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With Harry Stebbings, Rory O’Driscoll and Jason Lemkin This week’s 20VC x SaaStr covered a lot. Anthropic moved its IPO back a month. OpenAI’s internal forecast put total burn through 2030 at $278 billion. Meta shipped the first consumer AI product that competes head-on with ChatGPT and added about $100 billion in market cap in a week. TypeSafe launched Jev, a “System One” model that only returns decisions, on a $40M seed. And the IC voted on Factory at $5B, Legora at $11B and Crusoe at $30.9B. Under all of it was one question: what does a VC or LP actually do differently when everyone agrees we may be near the top of the cycle? Here are the 10 top learnings. #1. Anthropic Moves Its $2 Trillion IPO From October to November, and the Reason Is Its Q3 Numbers The news: Anthropic has pushed its IPO, reportedly at a valuation around $2 trillion, from October to November. Some read it as a crack in the market. The other read: Anthropic wants a clean Q3 in the prospectus. Rory’s read: It’s about the bankers, not the market. Anthropic had a huge Q2 and passed OpenAI in revenue. OpenAI hit back hard in July with its own Q3 story. If you list in October, you’ve closed the quarter but can’t show the audited numbers yet, and that’s the worst window for a company whose most important quarter just ended. “If we do this in October, it’s going to be a lot of explaining. If we do this in November, the numbers will talk.” Harry’s pushback: A company that expects to be 20x or 30x oversubscribed in the biggest IPO of our lifetimes doesn’t normally wait. The delay could mean there’s a little stress in the pre-marketing conversations. Where they landed: Waiting is what a company does when it thinks it’s in a strong position with time on its side. Rory would call it a good decision 90% of the time. In the other 10%, the window shuts, which windows do without warning, and you look back and say “damn, should have taken the $100 billion.” On liability insurance: Harry passed along a question from a multi-billionaire investor friend: how does a frontier lab go public when no insurer will cover product liability for swarms of autonomous agents? Rory: a $2 trillion company can self-insure. It doesn’t need Munich Re, which is worth $200 billion, to backstop it. Securities law doesn’t require that a stock carry no risk. It requires that you disclose the risk. When your own leadership has publicly compared the product to the atomic bomb, the risk is well disclosed. Jason expects Anthropic to handle it the way big tech handled IP trolls: a 200-person in-house legal team plus the top law firms, fighting every case for as long as it takes. “It’s game on.” #2. OpenAI Forecasts $278B in Burn Through 2030 and About $700B in Capex, Most of It on Other Companies’ Balance Sheets The news: Reports put OpenAI’s projected cash burn at $278 billion through 2030, with cash running out around 2028. Another round, reportedly at $1.5 trillion, is rumored. Jason’s take: “I bet it’s more.” No portfolio company in history has burned less than it planned. With every top-line rocket he’s backed, burn came in 30% to 50% over the model, whoever the CFO was. OpenAI may need $400 billion. Rory’s breakdown: There are three numbers, not two: - Revenue: about $35B ARR at the end of this year, growing to about $350B in three to four years. That’s 10x over several years. Anthropic did 10x in one year, so the forecast is close to modest. - Net burn: $278B against about $122B of cash on hand. There’s time to raise the rest. - Capex: about $700B to get to $350B in revenue. OpenAI only burns $278B because Oracle, Nvidia and others build the data centers and lease the capacity back, with revenue support and backstops behind them. Unlike B2B software, this is one of the most capital-intensive businesses ever built. Rory’s summary: “Intelligence is not cheap.” #3. Meta’s Muse Hits #1 on the App Store and Adds About $100B in Market Cap in a Week The news: Muse, the AI agent from Alexandr Wang’s team at Meta Superintelligence Labs, took the #1 spot on the US App Store about a week after launch, ahead of ChatGPT. Meta stock rose 7 to 8% in the week, roughly $100 billion in market cap, and is up about 34% for the month. Jason’s take: Muse is the first real consumer competitor to ChatGPT, and Jason called it one of the best pieces of software he’s ever used. It’s also a Trojan horse. The underlying LLM is good. When you use Muse’s agents you also ask it everything else: how’s the new superhero movie, how was the latest 20VC. It’s free, it gives you far more tokens than ChatGPT, and its agents are autonomous in a way ChatGPT’s aren’t. “If it’s free, why would an ordinary person pay?” The part people are missing: in two weeks, Muse built Jason a working CRM that tracks all 150 SaaStr sponsors, reads every related email and updates in real time, at zero cost. It’s a CRM of one. It can’t collaborate, so it isn’t replacing a sales team’s system. Still, it’s the first composable software Jason has actually seen work, after years of the idea being mostly talk. Rory’s read: He agreed, and noted this is the opposite of the Meta AI spending the show has criticized. It uses Meta’s distribution, and if you already live in Instagram and Facebook, the trust question is settled. It also changes Meta’s story: “a great business pouring money into a bottomless pit of enterprise AI” becomes “a great business that could have a second act in AI.” A $100 billion move off one product also explains why OpenAI might look at the consumer agent space and think about buying Instinct. #4. Amazon Blocks Muse, Shopify Partners With It, and Agents Will Route Around Any Middleman That Blocks Them The news: People use Muse to buy things from many merchants. Amazon blocked it. Shopify partnered with it. Rory’s read: Both decisions make sense. Amazon’s ad business now brings in more than its e-commerce profit, and agentic checkout cuts ad revenue to zero. Walmart’s data also shows smaller baskets, because the agent buys the one item and never sees “people also bought.” Amazon also has leverage: block them and they’ll come to you anyway. Shopify represents a long tail of merchants with no ad business that are glad of the extra demand, as long as orders run through Shop Pay. The bigger lesson is that the agent payment protocols from six months ago barely mattered. “Demand creates urgency to sort all this out.” Once someone aggregates consumer demand and starts hitting your API, everyone has to decide. Resy and OpenTable are almost certainly writing their agent API policies this week. Jason’s take: Every system of record that fights the agents loses over time. “It’s the last stand of the unnecessary system of record.” Agents don’t accept blocked paths. They find the unofficial API, call the restaurant directly, or find the DoorDash surface someone left open. His example from the morning of the recording: one of SaaStr’s core vendors sent a large price increase. The agent immediately proposed dropping them and laid out a 12-month migration plan on its own. “They will not tolerate this crap.” He was specific that Amazon won’t die: “It’s not that I think they’re going to be killed. I think they’re going to be maimed.” If agents take even 10% of Amazon’s ad revenue and the stock falls 20% on margin compression, that’s a big deal. Rory’s pushback: Amazon’s warehouses and fulfillment will be its strongest defense. He accepted the “maimed” framing, though, and cited the early internet saying that “the internet abhors inefficiency.” Expedia and Airbnb squeezed travel middlemen. AI will squeeze anyone whose whole value is holding information and lowering search costs, because an agent doesn’t mind checking all 17 websites. That only costs compute. #5. TypeSafe’s Jev Raises a $40M Seed for a Model That Only Returns Decisions, at a Fraction of Frontier Prices The news: TypeSafe AI launched Jev, a “System One” model that makes decisions instead of chatting, backed by a $40M seed. It became one of Vercel’s fastest-ever launches. Jason’s take: He spent all weekend on it. On Saturday nothing worked, and he thought it had failed. On Sunday, after he rewrote his prompts, it worked. His use case: deciding who in the SaaStr community should meet. Should a CRO at a Series B company in one city meet a CRO at a growth-stage company in another? Once set up properly, Jev answers in milliseconds at about 1/100th the cost of Anthropic’s models. Still, he said it’s a classifier backed by an LLM more than a new kind of model, and it covers only about 20% of the LLM calls in his app. “It reminds us we waste a lot of tokens on simple stuff that frontier models shouldn’t be doing.” It won’t replace ChatGPT. Rory’s math: Jev returns true/false, a ranking or a score. Output tokens are so few that TypeSafe doesn’t charge for them, and the name refers to Kahneman’s fast thinking. Rory’s partner meeting ran the numbers: - About $100B is spent today on LLM calls across OpenAI, Anthropic and open source, heading toward about $500B in five years. - About 20% of that is simple work that doesn’t need a frontier model: roughly $20B today. - System One models do that work at 1/5th the price, so the $20B becomes $4B. - TypeSafe’s bet is that the $4B becomes theirs. It’s a slice of the market cut off from OpenAI and Anthropic, not the end of foundation models. Jason’s correction: at 1/100th the price, the slice shrinks even more. Rory’s broader point: The split is happening at the developer layer, not in the ChatGPT app. What consumers need from AI and what developers need are separating. A developer often just needs “is this A or B.” “I don’t want to blather and have you tell me that’s a great question, Rory.” #6. Picking Models Is Getting Harder, and OpenAI and Anthropic Have Chosen Not to Compete at the Low End Jason’s take on routing: Jev’s main lesson for him was about model selection. Jev gets “should Rory and Jason meet for coffee” right. It gets “who’s the better partner hire for 20VC” wrong. You have to QA every single use case to capture the savings. Replit then moved him to an auto-router that switches between frontier and open-source models, he couldn’t tell which model was running, performance dropped, and he switched everything back to a single frontier model manually. “I can’t pick these models anymore. I’m tapping out.” People proposing Jev as an instant router have the same problem. If it picks the wrong model 20% of the time, and you’re coding a mission-critical feature, you spend the day chasing bugs. Harness pitches like “we route 80% to open-weights” sound great to a CFO, but they didn’t work for him. Jason expects a DevOps-style renaissance: teams running evals around the clock across endless combinations of models. That’s good for investors and harder on builders. How the labs respond: Rory expects OpenAI, which has become ruthlessly commercial, to ship something like Jev within weeks. Anthropic says it’s building AGI. Jev’s launch video was literally titled “not God,” and Anthropic doesn’t need to compete there. Jason’s read: Both labs have already made that choice. Their small models, OpenAI’s mini and Anthropic’s Haiku, fail his evals nearly every time. Ask whether Rory and Jason should get coffee, and the answer is “send them to London.” These are check-the-box offerings that serious developers don’t use. What worries the labs more is open-weights models closing the gap sooner than expected. #7. A $20M Raise Is the New Floor, and a16z Is Moving Pre-Inception The news: Jev’s first raise was a $40M seed. Harry’s team said they rarely see a first raise under $20M now, and even ordinary seed rounds for spinouts from strong companies are $8M to $10M. The same day, a16z launched a $40M program for pre-inception investing, essentially the Thiel Fellowship scaled up. Jason’s take: “You got to go pre-Inception if you want to do seed now. Inception is too expensive.” Peter Thiel saw this 18 years ago and executed. He just never scaled it. Jason thinks Z Fellows gets the best people today. Programs like this only work with enough people coming through, which is why the a16z version is interesting. You used to be able to run a fund on a blog and get deal flow for five straight unicorns. Now you need a podcast and a professor. If you still write traditional seed checks, you accept lower ownership, and the math only works if you pick $25B outcomes. Put $3 to $4M into a Jev, and it has to exit above $25B after dilution to give you 50x to 100x. Rory’s point on inflation: Nominal GDP, meaning inflation plus real growth, is up about 2.5x since 2010. “If you were writing $4 million checks in 2010, you should be writing $10 million checks in 2026. That’s just math before anything else has changed.” Add a market where everything in tech looks great, and you reach $20M quickly. Harry asked whether this is actually a better time, since outcomes have grown more than 2.5 to 3x. Rory said no. Today’s huge outcomes came from checks written five years ago at much lower prices. Assuming today’s bigger checks produce the same results assumes today’s $25B outcome becomes $50B in three years. Exits have outrun GDP and public markets over the long run, but part of today’s valuations is a cyclical boost that may not last. #8. Instinct Raised Four Rounds in Four Months, From $50M Pre to a Reported $10B. The Four Rounds Don’t Carry the Same Risk. The news: Menlo Ventures’ Venky Ganesan published a widely shared piece on investing at the top of the cycle. His framing: investors playing with house money (Menlo put itself in this group after Anthropic) versus investors who missed the early rounds and are now trying to catch up. Between those two groups, a lot of checks are written out of FOMO. Harry’s pushback: He called it a blowhard piece. “Be cognizant of the music stopping. Thanks.” And from a firm that has paid the highest price on many rounds. “Why do you need to be careful of the music stopping? You raise a fund every 18 months.” Jason agreed it read as a bit condescending: he hadn’t realized the AI gold rush might end someday. Rory’s defense: Sarah Guo and Conviction seeded Instinct at $50M pre. Within about four months it raised at $350M pre from Kleiner, then at $2.5B, and it’s now reportedly raising at $10B. “You cannot say the risk in all four of them is the same, because one of them is 20 times more expensive.” Get aggressive at the $50M round, and think hard at $10B. On Buffett’s margin of safety: at $50M pre, with a world-class technologist, it’s effectively unlimited. At $10B you might get 1x back in a downside. Rory’s closing line was a Bernard Baruch quote: if you look in the mirror every day and say two and two makes four, you’ll avoid most mistakes. What hurts you is usually something obvious that you chose to ignore. Jason’s version: In 2021 he made one investment, the seed of a company that just closed at $2.3B (Owner.com). You can do the same now: one or two deals over the next four years, each with a real dislocation. Otherwise, it’s other people’s money and you have to deploy it. The quiet compounder problem: Jason questioned the conservative alternative. Do you back a $50M-revenue company growing 60% and hope it re-accelerates or sells to Bending Spoons at 3x revenue? Bending Spoons looks at a thousand deals a year and does two to four. “I love the quiet compounders. There’s just no market for them anymore.” Rory thinks the market for them could come back above a certain scale, but agreed it’s thin right now. On Gokul’s “triple, triple, double, double”: Gokul Rajaram tweeted that he’ll happily fund capital-efficient T2D2 companies all day. Harry: “You can do those triple triple double doubles, have average IRRs, and your LPs will leave in droves,” when managers like Sarah Guo post high IRRs fast. Jason’s answer: do both, if you can pick. Top-quartile funds showed about 90% IRRs in 2021 and will again in 2026, but that doesn’t last. Compounding 90% over a decade is mathematically impossible. Low 30s is as good as it gets in the real world, and a manager who repeats north of 30% will keep LPs. #9. What LPs Should Back Now, and Why It’s Harder to Be an LP Than Ever Rory’s advice to seed and Series A LPs: Back managers doing the best seed and A deals in the newest, fastest-growing spaces where you believe they have a real edge. Don’t rule them out for low ownership. Rory targets about 10% at the A and would like 15 to 20% at seed. A seed investor who keeps showing up in the best deals with smaller stakes gets more ownership over time, because they get more capital and more chances. The risk is concentrated later: as rounds get bigger, the dollars go up and the multiple goes down. Growth was a great place to be in 2023 through 2025. It may look more like 2021 now. Jason’s read: Chasing returns is tempting and hard. Everyone wants into Sarah’s next fund, which would be 50x oversubscribed off one email. Most LPs also believe returns decay once a manager peaks. Meanwhile, many of the best new seed managers have strategies that look strange, and LPs chasing returns tend to screen them out. Rory’s data point: In public markets, fund performance barely persists, and chasing last year’s returns makes no sense. In venture, persistence is quite high because success compounds until something breaks. Partly chasing returns in venture is rational in a way it isn’t in public markets. #10. Jason’s IC: Yes to Factory at $5B, No to Legora at $11B, No to Crusoe at $30.9B Factory: $200M at a $5B valuation, about triple its last price. Factory sells enterprise coding agents through its Droid product, and revenue has grown quickly. Jason approved it. Demand for coding inference already exceeds the most aggressive models. At Dreamforce, the theme he heard most from C-level executives was sovereignty and model choice. “They don’t trust Anthropic and OpenAI with their data.” This isn’t something invented on X. “They’ve trained on all of our data. Every YouTube, every piece of open source, every piece of closed source. Of course they’re going to train on your data.” When he was an SVP at Adobe, any pollution of source code was code red. His team was the first at Adobe to use GitHub, only after engineers threatened to quit and after months of arguments and air-gapping. An enterprise coding vendor that doesn’t also sell you the model is a strong pitch. Rory agreed: “Coding is the motherlode.” It’s 10x everything else in AI value creation today, and you can’t have too many bets across the stack: coding, QA, testing, review. With Cursor off the table, Factory and Cognition are the two going to large enterprises and saying: “Your board is on your ass to do way more coding. We’ll make this go away.” Rory’s answer to Harry’s question about how to act on fear of a slowdown: lean into trends that keep compounding even if overall AI adoption slows. Coding is one of them. Legora: announced $200M ARR, next round reportedly at $11B. Jason likes both Legora and Harvey. The Information reported that Harvey’s gross margins are around -50%, and if that’s true and still falling rather than recovering, he wouldn’t lead. Rory’s counter: the old knock on Harvey was that lawyers didn’t use it enough. If margins are really -50%, “lawyers are pounding on our shit,” and once the company runs on its own models, usage won’t drop. His sharp version, after Harry called out his long answer: “No, I won’t do it at $10 billion, because when you count the legal heads, you don’t get to $10 billion.” Legal AI will never spend as much per head as engineering does, because much of a lawyer’s work stays with the lawyer. Crusoe: $3.9B Series F at a $30.9B valuation, with about $140B in total contracted value across data centers, GPUs and managed inference. Jason passed, reluctantly. Crusoe sits at the top of the second tier, with modular data centers and ownership of everything from power to tokens. But it’s a spreadsheet investment, and at this price it lands just below the fund’s return threshold. “$10 billion would have been great.” Rory’s framework: This is where fear of a slowdown actually changes decisions. Coding agents and AI apps aren’t leveraged bets, and they can survive a one-year dip. Data centers are leveraged four or five to one against AI usage growth, not just AI usage. That’s great on the way up and brutal on the way down. He wants some exposure (look at CoreWeave) but not a portfolio built on AI capex. His appetite would rise with the share of long-term commitments from Microsoft-tier customers and with three years of visible debt runway. He’d take a slightly lower price and more dilution for that protection. His aside: a single Wall Street Journal edition ran “deals pulled as Wall Street worries about data center trade” in the morning and “Nasdaq explodes as AI capex fears recede” by evening. Fear flipped to greed in eight hours. Bonus: Keith Rabois vs. Airwallex, and Why Every Decacorn CEO Needs an Army of Advocates The news: Keith Rabois and Joe Lonsdale, both tied to Ramp, went after Airwallex again on X over alleged China ties. Harry’s take (as an Airwallex investor): The allegations have shrunk over time, from “CCP agent” to “more than 20% of the cap table is Chinese,” which he says is also false. Nearly every large company, from Microsoft to Zoom, has employees in China. “It’s ridiculous.” Jason’s pushback: He doesn’t like the tweets and wouldn’t send them. But China exposure is a real issue in deals, not only on Twitter. A recent multi-billion-dollar Salesforce acquisition required removing every Chinese open-weights model before close. One of Jason’s own portfolio companies got the same requirement in an M&A offer last week. Rory’s view: Nobody elected Keith, Harry or Jason. The US government needs to set clear rules on what commerce with China is acceptable. Right now it’s ad hoc, and whether chips can be sold depends on whether Jensen checks in. Jason’s founder learning: TypeSafe’s launch of Jev was top-tier PR. Every influencer had a tweet, a thread and a demo lined up on day one. Jack Zhang, meanwhile, is personally debating Keith on X. “Jack should not have to defend himself to Keith and Joe.” A decacorn CEO needs 20 advocates posting the cap table screenshots and the real China headcount next to Microsoft’s, while the CEO just clicks like. Harry noted that Airwallex’s big backers, DST and Lee Fixel, don’t do social at all. Jason: “Find your tribe. It’s the job.” Or rent Matthew McConaughey. Top Quotes From the Episode Jason Lemkin - “I love the quiet compounders. There’s just no market for them anymore.” - “It’s not that I think they’re going to be killed. I think they’re going to be maimed. Talking to our agents all day, I can tell you they don’t put up with this.” - “They’ve trained on all of our data. Every YouTube, every piece of open source, every piece of closed source. Of course they’re going to train on your data. Give me a break.” Rory O’Driscoll - “Coding is the motherlode. It’s 10x everything else in terms of value being created from AI today.” - “Intelligence is not cheap. It is going to consume, directly or indirectly, $700 billion of capex to get to $350 billion in revenue.” - “In tech, none of that ever matters. What really matters is someone aggregates consumer demand and starts pounding on the API. Demand creates urgency to sort all this out.” Harry Stebbings - “How do these frontier model providers go public when no one is willing to provide liability insurance? Who’s liable?” - “You can do those triple triple double doubles, have average IRRs, and your LPs will leave in droves.” - “Why do you need to be careful of the music stopping? You raise a fund every 18 months.” Sources:
With Harry Stebbings, Rory O’Driscoll and Jason Lemkin This week’s 20VC x SaaStr covered a lot. Anthropic moved its IPO back a month. OpenAI’s internal forecast put total burn through 2030 at $278 billion. Meta shipped the first consumer AI product that competes head-on with ChatGPT and added about $100 billion in market cap in a week. TypeSafe launched Jev, a “System One” model that only returns decisions, on a $40M seed. And the IC voted on Factory at $5B, Legora at $11B and Crusoe at $30.9B. Under all of it was one question: what does a VC or LP actually do differently when everyone agrees we may be near the top of the cycle? Here are the 10 top learnings. #1. Anthropic Moves Its $2 Trillion IPO From October to November, and the Reason Is Its Q3 Numbers The news: Anthropic has pushed its IPO, reportedly at a valuation around $2 trillion, from October to November. Some read it as a crack in the market. The other read: Anthropic wants a clean Q3 in the prospectus. Rory’s read: It’s about the bankers, not the market. Anthropic had a huge Q2 and passed OpenAI in revenue. OpenAI hit back hard in July with its own Q3 story. If you list in October, you’ve closed the quarter but can’t show the audited numbers yet, and that’s the worst window for a company whose most important quarter just ended. “If we do this in October, it’s going to be a lot of explaining. If we do this in November, the numbers will talk.” Harry’s pushback: A company that expects to be 20x or 30x oversubscribed in the biggest IPO of our lifetimes doesn’t normally wait. The delay could mean there’s a little stress in the pre-marketing conversations. Where they landed: Waiting is what a company does when it thinks it’s in a strong position with time on its side. Rory would call it a good decision 90% of the time. In the other 10%, the window shuts, which windows do without warning, and you look back and say “damn, should have taken the $100 billion.” On liability insurance: Harry passed along a question from a multi-billionaire investor friend: how does a frontier lab go public when no insurer will cover product liability for swarms of autonomous agents? Rory: a $2 trillion company can self-insure. It doesn’t need Munich Re, which is worth $200 billion, to backstop it. Securities law doesn’t require that a stock carry no risk. It requires that you disclose the risk. When your own leadership has publicly compared the product to the atomic bomb, the risk is well disclosed. Jason expects Anthropic to handle it the way big tech handled IP trolls: a 200-person in-house legal team plus the top law firms, fighting every case for as long as it takes. “It’s game on.” #2. OpenAI Forecasts $278B in Burn Through 2030 and About $700B in Capex, Most of It on Other Companies’ Balance Sheets The news: Reports put OpenAI’s projected cash burn at $278 billion through 2030, with cash running out around 2028. Another round, reportedly at $1.5 trillion, is rumored. Jason’s take: “I bet it’s more.” No portfolio company in history has burned less than it planned. With every top-line rocket he’s backed, burn came in 30% to 50% over the model, whoever the CFO was. OpenAI may need $400 billion. Rory’s breakdown: There are three numbers, not two: - Revenue: about $35B ARR at the end of this year, growing to about $350B in three to four years. That’s 10x over several years. Anthropic did 10x in one year, so the forecast is close to modest. - Net burn: $278B against about $122B of cash on hand. There’s time to raise the rest. - Capex: about $700B to get to $350B in revenue. OpenAI only burns $278B because Oracle, Nvidia and others build the data centers and lease the capacity back, with revenue support and backstops behind them. Unlike B2B software, this is one of the most capital-intensive businesses ever built. Rory’s summary: “Intelligence is not cheap.” #3. Meta’s Muse Hits #1 on the App Store and Adds About $100B in Market Cap in a Week The news: Muse, the AI agent from Alexandr Wang’s team at Meta Superintelligence Labs, took the #1 spot on the US App Store about a week after launch, ahead of ChatGPT. Meta stock rose 7 to 8% in the week, roughly $100 billion in market cap, and is up about 34% for the month. Jason’s take: Muse is the first real consumer competitor to ChatGPT, and Jason called it one of the best pieces of software he’s ever used. It’s also a Trojan horse. The underlying LLM is good. When you use Muse’s agents you also ask it everything else: how’s the new superhero movie, how was the latest 20VC. It’s free, it gives you far more tokens than ChatGPT, and its agents are autonomous in a way ChatGPT’s aren’t. “If it’s free, why would an ordinary person pay?” The part people are missing: in two weeks, Muse built Jason a working CRM that tracks all 150 SaaStr sponsors, reads every related email and updates in real time, at zero cost. It’s a CRM of one. It can’t collaborate, so it isn’t replacing a sales team’s system. Still, it’s the first composable software Jason has actually seen work, after years of the idea being mostly talk. Rory’s read: He agreed, and noted this is the opposite of the Meta AI spending the show has criticized. It uses Meta’s distribution, and if you already live in Instagram and Facebook, the trust question is settled. It also changes Meta’s story: “a great business pouring money into a bottomless pit of enterprise AI” becomes “a great business that could have a second act in AI.” A $100 billion move off one product also explains why OpenAI might look at the consumer agent space and think about buying Instinct. #4. Amazon Blocks Muse, Shopify Partners With It, and Agents Will Route Around Any Middleman That Blocks Them The news: People use Muse to buy things from many merchants. Amazon blocked it. Shopify partnered with it. Rory’s read: Both decisions make sense. Amazon’s ad business now brings in more than its e-commerce profit, and agentic checkout cuts ad revenue to zero. Walmart’s data also shows smaller baskets, because the agent buys the one item and never sees “people also bought.” Amazon also has leverage: block them and they’ll come to you anyway. Shopify represents a long tail of merchants with no ad business that are glad of the extra demand, as long as orders run through Shop Pay. The bigger lesson is that the agent payment protocols from six months ago barely mattered. “Demand creates urgency to sort all this out.” Once someone aggregates consumer demand and starts hitting your API, everyone has to decide. Resy and OpenTable are almost certainly writing their agent API policies this week. Jason’s take: Every system of record that fights the agents loses over time. “It’s the last stand of the unnecessary system of record.” Agents don’t accept blocked paths. They find the unofficial API, call the restaurant directly, or find the DoorDash surface someone left open. His example from the morning of the recording: one of SaaStr’s core vendors sent a large price increase. The agent immediately proposed dropping them and laid out a 12-month migration plan on its own. “They will not tolerate this crap.” He was specific that Amazon won’t die: “It’s not that I think they’re going to be killed. I think they’re going to be maimed.” If agents take even 10% of Amazon’s ad revenue and the stock falls 20% on margin compression, that’s a big deal. Rory’s pushback: Amazon’s warehouses and fulfillment will be its strongest defense. He accepted the “maimed” framing, though, and cited the early internet saying that “the internet abhors inefficiency.” Expedia and Airbnb squeezed travel middlemen. AI will squeeze anyone whose whole value is holding information and lowering search costs, because an agent doesn’t mind checking all 17 websites. That only costs compute. #5. TypeSafe’s Jev Raises a $40M Seed for a Model That Only Returns Decisions, at a Fraction of Frontier Prices The news: TypeSafe AI launched Jev, a “System One” model that makes decisions instead of chatting, backed by a $40M seed. It became one of Vercel’s fastest-ever launches. Jason’s take: He spent all weekend on it. On Saturday nothing worked, and he thought it had failed. On Sunday, after he rewrote his prompts, it worked. His use case: deciding who in the SaaStr community should meet. Should a CRO at a Series B company in one city meet a CRO at a growth-stage company in another? Once set up properly, Jev answers in milliseconds at about 1/100th the cost of Anthropic’s models. Still, he said it’s a classifier backed by an LLM more than a new kind of model, and it covers only about 20% of the LLM calls in his app. “It reminds us we waste a lot of tokens on simple stuff that frontier models shouldn’t be doing.” It won’t replace ChatGPT. Rory’s math: Jev returns true/false, a ranking or a score. Output tokens are so few that TypeSafe doesn’t charge for them, and the name refers to Kahneman’s fast thinking. Rory’s partner meeting ran the numbers: - About $100B is spent today on LLM calls across OpenAI, Anthropic and open source, heading toward about $500B in five years. - About 20% of that is simple work that doesn’t need a frontier model: roughly $20B today. - System One models do that work at 1/5th the price, so the $20B becomes $4B. - TypeSafe’s bet is that the $4B becomes theirs. It’s a slice of the market cut off from OpenAI and Anthropic, not the end of foundation models. Jason’s correction: at 1/100th the price, the slice shrinks even more. Rory’s broader point: The split is happening at the developer layer, not in the ChatGPT app. What consumers need from AI and what developers need are separating. A developer often just needs “is this A or B.” “I don’t want to blather and have you tell me that’s a great question, Rory.” #6. Picking Models Is Getting Harder, and OpenAI and Anthropic Have Chosen Not to Compete at the Low End Jason’s take on routing: Jev’s main lesson for him was about model selection. Jev gets “should Rory and Jason meet for coffee” right. It gets “who’s the better partner hire for 20VC” wrong. You have to QA every single use case to capture the savings. Replit then moved him to an auto-router that switches between frontier and open-source models, he couldn’t tell which model was running, performance dropped, and he switched everything back to a single frontier model manually. “I can’t pick these models anymore. I’m tapping out.” People proposing Jev as an instant router have the same problem. If it picks the wrong model 20% of the time, and you’re coding a mission-critical feature, you spend the day chasing bugs. Harness pitches like “we route 80% to open-weights” sound great to a CFO, but they didn’t work for him. Jason expects a DevOps-style renaissance: teams running evals around the clock across endless combinations of models. That’s good for investors and harder on builders. How the labs respond: Rory expects OpenAI, which has become ruthlessly commercial, to ship something like Jev within weeks. Anthropic says it’s building AGI. Jev’s launch video was literally titled “not God,” and Anthropic doesn’t need to compete there. Jason’s read: Both labs have already made that choice. Their small models, OpenAI’s mini and Anthropic’s Haiku, fail his evals nearly every time. Ask whether Rory and Jason should get coffee, and the answer is “send them to London.” These are check-the-box offerings that serious developers don’t use. What worries the labs more is open-weights models closing the gap sooner than expected. #7. A $20M Raise Is the New Floor, and a16z Is Moving Pre-Inception The news: Jev’s first raise was a $40M seed. Harry’s team said they rarely see a first raise under $20M now, and even ordinary seed rounds for spinouts from strong companies are $8M to $10M. The same day, a16z launched a $40M program for pre-inception investing, essentially the Thiel Fellowship scaled up. Jason’s take: “You got to go pre-Inception if you want to do seed now. Inception is too expensive.” Peter Thiel saw this 18 years ago and exec
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