Inside OpenAI’s $500B Valuation | Altimeter’s Largest Bet

With $10B+ in AUM, Altimeter has made their largest bet in its history on OpenAI. Known for disciplined bets in generational companies like Snowflake, SpaceX, Anduril — and now OpenAI, the firm is now fully committed to the AI supercycle. In this episode, Apoorv Agrawal, Partner at Altimeter focused on AI and software, joins Molly O’Shea to break down how the firm evaluates opportunities in the AI supercycle. Apoorv explains why power laws govern venture returns, how Altimeter evaluated the $500B+ OpenAI opportunity, and why speed is the only defensible moat in AI. We also explore the AI talent wars reshaping Silicon Valley, the rise of XBOW (where AI agents are already outperforming humans), and lessons from Apoorv’s formative years as a forward deployed engineer at Palantir — a culture that has produced billions in founder-led value creation. For institutional investors, allocators, and operators, this conversation offers a rare inside look at how one of the sharpest minds in venture is positioning around AI — from infrastructure and chips to applications and talent. Connect with us: 1. Apoorv Agrawal: https://x.com/apoorv03 2. Molly O’Shea: ⁠https://x.com/MollySOShea⁠ 3. Sourcery: ⁠https://x.com/sourceryvc⁠ Brought to you by: • Brex—The modern finance platform, combining the world’s smartest corporate card with integrated expense management, banking, bill pay, & travel. As a Sourcery Listener you get: 75,000 points after spending $3,000 on Brex card(s), white-glove onboarding, $5,000 in AWS credits, $2,500 in OpenAI credits, & access to $180k+ in SaaS discounts. On top of $500 toward Brex travel, $300 in cashback, plus exclusive perks (like billboards..) visit → https://brex.com/sourcery • Turing—Turing delivers top-tier talent, data, and tools to help AI labs improve model performance—and enables enterprises to turn those models into powerful, production-ready systems. Visit: https://turing.com/sourcery • Kalshi—The largest prediction market and the only legal platform in the US where people can trade directly on the outcomes of future events: https://kalshi.com/sourcery • Fourthwall—The #1 way to sell merch online — Fourthwall is the easiest way to launch a fully branded merch store—used by big brands like MKBHD, Acquired, & even the Smithsonian. 100+ products. No upfront cost. Visit Fourthwall to start today: https://fourthwall.com/ Follow Sourcery for the latest updates! https://www.sourcery.vc/ Chapters: (00:00) Welcome & Altimeter’s $10B+ AUM strategy (01:57) Why Altimeter concentrated its largest investment ever in OpenAI (04:45) The power law in venture capital: why 0.3% of companies drive 99% of returns (09:25) Consumer adoption of ChatGPT & enterprise spillover (12:08) Velocity: Why speed is the ultimate moat in AI (14:18) Parasocial relationships with ChatGPT (19:29) Adoption curves: raising the ceiling vs. raising the floor (23:30) Defining AGI & DOOM (25:40) GPT-5 release: volatility, iteration, & anti-fragility (32:00) XBOW AI: When AI becomes the world’s top hacker (38:22) Where AI value accrues — chips, infrastructure, or applications (42:50) The billion-dollar talent wars & Meta’s AI strategy (44:12) Palantir’s billion-dollar FDE culture (50:10) Lessons from Shyam Sankar & Alex Karp (53:52) Evaluating founder performance & IPO readiness in today’s market (55:36) IPO Predictions: Klarna, Discord, Databricks, Cerebras, Anduril

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Do you think that the winner of the AI super cycle is going to be worth more than 150 billion at the time? It is clear that ChatGPT has become a verb. OpenAI is on its path to being a winner of the consumer AI super cycle. And you know, OpenAI is our largest investment in the history of Ultimater. Talent, this is where the war is now. You mentioned Meta, like they're generating about 100 billion dollars of operating cash flow. To put that in perspective, OpenAI just raised this massive round, 40 billion dollars. So they got the text moment, they got the voice moment, they got the image moment. Just this year alone, you've had operator and deep research and ChatGPT agents and Coder and GPT-5 now.

• 0:34Molly O'Shea?

  This was an interesting release because I don't think it went the way many had expected. There was a lot of hype around it. It didn't go so well.

• 0:42Speaker 00

  We are halfway through the year and they've had such big releases. I feel like that is the secret sauce. That is the defensibility. Speed is the only note. Talent was actually a very misunderstood business for majority of its existence. And in large part because of how customer obsessed Talent is.

• 0:56Molly O'Shea?

  Give us the rundown on Expo. How did you get into this investment? What do they do?

• 1:01Speaker 00

  Well, the punchline is the number one hacker in the world is no longer a human.

• 1:10Molly O'Shea?

  Apoorv, welcome to Sorcery.

• 1:13Speaker 00

  Delighted to be here. Thanks for having me.

• 1:15Molly O'Shea?

  Thanks for having me. We're in Ultimater's office.

• 1:17Speaker 00

  That's right. Welcome to Ultimater.

• 1:20Molly O'Shea?

  Thanks. We have a lot to discuss today. We're going to go deep into OpenAI, the 500 billion dollar valuation, the philosophy around ChatGPT's parasocial relationship that's formed with its users.

• 1:35Molly O'Shea?

  We're also going to talk about the talent wars, all the money flowing into that, some of your recent investments. And then we'll go into your time at Palantir, how that cemented your career and how you think about things as a forward deployed engineer. So to start, let's get into the 500 billion dollar valuation.

• 1:55Molly O'Shea?

  When did you first invest into OpenAI?

• 1:57Speaker 00

  We've tracked OpenAI for a bit, obviously a very important company. We first invested last year and

• 2:08Speaker 00

  Ultimately, what gave us the confidence was it is clear that ChatGPT has become a verb. OpenAI is on its path to being a winner of the consumer AI supercycle. And if you study the past supercycles, let's start with the internet. One of the biggest technology supercycles produced a large winner in Google, two and a half trillion in market cap. The next one, mobile, created a large winner in Apple, three and a half trillion in market cap.

• 2:39Speaker 00

  Social, Meta, one point eight trillion in market cap. Enter GenAI. Do you think that the winner of the AI supercycle is going to be worth more than one hundred and fifty billion at the time? Definitely, right? And so the real question was, is OpenAI that winner?

• 2:58Speaker 00

  And while it's not a layup, we got increasing comfort that OpenAI with ChatGPT had caught magic in a bottle. It was a brand, not a technology, not a product. It was a brand, something that was impacting our day to day lives. And that's what ultimately gave us the confidence. I'm happy to dig into the numbers.

• 3:17Molly O'Shea?

  Let's dig into the numbers.

• 3:19Speaker 00

  You know, I'll start with the consumer side of things. Consumer markets tend to be winner take most, if not winner take all sometimes, right? You look at search, Google has ninety percent plus market share. If you and I were internet investors in the year two thousand and one and we had the hard job of deciding which search engine to back, you could have backed Lycos and AltaVista and Ask Jeeves. And the truth is, you'd be right because the numbers are all going top and to the right. All you had to do was wait until two thousand and four, Google's IPO, and you'd have gotten ninety nine percent of all gross profits generated in search.

• 3:54Speaker 00

  That's a little bit of how I feel about what's happening with ChatGPT right now. If you, you know, they've publicly disclosed they've got seven hundred million weekly active users. They recently crossed a big revenue milestone. I believe they announced ten billion dollars revenue run rate in June. And

• 4:14Speaker 00

  A similar dynamic is going on in AI, where if you took the user count of ChatGPT on one side and all the other AI apps on the other side, let's do perplexity and Claude and Croc and Gemini and, you know, long tail of apps that people use, add them all up, multiply it by 10, it's probably still less than ChatGPT's user base. Same on revenue. And so we saw this, you know, this is the sign of, you know, a power law playing out. The same power law that played out in the internet, same that played out in mobile, the same that played out in social. And so the rough math, as we see it, as, you know, they've announced 700 million weekly active users. Let's say on a monthly active user basis,

• 4:59Speaker 00

  it's somewhere between 1 and 2 billion. Revenue was announced at, you know, 10 billion. So, you know, roughly 1 billion users, 10 billion revenue, earning about $10 per user per year, you know, round numbers. We think over time, the number of users could go up to 3 to 4 billion users. We think on the monetization side, over time, it could go from 10 today to 60, 70 dollars per user per year, which is where Meta and Google and other large consumer platforms are. And together, you know, P times Q, 3 to 4 billion users multiplied by 60, 70 dollars a user gets you to about 200 billion dollars of a revenue opportunity over some medium amount of timeframe. That is the consumer AI opportunity.

• 5:44Speaker 00

  That is what we're playing for.

• 5:47Speaker 00

  The other parts of the business, you know, the enterprise business, the API business,

• 5:53Speaker 00

  we'll have a shot as good as any other. And enterprise markets tend to be more fragmented, not as concentrated. And that's where we're ChatGPT Enterprise is doing really well. We use it. So that's, those are the numbers on OpenAI. That's how we think about it.

• 6:11Molly O'Shea?

  And how much did you invest at the time? What was your valuation?

• 6:14Speaker 00

  We entered last year at the 150 billion dollar round.

• 6:17Molly O'Shea?

  What's unique with you and Altimeter is that you've only invested into OpenAI, whereas other funds have spread their bets amongst other LLMs. So how do you think about that concentration? And what is your investment strategy?

• 6:33Speaker 00

  Our business is defined by power laws. The rough math of our business is there's 5,000 companies that raise every year that we track.

• 6:42Speaker 00

  500 of those, so 10% of those will raise from tier one investors.

• 6:48Speaker 00

  15 of the 5,000 will return over 90% of the gross profits for a given vintage. You might have heard of the Pareto theory, you know, the 80/20 or the 90/10. 15 of 5,000 is 0.3%. This is not 80/20, this is not 90/10, this is 99.7% and 0.3%. The power law is so sharp that very few companies actually return majority of the gross profits. And so

• 7:17Speaker 00

  when we think we found one that is on that power law, we think OpenAI is definitely on that power law. There's a couple others on that power law in our business.

• 7:26Speaker 00

  We try to concentrate into them. We had a similar experience with Snowflake. And you know, OpenAI is our largest investment in the history of ultimatera.

• 7:35Molly O'Shea?

  What sets OpenAI apart from Anthropic and even Google?

• 7:39Speaker 00

  You know, it's a great question. All of those are formidable, formidable teams with incredible products.

• 7:47Speaker 00

  You know, obviously Anthropic's crushing it with their API, probably one of the most loved developer products out there, both with the API but also the Claude code product.

• 7:57Speaker 00

  And so I would say they are winning in their own right in the enterprise segment with developers on the coding use case and doing a great job at it.

• 8:06Speaker 00

  Google, same thing. I mean, there's a lot of great products they've built. I would say that a lot is yet to be seen from them. I feel like the best of Google is ahead of them. So a lot more to come there.

• 8:23Speaker 00

  But at least that's how we see it. I think those are the two most formidable players, as you mentioned.

• 8:27Molly O'Shea?

  Going back, like circling back a little bit more. So 700 million weekly active users, they've developed a very strong consumer brand. They have a cult, they have a cult brand. They've achieved this. It's actually insane the rate they did this. I think you published a chart on this. And then also the other charts from East, Me, West. Those are great. We'll add all these in. I'm curious from your standpoint as an investor and a user and understanding their commercial efforts, how does the consumer adoption bleed into enterprise?

• 9:03Speaker 00

  Look, I think

• 9:05Speaker 00

  Ultimately, there's three legs to the stool on the usage numbers that you highlighted. It's obviously the number of users. It's how much time they spend per day. And what is the retention of those users? I would say those are the three legs of the stool that we measure to have a sense of the health of a consumer app. And on the first one, as I mentioned, I think they are far and above larger than any standalone AI app today.

• 9:35Speaker 00

  On in terms of time spent, you know, we published this analysis, they are far and above time spent per user per day compared to any of the other apps, AI apps today. Actually, not just AI apps, I, you know, one of the analysis we did is they are now larger than some of the mainstream consumer apps like X. Yeah. And retention, you know, the it's it's it's pretty wild, actually. So what we call smile curves are starting to show up in retention, meaning that, you know, in a chart where you see the number of users that stay on an app after they started the journey. Typically, you would expect an exponential decay of the users on this app. For very few apps, the the curve actually goes up, so it looks like a smile.

• 10:21Speaker 00

  And ChatGPT exhibits a smile curve. The other apps that exhibit smile curves are Instagram and TikTok. And the implication is

• 10:35Speaker 00

  users are finding so much value on this on this on this app that they that they want to come back to it. And how this bleeds into habit formation is I have this beautiful thing at my personal phone that I use for my personal life, and I want that at work. And so your question about how this bleeds into enterprise motion is, you know, similar to similar to the effect that Good software has, or good devices have, is you want to use at work what you have at home. And that's the success of ChatGPT Enterprise.

• 11:05Molly O'Shea?

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• 11:51Molly O'Shea?

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• 12:07Molly O'Shea?

  How did this momentum start? I know that they have this crazy strategy, but I'd love to hear it from you. We've talked about this before, but could you just go deeper into the momentum that they build? And how this is, like, what's the formula?

• 12:23Speaker 00

  Look, I think it all starts with going back to November 2022. I actually have vivid memories of it. It was the same month that my wife and I got married. And so we were at our honeymoon, playing with ChatGPT, just obsessed with it.

• 12:39Speaker 00

  And I think they called magic in a bottle. That moment is so precious. There is no amount of launch prep you could do for what happened in November 2022. Magic in a bottle, right? They had incredible, like, viral adoption, right? Hundreds of millions of users within a bunch of weeks. And Over time, I mean, if you if you look at the charts, the user growth has been a steady climb. And you can see the kinks when they've launched significant milestones. So for example, you know, in twenty twenty, twenty twenty three was like a steady climb. At some point, they took off the sign in page and that you didn't have to log on to use ChatGPT and you saw like a kink.

• 13:23Speaker 00

  Then you saw the next big one in twenty twenty four with advanced voice mode. Right? Then you saw another one with Studio Ghibli, the image gen. You know, so they got the text moment, they got the voice moment, they got the image moment. And so it's,

• 13:37Speaker 00

  I think the strategy is as much as it is like it's like the the grind of the feature velocity is so high. Keeping up with OpenAI product updates is a full time job. And that's the that's the strategy is ship fast.

• 13:55Speaker 00

  And I feel like that done at a consistent pace. I mean, just in just this year alone, you've had operator and deep research and ChatGPT agents and codex. And these are in G P five now. I mean, we're halfway through the year and they've had such big releases. I feel like that is that is a secret sauce. That is the defensibility. Speed is the only mode.

• 14:17Molly O'Shea?

  This is this is where the story gets funny, right? What we saw with G P T five was they turned off G P T four. And that caused a lot of unrest with users. And there was a story that really came to surface. And Sam wrote a tweet about this, but it was really clear. People had parasocial relationships with the chat itself. And so how do you think about this? It's probably a little bit more philosophical. But how do you think about that connection with the product?

• 14:48Speaker 00

  ChatGPT is no longer a website. It is no longer a product. It is a relationship

• 14:56Speaker 00

  that users have with technology similar to the way the relationship that I have with this phone. I use this for half a dozen hours a day. If I forget this at home, I will know within minutes. And I think ChatGPT is starting to get to a point where it is such a steady stream of a companion, a shop. I mean, I don't buy anything above a hundred dollars without consulting ChatGPT.

• 15:20Molly O'Shea?

  Really?

• 15:21Speaker 00

  Like a lot of research, spend so much time doing research,

• 15:26Speaker 00

  so much travel, so much learning, language, learning, planning.

• 15:31Speaker 00

  And I think what happened over the weekend was, you know, the hashtag for forever.

• 15:38Speaker 00

  Was a reminder that like most important technology releases, this is changing the human experience. The same thing happened with desktop and PC. Gaming, you know, people got addicted to gaming. The same happened with mobile. The same happened with, you know, social networks. And if I was to construct two axes, Molly on.

• 16:02Speaker 00

  Call it dopamine factor, how much sugar is there in the product? And and social, call it network effects. I would say chat G P T is actually quite. Safe and quite healthy because, you know, on the on the dopamine factor, there's no doom scrolling, there's no cat videos. There's the amount of dopamine that you have on. Chat G P T versus, I don't know, pick your favorite. Twitter or Instagram is a lot lower. X, the level of sugars a lot lower. Whereas and and on the other one, which is network effects, like if my friend. Molly, Brad, Jammin or Eric are on. Instagram, I'm more likely to be there. If they're on X, I'm more likely to be there. But if they're on chat G P T, you know, I don't have an incremental reason to be there.

• 16:47Speaker 00

  So the network effects are not as. Sharp right now. Despite that. This has become a dominant.

• 16:57Speaker 00

  Call it experience for for relationship with people and think that's what. The biggest takeaway I had over the weekend was, wow, this is. Becoming a.

• 17:08Speaker 00

  Technology that people have a relationship with. In a way that.

• 17:13Speaker 00

  Makes it incredibly sticky. Memory in particular, I think, adds so much context for your life. That switching out of it would be so hard. And so I think those were some of the takeaways that I had as that weekend unfolded.

• 17:25Speaker 03

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• 18:01Molly O'Shea?

  I use Kalashew markets within these conversations. And one of my favorite ones to ask is, what do you think the best AI will be this month? Usually, it's whatever their favorite is. Time and time again, mostly, unless it's a CTO, it's ChatGPT. And it's because of the context and the memory. And we fill it over and over and over again. Not everyone is having a parasocial relationship. Some people are extreme doomers. And David Sacks tweeted not too long after this, exposing the doomers, talking about the doomers. Guess what? We're not there yet. And what we've seen with these model releases over and over again, that the world is not ending. We're not going to get destroyed quite yet. It's going to take longer to build.

• 18:45Molly O'Shea?

  Where do you think we are in the AI adoption curve?

• 18:48Speaker 00

  Yeah. Jeffrey Moore wrote this book called Crossing the Chasm. I'll refer to some of the concepts that he describes in that book. But I think David's not wrong. Look, there's, with any sufficiently advanced technology, you've got the tech enthusiasts, or call it the developers or the superusers, who will adopt it first. And so I think of it as like, technology that raises the ceiling, and technology that raises the floor. And on the ceiling, you've got superusers, tech enthusiasts, typically 10 to 15% of the population. And then at the ceiling, you've got the vast majority of the population. The majority, even the skeptics.

• 19:29Speaker 00

  And so I think the way adoption plays out for really any technology is I evaluated on, hey, is this new product release raising the ceiling? Or is it raising the floor? So, for example, let's talk about raising the floor. What people who are, the vast majority in Laguard's need is remove friction. Look, they don't wake up to use AI. They wake up to do their, live their lives and AI has got to find a job, find a way to be a part of their life. Advanced voice mode reduces the friction. I've got a action button dedicated to voice mode. Incredibly useful.

• 20:08Speaker 00

  Eight, one eight hundred chat G P T. WhatsApp with chat G P T or Meta AI, you know, those are, I would say,

• 20:16Speaker 00

  ceiling feature releases that'll accelerate the adoption over the six, twelve, eighteen months.

• 20:24Speaker 00

  Sorry, raising the floor. The reason the ceiling would be, you know, these are users who are vibe coding, they're coding their own daily software, they're experimenting with chat, chat agents, they're playing with codecs, they're playing with cloud code and there's a set of features that are targeted to them this year. This would be deep research, this would be the computer use operator. And I think both of these will go up over time. And ultimately, you know, the, the, the biggest, I would say, experience will ultimately transcend beyond chat, you know.

• 20:59Speaker 00

  Maybe we'll have variables, maybe what Johnny Ives working on is a wearable, like a, I don't know, like a, I, I don't know what it is, but it could be a pair of glasses, could be a phone, could be a device, could be a watch. And how you engage with that could really remove friction of communicating with a future interface. So I think those are some of the big things that I'm looking forward to.

• 21:25Speaker 00

  What a time to be alive.

• 21:26Molly O'Shea?

  Maybe we've reached AGI, maybe we're gonna reach it soon. I don't know, the definition is unclear.

• 21:33Molly O'Shea?

  But with AGI and superintelligence, we are approaching a lot of really fun philosophical evolutionary existential questions. How do you think about humans' relation with technology and where this might go?

• 21:49Speaker 00

  AGI. And I think if I start with, start of the start, like, you know, I started my career. I was an intern at a firm called Rocket Fuel, and we were using AI to target ads to maximize some reward function. And that predictive machine learning or AI to, to, to, to, uh, felt magical to me. Two years later, and you know, in 2012, the team at AlexNet beat the ImageNet benchmark identifying cats and dogs better than humans. I thought that was magic. A couple of years later, I was a palter, I was a software engineer. I started using this tool called Kite. It was a coding copilot that would help you code faster. I thought that was magic. I thought that was great AI. Self-driving cars, GPT-3, voice mode.

• 22:34Speaker 00

  I mean, every two years or so, there's been a moment where I was like, wow, that's magical AI. Isn't that AGI? And so I guess my point is that the bar for what AGI is, is going up fast. And what we define to be, I guess, a lot of great intelligence is going up. And so now to actually answer your question, how that changes society. I think, you know, I, I, I wasn't around, but I'm told that there was a time when there was teams at banks whose job was to calculate the interest rates.

• 23:09Speaker 00

  And then the calculator came through.

• 23:12Speaker 00

  I, you know, I remember this as a child, somebody gifted me an encyclopedia. I remember reading it cover to cover, but it's completely not, not really needed anymore. You could just look it up pretty quickly. You could look up really any fact pretty quickly.

• 23:25Speaker 00

  I feel the same way about most forms of intelligence. I suspect in five to 10 years, we wouldn't have to work to earn a living anymore.

• 23:39Speaker 00

  And what, what, what do you do in a world where you don't have to work to live? You know, it.

• 23:47Speaker 00

  I suspect relationships would matter a lot. I suspect, as Victor Frankl talks about in his book, Man's Search for Meaning, creation would matter a lot. You would get a lot of joy in doing things that you enjoy in, you know, finding meaning. And but it's certainly going to look like a brave new world.

• 24:08Molly O'Shea?

  It's a great book. As we talk about the new releases, we have to talk about GPT-5. This was an interesting release because I don't think it went the way many had expected. There was a lot of hype around it. It didn't go so well. But my biggest observation, what I thought was pretty interesting out of this was, Sam actually took action really fast. He immediately went back to work and was iterating off of the feedback. What do you think that means today for what it takes to be competitive as a founder, as a CEO?

• 24:46Speaker 00

  You know, I think one of the things that strikes me about Sam and the OpenAI team in general is the single word to define them is anti-frigile. I've seen this for years now. Remember the time the blip happened when Sam was, for a hot second, not at OpenAI? And it felt like, oh, what's going to happen to this org? You know, this is obviously a pretty big, big deal. But I feel like they've gotten stronger. Very few orgs get faster over time. I feel like OpenAI has gotten stronger over time, faster over time. And the GPT-5 release is not anything different. I feel like for an org that is anti-frigile, they're taking incredible amounts of feedback. They're working with all the teams and they're gathering feedback and fast.

• 25:30Speaker 00

  The doors are open, the lines are open. And again, a sign of evolving fast, ultimately being the mode, speed is the mode.

• 25:39Speaker 00

  And so that's what I think about the G P T five release. You know, I think it was a complicated release. It was the first time they released a system, not a single model. It has a model router. It makes, it bakes in a lot of thinking of how much compute resources to allocate to a query coming in. And so, again, a sign of anti fragility.

• 25:57Molly O'Shea?

  Why was it so significant, technically speaking?

• 26:00Speaker 00

  Yeah, you know, I think each of our, each of the opening prior releases have been typically a model or a set of models. This was the first time they released a system, a unified system that makes the decision of how much compute to allocate. Should I give you a quick response to a simple query like, what's the capital of the United Kingdom? Which could just be a lookup. You don't even need to scramble the jets for that one. As opposed to a more complicated query like, you're an investment analyst. Help me analyze, gather feedback for, summarize the feedback on all the users of Neuralink so far. That would employ an orchestrator agent. It would go scrape the web. It would gather all the data. It would summarize it. It would probably package it together in a table. And that's probably like a three or four minute query.

• 26:46Speaker 00

  That delineation, which was previously something that the user would have to make in a model selector, is now made for you.

• 26:55Speaker 00

  That's a pretty significant change because remember the framework of the raising the ceiling versus raising the floor. This is raising both. For a user who is, you know, the majority of users who don't really want to select a model, this is great. This is simple, clean U X, a Google like bar. You just start. And the same for, you know, on the ceiling. If you, if you wanted to ask complicated questions, you don't have to think about, hey, what is the best model for me to do this? Is it O three? Is it O four mini? Is it O three pro? And I think that was one part of significance, call it the consumer experience.

• 27:33Speaker 00

  The second part was, you know, obviously they focused very heavily on a couple of domains, coding and design and healthcare and so on. And so the feedback for those focused domains has been pretty good.

• 27:45Molly O'Shea?

  Given they are a consumer product, do you think the structure of having these demos, these releases is the most effective way for them to communicate? Or do you think they should adopt other things? I mean, they do go on podcasts, like Sam definitely did a podcast tour beforehand. But how do you think about that in talking to who their real customer is? Because then people bring up benchmarking and how they perform and all of this sort of like stuff that you know is not what, you know, I don't know, Sally is doing on the couch before homework, you know?

• 28:19Speaker 00

  Yeah. You know, Ben Thompson wrote a great note on it. I'll share something he wrote about, which is that once you have a large enough user base, it's really hard for you to satisfy everybody. You just have to make choices. And not every

• 28:36Speaker 00

  upgrade, model upgrade, new product will please everybody. And I think that's kind of what happened here.

• 28:45Speaker 00

  Ben, on your question of benchmarks, I think the benchmarks are the starting line, but they're by no means the finish line. The benchmarks are kind of saturated, right?

• 28:56Molly O'Shea?

  Do we need them? Like, what is the point? What do they even mean? I feel like they're all arbitrary.

• 29:00Speaker 00

  They're a starting line, exactly. They're not the finish line. You typically start with, you know, a bunch of models that are at some, call it zip code of a performance metric. And what you actually need is more proprietary evals. Let's say for sorcery, you had 100 or 200 custom evals that work for your use case that you know work well. And then, hey, is the model doing better than a human on those evals? Or you might have particular features or just making sure there's no like, you know, better tool use or error debugging, etc. There's like a long tail of custom evals that I think do a better job now than benchmarks.

• 29:43Molly O'Shea?

  It was interesting to watch. I obviously love Kali. But I was watching the Kali chart on this for best AI of the month. And during that, it was live. And so during that, that release, they had a couple of different demos. After the second demo, ChatGPT had the top spot. And then it completely did a reverse Uno and switched. It actually switched, I think with Gemini, I believe. And so, so it was really interesting to see. But like my observation off of that is of course like retail sentiment. If they were public, this is how volatile this would be. It's like the most competitive market ever. And then you have Twitter chatter, but then you have like, okay, you actually went to CEOs. You did market research.

• 30:28Molly O'Shea?

  That's the reality. So there's a disconnect there. But it was an interesting observation into, okay, how, how volatile would this be if it was public?

• 30:37Speaker 00

  Very volatile. It would be very volatile. I mean, as there's been multiple moments like the blip, like the time last summer when there was, you know, a couple of departures, like the GPT five release. There's all these, all these moments where I feel like the narrative, as, as Kali and, and, and tracks appropriately, completely flips up and down. It would be a very volatile stock.

• 31:01Molly O'Shea?

  So Brad Lightcap, he captured some of the momentum from their customers with GPT five. I want to go through some of these and then get something that you've heard from your portfolio. So he heard some really good momentum and I'll show the tweet from Cursor, Levable, Harvey, Amgen, Uber and Notion. I know you have a particular instance. Do you want to share this?

• 31:24Speaker 00

  Absolutely.

• 31:26Speaker 00

  This is another example of what you said, which is the Twitter narrative was so volatile. But if you actually call the experts, the actual customers, and so we called a series of customers. We called all of the coding AI companies we know from, from, you know, a bunch of their customers, customers of the, the two or three largest labs to customer service and cybersecurity. And, you know, Early, right, two weeks in, but good so far, working with the team, as we said, there it's a product in motion. But one particular one stands out is our company, Expo. Their benchmark, on their benchmarks, they saw a significant step up with G P T five,

• 32:07Speaker 00

  much better than any other coding or design or math startup saw in their ability to find cyber exploits. And, you know, summarizing, basically, the jump was from just under sixty percent to eighty-one percent, switching from Anthropic's latest model to OpenAI's latest model, G P T five. And the jump was so significant, I think it was higher than what the team had expected, but certainly the Expo team had expected by quite a bit, but maybe even the OpenAI team. And so they've published the results and it's becoming pretty clear that A I is much better than humans at this thing called hacking.

• 32:48Molly O'Shea?

  We need to talk about Expo.

• 32:50Speaker 00

  Let's do it.

• 32:51Molly O'Shea?

  So give us the rundown on Expo. How did you get into this investment? What do they do?

• 32:57Speaker 00

  Well, the punchline is the number one hacker in the world is no longer a human. It's a set of A I agents that work together and break through perimeters and find vulnerabilities, sometimes even exploit them. And so there's this platform called HackerOne, which is a marketplace where corporations could find ethical hackers to help them red team or penetration test their perimeter. Expo has been let loose on this platform and a couple of weeks in, they became the number one player in the United States. But two weeks ago, right at Black Hat, they achieved the number one spot globally.

• 33:37Speaker 00

  And so it is, it's a pretty big milestone and for, you know, there's a lot of things that A I has gotten better than humans at, obviously image recognition, a bunch of games. Hacking is now there.

• 33:54Speaker 00

  And so that's the significance of expo. And you know, the one thing I will say is while expo is a will be a great business. It is a business that must be built for the safety of the of the of the of the Western world. And ultimately cybersecurity ends up being a game of cat and mouse. And you've got the good guys who'll make some progress and the bad guys. And you know, the chat GPT moment of cyber hacking hasn't happened yet. It'll happen and they will offensive actors will use AI to do so. And we need expo and players like expo to help keep the world safe.

• 34:31Molly O'Shea?

  What are the applications?

• 34:34Speaker 00

  You know, typically, what? So the way expo you would have done this previously is let's say you're a developer, developer, developer team and you would write software, you would ship software. And then you would have a red team or penetration testing team analyze all the vulnerabilities in your code. You you might you might do this test once a year or twice a year. And you're typically rate limited by the number of penetration testers there are in the world. There's not that many of them. It's expensive. It's ultimately a manual, you know, exercise. Today, a lot more code is being shipped, right? You're writing a lot of code using AI. And by the way, that code is being written by models that were trained on open the open

• 35:17Speaker 00

  source curve base with a lot more vulnerabilities. And so it's a lot more vulnerable. So the biggest application is going from this really analog red team penetration testing process that happens once a year to continuous testing that happens as you're writing code, as you're shipping faster continuously. Why be rate limited on the testing? And so that's the biggest application used by companies, small and large, across the spectrum.

• 35:46Molly O'Shea?

  This must have been incredibly competitive to win. How did you meet Uhe? And how did you win this?

• 35:53Speaker 00

  The team at expo is incredibly, incredibly talented. Uhe Demur, you know, founder and CEO of expo, taught computer science at Oxford for a couple of years, a couple of decades, sorry.

• 36:08Speaker 00

  Then built GitHub Copilot with Nat Friedman and team at Microsoft. And this is sort of his yin to his yang as he saw as he was building GitHub Copilot and saw all the vulnerable code that was being written, started Expo last year. You know, we're obviously incredibly lucky to be working with them and we haven't won anything. The work has just begun. We've just started partnering with them, but I would say it was a, I think they were looking for missionaries and I feel like our first meeting, my first meeting with Uheh felt like our fifth meeting. We'd done quite a bit of work even before I met them and I would say ultimately, as a former software

• 36:50Speaker 00

  engineer, I could intrinsically understand the problem, you know, and how I'd reached out to them was also because it like, it makes so much sense. Like I understand the root cause. I see how they were going to fix it and how you build a large company doing it. And so our first meeting felt like our fifth meeting. I think we met on a Friday morning and we decided to work together by the Saturday evening afterwards.

• 37:10Molly O'Shea?

  Wow.

• 37:11Speaker 00

  Yeah, fast and furious.

• 37:13Molly O'Shea?

  That is quick.

• 37:14Speaker 00

  Yeah, yeah, yeah. The shape of Expo is so unique. As you said, it's a small team, less than 50 people, all across the world. Uheh lives in Malta. Nico, the CTO, lives in Argentina. They've won thousands and thousands of dollars, which are awards that are publicly available for anybody to win, but also ultimately to create training data and focus on our target segment, which is large enterprise. Thank you. Large financial services customers, large insurance customers, large healthcare, large technology businesses on one end, and that's, so that's our customer base. And on the other end, it's, call it small to

• 37:58Speaker 00

  medium sized businesses who want to just have faster compliance. And so that's where we spend the tokens. And it is true, we spend more on tokens than we spend on humans. And it is, I guess, the shape of an AI needed form.

• 38:09Molly O'Shea?

  Okay. Well, we need to talk about where is spend today in AI and where will this be in the future? I know you have some great charts about this. They're not pyramids.

• 38:22Speaker 00

  It is probably the biggest question in AI right now. Where will the value accrue? And we've been studying this now for for a couple of years. And you know, I, you know, I'm sure the audience sees the large CapEx announcements that the hyperscalers are making.

• 38:45Speaker 00

  They add up to hundreds of billions of dollars that are going into the ground, building data centers, acquiring chips and the infrastructure to to enable all that. And so we decided to study the same with the build of prior supercycles with the internet, cloud, mobile, AI. And we put it together to see where does value accrue over time? And you know, summarize it for for the audience here, which is in the cloud supercycle, you know, you have about $400 billion of revenue that is being earned by the applications. Let's call it the layer one. You've got about $200 billion spent on infrastructure. This is AWS and GCP and Azure, et cetera.

• 39:30Speaker 00

  And you've got about $50 billion on semis. This is Intel, AMD, et cetera, the CPU layer. The shape of this is 400, 250.

• 39:40Speaker 00

  Enter AI.

• 39:43Speaker 00

  Nvidia earned, Nvidia alone at the chip layer earned roughly $40 billion in data center revenue in Q1 last quarter. So analyze that's about 160. Let's say the total industry is 170, 180, maybe let's say 200.

• 39:56Speaker 00

  The infra layer is 20, 30 billion. This is inference revenue that's being earned by the hyperscalers and and neo clouds and and so on. So a fraction of this, right? A fraction of the of the 200. And then the revenue being earned by the application layer, this is players like OpenAI and Anthropic and Perplexity and and Gleam and and so on, is on the order of, you know, 30, 40 billion dollars. The shape is this way. Cloud is this way. And and it is so different from cloud that it begs the question, when will and if this will import? And that's the biggest question we've been analyzing. And and so, you know, we we walked through a bunch of analogs.

• 40:40Speaker 00

  I've written about this publicly as.

• 40:44Speaker 00

  It takes a while for this to invert. You know, the canonical example is AWS. Started in the year 2000. In 2012, sorry, it started in the year 2004. Eight years later, in 2012, they got their first outside customer in Netflix. Eight years. And for eight years, there was a lot of the build, laying down the railroads, until you started to monetize it. And so, you know, that's the, that's the, that's the thing that's going on right now. It's, you know, 90% of all AI dollars are actually in the semis layer.

• 41:15Speaker 03

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• 41:58Molly O'Shea?

  There's one important line item that you missed. It's the talent.

• 42:02Speaker 00

  The talent.

• 42:03Molly O'Shea?

  The billion dollar talent.

• 42:05Speaker 00

  The most important.

• 42:06Molly O'Shea?

  I mean, who knew that if you went to college for being an AI researcher, you'd become a billionaire? That is creating an entirely new class of graduates.

• 42:17Speaker 00

  That's right. That's right.

• 42:19Molly O'Shea?

  I want to get into this because I think it's so interesting and we're seeing it from multiple sides. You'll see it from the OpenAI side, but also the meta superintelligence scene. Can you break, like, what is going on here?

• 42:30Speaker 00

  The ingredients of.

• 42:35Speaker 00

  Call it being on the frontier or

• 42:38Speaker 00

  compute, CapEx, we spoke about it. Data, which is sort of accessible to everybody. We are saturated on the training data that exists. Talent, this is where the war is now.

• 42:53Speaker 00

  And if you look at, if you look at, you know, you mentioned Meta, like Meta has an incredibly large balance sheet, right? They've got, I want to say like

• 43:06Speaker 00

  seventy to eighty billion dollars on the balance sheet of cash.

• 43:12Speaker 00

  Every quarter, they're printing twenty-five to thirty billion dollars of operating cash flow, which, you know, they might use to invest in CapEx or buy their stock or distribute dividends or whatever. But, you know, on an analyzed basis, they're generating about a hundred billion dollars of operating cash flow.

• 43:31Speaker 00

  To put that in perspective, OpenAI just raised this massive round, forty billion dollars, and Meta is producing more than twice that in a year. So if I was Zuck, and you know, gotta give it to him, founder-led organization, incredibly bold, not afraid to make tough decisions, big decisions, is acquiring the most important ingredient, talent, leading indicator of all value. And, you know, they've got plenty of cash to do it. So bold move by a bold leader. And what a time to be, what a time to be watching this.

• 44:09Molly O'Shea?

  What a time to be alive.

• 44:10Speaker 00

  What a time.

• 44:12Molly O'Shea?

  You were part of one of the most intensive, one of the most impactful,

• 44:19Molly O'Shea?

  one of the most well-branded teams that came out of technology. And this is Palantir's Forward Deployed Engineers. I want to break this one down because it's insane. I was just looking at a Instagram post. This is where I get some of my news, groundbreaking.

• 44:37Speaker 00

  Right, right.

• 44:37Molly O'Shea?

  So I'll read this out. Palantir X employees, this is one of the best cultures ever.

• 44:45Molly O'Shea?

  They've raised over thirty billion dollars total, averaging eight hundred million per company, and more than six percent have founded billion-dollar startups. These are companies like Calshi.

• 44:57Speaker 00

  Yes.

• 44:57Molly O'Shea?

  Tarek. Tarek was a forward deployed engineer. Sourcegraph.

• 45:02Speaker 00

  Quinn.

• 45:03Molly O'Shea?

  Quinn.

• 45:03Speaker 00

  Yeah.

• 45:04Molly O'Shea?

  Yep. Ironclad.

• 45:06Speaker 00

  Mm hmm.

• 45:08Molly O'Shea?

  Adipar.

• 45:08Speaker 00

  Of course.

• 45:09Molly O'Shea?

  Joe Lonsdale. Does that count?

• 45:11Speaker 00

  Yeah.

• 45:11Molly O'Shea?

  It counts. Okay. And we have eleven labs.

• 45:15Speaker 00

  Eleven labs. Marty, Andrew, obviously, legendary firm, Chapter. I mean, the list is long, and I promise you it's getting longer.

• 45:22Molly O'Shea?

  It's getting longer.

• 45:23Speaker 00

  There's gonna be ten X more of those. So the world, watch out. The panels are coming.

• 45:28Molly O'Shea?

  So what is it? How does this create thirty billion dollars in raised capital? What are the principles of being a forward deployed engineer?

• 45:37Speaker 00

  Yeah, it's a good place to start. Look, I think for the two decades or so that Pancha has been around, for majority of it, nobody understood forward deployed engineering. Pancha was actually a very misunderstood business for majority of its existence, and in large part because of how customer obsessed Pancha is. You know, the shape of the business is like nothing, no other software business, or median ACV is over five million dollars. The reason forward deployed engineering came to be is anytime there's a sufficiently advanced technology that cuts across horizontally through a bunch of different industries from defense, oil and gas, finance, consumer packaged goods, automotive, airlines, etc.

• 46:22Speaker 00

  What you need is experts who are really good at the technology,

• 46:29Speaker 00

  which was a lot of people at Pancha, but we knew nothing about those industries. You know, as somebody who worked in a bunch of these commercial industries, I did not know much about oil and gas, or finance, or CPG, and so we

• 46:42Speaker 00

  we would spend a lot of time with our customers, learning about their industries to deliver an experience that was soup to nuts, controlled by us, because the industry was so used to buying software that that while might be good on paper would not actually move the needle.

• 47:00Speaker 00

  The same is true right now. Why is forward deployed engineering all the vogue? Is because you have a very advanced technology in AI that cuts horizontally across a lot of different industries. Software engineering, customer service, legal, healthcare, finance, and so on. And applying it appropriately to those industries requires a lot of context. And, you know, I think that was a that was the principle behind creating forward deployed engineers. Basically, engineers who would go and obsess about their customer problem. And oftentimes, the customer problem was not a sexy problem. It was building a data pipeline. Sometimes it was dealing with org change. Sometimes it was figuring out processes. Sometimes, other times, it was figuring out permissions and, and, and, and, and, you know, all sorts of things.

• 47:44Speaker 00

  And I think that's why forward deployed engineering came to be.

• 47:48Molly O'Shea?

  They've reached over a billion dollars in quarterly revenue. They have forward deployed engineers. They just keep on going on a tear. What is it about their culture that primes their employees for success?

• 48:00Speaker 00

  You know, Palantir is a very, very special place. Obviously, a special place for me. I'm biased. I think it's a, it's a, it's again, a very important mission. It is a, it is a firm that while a great business, as you said, must exist for, for, for, for the Western world and allies. And I would say the, there's a lot of great things, and I've written a whole article about it, is it all starts with a mission. The single thing that unites folks at Palantir and, and, and what we do and, and what we did while I was there is that there is something that is larger than your own existence that you're there for. It could be helping the, the, the US government, the allied governments. It could be a mission.

• 48:45Speaker 00

  And, you know, we, for that same reason, every, in a lot of the Palantir language.

• 48:53Speaker 00

  Mission language was was overused. Everything was a winner loss and that brought people together.

• 49:01Speaker 00

  And you need that in wartime. You need something to unite the troops in a wartime. The second was an uncompromising bar on talent. I mean, for all the time that I was there, every single candidate we hired was interviewed by the founders. Imagine the operational

• 49:19Speaker 00

  complexity that creates, but that that was required to keep the bar really high. And I felt like we got, you know, as Karp would say, a colony of artists that that Palantir kept together.

• 49:32Speaker 00

  And finally, I would say, you know, it's

• 49:36Speaker 00

  really deep obsession with customer outcomes. Not sexy problems, not the shiny problems, not vanity metrics, but like really moving the needle for the customer in a way that you can only do when you own the entire process soup to nuts. And so now it shows up in the metrics at Palantir, as you said, the latest quarterly earnings, incredible quarterly earnings. And I think revenue and revenue growth are lagging indicators. The leading indicators are what I described to you is what Palantir excels at.

• 50:08Molly O'Shea?

  They have an incredible company. They have a cult brand, software that dominates. Alex Karp, Shom Senkar. What was the biggest lesson that you learned from Shyam?

• 50:20Speaker 00

  Wow. Too many.

• 50:25Speaker 00

  Honestly, Shom's a great leader. Shom's a great systems thinker.

• 50:30Speaker 00

  And, you know, I'll share one anecdote that I learned with them. I remember this was

• 50:37Speaker 00

  we were in London, working on a customer deployment. And as a young engineer and engineer leader at the time, I remember feeling how the entire mental model for for for Shom and his focus was around winning. It was not around a great plan. It was not around a great team or a roadmap or a set of features that we're going to build. It was around winning. He actually wrote a lot of this in his article called The Primacy of Winning in Pirate Wires. I recommend, you know, folks to check it out. But it is the single unifying.

• 51:19Speaker 00

  Orientation that Sean has always had. And you know, he would say that like winning takes care of everything. If you're winning, you don't have to worry about the other stuff. But just to highlight the trade offs, the trade offs of that. If you're oriented around winning is you will have to deal with a lot of chaos. You might not have a beautiful roadmap in wartime because you know, the fact that you. I mean, I feel the same way about a lot of what's happening in AI right now. Things are moving so fast that if you have a beautiful product roadmap, something's not right. You're probably not moving fast enough. And I'd say that was the biggest thing that I learned from Sean. And I think it's particularly relevant now.

• 52:04Speaker 00

  One of the other things he used to say is for deployed engineers ingest pain and excrete product. And you know, one of those Seanisms that is so true. Basically to remind you that no task is beneath you, even if that includes plumbing of data pipelines to change management. It's ultimately all pain for a bigger, more important mission. So many, many things I could speak at length about all the great, great things I learned from Sean and his leadership.

• 52:36Molly O'Shea?

  What's the biggest lesson you learned from Alex Karp?

• 52:39Speaker 00

  Another legend.

• 52:41Speaker 00

  Too many.

• 52:44Speaker 00

  You know, one of the things that Talents does, even to this day, I believe, is before you start, they ship you a set of five books.

• 52:53Speaker 00

  A book on obviously getting things done and user experiences, which you would expect. A book on counterterrorism, which you'd expect, but a book on improv comedy.

• 53:04Molly O'Shea?

  Okay.

• 53:04Speaker 00

  But a book on, you know, but basically the biggest lesson I took away from Alex Karp is, you know, he thought a lot about.

• 53:16Speaker 00

  Keeping this artist, the colony of artists at Pounder together.

• 53:21Speaker 00

  He thought a lot about what is the appropriate way for Pounder to be represented in the world. So for example, you know, Carp really led all our go-to-market efforts while the time I was there. And it was a very small go-to-market team led by Alex. And he had some very particular thoughts on how we approach our customers.

• 53:42Speaker 00

  But an incredible CEO and obviously crushing it at Pounder.

• 53:48Molly O'Shea?

  A true lesson in performance.

• 53:50Speaker 00

  That's right. That's right.

• 53:52Molly O'Shea?

  Well, speaking of performance, Sorcery is sponsored by Brex. They're all about spending smarter, moving faster for over 30,000 companies, 200 public ones.

• 54:04Molly O'Shea?

  And I'm curious, from your perspective, as we think about companies today in this super cycle, moving super fast, becoming really competitive, what are the key characteristics that you look for in founder performance?

• 54:16Speaker 00

  Ultimately, you know, we invest across venture growth, pre-IPU and then public markets. I would say there's really two questions that I'm trying to assess when looking at businesses. Is this a great business? Is this a generational business that'll go create a large company, maybe a public company? Question number one.

• 54:40Speaker 00

  Question number two, is this a great price? Is this a great structure? Does math pencil out, et cetera? And I spend 99% of my time answering the first question. Is this a generational business led by incredible leaders, inspired by a great mission? And while every business is unique, it's hard for me to give you a template for all of them. I think the thing that I obsess about the most is asking the right questions. You know, every time we study a business, there'll be two or three or four questions that'll define the business at that given time.

• 55:16Speaker 00

  Knowing the right questions to ask, actually, that's probably the process that takes a little bit of time. The answers will typically either be knowable or not, and we can get to them. But that's where I spend a lot of time.

• 55:28Molly O'Shea?

  That's pretty good. That's a good answer.

• 55:29Speaker 00

  I appreciate it.

• 55:30Molly O'Shea?

  As we wrap up, we're gonna do a quickfire round.

• 55:34Speaker 00

  Fun.

• 55:34Molly O'Shea?

  Okay.

• 55:35Speaker 00

  Fun, fun.

• 55:36Molly O'Shea?

  First, I wanna know if you're bullish or bearish on these companies. And then I'll ask a secondary question. So these are all based on Calci's recent IPO charts. You can go there and see, you know, place your trade on who you think is gonna IPO first. But I wanna know if you're bullish or bearish on these companies.

• 55:56Speaker 00

  Sure, alright.

• 55:57Molly O'Shea?

  It's five of them, okay? Are you ready for this?

• 55:59Speaker 00

  Let's go. Ready, ready.

• 56:00Molly O'Shea?

  Klarna, Discord, Databricks, Cerebras, and Dural.

• 56:07Speaker 00

  Bullish, bullish, bullish, bullish. Wait for it. Bullish. Bullish on all of them. Those are really good companies. Those are all really good companies. I mean, four of them are ultimate portfolio companies.

• 56:18Molly O'Shea?

  Four?

• 56:19Speaker 00

  Four of the five. So those are all really good companies. Sorry, I'm not bearish on any of them.

• 56:25Molly O'Shea?

  What do you think the odds are that any of those will go public this year?

• 56:29Speaker 00

  I think at least one of them will go public this year. I'm a big fan of the public markets, building the public. You can still have a lot of innovation in the public markets. As some of the greats have shown.

• 56:42Molly O'Shea?

  Any particular name?

• 56:44Speaker 00

  Oof, tough.

• 56:47Molly O'Shea?

  Are we gonna trip compliance?

• 56:50Speaker 00

  That's right. Well, we can always remove this, but well, as you saw, Databricks just announced a big round this morning. So had that not happened, I would have said Databricks, but that's probably not the number one leader right now.

• 57:06Molly O'Shea?

  I guess I think a more interesting question I wanna ask is, do you think companies even need to go public anymore? I mean, we've seen through secondary markets, through these massive private rounds over and over again, that there's just so much capital. Do they still need to go public?

• 57:24Speaker 00

  I'm a big proponent of companies going public. It has an incredible amount of alignment with shareholders. Create hygiene, not to mention gives you a pretty good feedback on the company's priorities.

• 57:45Speaker 00

  I suspect there's a class of companies now like SpaceX, like Stripe, that have found a way to have repeatable liquidity for their employees at a regular cadence. Let's say 12 to 18 months.

• 57:59Speaker 00

  But I'm personally a huge fan of companies going public and I think it'll always be a great way to build companies at scale, durably.

• 58:10Speaker 00

  While admitting that some of the best businesses are now being built while they're private, the private, late-stage private, call it asset class, is just full of so many great gems. You know, SpaceX, Stripe, Android, OpenAI, Anthropic, some greats there. So, not to be discounted, but companies are staying private longer, building with access to capital, both primary and secondary.

• 58:37Molly O'Shea?

  Okay, we'll have to check back in about six months?

• 58:41Speaker 00

  Six months.

• 58:42Molly O'Shea?

  Okay.

• 58:43Speaker 00

  Right, right.

• 58:43Molly O'Shea?

  Well, Pore, this was fantastic. Thank you so much. We covered so much ground.

• 58:48Speaker 00

  This was fun.

• 58:49Molly O'Shea?

  But...

• 58:49Speaker 00

  Thanks for having me.

• 58:50Molly O'Shea?

  Yeah, thank you so much for coming on.

• 58:51Molly O'Shea?

  Hey, it's Molly. If you enjoy our interviews, check out our newsletter, sorcery.vc, where we deliver a once-a-week top deals and tech headlines email and also go deeper on our podcast interviews. Subscribe to Sorcery today and don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up.