscription markets, which is $20 a month subscriptions."* Harry Stebbings named a likely victim: "You're seeing it cannibalize Canva's growth... A lot of the low-hanging fruit on the consumer design side that Canva used to serve can now be done in OpenAI in particular." Mashrabov agreed, and said that's why Higgsfield focuses on getting customers to spend "over $1,000 a year." One business customer went from a **$99 a month** subscription to a **$6 million a year** contract in six months. **Coding tools are switched quickly.** Asked what his engineers use: "From March to June, everyone really moved to Claude, including the creative team... But then we started to see that all the coders quickly moved from Claude to Codex as of mid-June... But look, I do believe that it's cyclical." Internally, Higgsfield spends **more than $4 million a month** on models across about 400 people, over $10,000 per person. One employee spent **$30,000 in a week** on Astra. **On moats.** Stebbings: "I largely think they're bullshit... Instinct is a wrapper. Of course it is." Mashrabov's answer gave founders a better test. There are "only two ways of modern value creation or moats today. First is when you deliver the outcome... And the second thing is network effects. Unfortunately, AI does not replace network effects." (20VC, "$1BN ARR in 18 Months; The Untold Story of Higgsfield | Spending $4M Per Month on Models | Why Moats in AI are BS | Scaling a Content Team to 150 People with Alex Mashrabov," September 28, 2026) ## Legal AI, round two: the margin problem and the "priority list" defense Last week OpenAI launched a legal product. This week the podcasts argued about whether Harvey and Legora can hold on. **The money keeps coming.** On LawNext, Noah Waisberg (co-founder of the legal AI pioneer Kira) noted that Harvey had just announced **another $550 million raise at about $15 billion**. He said what impressed him more was "the **$400 million** of revenue" at Legora (LawNext, "'The Hardest Thing I've Ever Done': Noah Waisberg on Why Selling a Business Hurts, and How Zuva Aims to Fix It," October 1, 2026). On The a16z Show, Harvey's VP of Talent Maggie Landers said the valuation went from **$3 billion in February 2025 to $11 billion in March 2026**. Harvey added more than 1,000 employees last year and expects about 2,000 by year-end (The a16z Show, "Building a Team at AI Speed | Harvey's Maggie Landers," September 27, 2026). **The margin problem.** On Topline, Sam Jacobs gave the most specific version of the Harvey margin story we've heard: *"In 2026, the company began with gross margins around positive 50%. But... by June, Harvey's gross margin due to rapid agentic adoption, right, agents going out and consuming tokens on behalf of their human users, Harvey's gross margin had fallen to negative 50%... That means its cost to deliver $1 of revenue went from 50 cents to $1.50."* (Gross margin is the share of each sales dollar left after the direct cost of delivering the product. Negative means it costs more to serve customers than they pay.) Jacobs said Harvey's way out is "training their own models, hosting their own models, building open weight models, and moving away from using state-of-the-art models." He also cited Iconic data. Scaling AI companies averaged gross margins of about **41% in 2024, 45% in 2025 and a projected 52% in 2026**. Application-layer companies came in lower at **33%, 38% and 45%**. Classic software companies ran in the mid-70s or higher. Asad Zaman named the platform risk directly: *"You're also then completely at the behest of these model companies. Like if they decide to provide you a cheap model, like OpenAI is using pricing to capture market share, you're in a good spot... And if they feel like they don't need to do that in that particular moment, they can just like capitalize on price as much as they want. And you're screwed."* AJ Bruno was blunter: "At negative 50% margins, you don't have a business" (Topline, "If the AI Money Dries Up, Which Companies Burn?," September 27, 2026). **The defense: legal isn't the labs' top priority.** a16z's David George, whose firm is a large Harvey investor, made the most careful bull case on Uncapped with Jack Altman: - Legal is "probably like 12 months behind coding" in how widely it's used, and "end clients are demanding that their law firms use Harvey... for product and we care about it for cost." - For the labs, legal is "somewhere between number six through 15 on the priority list." - He quoted an old Microsoft rule: "A platform is only a platform if all of the things built on top of it generate more revenue than it." - Winning in legal takes two things the labs are unlikely to do: "the last details of the product really matter to the users," and "it requires like real go-to-market and on-the-ground efforts." On a lab's own legal product: "No one really uses it." - He was also candid about a scare: when the law firm Kirkland said it would spend **$500 million** building its own tech, "people were like, oh man, what's that mean for Harvey?" (Uncapped with Jack Altman, "Uncapped #58 | David George from a16z," October 1, 2026) Waisberg made a similar point from the user side. "$550 million is sort of objectively a lot of money, but it's actually not that much money to an OpenAI or an Anthropic." But the labs "haven't been able to put in the effort to build those specific things" because "they have lower hanging fruit." His test is simple: "If they thought they could get the same thing at a Claude, they would get the thing at a Claude." Stebbings went further on 20VC, calling OpenAI's legal launch "complete bullshit" because big-firm legal work requires "very, very deep and specific functionality" and "a multi-year sales cycle" (20VC, September 28, 2026). Waisberg also had a warning for everyone else. At Kira, a better in-house model gave him "a decade probably where our results were just better than anyone else's." Today, "the AI is not going to be that different using one tool versus another tool... even like the foundation model developers don't really have a durable, competitive advantage." ## Infrastructure startups: the plumbing gets squeezed too - **Web search for agents is mispriced.** Parag Agrawal, the former Twitter CEO who now runs Parallel, said on 20VC that if you build an agent on a cheap model, "you'll be spending 80 to 90% of your dollars on web search and 10% on the model. Seems entirely silly." Parallel charges "$1 for a thousand" searches while "almost every other web search in the market available to you right now will cost you 7 or 10 or 14," and he expects "another 10x" price drop. On model routers, the services that send each request to the right model: "Today there is real value," mostly because GPU capacity is tight. Whether that lasts depends on whether the shortage does (20VC, "Five Predictions for a World of Agents | The Ads Business Model Will Die | Biggest Lessons from Working with Elon Musk at Twitter with Parag Agrawal, Parallel," September 26, 2026). - **Routers say a markup alone won't last.** OpenRouter's Alex Atallah, talking on Latent Space about his company's new tie-up with Stripe (the product, name and brand are staying the same), argued that routing businesses can't rely on "just adding a markup on top of inference." One reason: "the pressure from the labs and from good inference providers to do a commit and bring your inference elsewhere." OpenRouter now has "over 10 million" developers (Latent Space, "OpenRouter: from Seed to Stripe, with OpenRouter's Alex Atallah & AMP's Anjney Midha," September 25, 2026). - **Claude Code becomes a platform for "mods."** Anthropic's Thariq Shihipar described plugins that let users change both how Claude Code works and how it looks: custom tools, mode selectors, even a model router built as a mod. He said Anthropic doesn't do automatic model routing by default because "it's a hard problem... you will get it wrong" (Latent Space, "Claude Code's Next Era, Thariq Shihipar, Anthropic," September 29, 2026). Every feature like this turns the lab's own coding tool into a place for third-party add-ons, which competes with standalone developer-tool startups. ## Consumer agents: Instinct's numbers, and OpenAI goes premium We covered Instinct and Meta's Muse last week. This week brought the founder's own numbers and OpenAI's answer. - **Instinct raised $1 billion at $10 billion** (20VC, October 1). Jack Altman, whose partners at Benchmark led the round, compared it to coding: "It kind of reminds me of what happened with coding and with Cursor and Cognition in the face of the labs... when something is that important, a lot of things can win." - **Founder Noah Shinn, in his first interview**, gave the numbers. "Over a billion dollars flowing to the platform now every year," with transaction volume growing about "10 percent day over day." Three weeks in, "40% of the user base" has shared a credit card. Users who share one sensitive piece of information retain at "like an 80%" rate. His plan is to keep the product free and charge merchants a small percentage on each transaction, like Apple Pay. On cost, he claimed Instinct delivers "the same performance as, honestly, Opus 5" at "a cost that is very, very low," partly by running background work in batches that are "3x, 5x, 8x more efficient." His biggest worry is buying computing capacity months ahead while growing that fast: "If you're wrong, you're very wrong. You get charged 3 or 4x" (Invest Like the Best, "Noah Shinn - Building Instinct: The Personal Agent - [Invest Like the Best, EP.493]," September 28, 2026). - **Travel sites are exposed.** On My First Million, one of the hosts relayed Shinn's figure that 40 to 50% of Instinct's transactions are travel: "Imagine you're Booking.com or your Expedia. Sorry, boys." (My First Million, "We tested Instinct, MUSE and Grokbot. They're ridiculous.," September 30, 2026) - **OpenAI's answer is premium, not free.** Altman on Dots: "It's starting as a premium product... we're putting our best model Astra in this... it is priced higher than other things in the market." On Muse and Instinct: "I've tried them. I don't use them regularly. I have not found a sort of place for them in my life" (Bloomberg Talks, September 29, 2026). ## A small but telling detail: OpenAI's investment offer to YC startups On This Week in Startups, the founder of Ploy, a current Y Combinator company, described the deal OpenAI offered every YC company this spring: "one and a half million credits and uncapped note, no MFN." (An uncapped note is an investment that converts into shares later at whatever valuation the company reaches. "No MFN" means the startup doesn't promise OpenAI the best terms later investors get.) He said "a good portion of my batch" took it. Jason Calacanis gave the warning: "At some point OpenAI is just going to be like, we have a website builder and they're just going to go scrape your website and build the website builder and copy all your innovations." The founder's answer was honest: "I actually don't think the data that Ploy has is necessarily like crazy valuable to them" (This Week in Startups, "A rogue OpenAI agent hacked Australia's government. Does this matter? | E2342," September 25, 2026). After the Jev episode, that trade-off reads differently. The lab doesn't need your data to copy you. It only needs to notice that customers want what you built. ## Exposed vs. Defensible (as called out this week) **Exposed** - **Single-feature API startups whose pitch is "cheaper than the lab."** Jev's decision model was cloned on top of OpenAI's existing Luna model in about a week, with vision included for free (Latent Space, "Why Dwarkesh is Wrong about Computer Use + How OpenAI shipped its Jev competitor in 1 Week," September 30, 2026). - **Apps whose users could be served inside ChatGPT.** ChatGPT now suggests apps mid-conversation to 1.2 billion weekly users and lets them bring their AI allowance along, putting OpenAI between apps and their customers (Tech Brew Ride Home, "Dippin Dots," September 30, 2026). - **AI note-takers and meeting tools.** OpenAI launched its own meetings plugin and built first-party products on its new Agents API (Tech Brew Ride Home, September 30, 2026; Latent Space, September 30, 2026). - **$20 a month prosumer creative subscriptions.** Mashrabov expects the labs to "completely demolish" this tier, and Stebbings pointed to Canva's growth (20VC, "$1BN ARR in 18 Months; The Untold Story of Higgsfield…," September 28, 2026). - **Vertical apps running mostly on closed frontier models.** Higgsfield keeps 20 to 30% margin on closed models versus 80%+ on open or its own. Harvey's margin reportedly swung from +50% to -50% as agent usage grew (20VC, September 28, 2026; Topline, "If the AI Money Dries Up, Which Companies Burn?," September 27, 2026). - **Startups that rely on Anthropic volume discounts.** Discounts of about 15% now stop at usage caps, and CodeRabbit and Replit have moved work to OpenAI (The Information's TITV, September 29, 2026). - **Routers and resellers living on a markup.** Labs are pushing customers into direct commitments (Latent Space, "OpenRouter: from Seed to Stripe…," September 25, 2026). - **Travel and comparison-shopping sites.** 40 to 50% of Instinct's transactions are travel, and personal agents do the comparing for the shopper (My First Million, September 30, 2026; Big Technology Podcast, "Meta's Muse Revival, Frontier AI Under Threat, The Rise Of Dopamine Sites," September 25, 2026). **Defensible** - **Companies that route across models and own some of them.** Higgsfield picks the model in 40%+ of jobs and earns 80%+ margins on its own and open models. SAP's agents "have seen the fifth model" as it keeps switching for price and quality (20VC, September 28, 2026; Big Technology Podcast, "SAP CEO: AI Won't Kill Software…," September 30, 2026). - **Vertical companies with deep workflow details and boots-on-the-ground sales.** David George: legal is priority "six through 15" for the labs, and winning requires product detail and field work they won't do. Legora is at about $400 million in revenue, and Harvey raised at about $15 billion (Uncapped with Jack Altman, October 1, 2026; LawNext, October 1, 2026). - **Narrow specialists in markets the labs treat as "horizontal."** EliseAI's Minna Song said the labs "have a lot of things to go after" and "focus more horizontally," while EliseAI goes deep in specialty medical practices like dermatology and ophthalmology, using a mix of lab models and its own (The Information's TITV, "OpenAI Announces New AI Tools at DevDay, Google Is Paying Digital Publishers for AI Overviews," September 30, 2026). - **Systems of record that control how agents get in.** SAP lets OpenAI and Anthropic agents access its data, but only "through our API gateway and the agent gateway," while keeping its own AI coworker as the main interface (Big Technology Podcast, September 30, 2026). - **Outcome sellers and network effects.** Mashrabov's only two moats: delivering the outcome, and network effects. Higgsfield went from about 10 seeded open-source community projects to over 10,000 in eight weeks (20VC, September 28, 2026). - **Infrastructure priced for the agent era.** Parallel sells search at about one-tenth of rivals' prices. Modal ($15 billion) and Baseten (talks at $26 billion) benefit no matter which model wins (20VC, September 26 and October 1, 2026). - **Consumer agents that own the transaction and earn trust.** Instinct: $1 billion+ in yearly transaction volume, 40% of users share a card within three weeks, about 80% retention after that (Invest Like the Best, September 28, 2026). Still up against Muse's free distribution and OpenAI's Dots. ## Founder Takeaway The labs gave startups two clear signals this week. Cheap, near-top intelligence is now everywhere, and the labs will build whatever developers are excited about. Here's how to act on that. 1. **Assume your clever API can be rebuilt on a model the lab already owns.** OpenAI didn't train a new model to copy Jev. It used Luna, added structure and speed tuning, and shipped in a week. If your product is "a lab model plus a smart wrapper that saves money," plan for a copy within a quarter. The parts that took the lab more than a week, like real calibration, accuracy and speed, are where to put your engineering effort. 2. **Make model choice a core part of your product, not a supplier decision.** Higgsfield's numbers are the clearest guide of the week: 20 to 30% margin on closed models, more than 80% on its own and open ones, and the company picks the model in over 40% of jobs. SAP re-tests every agent against new models as they ship. If you can't switch models in a day, every price change or ended discount from a lab hits your margin directly, as CodeRabbit and Replit just found. 3. **Measure cost per finished task, not cost per token.** Sonnet 5.5 looks cheap per token but used enough tokens to cost more per task than Opus 5.5 in one benchmark. Harvey's margin reportedly flipped from +50% to -50% because agents used far more tokens than people did. Build cost per job into your dashboard and your pricing before your agents scale up. 4. **Be deliberate about distribution through ChatGPT.** A suggestion inside ChatGPT can reach 1.2 billion weekly users. It can also make OpenAI the owner of your customer relationship and teach it exactly what users want from you. Use it to acquire customers, but make sure the work they save, their history and their billing live in your product. 5. **Build in the lab's blind spots: long sales cycles, messy details, small markets.** David George's "priority six through 15," Waisberg's "lower hanging fruit," and EliseAI's specialty practices all describe the same opening. The labs go after big, horizontal prizes like coding, consumer agents and enterprise chat. Work that needs field sales teams, industry-specific rules and years of customer trust is still yours to win, as long as your margins survive the model bill. 6. **Read the lab's term sheet as a competitive document.** Free credits and an uncapped note from OpenAI can keep a young company alive. Just remember who's watching what you build. After this week, "they won't bother copying us" is not a plan. The week in one line: the labs made near-top intelligence cheap, then showed they can copy a hot startup in seven days. Build what takes longer than a week to copy, and keep the freedom to switch models. --- ## Get the full transcripts on Matterfact This briefing was produced with Matterfact, which indexes the complete, full-text transcripts of 120M+ podcast episodes alongside SEC filings, earnings calls, and financial datasets. 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