What Gets You Funded in AI: Insights from Felicis, 500 Global & Mayfield

Astasia Myers (Felicis), Tony Wang (500 Global), & Patrick Salyer (Mayfield) joined Sourcery’s Molly O’Shea at the Startup Grind AI Summit, presented with Mayfield & Snowflake, for a conversation on what it really takes to raise from Seed to Series A in AI. Together they unpack the realities of fundraising in AI today—from what traction truly looks like, to defensibility and integration moats, to shifting stage definitions and round compression. The panel digs into: ​Why AI voice and agents are opening massive new TAMs​How founders should navigate inflated growth expectations & conversion rates below 20%​Red flags investors watch for in ARR, pilots, and “vibe revenue”​How AI is reshaping business models, talent, and even the venture model itself​The premium (and pitfalls) in AI valuations This panel goes beyond the hype to share what top VCs are really looking for when backing the next generation of AI companies. Funding data provided by Carta. 5 Key Takeaways ​Traction Isn’t Just Revenue – For AI startups, traction is measured by engaged usage, speed of iteration, and evidence of repeatability, not just top-line growth.​Defensibility Is Everything – Proprietary data, strong technical teams, and hard-to-replicate integrations are the biggest signals for Series A readiness.​GTM Strategy Sets Winners Apart – Investors want to see early playbooks for distribution, not just product innovation. GTM clarity is now as critical as model performance.​Red Flags to Avoid – Pitching with no moat, chasing hype, or relying entirely on third-party APIs without differentiation will stop fundraising momentum cold.​Series A Is a Graduation – Moving from seed to A is about proving repeatability, durability, and clear paths to market leadership. 1. Astasia Myers: https://x.com/AstasiaMyers 2. Tony Wang: https://x.com/TonyW 3. Patrick Salyer: https://x.com/patricksalyer 4. Molly O’Shea: ⁠https://x.com/MollySOShea⁠ 5. Sourcery: ⁠https://x.com/sourceryvc⁠ Brought to you by: • Brex https://brex.com/sourcery—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 https://turing.com/sourcery—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 • Carta http://carta.com/sourcery—Carta connects founders, investors, and limited partners through software purpose-built for private capital. Trusted by 65,000+ companies in 160+ countries, Carta’s platform of software & services lays the groundwork so you can build, invest, and scale with confidence. Visit: https://carta.com/sourcery • Kalshi https://kalshi.com/sourcery—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 Follow Sourcery for the latest updates! https://www.sourcery.vc/ Chapters: (00:00) Investor backgrounds (01:39) Astasia on AI voice models (04:02) ROI of voice-native apps (05:02) Patrick on AI agents & TAMs (07:27) Tony on MCP & voice agents (09:15) Growth expectations from Seed → A (11:15) Conversion rates & red flags (13:29) Patrick on stage shifts & round compression (15:36) Tony on hypergrowth traps (19:08) Astasia on fastest-growing AI companies (21:13) Patrick on AI supercycle & greenfield markets (23:23) Panel on integration moats & stickiness (25:41) How AI is reshaping business models & VC (32:57) Building AI-native teams & learning velocity (38:43) Hiring trends & younger AI-native talent (40:48) Valuations, premiums & market realities

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

• 0:00Molly O'Shea?

  Okay, well, welcome to the VC panel. Today, we have three outstanding investors, Patrick, Tony, Anastasia. We're going to cover what it takes to go from seed to series A. And to begin, let's start with Patrick. Give a little bit on your background and what you're investing into, and then we'll go around.

• 0:18Speaker 01

  Thanks, Molly. And hi, everyone. Again, my name is Patrick Salyer. I'm a partner at Mayfield. Mayfield is a 55-year-old, early-stage venture capital firm. So we invest in lead and co-lead rounds at seed, series A, series B, checks ranging from a couple million up to 15 million. Personally, I'm focused in AI middleware and applications. So a lot of the areas that I'm seeing and interested in are AI agents. Everything's AI agents today for various horizontals and verticals. We're seeing a lot of innovation on business models, like services companies that are now AI-enabled. We're seeing all sorts of AI builders, like lovable for marketing, lovable for sales. So these are kind of some of the more interesting things that we're seeing right now.

• 1:02Speaker 02

  Yeah, thanks, Molly. I'm Tony Wang, managing partner at 500 Global. We got started in Silicon Valley. And now in our 15th year, about 2.1 billion AUM, we are pre-seed and seed. So we're often the first check-in. We're early investors into companies like Canva, Topdesk, Intercom. And then more recently, our AI companies have really just accelerated. So Sakana AI, we are the first investors there. Deep Infra and Play AI, which is now part of Meta's superintelligence lab. So we're often talking to founders before they even start their businesses. So we're definitely open to chatting.

• 1:39Speaker 03

  Hi, everyone. I'm Anastasia Myers. I'm an AI-focused general partner at Felicis Ventures. Felicis is an early-stage VC fund that invests across inception, seed, series A, and series B. We tend to lead and write checks between about 1 and 40 million. We've been really humbled to be part of the journeys of many of these hyper growers from Merckor to Canva, Notion, Runway, Superbase, among others. We think about AI across the stack, and so I've been looking at infra, security, AI apps, and even the application to frontier technologies like robotics with our investment in skilled and defense tech as well.

• 2:26Molly O'Shea?

  A heavy bench of investors and experts in this next generation of AI. Not to skip over myself, but I am Ali O'Shea. I run Sorcery. It's a tech and investing newsletter and podcast. And I do know that Sam Altman is related to Jack Altman. I don't know if you've seen that clip, but it's not.

• 2:49Molly O'Shea?

  It was, uh, yeah, it was fake. Anyways, um, so to begin, I just want to know, so what is the current trend that's happening right now? We've gone through so many different waves in AI. What is happening at the earliest stages? Astacia, maybe do you want to break this down?

• 3:04Speaker 03

  Mm hmm. One theme in AI that we're incredibly excited about is AI voice. We've been investing across the stack with AI voice and audio models at the infrastructure layer, middleware, so the tooling to build the workflows themselves and AI voice native applications. Actually, yesterday, uh, I announced I had led, um, an investment in a business called Assort Health, which is using AI voice for patient communication and access. And it's one of these hyper growers in healthcare. Um, one of the reasons being is AI voice is a very powerful technology.

• 3:46Speaker 03

  Previously, you had people manually picking up calls, either people working internally at companies or in contact centers. It was a very, uh, painful process with very little automation. Now with the immeasurable improvements in AI voice models, you can not only automate the task, but often the NPS of the experience is, is better. And so the ROI for these gen AI app companies that are voice native is incredibly high. And customers are just ripping it out of the hands of these, um, early stage offerings. And so we are super excited about AI voice and thinks it's a technology that's in right now.

• 4:32Speaker 01

  Yeah, just

• 4:33Speaker 03

  Feel free to data work.

• 4:34Speaker 01

  Yeah, um, and, and I think like a sort health is an amazing example of what's happening in general with AI agents. And what we're calling AI teammates are this idea that like the last generation of software was really about SAS, like systems records, systems of engagement. And this next generation is about automating work. It's, it's automating and, and giving each role and persona kind of their own teammate or assistant to complete their workflows and become anywhere, I think from like thirty to seventy percent more effective and more efficient. I think what's exciting about that is I think the Tams are literally ten X what software was, because you can actually think about like every individual on the planet and how do they get fifty, sixty, seventy percent more productive and efficient.

• 5:15Speaker 01

  And I think over the last couple of years, we saw a lot of companies around horizontals. So, sales, marketing, customer success and every persona within that. And now this is moving towards things like verticals. I think a sort health is an amazing example. Uh, as, as an example of that, we're seeing this in real estate. We're seeing this in education. We're seeing this in legal and, and every iteration of that, which I think is super, uh, super exciting, super interesting. And, um, and there's a lot of enablers for these businesses. So like one of the trends we're seeing is reinforcement learning post-training. And actually like every agentic flow before you're having humans say, was this good or bad? Now, actually, agents are being used to say whether these flows are working or not working.

• 5:59Speaker 01

  And there's companies that are producing that. So the infra levels being impacted by this as well. So I think, I still think we're actually pretty early in this trend. And what's exciting for builders is like you're not replacing software anymore. These are greenfield markets. Some of these Tams don't exist, but we think they're very large. And you're seeing kind of every iteration and the companies that are building around this, the founders, tend to have a lot of insight in those areas. They tend to have been building in those areas for the last ten or fifteen years. So you're seeing repeat entrepreneurs, first time entrepreneurs, but folks that have like a lot of perspective about those areas.

• 6:29Speaker 02

  Yeah, I, I think, um, you know, when we look at a company, what I always want to ask is, how fast is the technology moving and how broken is the experience? So maybe a couple of years ago, it's pretty clear that text and images, the winners were becoming more and more clear there, but we didn't know who the winners in voice were, right? So, you know, that's why we, uh, invested in voice agents, uh, voice model companies. But today, As as David has mentioned, you know, we probably wouldn't have, we probably wouldn't invest in another voice model company, but voice agents is something that I think the experience is still relatively early and still relatively broken, but the surface

• 7:11Speaker 02

  area is huge. And that's where we look at, okay, well, there's definitely a a problem to be solved. If you're building in that space, the question I would have for you is, as a founder, the voice agent approach, which we love, still feels like an interim approach because in the end, everything will either be API or MCPed, but the voice agent is actually a really good interim approach where if you don't have an MCP, if you don't have an API, you get to just pick up the phone and call. So we really like that. And and as a founder, you kind of want to start navigating, how do you go from sort of this interim approach

• 7:52Speaker 02

  to something that would be, you know, durable? So that's one thing that we ask founders when they're building in the voice space. Other areas that we like, uh, security, anytime you have a new technology, there's obviously new adversarial vectors that open up, healthcare, fintech and others, uh, as as the folks on the panel mentioned, lots of verticals and and service area there.

• 8:14Molly O'Shea?

  Tony, before we go too far, could you just share what MCP means for people that don't know the term?

• 8:19Speaker 02

  Oh, yeah. So basically, the the model context protocol where, you know, Anthropic came out with that first, it looks like all the other labs are now starting to adopt it. It's basically a way to interface with, you know, if you don't have an API, if you don't have a a specific way for a model to be able to relate to your database, that's a that's a protocol that, you know, OpenAI, Gemini, Anthropic, uh, all these labs that are building agents are now using. So basically, it gives you a a way to, you know, interface with with anything. So it'll be a future agent to agent interaction as opposed to sort

• 8:59Speaker 02

  of mouse and keyboard using a UI. So that's a really exciting, um, future. Uh, obviously, there's lots of, uh, applications, sites, um, merchants, brands that don't have it uh, supported yet, but we believe that that's where it's going. But in the interim, you'll you'll see this messy middle.

• 9:19Molly O'Shea?

  Hot topic, it's a trending term, have to have to clarify that. Okay, so now as we, um,

• 9:25Molly O'Shea?

  I I just want to lay out more on the investing side, like we have access to some phenomenal investors right here in front of us. So I'd love to understand more about the growth expectations between the seed to series A because the graduation rates have kind of dramatically taken a turn, whether or not you're an AI company or you are. And so I'll bring up this slide deck from Carta that we have access to. And feel free to tell me to go to any slide that you want, but I guess to start off, what level of growth do you now expect between seed to series A companies? It's become kind of a contentious topic with this recent clip of Hamon on 20 VC

• 10:06Molly O'Shea?

  talking about getting exponential growth much, much faster, but where do you realistically see this kind of growth?

• 10:13Speaker 03

  Yeah, that was a a pretty interesting clip of um you have to go from one to fifteen to a hundred million. And that is interesting versus the one triple, triple, double that uh many builders have been anchored around for the past few years. Growth expectations are higher than ever before. There is a perspective that the time to build in AI is now and there's so much revenue to go after and so much opportunity that founders should be heat seeking missiles for cash in the back essentially.

• 10:53Speaker 03

  And so we tend to see businesses that are in a strong, healthy position to raise their series A at different levels of growth, depending on the category. I think I do a lot of dev tool and infrastructure investing and that one million error R number is still a good signal, but the time horizon to get there is no longer a year. Usually there's an expectation of that shrinking to the first two quarters or three quarters. And then for Gen AI applications, the bar is higher of two to three million.

• 11:33Speaker 03

  I think this, one of the slides that I think is the most interesting to look at of the data that you pulled is the conversion rate today from seed to series A.

• 11:44Speaker 03

  It's hovering below 20%, and that is a function of the expectations and flight to quality for investors. Because when they see these breakout growth rates, they'd rather be part of a journey like that, that they think can go the really long distance. The other thing that we are observing is kind of the manipulation of the data to try to demonstrate that you've achieved these milestones. And so we take a very fine tooth comb to

• 12:23Speaker 03

  looking at the definitions of revenue and ARR that the founders are presenting, because often they're trying to use other proxies like credits, or pilots, or NREs to show that they have this level of growth. But really, in fact, it's a much lighter weight form of customer engagement. The last thing I'll say about this change in expectations with, from investors, is that one thing that we pay a special attention to, and one thing that we think is out, is kind of this vibe ARR that may not necessarily be enduring.

• 13:07Speaker 03

  So even if you are very upfront and direct and using the correct definitions of revenue and ARR, we still want to make sure that that is sticky, there's loyalty to the product, that the migration from your product is hard, so that when we're modeling out the second and third years, we can have confidence in those numbers.

• 13:29Speaker 01

  Yeah, just, just building off that, one thing that I think may muddy the waters a little bit is, is basically the, the sort of stages and names have changed a little bit. So like, a Series A is kind of like what the Series B used to be in terms of check size and valuation, and the seed is what the A used to be. And it's not exactly, you know, apples to apples, but I would say like roughly, that's what we've seen. And so you're going to have a shift in graduation rates, because you've always had sort of a certain jumping off point. So I think that that's one thing that's worth, so founders can maybe have a sigh of relief of like, maybe it's not so different than before. But what I do, I do think is, Is valid right now, which is these markets are so up for grabs and they're so green fielded

• 14:11Speaker 01

  and there's so much latent demand when you really hit something that the growth should be faster. Because if you think about like, at least at the application layer in SaaS, you're almost always replacing something that came before. Maybe it was like on-prem to cloud early on, but then it was like cloud version one, cloud version two. And that has a certain adoption cycle. But here you just have like, there's not software in place. So if you hit demand, it should grow quickly. So the advice I've been giving to founders is just like, take, early on, take your time. Really focus on like deep product market fit, like focus on finding demand. Cause you can always sell and find some customers, but really focus on where there's latent demand and let the customers pull you. And if they start to pull you, that's where you'll see this fast growth.

• 14:53Speaker 01

  Otherwise you still are in the exploration, the PMF phase, whereas you could have like, strong-armed your way to like a millionaire R before and probably got that next round done. It's probably not the right thing. You really want to wait and make sure you have something. And then, and then that's probably the signal where it's, it's the right time to raise.

• 15:08Speaker 02

  Yeah, you know, you observed the stage shifting earlier and I'll tell you a little bit about what, what's behind it. Because in addition to stage shifting, we're seeing round compression, right? Where people are raising multiple rounds in a very short period of time. And you know, you'll have your pre-seed and then your seed, and then your seed extension and then pre-ae. All that happens now within months. And the other thing that we're seeing is that the compression is happening so fast. We're now seeing round conflation where you, you have a $30 million seed that's raised, right? In no world would that would be a real seed round, right?

• 15:48Speaker 02

  So, but what in reality is happening is that that's more like a seed and series A, like as if you raised five million seed and a 25 million series A all at once. So the expectations when you go out and raise your series A, your real series A, is that you would look more like a series B company. So that's what's causing that stage shift. And you know, some of the, the fastest growing companies are experiencing that where they do that 30 million seed and they're able to get to that series A. But that's certainly, I think, the exception that we're seeing. But it's certainly a dynamic to be observing.

• 16:28Molly O'Shea?

  So what are the real signals that you're looking for within the product, within the growth, within the revenue?

• 16:34Speaker 02

  Well, I, I think, you know, it's always the velocity of growth, right? The, I, I think, uh, Heymon got unfairly trolled for his comments, um, that he made, uh, but I, and the reason is that if you look at what people said is like, well, that only applies to certain, you know, the small percentage of of companies that can actually achieve that. But that is the point of venture, uh, to be fair. Um, but I think when people, you know, when, when investors and founders think about velocity of growth, they fall under three different traps when they see these revenue ramps. Um, so there are three different traps that are most common. It, they can fall into the first category, which is, uh, what I call a, a cost wrapper.

• 17:19Speaker 02

  Really, everyone's heard of the, the model wrapper or GPT wrapper. This is more of like revenue, uh, that is actually a cost wrapper. Um, because your cost of, of delivering the good or service is really high. So obviously that's, uh, buffeting the revenue, uh, as opposed to margin, margin actually buffeting the revenue. So that could really drive revenue up because your costs are really high. The second is, the second sort of trap or, or category that these hyper growth companies can, can fall under is that there's a surge in demand or a pull forward of demand that's not necessarily durable. So we saw that during the pandemic with Zoom and others in e-comm. Um, and then the last category is more, if there's some sort of regulatory shift or some incentive,

• 18:04Speaker 02

  you know, like renewable energy and, you know, had, had a bunch of incentives. And the real question is, can it still survive or show that acceleration without that regulatory, uh, support? And if you can run through that sort of, um, that gamut of pitfalls, you can get to the last category, you know, category four, which is. Actual real innovation that's happening with lots of demand. And that's, you know, hopefully, if you can sort of run the gauntlet and get to a category four, you're in that promised land. And there are companies that are like that. We saw that in the, you know, sort of original internet as well as now with AI.

• 18:40Molly O'Shea?

  Estacia, you brought up a really good point between real revenue and buy revenue. And there's like, there's multi dimensions to this as well because we're seeing like the time from zero to a hundred million dollars become like a new benchmark and like this new racing ground. But I'd love for you to maybe unpack that a little bit more and like, maybe let people know like what are the types of companies that are growing that fast? Because we do have the AI labs. We also have lovable. Like there's all these different kinds of categories within it, but just to like set the expectations between those and then maybe more AI agents and other like infrastructure companies that you guys are looking at. Could you just describe that a little bit further?

• 19:18Speaker 03

  Yeah, so a pattern that we have seen is generally there's two buckets of companies that are some of the fastest growing of all time. This first bucket are a lot of the businesses that are actually selling in to the model labs who have incredibly urgency and very large budgets to fulfill their needs. And that is best exemplified by these chain of thought companies from Merckor to Handshake and others where there's almost infinite budget for the products themselves and with Merckor, this is one of many acts that they're excited to release to the public.

• 20:00Speaker 03

  The other bucket that we're seeing hypergrowth in is in the consumer market where AI has created a really game changing, delightful experience that wasn't possible before. Usually it is the democratization of a skill that was more specialized to a broader audience. We saw that with Canva that lowered the barrier for design away from the Adobe suite to make anyone the designer and the billions of people that they could sell to. And now we're seeing it with software engineering. Anyone can be a software engineer now with the cursors and lovable of the world.

• 20:40Speaker 03

  So those are the two big buckets, often companies selling into the the model businesses themselves and really delightful Gen AI native businesses that unlock a skill for the billions of people out there who are creators.

• 20:58Molly O'Shea?

  And where do you see, maybe Patrick, I'm curious from your standpoint, where do you see us right now within this AI super cycle? What trends have already passed and what are you looking forward to, you know, get into next that people are overlooking?

• 21:13Speaker 01

  Yeah. Gosh, isn't that the ultimate question? Where are we at? Like, is this, is this a bubble or is this not? I have like three text messages, people, my friends asking me this question. I, I, if I knew the answer, um, there'd be a lot of things I'd be doing on my own personal bank account. I mean, my own personal investing account, but the, um, you know, I, um, I still think like, I mean, the various layers, right? Like the modern sort of data center, power, cooling, all this stuff, there's, there's still some innovation to be done there over the next handful of years, I believe, right? And at the infra layer, I mean, you're seeing obviously stuff around agents, MCP, some of these layers. The, the thing that you have to be aware of there is this time around, the cloud providers are like, they're ahead,

• 21:57Speaker 01

  like the, the big hyperscalers, they, they were the innovators in this market. And so like during the cloud era, they were behind and it was easy to innovate there. And there was a lot of successful cases and we had a bunch of investments there. But at the infra layer, I think they're, they're ahead this time. So you have to kind of like, and they're moving quickly. So you have to be, I think, very aware of that. It's the application layer, um, where all these agents are impacting workflows. And there's just all, what I see is net new markets that I think are quite exciting for entrepreneurs. And, and there's, there's a lot of them. I mean, I think there's like literally trillions of dollars up for grab in these markets. And I think it requires context. I think it requires understanding workflows, understanding buyers. And so I, I, I think there's a lot of room for innovation.

• 22:41Speaker 01

  Now that counter to that. Would be as these models get better and and they do reinforcement learning on agentic workflows, like what will they take over? What will they own? And and so that would be the counter. And there's certainly proof that they're going to go after things like coding um and probably some other large categories. I I sort of believe in a hybrid world personally. Like, I think that it requires so much context and understanding of the end customer and how customers buying kind of all of this stuff that it's going to be hard to go after this long list of use cases. So that would be the area that, you know, if I was starting a new company, I would really focus on um because I think it will be defensible. I think there'll be a lot of room for innovation and and it's where we're doing a lot of our investments right now.

• 23:23Speaker 03

  That is, I think that's a great point, Patrick, and something that we think a a lot about, which is what is the integration universe that your product uh can bring to bear and leverage to um have the context for the AI agents? If it is in more esoteric spaces, um it's highly unlikely that the model companies or the cloud service providers will be operating there. And so your earned insights from working in the domain, building these technologies, and particularly the integrations, which can be very hard if they're legacy systems, provides more of a technical moat. And if you're automating traditionally manual processes,

• 24:06Speaker 03

  the it's such a sticky product with high ROI, the switching costs will be very high for the buyers as well. So I I really agree with the points that you've made.

• 24:17Speaker 01

  Well, one, one thing I'll just build off of like as an observation I've been trying to think about, which is these, a lot of these agents, at least to date, have been um the last mile of building them is actually quite complex and hard, which is a good thing, by the way, if you're a builder, because I I think that creates service area. But a a lot of how that's being addressed is either through business model innovation, e.g., you don't provide a technology, you provide a service that's human in the loop, or uh forward deployed engineers, which is this terminology for like basically parachuting a engineer into a customer to get that last 10%. And what that's meant, basically, I think, in terms of like ICP, is you either can make this tractable by going after very large customers, which is kind of what like a Sierra or a

• 25:02Speaker 01

  Palantir has done and you have big contracts or you're very consumer oriented, longer tail type approach. But there's this like middle ground, which actually like SaaS lived off of, which is like this 30 to 50 K contract. It was all hosted in the cloud. It was a 5K setup. You got going in a month. That market is actually like a little bit harder to go after, I think, in this right now, by the way, I think it might become more tractable. But I think as, as entrepreneurs thinking about where to build, it's like being aware of not just like what you're building, but who the ICP is and is it tractable to deliver this type of solution now versus in the future, I think becomes really important.

• 25:40Speaker 03

  Yeah, you're essentially talking about the to deliver the service for the mid-market and how if you look at the acquisition costs of CAC and delivery against the LTVs, the numbers just don't really add up at this point.

• 25:55Molly O'Shea?

  Yeah, I was gonna ask, so how have business models majoritively shifted? SaaS era is one thing, but this new AI era is causing different kinds of forms and functions associated to your point vibe, experimental, there's, there's that cohort. But maybe Tony, if, if you could answer this one, how are you seeing the, the shift in business models?

• 26:17Speaker 02

  I think the shifts in business models are both hitting the startups as well as VCs in general. I think AI is actually disrupting the VC model. And I see that in two ways. One is,

• 26:30Speaker 02

  we're now seeing founders that are, they, they're not giving as much, that, you know, that the household names, the tier one venture capital firms that have just been around forever. We're now seeing founders that are shifting to a different type of investor, ones that, that are a little bit more technical. So that was an interesting observation that started happening a couple of years ago. And because we're so early and we have a pretty wide portfolio, we see some of the meetings that founders are taking versus, you know, their passing on. And it was surprising to us when we saw that shift happening. The second shift I think that's happening within venture is, you know, I think about

• 27:15Speaker 02

  like some of the techniques that model builders use in post-training, like there's a technique called reinforcement learning. And traditionally, the criticism there is that it's very sample inefficient and it's also very costly. So you need, you know, a lot of compute. And now that we have a lot of compute, you know, you RL tends to work really, really well. Now, if you were to put that into startup building, if the cost of building, the cost of capital is coming down, and now you literally have models that are coding for you. We are almost seeing this shift to almost like an RL version of a startup building,

• 27:55Speaker 02

  where you have a big rise in solo contributors that are not raising money. And so you could almost think of them as just a bunch of founder agents going out there, trying things and seeing what works. Now, that wasn't possible before the coding agents. It definitely wasn't possible before cloud.

• 28:14Speaker 02

  But that's certainly a real trend that we've observed over the last few years. And it'll be interesting because I think the last, you know, few decades of venture has been more akin to a post-training technique called supervised learning or supervised fine-tuning, where we as venture investors, like, oh, yeah, you know, we've operated and scaled companies before. Here's how you would do it. That's sort of like supervised learning. And then you go out and raise from downstream capital and then your next, you know, like these downstream investors will say, hey, we like this company, but we don't like that company. And what does that sound like? That that sounds like RLHF, reinforcement learning with human feedback.

• 28:56Speaker 02

  So our entire venture industry has almost been built, all the playbooks and all the best learnings and practices have been built through that. But I'm really excited to see, you know, this whole RL surge of, you know, founders going out and trying things that may be different. And we're going to learn from those founders as an industry.

• 29:16Speaker 01

  Yeah, I mean, this is a It is an interesting, it's one I think a lot about because we think about like the construction of on these early stages, what creates a great company that has the best chance of when if you look historically, you will find things that like companies that have multiple founders are actually like statistically way more likely to build an outlier company. If you look at things like founders here in the Bay Area, although people can argue a lot about geography, great companies can be built anywhere and they are built anywhere, but statistically, they're way more likely to succeed here. Founders that raise greater than five million of a seed, although people say don't raise a lot of money, are statistically likely, more likely to build a great outlier company. And so I think there's reasons for this, by the way, I think founding a company is

• 29:58Speaker 01

  hard. Doing this on your own is really hard. Like, having more talent around you that's complimentary is, is, is just does make sense. Being surrounded by networks of company builders that can be advisors and investors and all this stuff here in the Bay Area and builders like makes sense. But these things, you know, will change and evolve over time, right? So I think it's like, that is the thing is just being thoughtful around like, what's going to be the same, what's going to be different, what can you learn from? Is something that, that, that we'll have to see, but you also have to look at the data. Like, I try to go back to like, when I advise founders is like, give yourself the best chance of succeeding. And it tends to be, I think, with these larger teams in the Bay Area, some of these things end up being true.

• 30:38Molly O'Shea?

  So no billion dollar, one person companies yet?

• 30:43Speaker 01

  You know, the thing I think about with that, I, I, I'm sure I will be proved wrong, I probably already am, I just don't know it, is that it's like, if that's a good idea, some company will raise that much and then they'll just hire like 500 people and they'll probably do, might do better, right? Like, because it's, because it is a competitive environment. And so what I've seen is like, companies aren't raising less, they're raising the same, actually, in most cases, they're raising more. And they're just moving faster. And their, their ambitions are big. And so, it's, that's, and that's the way it works, right? Like, I think that, that's just kind of like a natural evolution in early stage companies, they want to bite off more. And so like, that, that tends to be where, where I lean is, and I was an entrepreneur before and, you know, built a company, that's just

• 31:27Speaker 01

  kind of like what I've observed. But again, it could be, it could be different this time around.

• 31:31Molly O'Shea?

  So one thing that I think is like, quite interesting, and I've been asking a lot of different investors and CEOs in my interviews, is how are you reconstructing or? Constructing your companies now with AI native like at the the core. So now that's central, like you need to have an AI native company. Maybe it's been around for five years, but like has every single person on your team, you know, kind of like converted over. I I think it was Brian Armstrong, who was on Cheeky Pints. And he was talking about how he gave his like engineering team like one week to study different like AI coding agents and that sort of thing. And if they didn't, then they were cut. So how do you guys think about that um in terms of funding companies and looking at the teams that they've built around them?

• 32:16Speaker 03

  Yeah, we we think about this all the time and we like to walk the walk as well. So we are using a lot of these gen AI technologies. We're actually hiring someone to be our first and founding AI engineer here at Felicis. So if you are interested in building tools around AI at a VC, at a leading VC firm, please reach out to us. But, you know, we want to embody the spirit of being on the frontier of where AI is going. And we look for the same thing in the founders that we partner with. And so I will literally go in and ask them, what tools are you using across all of your different functional groups so that you are getting the most ROI and return out of your employees?

• 33:00Speaker 03

  And that they can be increasingly productive. We also ask them about the core infrastructure that they're building on and the architectures that they're taking. And we do try to pressure test, oh, have we've talked a lot about MCPs. Are you using a multi-agent system? Are you thinking about MCPs? Where is your enduring technical advantage in your stack if you're building both at infra and at the app layer? And so we really do like to think, think through first principles of what does a brand new company look like that's being built in this era?

• 33:40Speaker 02

  Molly, I'll give you a counterintuitive example of the type of thing that you mentioned in terms of relying on AI. It came from somebody that came to our hackathon recently, and I thought it was so interesting, which is with AI, they're now doing much more manual work. I'll give an example. It used to be, you know, sort of the corporate protocol was you can only have anywhere between seven to 10 reports, you max out on 10 reports. That is, if you're not Jensen and you have 60 reports. But for all the mortals, you probably max out on 10 reports. But what they are mentioning was that because now you can do a lot more with AI, a lot of

• 34:21Speaker 02

  the management and organizational workflow efficiencies, now they're actually managing, instead of seven to 10, they're managing 15 to 20 people. And the organization is actually getting flatter. And so there's actually much more attention that they need to have with each individual, I see. And so that was an interesting way of manifesting the reliance on AI in a way that looks to be more manual. So I think also Zach had mentioned, you know, now that they have a voice agent that they acquired, they're now potentially able to have customer service reps, which, you know,

• 35:04Speaker 02

  in the past would have cost billions of dollars to be able to do that. But now, maybe they will hire, you know, a few thousand or hundreds of thousands of people because they can actually have an agent do that first line. So it's like, there are counterintuitive examples here of AI working, but actually shifting the type of manual and human work that's involved.

• 35:28Speaker 01

  Just wanna build off what Tony, you just mentioned. So it goes without saying, like, you have to be using these AI tools, and if you aren't, you're like dead in the water. But... What I I I would say is like it's never become more important just to have conversations with customers and prospects and have unique insight about the market. Like it's it's it's sounds like an obvious thing, but like it's so evident when you come across a founder who has just spoken with not like one, two, three customers with anecdotes. They've talked and they spoke with a hundred customers or a hundred prospects and they have just this this texture of the market. And that I think in this moment in time where you can build anything that becomes so critical is to have some unique insight that someone else doesn't have.

• 36:10Speaker 01

  And how do you embed that into the product that anyone can build is like the real the real question. So that that I think almost becomes like the IP early on at the seat at the seed stage that the that the founder needs to bring to bear.

• 36:22Speaker 03

  We think about that as well. We um we call it learning velocity is more important than ever before. And you know, technologies, evolution and adoption patterns are changing faster than any generation past. And AI really operates at warp speed. And so the only thing that is consistent is change itself. And so as a founder, you need to be naturally inquisitive, as you said, always talking to customers. Because your enduring advantage is being in front of others, which is through learning.

• 37:00Molly O'Shea?

  What have you found to be the best practices for recruiting this type of talent? And where do you find these talent pools?

• 37:08Speaker 02

  Well, I I think we're now in a sort of a proof of work um regime where you no longer have to take people's word, uh, right? Like, you know, obviously, there's there's the uh GitHub contributions that you make. But now it's like, what you should be able to show a whole portfolio of things that you've built and have on your your on your own, right? Um, especially because the costs has have come down. And even if they're just, you know, simple projects, I I always like to see like, okay, well, what have you built? What what have you played with? Because As the technology and infrastructure gets better and better, that might not have worked.

• 37:49Speaker 02

  But it will, you will certainly be in a better place once those things start clicking. I, I, I teach an AI course at the School of Engineering at Berkeley. And we were, you know, as the, the, the week after Andre Capati talked about vibe coding, we all tried it and it just didn't work, right? It just, and I, and I just, I bet that if we ran that experiment again in the new semester, we would have a very, very different outcome. So I think right now that whole proof of work is like, okay, well, what, what have you built? What have you learned? And that relies less on, oh, I, you know, I, I worked at this large hyperscaler and, you know, built my resume through that means. And I think that's what's causing a lot of these people that are in college

• 38:35Speaker 02

  to just start building and, and start, uh, start companies in a way that we haven't seen before.

• 38:41Molly O'Shea?

  To close out, I know.

• 38:43Speaker 01

  Oh, please.

• 38:43Molly O'Shea?

  Sorry, go ahead, Patrick.

• 38:45Speaker 01

  Yeah, just one, one, we, one area we help our founders a lot, especially the C-stages, like bringing in like initial VPs and executives. And one of the things I'm thinking a lot about is like, before you'd look for, you know, time in the job, a lot of experience with companies like that, maybe they have fifteen, twenty years of experience. I think because of AI impacting a lot of these roles and personas, that actually, those playbooks can actually run counter instead of being helpful. And, and it's actually in some areas like marketing, for instance, it's really transformed in the last like handful of years. So we're actually finding like, maybe you need to look for someone that's a little earlier in their career. There's more hands on keyboard, it's kind of, and that's an example of just one area, but, but that's shifting a lot, I think, the type of talent you're trying to hire early on in these companies.

• 39:27Molly O'Shea?

  Yeah, I've definitely seen a broad shift towards younger, more AI native talent that's trying lots and lots of things. So as we wrap up, we only have a couple of minutes left. I wanted to save a juicy question for the end, and I want to talk about valuations. So let's, Patrick, let's start with you. So where are you right now in your appetite for AI valuations? I'll share some of the card of data. Cause AI companies, they're getting a premium.

• 39:57Speaker 01

  Look, I think at the.

• 40:00Speaker 01

  First of all, a little bit is it's a market, right? It's whatever the market will bear. And and that's how you have to look at it as an investor. That being said, there tends to be like bounds, I think, that makes sense at the early stages. Companies need to raise a certain amount of capital. And let's so like at the seed stage, you also know, series As are getting done in a certain range. And so, and you're going to want to like not price for perfection, meaning you can raise your seed and then have room to raise your next round, even if it's not like a perfect round or valuations compressed and so forth. So I find you can actually at the seed stage be pretty open with founders in terms of having that discussion and sort of finding what makes sense for them and the market. And there's a bound and then there's whatever the market will bear, right?

• 40:43Speaker 01

  Now, I've seen frankly, the series A can, it can very wildly, really dramatically. And so that, that I think is more a matter of like momentum, founder quality, and then how big the market is, leadership and some of these things. But look, at the end of the day, if A I is transformational as we think it is, then it all is going to work out. Now, the truth is, there's only so many companies that are worth a hundred billion. And the list isn't very much. There's only so many companies worth 10 billion. And there's not actually that many that have actually sold or IPO'd even a handful of billion. So I do think there's sort of this natural, like, we do have to stay sort of grounded a little bit on, on creating actual value.

• 41:26Speaker 01

  But you'll have to live with the reality of the market today. And then if I was a founder, I'd just say like, yes, you want to get maximum value, but don't limit your next round based on what your pricing of your current round is in a way that might penalize you or hurt you kind of moving forward.

• 41:41Speaker 03

  It's, it's very interesting. You know, we are in the business of outliers, outlier people, outlier categories, outlier technologies is get, is what gets you to be that hundred billion valued business. And at any different stage of venture, you know, the nineties, 10 years ago, the best teams with the best tech in the biggest markets have always had a premium. And today, that happens to be A I. In the nineties, it was networking. In the early 2000s, it was consumer. But so all really exceptional groups get a premium. So I don't think this is anything new.

• 42:23Molly O'Shea?

  So you're okay with a billion dollar seed round?

• 42:26Speaker 03

  We have not done a billion dollar seed round.

• 42:30Speaker 03

  But it's an exceptional team in a massive market, so I can see how people underwrote it.

• 42:38Speaker 02

  And I would say it's something that I have to constantly remind myself, but also just something for both investors and founders is just know the game that you're playing. Right? And just focus on, you know, what is it that that you're playing? What's the game on the field for you? Because that could be totally different for some someone else. Uh, for venture, there's just so many different ways of approaching venture because there's always the controversy over um being, you know, small and nimble and going for carry versus these large mega mega cap VCs. I think they're all going to do great, um, as long as they're really good at their craft. And so for founders as well, you know, you may not be able to get those uh premium valuations or you or or maybe you do, but

• 43:22Speaker 02

  as long as you're focused on the game that you're playing, uh either as investor or founder, at some point, um, I think the spotlight and the market will find those areas that if you're right, they should find you, right? And just like, you know, reinforcement learning is getting a big surge now. It wasn't in style for the last, you know, decade or or two. Um, but you know, people that were uh do in that area just kept pushing on it and now now that it's it's their time to really move the technology forward. So similar to to founders and investors, know the game that you're playing uh and keep doing that as best as you can.

• 44:04Speaker 02

  Because if you're right on that hypothesis, the market will find it.

• 44:08Molly O'Shea?

  Wonderful. Well, great way to close it all. Thank you so much and thank you to everyone who's listening. Um, if you want to find everyone here, I'm sure we're going to be sending out Twitter links, LinkedIn links, and you can find us all online.

• 44:21Speaker 01

  So thanks, Molly.

• 44:22Speaker 02

  Thank you.

• 44:23Speaker 03

  Thanks, everyone.