Astasia Myers, GP at Felicis, $3B AUM, AI Agents & Autopilots, New Infra Opportunities, & Roadblocks

[00:00] I think the individual will be more empowered than ever before to focus on the [00:08] what they care about and get the most joy from. [00:11] Maybe that is too... [00:12] a big picture, like if high in the sky. But if you think about the creativity tools that we see today, it's like, [00:22] democratizing access to creation. It brings people so much joy. If you think about the enterprise products that are autopilots or copilots, so allowing people to really focus on the strategic aspects of their role that are more creative and interesting. And so I think it will enable more choice and opportunity in the future. [00:52] you [00:55] Welcome to Sorcery. I'm Molly O'Shea, founder of Sorcery. Today, we have Astacia Myers, GP at Thalesis, which recently raised a new core $825 million fund back in March of 2023. [01:10] That brings their total AUM to over $3 billion. Astasia is focused on databases, dev tools, infra, and AI from inception to series A. [01:20] with investments such as LaunchDarkly, [01:23] text and super base. We go deep into all things AI infra and even learn a bit about some of the predictions for 2024.

[01:31] I hope you enjoy. [01:33] Hi, Astacia. It's a pleasure to have you on. [01:36] Yeah, thanks so much for having me, Molly. Well, I'm really excited about this conversation. I think we're going to go deep in many aspects of AI infrastructure, data stacks. But to start, I'd love to start wide and talk about Felicis, its history, and its founding story. [01:55] Totally. So Aiden was one of Google's first product managers and an international sales leader. And he left Google in 2006 with a deep passion to partner with early stage founders. He went up and down Sandhill Road looking for a role at a VC company, and he got feedback that he didn't have the right pedigree to become an investor. [02:25] challenge traditional venture norms. And so for us, Felicis widens the opportunity for founders from unconventional backgrounds. As a team of outsiders to VC, we know that great ideas can come from anywhere. And so our mission is really simple. We want to back amazing companies that are inventing the future and become founders, most trusted partners by investing directly in their [02:55] to founders, to our LPs, to our employees, and to people across the industry. [03:01] And so what's really cool about Felicis is even the name like we Felicis means luck and fortune. And we believe that our luck is through preparation, meeting opportunity. And so that's exactly how we like to invest. And so who are the partners that make up the fund and what areas do each of you focus on?

[03:21] Yeah, so we are a generalist fund with specialists underneath. We have 11 investment professionals and we cover areas like AI, infrastructure, security, vertical SaaS, global resilience and health care and bio. [03:39] For me personally, I spend time in enterprise software, working with these founders across AI, infra, security and SaaS. I have great teammates like Sandeep, who work and do a lot with vertical SaaS and bio and health care. Viv, who does enterprise software, infra and app layer and global resilience. [04:09] Victoria, who has a background as a marketing leader doing vertical SaaS, fintech and consumer. And then Aiden, who works across all the different domains. Your recent $825 million fund brought Felicis AUM to over $3 billion. So I have to ask, what is new with this fund and what is the team currently excited about? [04:33] Yeah, so our heritage is being inception and seed investors. And over the span of time, you know, we've evolved our strategy to still love all things early stage. And we do a lot of our work there, but also doing series A's and very selectively series B's. And so this fund supports that mission of really our goal of just working with the world's best founders that are

[05:03] We're spending a lot of time in this fund looking at clearly AI. I feel like it's a must do, right, in different aspects. So Infra, App Layer, AI-enabled services, foundation models, and how this could impact frontier categories like biohealthcare, robotics, and connected services. [05:33] So that we can support very early stage teams at inception and then build relationships to lead rounds in slightly later stage companies. [05:44] So is your team mostly in the Bay Area or are you scattered globally across the nation? [05:50] So we are based in the U.S. Our headquarters is in the Bay Area. [05:57] We have offices in San Francisco and the Presidio, in addition to on Sand Hill down in Menlo Park. And we have team members that are also in New York City. So that's kind of emerging space for us. We have a new office out there, which is very exciting. We do invest globally. And we really take this perspective that the best founders can exist anywhere. [06:27] Dovetail out of Australia, Audion out of Europe. Really, we look across the world when we partner with founders, but we're based here in the U.S. Amazing. And I do want to dig into your particular background. So you're a name to know in enterprise software and you have quite accomplished in many different seats.

[06:57] to keep you building your career and becoming a GP. [07:01] So I was born and raised in the Bay Area and kind of grew up in a single parent household where I was very fortunate because my mother worked in the semiconductor industry, always at early stage startups. [07:31] providers. And so it was very exciting to me. And one of the most seminal moments in my childhood when I was [07:38] And, you know, later in elementary school was she was working at a company called MIPS and they designed this chip that went into a Sony robotic dog. And, you know, as someone that loved dogs and animals, I was like. [07:50] oh my God, like this is totally amazing. I can't believe you have like a robot dog pet now. And I can't believe like my mom was a small part of making that reality happen. And really through these types of experiences, I gained a deep appreciation for hard tech and doing something that has never been done before. And so in undergrad and grad school, I was always researching [08:20] in society and economies and coming out of grad school, I really realized that I wanted to be part of the action. And so I evolved my career to being in sell side equity research, really understanding publicly traded companies and IT networking and security. What were the levers of their success, the management teams, product lines and how they manage the financial positions of the company?

[08:49] The opportunity to deep dive into businesses and really appreciate what success looked like and how you got there deeply informed how I view companies today. From working in sell side equity research, I joined Cisco. It was a company that I actually covered, so I knew their product lines really well and had an opportunity to get to know some of the management team. [09:19] because they always had really amazing NPS scores when we did buyer surveys. And they were super creative when it came to thinking about venture, M&A, and strategic partnerships. I mean, Cisco was the most acquisitive tech company of all time and had very cool innovations like the spin-in, where you could have a call option to buy amazing startups. And so I wanted to get closer to the [09:49] for their core business lines of servers and networking. Through that experience, I naturally gravitated to the investing side of the house. My sub team was the most active across all the BUs, and it was incredibly fun to partner with businesses like Cohesity and Gardacore.

[10:19] It was very enjoyable to tie founders into Cisco's go-to-market engine through doing events together, co-designs, and really trying to help move the needle for businesses. And I felt like I could do that at the earliest stages. And with the love of focusing on deep tech and hard tech, I stayed in infrastructure and joined Redpoint's early stage team for a number of years. [10:49] partnering with businesses like Hex over there. Most recently, I was at an early stage team called Quiet Capital, where I was helping build out the enterprise software practice. And then earlier this year, I joined Felicis as a GP to continue those efforts. Yeah, as I said, across AI and infrastructure. So I think really for me, the North Star has always been working [11:19] incredibly fulfilling process where you can be a first believer in them before everyone knows about what they're building and their big vision and really try to tangibly help these teams. Even yesterday, it was very fulfilling. One of my seed stage companies hired their founding engineer and literally right before this podcast recording, they said they closed their second engineer. And I was just like so excited for them. I was like, we were talking about these

[11:49] qualify them and they did a great job closing. I'm like, oh, my gosh, like that's so cool to be there for some of the firsts and be a sounding board and kind of be that rock and champion for them. [12:00] So you don't miss the days of being a hardcore sell-side analyst. [12:05] You know, I have to say, I love the experience because you get... [12:12] I feel like the skills that you learn there of like understanding financial modeling, having the opportunity to really dig into product lines, do primary research. Those skills are directly applicable to what we do in venture. And some of my favorite work when I was there was deep diving, like emerging technology. And at that point was like, what are the late stage startups that are going to disrupt the publicly traded companies? [12:42] storage back then, hyperconvergence infrastructure. This is all like so long ago people were like, what are these things? But it was a ton of fun. And it's the principles and best practices from that I try to apply as part of my VC role. [12:56] Yeah, and now it's an active role, right? Something you've been doing for many years now, but you're actively involved in investing into the research and into the innovation class. [13:09] Exactly. And that's a ton of fun. And it's also much more enjoyable to be. [13:15] build deep relationships with the founders themselves. I like to view myself as an extension of the team being available 24 by seven to think through any situation and try to support them as best as we can. And that level of interactivity with founders and team members, you just don't have in self-sidding research, right? You're so removed from that. And so, yeah, it's just fun to

[13:45] is a key component of the role. [13:47] So I'm curious, what other types of resources does Felicis offer your portfolio companies? Do you have a platform team? Is it you? Do you kind of handle most of the relations? Like, how does that work? Yeah, Felicis is fantastic. You know, in addition to the 11 investment professionals who work collaboratively to support our companies. [14:17] talent. And so we really try to bring to bear ourselves as investors, our success team and our network on behalf of founders. And yeah, it's been a ton of fun being here and just seeing like ways that our success team has moved the needle for our founders. A great example is one of my companies are collaborated with one of these seed stage companies to do some events with them. [14:47] We try to identify, qualify and provide leads for founding roles and executives. And so, yeah, it's a comprehensive approach to helping teams. [15:00] Hey, we'll get right back to the conversation after a word from our sponsor. [15:04] Sorcery is brought to you by Archer. I'm genuinely amazed at what Archer has been able to accomplish. Archer's goal is to transform urban travel, replacing 60 to 90 minute car commutes with estimated 10 to 20 minute electric air taxi flights. They are safe, sustainable, low noise, and cost competitive with ground transportation. Archer's Midnight is a piloted four passenger aircraft designed to perform rapid back-to-back flights with minimal charge time between flights.

[15:34] For passengers by providing safe and efficient access to people, places and events across the communities they live. Visit Archer.com. [15:44] Wonderful. So in preparation for our conversation, we talked about this a little bit, but you mentioned that Felicis is kind of taking... [15:54] AI in three buckets. So I'd love for you to explain out what those three buckets are and maybe dig into some examples for each of them. [16:04] Yeah. So we kind of think about three different aspects of AI today. One is AI infrastructure, which has been very hot over the past two and a half to three years, really trying to reimagine what AI engineers and builders need as part of their tool suite to build the gen AI applications. [16:34] looking at the models, frameworks, RAG systems. And this has been very active for us. So an example of that is myself and my great teammate Viv led the Series A in a business called Dictology that's helping with AI data curation for teams. How do you make that process of deciding

[17:06] We work with other businesses like Predabase and the fine tuning space. Strong belief that with the rise of open source models, teams can now take their proprietary data and create a better service through fine tuning. So AI infra is one big bucket for us. A second bucket is AI. [17:28] AI-enabled SaaS. And we think about it in two different ways. One is autopilots, kind of think GitHub co-pilot and how it helps accelerate code generation for engineers. And the second bucket is [17:43] Autopilot. [17:45] So any software that can actually create the entire program application on behalf of engineers, we apply these kind of principles to different domains within SAS. So office of the CFO, the sales stack, even for marketing or particular roles like data engineers or data scientists. [18:15] on their behalf, kind of thinking about it as areas of opportunity to automate with AI agents as anything that's when you're acting like a execution machine or workhorse, kind of like wrote tasks that are repeatable in nature as compared to strategic and creative thinking that we think will still be in the hands of the employee and is really their value add to any

[18:45] So something that is very interesting about the services market is, you know, for every one dollar of software that they charge for, there's usually four or five dollars of human capital value that is also being created. [19:15] change the margin profile and deliver a completed product faster and more effectively than traditional alternatives. So we've been looking at legal services where you have businesses like Lighthouse that are using AI agent software to help with the immigration process and application. Businesses like Crescendo who are in a contact center space that are really helping agents be [19:45] at a single time. So both of the [19:50] Of the three buckets, you know, we're equally excited about all of them. But we're really looking for teams that are trying to take over entire units of work so that they're, [20:05] There's a deliverable at the end that someone would have been manually doing in the past. [20:11] Yeah, that makes sense. And it's also really helpful to.

[20:16] to cut it up that way because I feel like even earlier on, it's just such abstract of a category. And even like it became just the term people were using of just AI, AI, AI, but breaking it down and focusing on these three categories of AI infra, AI enabled SAS, AI enabled services really does help kind of contextualize it and understand like, Oh, this is where I get it. Or this is actually how I should be thinking about a business model. [20:44] Totally. And I think, as I mentioned earlier, there was an extreme interest in AI infrastructure over the past few years because you kind of need the picks and shovels to move up the stack and create more value in SaaS or AI-enabled services. And there's been a lot of investment in AI infrastructure, which has been fantastic. And people kind of have those tools now. [21:14] Having a similar moment to what we saw in mobile, which is over the next few years, we're going to see massive AI for SaaS and services companies come together, just like we saw when mobile came out and you had Uber and Lyft and Shopify and a whole others that emerged. So we're paying very careful attention to what's going on at the app layer. And if there's any app layer founders out there, please reach out to us. [21:44] noted and we will put that in the notes um so i i actually want to break uh one of your points down a little bit more so

[21:57] Given your background in research covering this category enterprise for a while and, uh, [22:04] Now it's, you know, [22:06] become AI infrastructure rather than just infrastructure because AI is now going to like, I think I heard a statistic that it's going to be like two times more power intensive than what the US already consumes or generates. So that's kind of crazy. But in terms of that, like working from endpoints, that's the critical point to start figuring out how to build or rebuild. So I'd love to get from your perspective. [22:33] What was the arc of infrastructure and software stacks throughout time? How did enterprise really evolve? [22:42] kind of what were the main [22:44] critical points. Totally. Yeah. So, um, [22:49] Loving infrastructure and studying the evolution over time, really coming out of the late 70s and 80s when we started to see companies building applications for themselves and sometimes more infrequently for end users, either employees or consumers. [23:19] everyone is safe. And all of that was on premise, right? People were building their own data centers. Everything was appliance based where Cisco or Dell, you see, would go and build these like full stack services that you actually implement. And that was happening in the 90s so that more people could have the resources to go build software.

[23:49] Yeah. [23:49] Into the 2010s is the rise of cloud service providers like GCP and AWS and Azure, who are now democratizing access to infrastructure services because now the CapEx required to procure them is so much lower and is all self-serve and usage based. [24:19] Something that's been very interesting to see in the past 10 years is certain categories of infrastructure becoming more popular, kind of like the ebbs and flows. [24:49] across all different types of endpoints. [24:52] Then we saw a shift from a buyer perspective from the CIOs making technical decisions about what infrastructure is procured to more of the engineering teams and developers themselves, shifting from top-down sales to bottom-ups go to market. And that shifted interest in having developer tools.

[25:22] HashiCorp that had amazing bottoms up go to market motion because they had strong developer ergonomics and a lot of decision making was happening after there was usage in an account. And so it was no longer the traditional buying just steak dinners to close an account and force technology onto teams. It was really bidirectional with the end users as well. [25:52] Right before the 2020s, there was a huge push from looking at developer tools into the modern data stack, really with the rise of Snowflake and providing a cloud data warehouse. Everyone was now able to ingest numerous data sources into a single back end where they could perform analytics and processing. [26:22] But from that, in addition to Snowflake, like the ingest layer with Fivetrend and Airbyte, analytics with Looker, transformation layer with DBT and then Hex on like self-serve data workspace for teams. And so that was a very big category and theme for a number of years. And then more recently, to your point, it has been around AI infrastructure.

[26:52] machine learning stacks to [26:54] is less applicable today because of the model types that we're running. And so teams are no longer necessarily using like PyTorch and TensorFlow to develop prediction or recommendation models, but really using these foundation models to have conversational AI or agentic workflows. [27:24] And that has been why over the past few years there's been huge interest in that category. [27:31] One area of infrastructure that I feel like is emerging that I'm very excited to see is anything at the edge. So by moving compute and storage to the edge, you can have much better performance and responsibility from applications. And so I'm looking for startups that are working in that domain because I think it's the new frontier. [27:59] Well, you should definitely check out Armada. I'm a huge fan of them. [28:03] We are investors. Oh, there you go. OK. [28:09] That's awesome. So, Astacia, is there any particular edge computing company you're excited about in your portfolio? [28:17] Yeah, that's so funny. That's awesome. OK, so that's super helpful. And thank you for giving the context over time and how it's evolved.

[28:33] It's kind of like the same as like ad tech. It's just thousands of logos. Yeah. [28:41] It's crazy. It's crazy. And so I think that during that era when there was a lot of investment in the modern data stack, people were really – [28:55] Some people were really thinking about, gosh, if Snowflake is a 50 plus billion dollar company and this product is X dollars. How to put this? If someone is paying one hundred dollars for Snowflake annually, theoretically, and this startup can take X dollars of that, you can extrapolate that it can also be a multibillion dollar company. And for some companies, that has completely been true. [29:25] But then there's been some other segments that are smaller than people anticipated. And the larger, higher-flying companies have brought in their portfolio scope to kind of eat up some of those smaller domains. And so there's been a lot of talk actually recently, and you may have seen some of these blog posts about, like, the end of the modern data stack, essentially, where it's pretty clear who the emerging winners are. [29:55] investors like myself were looking to these other categories of like AI infrastructure and edge technologies. [30:01] Yeah, it's really interesting. I'd say like over the last year and a half, the GPT moment, that was like a critical component and point in which everyone really realized, oh, this is a big deal. I'd love to get into it a bit further.

[30:17] But from your perspective, how is AI different today than what we saw last year? And I'm not talking like just on like a marketing basis, but maybe down like below the surface. [30:29] Yeah, I would say that there's two things that seem very different. [30:34] this year than last year. One is... [30:40] At the beginning of last year, I felt, and I think the industry felt, that there was [30:45] a lot more hegemony. [30:47] with OpenAI, where it was like, [30:51] One model company will win it all. And that model company is OpenAI, which has clearly done fantastically well. And we should all wish we were investors in that business. However, I think that we've also seen the emergence of great alternative generalist foundation model companies like Anthropic and Mistral, where we're seeing increased. [31:15] interest and even Lama from Facebook increase interest because certain models perform better for certain tasks. [31:24] And so from a builder perspective, they're more agnostic to the models that they were using. [31:30] today than previously. We're also seeing the importance of foundation models that are tuned and oriented to specific tasks. So, you know, we are investors in RunwayML that has built their own foundation models for the creativity space for text to image and text to video. We're in poolside.

[32:00] There's a specialist model for cogeneration. We're seeing really amazing things for foundation models in the world of AEC, in the world of doing physics modeling. And it's very cool to see that there is now a number of different financial models, models that are either generalist or for specific verticals. [32:30] much activity. [32:32] last year. A second thing that's been a very big difference this year compared to last year is the rise of AI agents. You know, OpenAI talks about like their five tiers of getting to general intelligence. And last year was really level one focused on conversational AI. There was a lot of [33:02] And conversational AI was a great entry point for teams to get started building their rag stack. [33:09] And we saw some early innings with AI agents for auto GPT, but the resilience and reliability of those services just weren't there. And while we need better models for reasoning to go to level two and level three, it's been very cool to see teams start building services that are enabled by AI agents.

[33:39] that are autopilots. A wonderful example of that is 11X in the AI SDR space that really takes over an end-to-end workflow to help generate leads for teams. So the... [33:55] Aspects of having more tooling for AI agents, everything from frameworks to AI memory products like MGPT and the enhancement of the models itself has been very cool to see this year compared to last year. [34:14] Is there anything that you've particularly changed your mind on that you were really bullish on or maybe really against? And throughout the year... [34:25] you've just kind of came around or decided against it? [34:31] I did not anticipate that. [34:35] how quickly there would be a [34:39] surge of alternative... [34:42] models. [34:43] available to builders. And I mean, we're still seeing funding announcements now, right? And I think that's really exciting because it demonstrates not only the [34:57] market demand for [35:01] continuing to support new teams and specialized teams, but it influences how we invest in

[35:11] because it makes us think that it just reminds us that we're just so early in this AI journey that even if there is a big incumbent in the space, the market's moving so quickly that incredible teams with unique ideas and tech can get really far. [35:32] Do you think... [35:33] Like, we have any... [35:36] realm of [35:38] like [35:39] Getting back to a normal speed, do you think this is the speed going forward? [35:43] forward with anything, with all companies. [35:47] And then from your investment like hat or perspective, [35:52] How do you get into these companies and gain conviction? Like I, I'm obviously things sputter out over time and that kind of thing. But if they're running so fast opportunities top up all the time, how do you remain focused? [36:05] Yeah, it's been pretty crazy to see the innovation cycles accelerate over my career. I feel like especially when there was like a compliance angle, you know, 10 plus years ago is like, oh, you know, like every two to three years, we'll just, you know, kind of relaunch the product. [36:35] And the megatrends are happening in like six months cycles. It's so different. One way that we gain conviction as investors is it's we really anchor on the founders themselves. Like what is their founder market fit for a category?

[37:05] as well. Often we can do as much diligence on a space as possible and gain conviction from listening to experts. But sometimes you just have amazing founders. Once again, the Felicis philosophy is unconventional founders that come through the door that just have this earned insight. And you think, gosh, like that person is phenomenal. And I can definitely see that vision [37:35] And we do think about [37:39] You know, with someone's vision of the world, what do the other layers and the technology stack look like? Are there things that need to be. [37:51] created that don't exist today for them to be successful. If they win, who loses? Are there other technical unlocks that they will be doing or that they're reliant on others with? But it's really often the founder, their vision, and just how big of a market we believe they're playing in. [38:15] Do you have any ideas or are there any areas that you're watching that are white spaces that you've seen pop up or how do you how do you think about these? You start with certain endpoints and then hypothesize on it and kind of test around. Yeah. So we really do a lot of firsthand research. So here at Felicis, all the investors are doing their research by speaking with.

[38:42] builders and buyers or just experts in the field, but we also have a fantastic research team as well that we collaborate with to try to look at emerging signals and trends that are happening in the industry. And so through this primary research, we try to identify new spaces where there are emerging players that could become really big. So a big theme that we looked at [39:12] as the rise of web AI agents. [39:16] And technologies like Multion and BrowserBase, in addition to end user applications that try to empower everyone to more effectively use the web with agents. [39:46] that does immigration, O-1 visa construction and application creation. Like that's not that should not be the core competency of that team. Right. They're really focused on like the end user problem and making it delightful for them. [40:03] One area that we continue to be very excited about is thinking about unstructured data ingest and processing.

[40:16] value of unstructured data, image, text, video, and audio. And that amount of data is absolutely massive and continues to grow very quickly. And so looking at pipelining and processing engines that enable teams to get more value out of that data. [40:37] On the AI app side of the house, what we'll do is we'll go talk to buyers and kind of think through that construct that we talked about of what are areas where you're kind of execution horsepower driven that can be automated? What are areas where it's like the creative side of the role and then try to go identify people? [41:03] for technologists who are... [41:06] thinking about the areas that are right for automation. So it takes many forms, you know, [41:14] something that is [41:16] exciting about this role is like you can do a whole bunch of research and then you can also find amazing founders who, as I said, have this insight that you don't even have yet. And it's in a white space that you didn't even think about. You're just like, [41:30] Gosh, makes a ton of sense. And yeah, the the serendipity usually happens more than. [41:37] than one would think. [41:38] Yeah, it does. I especially I would say I'm like the AI enabled services and app side of the house, like the creativity that we're seeing from domain experts today that now have the AI tooling to make their vision a reality is amazing.

[41:57] really neat. We even saw as an example of that, [42:00] We saw a company that was trying to reimagine architecture firms. And so instead of selling new architecture software to architects themselves, you're like, we're just going to build a new firm completely around it. It's like this person has amazing expertise as an architect. And it's very hard to imagine, you know, three years ago, five years ago, this was even an opportunity for them. But now they can go build this reimagine architecture. And it's like, wow, that's pretty cool. [42:30] Very cool. Within this cohort of companies, have you started to see any themes of roadblocks that have emerged? Like, is it talent? Is it quality data? Is it resources? Is it pricing? Where have you seen more emphasis grow for where they're going to need support or where there might be like overlooked? [43:00] to a certain extent, like how fast you can go. [43:04] with what you have. [43:05] Totally. You know, talent talent is always a constraint somehow. Always needed one more experienced individuals to go build their vision. One thing that is nice, though, with this rise of gen AI technologies, you see a shift from data scientists and machine learning engineers, sometimes with PhDs to product engineers that can contribute to the creation of these solutions.

[43:35] And I think that's why this time with particularly like Gen. AI and for tooling feels so much more massive and weedy than, you know, five, seven years ago with the deep learning unlock, because it was still constrained to a smaller network of buyers and builders. [43:55] There's 10 to 15x the number of software engineers and data scientists, right? So that's been nice with this wave. But yes, it often comes down to talent, people with expertise and building these AI products. Many people don't have it, right? It's such a new phenomenon. And so always looking for great people. Another area... [44:19] that has been interesting is how with [44:23] AI first products or products that have now embedded AI into the experience is thinking about how do you like price and package things? [44:33] these solutions because we came out of a world of pricing and packaging. I mean, historically, site licenses all you eat to SAS, whereas per seat or usage based pricing, [44:48] But then people thinking, gosh, [44:50] You know, if I have a I am as part of my product, like, [44:55] What is the metric of value that I'm delivering to my customer today? You know, and teams have taken very different approaches to this. You know, some some teams have decided that they should have kind of their base seat costs and then kind of a quota for how many runs you can do of the product. You're kind of seeing this with notions pricing.

[45:25] teams like Figma, when they've released some AI-enabled Figma slides, it's an additional seat that's an AI seat. You also have teams like Hex. [45:36] who has done a wonderful job with their magic product. And they decided that, you know what, [45:43] AI is just embedded in our per seat costs because we can't imagine selling our product without AI. And the underlying cost of inference will be going down over time. So it just makes more sense to have one price. You know, we'll see a number of different pricing models here. And I think the. [46:05] the trick for [46:07] teams is that really understanding like the unit of value, because with these autopilot SaaS companies or the AI enabled services company, it is the deliverable at the end of the day. You can see Zendesk is moving towards pricing based on automated resolution for their employees. [46:37] putting on traditional law firms where they're moving away from [46:41] from charging per hour to charging per deliverable. It's having a huge impact on the industry. [46:49] And so it's been... [46:52] If I was an early stage founder... [46:54] building in

[46:57] an AI product, I would be trying to hold off actually truly deciding on the pricing and packaging plan as long as possible. I would be focusing on working with design partners to identify from their perspective what value I am providing to them in a unit of metric that [47:27] the design partnership and POC continuing to validate my success against the metric my customer cares about and holding off on pricing as long as possible until I feel like it is repeatable across businesses what that value metric is. Oh, yeah, that seems like a super complex. It doesn't [47:57] how we were going to go forward with seat based or [48:01] deliverable pricing was like even a year ago. This adds another element to it. Whether you add on the AI premium, you bundle it in, but it's super helpful to get your perspective. And I know that your team also wrote [48:15] a piece on this. So I'll attach that. Yeah, it's right now I feel like it's pricing and packaging of these products and having enough talent to deliver the product.

[48:28] Do you think we have reached a technical unlock now that we just didn't have before? And what do you think the effects are of that if we have? Yeah, I mean, for sure. It's been pretty mesmerizing [48:45] available today. I mean, every time I use runway, I'm just like, oh my gosh, it's just insane. So I definitely believe there's a huge technical unlock with foundation models. And really, it's just the beginning. I mean, we see the transformer art protector, now there's state space models, there'll be future architectures in the future that will be even more impressive. So yeah, huge unlock. What do you think the effects will be? [49:15] speed. [49:17] Yeah, I think I think that individual will be more empowered than ever before to focus on. [49:26] what they care about and get the most joy from. Maybe that is too... [49:31] a big picture, like high in the sky. But if you think about the creativity tools that we see today, it's like, [49:41] democratizing access to creation. It brings people so much joy. If you think about the enterprise products that are autopilots or copilots, so allowing people to really focus on the strategic aspects of their role that are more creative and interesting. And so I think it will enable more choice and opportunity in the future. Yeah, that's excellent. And just to end out

[50:11] What am I most excited for this year? [50:15] Oh. [50:17] I am excited for... [50:21] companies that I'm really excited for the first AI agent software company. [50:29] to be like a blockbuster. [50:32] We've kind of had a first wave a few years ago where we thought some of the Gen AI native SaaS company was going to be 100 million plus ARR and kind of there were. [50:45] It got a little shaky, but I think in the back half of this year, we're going to see some really blockbuster numbers come from the AI native SaaS companies, which we'll be really excited to see. I'm excited for that, too. Thank you so much for coming on, Astasia. It was a pleasure. I loved learning more about your background, the whole story behind Felicis, as well as your deep dive and perspectives on AI infra. It was a pleasure. [51:14] Well, thank you so much for having me, Molly. I really appreciate being here. [51:18] Of course. [51:20] *music* [51:30] There's no shortage of podcasts and deep dives into the secrets of VC, but the truth is that the world's best venture firms and GPs at their helm still remain an enigma. VC is as much of an art as it is a science.

[51:41] Other shows focus on the now adventure or even the last 10 years. But what separates the most enduring and generation-defining firms is the subject of a podcast called Turpentine BC from the Turpentine Podcast Network. [51:52] On this season of the show, you'll hear from Ben Horowitz, Alfred Lin, Mahmoud Hamid, and more. Subscribe to Turpentine BC for the rare and revealing conversations that can only be had investor to investor. [52:03] you