Podcasts about Index Ventures

Today we had the pleasure of hosting Erin Price-Wright, General Partner in American Dynamism at Andreessen Horowitz (a16z). Erin joined a16z from Index Ventures in 2024, where she was a Partner focused on software infrastructure and applied AI. She previously served as Head of Product for Palantir's data analytics and machine learning program. The American Dynamism practice invests in founders and companies that support the national interest spanning aerospace, defense, public safety, education, housing, supply chain, industrials, and manufacturing. We were thrilled to hear Erin's perspective on the rapidly evolving intersection of AI, energy, and industrial technology. In our conversation, Erin shares the story behind a16z's American Dynamism practice and its early investments in companies including SpaceX, Anduril, Applied Intuition, Shield AI, and Skydio. She discusses how the firm's conviction in the space developed well before the recent surge of interest in industrial technology. We explore the convergence of AI, supply chain vulnerabilities, geopolitical pressures, reindustrialization, and skilled labor shortages, which Erin believes are creating the conditions for the “next great American industrial build-out.” We discuss physical AI and robotics, the potential to automate dangerous, expensive, and labor-intensive activities across energy and industrial operations, the challenges of deploying physical AI, and the importance of taking a practical approach to automation by starting with specific activities where the economics make sense and expanding from there. We touch on why venture capital is returning to energy and industrial technology, China's advantage in deployment versus the U.S.'s strength in experimentation and entrepreneurship, and the importance of permitting and regulatory certainty. We also examine data centers and the broader question of whether America wants to build, Erin's thoughts for energy and industrial leaders looking to accelerate innovation within their organizations, and much more. Special thanks to Erin for joining! We look forward to partnering with a16z on our upcoming Energy & Industrial Technology Showcase in Houston on September 10. Mike Bradley kicked off the discussion by noting that the 10-year U.S. Treasury yield was trading at ~4.65%, down ~10bps on the week but still near its highest level of the year. Bond investors are focused on the ongoing Canada tariff dispute, Wednesday's Core PCE inflation report, and Chairman Warsh's Jackson Hole speech on Friday. Major equity indices finished lower last week but are modestly higher this week, with attention now centered on NVIDIA's second-quarter earnings report Wednesday and its 6- to 12-month capital spending outlook. Turning to energy markets, Mike highlighted that WTI crude oil had declined ~$5/bbl this week to ~$82/bbl following the Treasury Secretary's announcement of “Operation Economic Outcast,” aimed at increasing economic pressure on Iran. EU natural gas prices rose another ~$1/MMBtu to ~$23/MMBtu, with storage levels remaining a concern ahead of winter. The energy sector was modestly lower this week but remains up ~5% month-to-date, led by refiners (+~12%). In power, electric utilities are down ~5% month-to-date, while IPPs, large-scale generators, and distributed generation providers are down ~10%–15% on average, largely reflecting higher interest rates and growing state-level opposition to data center development.

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Joon Sung Park is the Founder and CEO of Simile, the AI simulation company building foundation models of human behaviour; allowing companies to test how real people may think, decide and act before making a decision in the real world. Simile has now raised $300 million in total, including a $200 million Series B announced last week at a $2 billion valuation, led by Greenoaks and Index Ventures. AGENDA: 00:00 We Will Pay $100M for a Single Query on Some Models 10:00 Why Stock Markets May Not Exist in 5 Years Time 15:00 The Best Companies All Have Unique Data Acquisition Strategies 19:00 The Best AI Companies Have Clear and Fast Reward Functions 24:00 How We Sign Fortune 500 Companies for $10M Contracts in Weeks 32:00 Does Similie Kill Kalshi and Polymarket? Prediction vs Changing the Future 42:00 Inside Similie's $300M Raise; What Every Founder Needs to Know    

When Martino Cadoni joined DeepL, he arrived with an unusual perspective—he already knew the company's product firsthand. Earlier in his career at Klarna, he had helped introduce DeepL as a translation solution, making the transition from customer to CFO especially meaningful. Today, DeepL is backed by investors including HV Capital, Benchmark, Index Ventures, ICONIQ, and Atomico, Cadoni tells us. Working alongside those firms, he says, continually pushes him “out of the comfort zone.”That mindset mirrors the company's trajectory. DeepL supports “almost 50 percent of the Fortune 500 companies,” Cadoni tells us, while continuing to grow and mature for its next stage of development.Rather than viewing language translation as a commodity, Cadoni emphasizes its strategic importance in critical business workflows. Pharmaceutical companies, for example, rely on accurate translation of regulatory documentation before commercializing new drugs, he tells us. Legal firms, airlines, manufacturers, and multinational organizations face similar challenges where translation quality directly affects operational outcomes.Customer adoption reflects those varied use cases. DeepL monitors daily and monthly active users, translated character volumes, language pairs, and traditional financial metrics, Cadoni tells us. He notes that demand often extends well beyond English, highlighting significant activity between Japanese and Korean as well as Portuguese and Spanish.Enterprise relationships frequently begin with a single geography or department before expanding across functions, Cadoni explains. One airline customer, for example, uses DeepL to translate aircraft maintenance documentation before selling planes internationally, illustrating how specialized AI can solve highly practical business problems while supporting global growth.

Paul Erlanger is the Co-Founder and CEO of FOMO, the social-first trading platform building the future of on-chain investing. Since founding the company in 2025, Paul has raised approximately $94 million, including a $17 million Series A led by Benchmark and a $75 million Series B led by Index Ventures with participation from USV, valuing the company at $550 million. Today, FOMO has grown to 600,000 users, processed over $4 billion in trading volume, and is adding thousands of new users every day—all with a team of just 17 people. AGENDA:  00:00 – Building a $550M Company with No Salaries, No Managers & No 1:1s 03:58 – Why Traditional Brokerages Will Lose in the Next 10 Years 09:30 – Why Robinhood's Strategy Is Wrong; The End of the Financial Super App?  13:05 – "Markets Aren't a Casino" — The Case for Retail Investors Fighting Wall Street 16:45 – The Radical Hiring Bet: Giving Employees Founder-Level Equity 23:40 – AI Kills Org Charts: Why FOMO Will Stay Under 25 Employees 29:30 – Why Taste Beats AI & The Biggest Mistake Most Consumer Startups Make 33:10 – The Social Media Playbook That Every Startup Gets Wrong 39:20 – How Benchmark, Index & USV Won the Deal—and the VC Advice Founders Need to Hear 46:10 – The Future of Investing: Social Trading, Creator Economies & Financial Networks  

Nick Reber, CEO and founder of Garner Health, joins Jacob and Nikhil to walk through what is arguably one of the most structurally underrated problems in American healthcare: that the single biggest driver of cost and quality variation isn't which hospital system you use or whether your plan is value-based — it's which individual doctor you see. Nick traces the intellectual journey from his time at Oscar Health, where he first encountered 4x variation in complication rates across physicians at the same brand-name institutions, to building Garner's core infrastructure: a dataset of 320 million patients used to score every doctor in the country on quality-adjusted outcomes, layered on top of existing employer health plans with financial incentives to steer patients toward top performers. The episode drops alongside the announcement of Garner's Series E with Index Ventures, valuing the company at approximately $2.7 billion. The conversation covers the technical depth required to actually measure physician quality fairly (and why existing methodologies are fundamentally flawed), why value-based care has largely failed and what actually moves patient behavior, how AI will reshape the front door of healthcare, what it will take for AI health companies to build durable businesses beyond 2030, and why the solution to the US healthcare cost crisis may be as simple — and as politically hard — as treating it like a corporate expense policy.   (0:00) Intro (0:30) Garner's Origin Story (2:05) Doctor Choice Is the Biggest Lever (3:45) How Garner Works (5:43) Why Old Scoring Methods Failed (7:39) The Knee Pain Problem (11:58) Consumer UX, Incentives, and AI (33:44) How Much Spend Can AI Actually Touch? (36:00) Why Doctor Choice Needs Plan Integration (39:18) Build vs. Buy: Garner's AI Philosophy (41:38) The Unified Data Flywheel (43:01) What Actually Predicts Doctor Quality? (46:28) Enabling Independent Providers (51:37) Quickfire   Out-Of-Pocket: https://www.outofpocket.health/

Episode 429 of The VentureFizz Podcast features Lior Div, CEO & Co-Founder of 7AI. One of the most exciting parts of this platform shift to AI is watching elite, repeat founders get back into the arena. It's often these experienced builders who have the appetite and the playbook to swing for the fences and create a truly category-defining company. Lior is exactly one of those entrepreneurs. Along with his co-founder, Yonatan Striem-Amit, this duo is uniquely qualified to build the leading agentic AI security platform. They have deep expertise in the cybersecurity industry and… by the way, they've done this before with their prior unicorn, Cybereason. 7AI empowers enterprises to shift security tasks to AI agents. The company recently made waves across the entire tech ecosystem by announcing a massive $130 million Series A round of funding led by Index Ventures, with participation from Blackstone Innovations Investments, Greylock, CRV, and Spark Capital. To put that into perspective: a $130 million Series A is the largest Series A round in the history of the cybersecurity industry. It is exactly that type of aggressive funding, along with blue-chip investors, that creates market leaders. In this episode of our podcast, we cover: A discussion around the rapidly changing landscape of AI and how that affects cybersecurity. Lior's background story, including being part of Israel's elite Unit 8200 intelligence group and how he got involved in the cybersecurity industry. Scaling Cybereason, plus why he chose to build his companies in Boston. The background story of 7AI and all the details on the company & platform. The distinct operational differences between building a traditional software business versus building a native AI company. 7AI's aggressive growth plans ahead, a look inside their company culture, and what it takes to build a trusted brand in security. The most important skills someone needs to be a successful CEO. And more! This podcast is brought to you by one of the strongest longtime supporters of the local startup ecosystem, Silicon Valley Bank, a division of First Citizens Bank. With more than 1,500 bankers and relationship advisors and $44B in loans as of Q4 2025 – SVB delivers expert guidance, specialized products and a team that knows the innovation economy inside and out. Learn more at SVB.com.

Brought to you by:Heights: a design agency founded by Gabri Grassi, helping impact-driven tech companies sharpen their brand, attract investors, and scale their reach. Grab your free brand checklist here or reach out to Gabri to elevate your brand.****Subscribe to the newsletter:New Wave | Hugo Rauch | Substack****

Today's episode features Sahir Azam, Partner at Index Ventures and former Chief Product Officer at MongoDB, where he helped scale Atlas into a multi-billion-dollar platform. This conversation breaks down what actually separates top enterprise sellers, from intellectual curiosity to resource orchestration, and why those traits alone aren't enough without leadership building the right operating model around them. Sahir also explains how sales leaders create scale through enablement, accountability, and structured engagement, not just hiring more talent. For leaders trying to build repeatability in complex sales, this is a clear look at what it takes. Sahir Azam is a Partner at Index Ventures investing in AI infrastructure, and former Chief Product Officer at MongoDB where he led the Atlas transformation into a multi-billion-dollar platform. He brings a rare operator's perspective on building go-to-market discipline, scaling sales culture, and navigating the product-distribution balance that separates winners from founders who fail. Connect with Sahir: Index Ventures LinkedIn Get the Force Management framework for navigating product-go-to-market fit and building the sales discipline that separates scaling companies from those that fail: The Predictable Revenue Framework: Guide for Leaders Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

Steve Domin, Founder & CEO of Duffel, the company rebuilding the infrastructure layer of the global travel industry.Duffel is taking on one of the most complex and outdated sectors, creating modern, developer-first APIs for flights, hotels, and more. Backed by investors including Index Ventures and Benchmark, the company has raised over $50M to transform how travel is bought and sold.Steve shares the journey of building Duffel from scratch, including: Why the travel industry is fundamentally broken  The insight that led to Duffel's API-first approach  Building deep supply-side integrations with airlines and incumbents  Navigating COVID when travel demand dropped to zero  The painful reality of scaling, resetting, and rebuilding product-market fit  Where long-term defensibility comes from in complex infrastructure businesses We also explore: What makes companies like GoCardless “talent factories”  How AI is changing how modern engineering teams are built  The future of smaller, highly efficient companies  Steve's view on the next generation of AI-native products This is a masterclass in persistence, infrastructure thinking, and building through uncertainty.

Sponsored by Chargebee, subscription and revenue management → check out their startup offer: https://www.chargebee.com/startups - Samar Abbas, Founder of temporal.io https://www.linkedin.com/in/samar-abbas-381997/   - Samar Abbas, co-founder of Temporal.io, shares the journey of building an open-source platform that ensures durable execution of code, allowing developers to focus on business logic instead of handling failures and reliability. - Temporal.io originated from years of experience at companies like Amazon, Microsoft, and Uber, where Samar and his co-founder iterated on workflow and state management systems, eventually creating a new category called "durable execution." - The company's open-source approach led to rapid community adoption, with major companies like Snap using Temporal for mission-critical workloads, validating the product's value and scalability. - Temporal.io monetizes by offering a fully managed cloud service with a consumption-based pricing model, aligning customer costs with the value delivered. - The company has raised significant funding, including a $300M Series D led by Andreessen Horowitz (a16z), with participation from Lightspeed Venture Partners and Sapphire Ventures, reaching a $5B valuation.

Lucy Tyrrell, General Counsel at Wordsmith AI, joins Scott Brown to explore how in-house lawyers can evolve by combining legal expertise with technology, process design, and commercial thinking.In this episode of Lessons I Learned in Law, Lucy shares her journey from private practice into high-growth tech environments, culminating in her role at a fast-scaling legal AI startup backed by Index Ventures and General Catalyst. Sitting at the intersection of law and product, she offers a unique perspective on what it means to be an “AI-native” legal function—where lawyers don't just advise the business, but actively shape how legal work is delivered.Her first lesson centres on thinking like the business. Lucy explains why legal advice cannot exist in isolation and how understanding commercial drivers—often expressed through metrics—allows lawyers to prioritise effectively and deliver more impactful guidance.Her second lesson highlights the power of networks, both internally and externally. Whether navigating uncertainty, making career decisions, or solving unfamiliar problems, she emphasises the importance of knowing who to turn to and building relationships before you need them.Finally, she focuses on curiosity—encouraging lawyers to embrace new technology, experiment with AI tools, and develop “legal engineering” skills. Rather than waiting for perfection, she advocates for a mindset of testing, learning, and iterating—mirroring how modern tech teams operate.This episode is brought to you in partnership with Wordsmith AI — the legal AI platform built specifically for in-house teams.Guest RecommendationsSong: The Chain – Fleetwood Mac Resources & Links Mentioned in This EpisodeRegister your interest in joining The Lodge In-house Legal Community: https://bit.ly/TheLodgebyHB Legal Engineering Project (Slack Community for in-house lawyers): [APPLICATION FORM LINK] Wordsmith AI: https://www.wordsmith.ai/ Listen to the PodcastSpotify: https://open.spotify.com/ Apple Podcast: https://podcasts.apple.com/4 YouTube: http://www.youtube.com/Connect with Heriot Brownhttps://heriotbrown.com/ About Heriot Brown: At Heriot Brown, we help lawyers find fulfilment in their careers. Beyond recruitment, we foster a thriving community of in-house legal professionals who share insights, experiences, and growth opportunities.Enjoyed this episode? Subscribe to Lessons I Learned in Law, leave a review, and share it with someone building their career in legal leadership.Chapters:00:00 Opening insight – Metrics, AI & modern legal roles 00:48 Scott introduces Lucy Tyrrell, GC at Wordsmith AI 03:39 Inside a legal tech business & AI-native legal teams 09:23 Lesson 1 – Think like the business 16:06 From private practice to in-house & startup life 20:48 Lesson 2 – Your network is everything 29:50 Career opportunities driven by relationships 31:31 Lesson 3 – Stay curious & embrace change 40:53 Hot or Not – KPIs, AI & legal team performance 47:21 Walk-on song, legal engineering & closing reflections 

Latin America is the third-largest fintech and payments market in the world — bigger than India, behind only the US and China. Gastón Irigoyen is Co-Founder and CEO of Pomelo, the fintech infrastructure company powering card issuing and processing for banks, fintechs, and global enterprises across eight markets including Brazil, Mexico, Argentina, Colombia, Chile, Peru, Puerto Rico, and Panama. Pomelo is backed by Index Ventures, Insight Partners, Kaszek, Monashees, and most recently Adams Street Partners in their first-ever Latin American investment.In this episode of The J Curve, Gastón unpacks the contrarian playbook behind Pomelo: why the team went regional from day zero on a $10M seed round instead of nailing one market first, how they built a "plug and play" hiring engine that's stayed at 90%+ since founding, why they tripled revenue without adding headcount, and what it actually takes to win enterprise customers like BBVA, Santander, Bci, Bancolombia, Binance, and Bybit when nobody trusts an infrastructure startup. He also shares the Series B-to-Series C lessons most founders never document — including the end-of-year memo that turned rejections into investor trust — and his framework for the AI transformation a five-year-old company is now being forced to run.This is a masterclass on regional-by-design strategy, B2B fintech go-to-market, founder-led fundraising in down markets, and building world-class companies from Latin America for the world.Subscribe to The J Curve Insider newsletter for deeper insights and follow Olga on LinkedIn and Instagram.

High-growth companies demand constant reinvention, yet most leaders underestimate how deeply roles, go-to-market models, and buyer behavior evolve over time. This episode explores what it actually takes to adapt at that level, from navigating internal resistance to aligning product and sales with how customers truly buy. Sahir Azam brings a rare operator-to-investor perspective, unpacking the realities of PLG to enterprise transitions, the cultural discipline required to scale sales, and how AI is reshaping both software and the sales function itself. The conversation also challenges common assumptions around SaaS models, tooling, and where value will accrue as AI infrastructure matures. Sahir Azam is a Partner at Index Ventures investing in AI infrastructure, and former Chief Product Officer at MongoDB where he led the Atlas transformation into a multi-billion-dollar platform. He brings a rare operator's perspective on building go-to-market discipline, scaling sales culture, and navigating the product-distribution balance that separates winners from founders who fail. Connect with Sahir: Index Ventures LinkedIn Get the Force Management framework for navigating product-go-to-market fit and building the sales discipline that separates scaling companies from those that fail: The Predictable Revenue Framework: Guide for Leaders Key takeaways from this episode:  00:00 – How Sahir Azam went from building MongoDB Atlas into a multi-billion-dollar platform to investing in the infrastructure shaping AI's next wave 06:24 – The secret to driving change inside a company before trying to win in the market 10:10 – What PLG and enterprise sales actually have in common when you design around the buyer 12:18 – What it's really like to move upmarket and why most companies underestimate the cultural shift required 23:50 – Sahir Azam's unexpected perspective on technical founders who struggle to scale 41:12 – A peek into where real value in AI is being built and why infrastructure is the leverage point 01:02:00 – What you can do right now to stay relevant as AI reshapes how top sellers operate Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

In one year, Shield AI's value has leaped 140%. This after it won a contract to be the software provider to Anduril's Fury fighter jet for the U.S. Air Force. Also, Index Ventures is said to be leading a $250 million to $350 million round. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Shardul Shah, Partner at Index Ventures, was one of the first checks into Wiz — the Israeli cybersecurity company Google acquired for $32 billion. It wasn't luck. It was a decade-long relationship with the founders, a willingness to wire money on conviction alone, and a philosophy that treats risk calculus as a fool's errand.In this conversation, Eric sits down with Shardul to unpack how the Wiz deal actually came together, what Google really bought for $32 billion, and why mid-sized acquisitions almost always fail. They get into how Index thinks about doubling down across funds, why Shardul refuses to invest in a founder he's only met over Zoom, and what he saw in the Wiz founders a decade before anyone else was paying attention.They also talk about what's next — the categories Shardul is hunting, the founders he's already betting on, and why he thinks everything that happened with Wiz should stretch every entrepreneur's sense of what's possible.Eric Newcomer covers the inner workings of startups and venture capital. Subscribe for interviews with the people building and funding the next generation of tech.

Shardul Shah, Partner at Index Ventures, was one of the first checks into Wiz — the Israeli cybersecurity company Google acquired for $32 billion. It wasn't luck. It was a decade-long relationship with the founders, a willingness to wire money on conviction alone, and a philosophy that treats risk calculus as a fool's errand.In this conversation, Eric sits down with Shardul to unpack how the Wiz deal actually came together, what Google really bought for $32 billion, and why mid-sized acquisitions almost always fail. They get into how Index thinks about doubling down across funds, why Shardul refuses to invest in a founder he's only met over Zoom, and what he saw in the Wiz founders a decade before anyone else was paying attention.They also talk about what's next — the categories Shardul is hunting, the founders he's already betting on, and why he thinks everything that happened with Wiz should stretch every entrepreneur's sense of what's possible.Eric Newcomer covers the inner workings of startups and venture capital. Subscribe for interviews with the people building and funding the next generation of tech.

What will tech look like in 2026 and are we heading for an AI bubble, or a boom? To gaze into the crystal ball for the year ahead, Katie and Danny speak to VCs Hannah Seal from Index Ventures and Jon Callaghan of True Ventures in Silicon Valley, and get them to make their predictions for the year ahead and the innovations to watch out for – AI solving healthcare? Robots replacing brickies?Image: Getty Hosted on Acast. See acast.com/privacy for more information.

Mirelo, a German startup that is building AI to add synced sound effects to videos, has raised a $41 million seed round led by Index Ventures and Andreessen Horowitz. Also, First Voyage has raised $2.5 million in a seed funding round from a16z speedrun, SignalFire, True Global, and other investors. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Episode 404 of The VentureFizz Podcast features Ben Sesser, CEO & Co-Founder of BrightHire. Well this is a first… It's common to time my podcast interview around a milestone for a company like a funding announcement. But, this is the first time that in the span between my interview with the founder to the publishing date that the company announced its acquisition. Last week, BrightHire announced that the company has entered into an agreement to be acquired by Zoom. It certainly is a combination that makes a lot of sense. BrightHire is an interview intelligence platform. We saw its influence firsthand this past summer when VentureFizz hosted a series of AI job searching events. Talent acquisition leaders consistently mentioned BrightHire as the most-adopted application—a signal that immediately led me to reach out to Ben for this interview. As Ben shares, when they started, "interview intelligence" was a brand new category in hiring, and they faced plenty of doubters. But fast-forward to today, and much like the success of companies like Gong for sales teams, BrightHire's value is now obvious. But isn't that the case for all great companies in hindsight? BrightHire's investors include Flybridge, Index Ventures, 01 Advisors, Zoom Apps Fund, and others. Chapters 00:00 Intro 02:48 State of Hiring in the AI Era 06:51 Ben's Background Story 19:40 Getting Started in the Tech Industry 25:26 Origin Story of BrightHire 33:06 Creating a New Category 35:31 The Value of Video in the Hiring Process 41:17 BrightHire Screen - New AI Screening Platform 45:51 Experience of Raising Capital 48:36 Biggest Lessons Learned 50:22 Common Mistakes Companies Make When Hiring 51:57 Lightening Round Questions Episode Sponsor: As a longtime champion of the local startup ecosystem, Silicon Valley Bank supports innovative companies with the solutions and financing they need through every stage of growth. With more than 1,500 bankers and relationship advisors, and $42B in loans as of Q2 2024 – SVB delivers the right people, service and resources to support your entire financial journey. Learn more at SVB.com.

In this episode of the Revenue Builders Podcast, our hosts John Kaplan and John McMahon are joined by Dan Fougere, a venture partner at Index Ventures and former CRO of Datadog. Dan shares insights from his extensive sales career, emphasizing the importance of developing adaptive and context-specific sales playbooks. He discusses the evolution of PLG (Product-Led Growth) strategies, the integration of AI in sales processes, and the critical need for continuous learning and adaptability. The episode also touches on Dan's philanthropic efforts, including his involvement with Homes for Our Troops and other charitable initiatives.ADDITIONAL RESOURCESConnect and learn more from Dan Fougere.Connect with Dan on LinkedIn: https://www.linkedin.com/in/danfougere/Support Homes For Our Troops: https://www.hfotusa.orgSupport Imagine Reading: https://imaginereading.com/Support No Person Left Behind Outdoors: https://www.nplboutdoors.orgRead the Guide on Six Critical Priorities for Revenue Leadership in 2026: https://hubs.li/Q03JN74V0Enjoying the podcast? Sign up to receive new episodes straight to your inbox: https://hubs.li/Q02R10xN0HERE ARE SOME KEY SECTIONS TO CHECK OUT[00:02:24] Advice for New Sales Leaders[00:02:52] Adapting Sales Playbooks[00:03:27] The Importance of Flexibility in Sales Strategies[00:03:54] Understanding Product-Led Growth (PLG)[00:06:44] Case Study: Datadog's Sales Evolution[00:07:57] Challenges in Scaling Sales Strategies[00:08:51] Building a Sales Organization for the Future[00:12:14] The Role of a CRO in Modern Sales[00:14:48] Adapting to Market Changes[00:26:23] Traits of Effective Sales Leaders[00:34:03] The Tip of the Spear: Leading from the Front[00:34:16] Medallia: Building a Sales Process from Scratch[00:36:58] Profile of a Successful Sales Leader[00:37:47] Recruiting and Building a High-Performance Team[00:39:25] The Importance of High Standards in Hiring[00:52:41] AI's Impact on Sales and Forecasting[01:02:07] Giving Back: Charitable EndeavorsHIGHLIGHT QUOTES[00:03:21] “A big mistake is trying to force fit a playbook from a previous company into a new company.”[00:06:01] “Approach it with a beginner's mind… it's actually an advantage you only get once.”[00:10:55] “Build your outbound before you need it, because at some point you're going to need it.”[00:13:33] “98.5% of companies realize, ‘I wish I had a great sales organization to go with this great PLG motion.'”[00:19:07] “The thing that tops people out is the inability to adapt and collaborate—they become too rigid.”[00:22:25] “If you know in your heart your team is mediocre, you're never going to be great. Raise those standards.”[00:31:36] “Don't just assume you can get rid of BDRs and have AI do it. I don't see anybody telling me that's working yet." Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This week, we chat with Rex Woodbury! Rex is the Founder and Managing Partner of Daybreak, an early-stage venture capital firm based in New York. Daybreak is a first-check firm built on the belief that the companies shaping the future will be those improving the lives of the next generation.Before starting Daybreak, Rex was a Partner at Index Ventures, where he invested in consumer technology and the creators building online communities. Beyond investing, Rex is also the writer behind Digital Native, a widely read weekly publication exploring the intersection of technology, culture, and the internet with over 70,000 readers each week.With his unique perspective as both an investor and a storyteller of the digital age, he brings deep insight into how technology continues to shape how we live, work, and connect.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Rex Woodbury: @rex_woodburyDaybreak: @daybreak_fund@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

Send us a textLearn how you can scale your care team with AI: https://link.CareCo.ai/rmvhvqIn this enlightening episode, I sit down with Chris Ellis, CEO of Thatch, following their impressive $40 million Series B funding round led by Index Ventures with strategic investment from ADP Ventures. Chris breaks down how Thatch is revolutionizing employee health benefits through Individual Coverage Health Reimbursement Arrangements (ICHRA), allowing employees to choose their own health plans while giving employers cost control and administrative simplicity. We dive deep into the fundamental problems with employer-based health insurance, explore the bipartisan political momentum behind health insurance reform, and discuss how decoupling insurance from employment could realign incentives throughout the healthcare system. Chris provides fascinating insights into how this shift could enable true preventive care, extend insurer-patient relationships, and create the consumer-driven healthcare marketplace that has been decades in the making. This conversation connects perfectly with the current administration's focus on patient empowerment and transparency, making it a must-listen for anyone interested in the future of American healthcare.Timestaps: 00:00:00 - Thatch's $40M Series B Led by Index Ventures00:03:29 - How Thatch's ICHRA Model Actually Works00:12:32 - Government's $1,200 Tax Credit for Small Businesses00:25:94 - The Cancer Detection Problem: Why Insurers Won't Invest in Prevention00:28:47 - The Vision: Decoupling Insurance from Employment00:40:49 - GLP-1 Coverage Dilemma: When ROI Takes Too Long

Practice Leader at UpperEdge Adam Mansfield and The Information's Kevin McLaughlin talk with TITV Host Akash Pasricha about Salesforce's challenges selling its AgentForce AI software and the murky ROI for customers. We also talk with The Information's Aaron Holmes and Adam Mansfield about Microsoft's new playbook for Copilot. We get into the evolving role of product management with Sahir Azam, the new partner at Index Ventures, and finally, we talk with Kareem Amin, CEO of Clay, about how his company is defining a new role in go-to-market.Articles discussed on this episode: https://www.theinformation.com/articles/marc-benioff-said-ai-easy-crazy-team-salesforce-proved-wronghttps://www.theinformation.com/articles/microsoft-hopes-hastened-ai-rollout-price-discounts-can-fuel-office-365-growthTITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to: - The Information on YouTube: https://www.youtube.com/@theinformation4080/?sub_confirmation=1- The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda

The most successful enterprises are about to become autonomous — and Eléonore Crespo, Co-CEO of Pigment, is building the nervous system that makes it possible. In this conversation, Eléonore reveals how her $400 million AI platform is already running supply chains for Coca-Cola, powering finance for the hottest newly public companies like Figma and Klarna, and processing thousands of financial scenarios for Uber and Snowflake faster and more accurately than any human team ever could.Eléonore predicts Excel will outlive most AI companies (but maybe only as a user interface, not a calculation engine) explains why she deliberately chose to build from Paris instead of Silicon Valley, and shares her contrarian take on why the AI revolution will create more CFOs, not fewer.You'll discover why Pigment's three-agent system (Analyst, Modeler, Planner) avoids the hallucination problems plaguing other AI companies, how they achieved human-level accuracy in financial analysis, and the accelerating timeline for fully autonomous enterprise planning that will make your current workforce obsolete.PigmentWebsite - https://www.pigment.comTwitter - https://x.com/gopigmentEléonore CrespoLinkedIn - linkedin.com/in/eleonorecrespoFIRSTMARKWebsite - https://firstmark.comTwitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/Twitter - https://twitter.com/mattturck(00:00) Intro (01:22) Building Pigment: 500 Employees, $400M Raised, 60% US Revenue (03:20) From Quantum Physics to Google to Index Ventures (06:56) Why Being a VC Was the Perfect Founder Training Ground (11:35) The Impatience Factor: What Makes Great Founders (13:27) Hiring for AI Fluency in the Modern Enterprise (14:54) Pigment's Internal AI Strategy: Committees and Guardrails (17:30) The Three AI Agents: Analyst, Modeler, and Planner (22:15) Why Three Agents Instead of One: Technical Architecture (24:10) Agent Coordination: How the Supervisor Agent Works (24:46) Real Example: Budget Variance Analysis Across 50 Products (27:15) The Human-in-the-Loop Approach: Recommendations Not Actions (27:36) Solving Hallucination: Why Structured Data Changes Everything (30:08) Behind the Scenes: Verification Agents and Audit Trails (31:57) Beyond Accuracy: Enabling the Impossible at Scale (36:21) Will AI Finally Kill Excel? Eleanor's Contrarian Take (38:23) The Vision: Fully Autonomous Enterprise Planning (40:55) Real-Time Supply Chain Adaptation: The Ukraine Example (42:20) Multi-LLM Strategy: OpenAI, Anthropic, and Partner Integration (44:32) Token Economics: Why Pigment Isn't Token-Intensive (48:30) Customer Adoption: Excitement vs. Change Management Challenges (50:51) Top-Down AI Demand vs. Bottom-Up Implementation Reality (53:08) The Reskilling Challenge: Everyone Becomes a Mini CFO (57:38) Building a Global Company from Europe During COVID (01:00:02) Managing a US Executive Team from Paris (01:01:14) SI Partner Strategy: Why Boutique Firms Come Before Deloitte (01:03:28) The $100 Billion Vision: Beyond Performance Management (01:05:08) Success Metrics: Innovation Over Revenue

At EUVC Summit 2025, few sessions packed as much history, humility, and hard-earned wisdom as the conversation with Bernard Dallé and Thomas Kristensen.What began as a one-man show in the early '90s—when venture in Europe was barely a concept—has become one of the most respected platforms in the global industry.“I joined Index before it even existed as a venture firm. It was still Index Securities.”This was more than a talk. It was a journey through time, with insights for every fund manager—new or seasoned—building for the long haul.Before Skype, before unicorns, before European VC had a flag to wave, it was about scraping together conviction and capital.Index Fund I: $17 millionRaised in 1999, following years of groundwork and trialNo real ecosystem, no pattern recognition, and no “easy” capital“You can't raise without a track record. So we used Fund I to create it.”And then came the landmark deal: Skype's acquisition by eBay for $3–4 billion. That one outcome shifted the trajectory of Index—and of European venture as a whole.“After Skype, we could raise with more ease. It gave us credibility.”One of the standout themes was Index's philosophy around team building:“The hires that worked? People we knew—or people who joined slightly below partner level and grew into the role.”In contrast, hiring senior talent cold—especially across geographies—proved far harder. Culture cohesion was key, and misalignment at the top often broke the system.The advice was clear:Grow talent internally when you canOnly bring in outsiders when they're “known entities”Avoid parachuting in partners who haven't lived the firm's values“At some point, having someone senior focused purely on operations becomes essential.”This wasn't about back office—it was about survival.Today's LP demands include:ESG complianceFund reportingExit prepOngoing fundraisingPortfolio support“You need to start thinking about this 10 years in advance.”“It takes 15 years to become somewhat successful in this business. And once you get there—you need to start thinking about who'll take over.”Venture isn't just about spotting founders. It's about building the kind of firm that can back them for decades to come.Bernard and Thomas left the stage with no fluff—just a quiet reminder:Build slowly. Hire wisely. Think in generations.And good luck to all of us doing the same.The Early Days: A Market Without MomentumScaling a Firm: Culture First, Titles LaterOps Matter More Than You ThinkThe Final Lesson: Play the Long Game

Notion te regala 3 meses del plan Business + IA ilimitada

Parag Agrawal is the co-founder and CEO of Parallel, a startup building search infrastructure for the web's second user: AIs. Before launching Parallel, Parag spent over a decade at Twitter, where he served as CTO and later CEO during a period of intense transformation, as well as public scrutiny. In this episode, Parag shares what he learned from his time at Twitter, why the web must evolve to serve AI at massive scale, how Parallel is tackling “deep research” challenges by prioritizing accuracy over speed, and the design choices that make their APIs uniquely agent-friendly. We also discuss: Why Parallel designs for AI as the primary customer Lessons from 11 years at Twitter and applying them to a startup Potential business models to keep the web open for AI Hiring philosophy: balancing high potential and experienced talent The evolving role of engineers in an AI-assisted world Why “agents” are finally becoming useful in production And much more… References: Bloomberg launch coverage: https://www.bloomberg.com/news/articles/2025-08-14/twitter-ex-ceo-parag-agrawal-is-moving-past-his-elon-musk-drama Clay: https://www.clay.com/ Index Ventures: https://www.indexventures.com/ Josh Kopelman: https://www.linkedin.com/in/jkopelman/ KLA: https://www.kla.com/ OpenAI: https://openai.com/ Parallel: https://parallel.ai/ Patrick Collison: https://www.linkedin.com/in/patrickcollison/ Stripe: https://stripe.com/ Where to find Parag: LinkedIn: https://www.linkedin.com/in/paragagr/ X/Twitter: https://x.com/paraga Where to find Todd: LinkedIn: https://www.linkedin.com/in/toddj0/ X/Twitter: https://x.com/tjack Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ X/Twitter: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: (1:26) Founding Parallel with an AI-first mission (3:23) From Twitter CTO/CEO to startup founder (6:20) What the AI era spells for companies (7:58) The CEO to founder pipeline (11:18) Reflections on Twitter's transformation (17:48) How Parallel was born (22:31) Early use cases for Parallel (31:42) How has Parallel's ICP changed? (34:37) AI's impact on competitor dynamics (36:06) When should founders launch? (37:43) Parag's fundraising framework (40:14) Building a high-impact engineering team (44:49) Counterproductive uses of AI (47:35) How will the software engineer role evolve? (49:10) How are Parallel's customers using AI? (53:27) Defining agents in 2025 (55:02) Parallel's long-term vision (1:03:43) Parag's growth as a founder

Ross went from lawyer to self-taught engineer to CTO at a 1,600-person unicorn—then quit to build Wordsmith AI. In 18 months, he's raised $30M and grown to mid-single-digit millions in ARR by doing everything differently. He tested co-founders by starting fights. Built in Slack for 10 months before adding a web interface. Kept his team at 8 people while competitors hired dozens. This episode breaks down his exact playbook: how to test co-founders before committing, why attacking someone's core job kills your sales cycle, and how he accidentally created the hottest seed round by ghosting every VC. Plus the reality of building a rocket ship with a newborn at home.Why You Should Listen:Why starting fights with co-founders can be a great way to test conflict.Why keeping your team at 8 people until PMF lets you move fasterThe accidental fundraising playbook that made VCs go crazyHow having a baby forces you to be 10x more productive as a founderKeywords:Wordsmith AI, Ross McNairn, AI legal tech, product market fit, co-founder selection, Series A, Index Ventures, Slack integration, startup pivots, legal AI00:00:00 - Intro00:01:31 - From Lawyer to CTO00:03:45 - Starting Wordsmith AI00:06:41 - Testing Co-Founder Relationships00:14:42 - Building the MVP00:20:44 - First Product Iterations00:26:39 - Finding Product Market Fit Through Slack00:37:42 - Go-to-Market Using Webinars and Influencers00:47:00 - Balancing Startup Life with a 10-Month-Old BabySend me a message to let me know what you think!

Martin Mignot is a Partner at Index Ventures, the best-performing fund in the world right now. In the last three months, they have sold Wiz for $ 32 billion, sold Scale for $14.9 billion, and IPO'd Figma as the largest investor. In addition to this, they are the largest or second-largest shareholders in Roblox, Revolut, Adyen and Datadog.  Agenda for Today: 00:00 – Why Gross Margin is the Biggest Sin in the Early Days 04:50 – Why Most People Shouldn't Become VCs 07:40 – Why it is BS to Suggest the Future of VC is Boutique vs Mega Fund 09:10 – Do Multi-Stage Funds Really Give a S*** About Seed 13:50 – The Founder Trait That Trumps Market Size Every Time 18:45 – How Spotify Still Haunts Index Ventures & What They Learn From It? 28:50 – The Brutal Truth About European vs. U.S. Founders 34:20 – The Case for a European AI Giant (and Who Might Build It) 40:50 – The Return of the 7-Day Founder Work Week 52:10 – Biggest Lessons from Leading Revolut's Series A 56:40 – Betting Against Nick Storonsky? Don't. 1:03:10 – The One Competitor Index Ventures Admires    

Welcome back to another episode of Upside at the EUVC Podcast, where Dan Bowyer, Mads Jensen of SuperSeed and Lomax from Outsized Ventures unpack what's happening in European tech and venture capital.This week: Why Meta and Microsoft are minting cash from AI, what Figma's IPO signals for SaaS, whether the EU got rolled in its new trade deal with the US, and how Europe's AI scene is finally delivering billion‑dollar exits. Plus: OpenAI's new “Study Mode” and Harry Stebbings' Project Europe—an “anti‑YC” deep‑tech accelerator for founders under 25.

On Figma's first day trading on the NYSE, its stock soared over 250%, closing at $115.50 and giving the design software company a market cap near $68 billion. That's more than triple the $20 billion Adobe offered before regulators killed that deal in 2023. Silicon Valley is celebrating. The IPO raised $1.2 billion, mostly benefiting early investors like Sequoia, Greylock, Index Ventures, and Kleiner Perkins, whose combined stakes are now worth about $24 billion. It's a huge win for Silicon Valley VCs after a prolonged IPO drought that began in 2022.Featured Guests: Ben Narasin, founder and general partner, Tenacity VC | Jaya Gupta, partner, Foundation Capital

This week on the Newcomer Podcast, we're joined by a very special guest: Danny Rimer, seasoned investor and longtime partner at Index Ventures, for a timely conversation around Figma's highly anticipated IPO.Danny takes us behind the scenes of Index's early bet on Figma and its visionary CEO Dylan Field, sharing how the deal came together and what made the design platform stand out in a crowded startup landscape. From there, we zoom out to talk about the current venture capital climate — what's changed, what's stayed the same, and what the smartest investors are watching right now.We also dig into AI's evolving role in the startup ecosystem, the tension between hype and real value, and where Danny sees the next big opportunities emerging. Whether you're a founder, investor, or just love a good origin story, this is an episode you won't want to miss.Timecodes:00:00 Introduction to Danny Rimer02:29 How Rimer met Figma and the beginnings of design as a category16:42 Figma's failed Adobe deal and comeback25:39 How Index approaches AI deals 31:00 AI's iPhone moment and looking beyond the chatbot39:10 Shifts in the venture capital industry

Index Ventures partner Jahanvi Sardana has a reminder for all those founders worried about finding TAM for their product or service: many startups have emerged from markets that, at the time, were essentially nonexistent. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Bonus track avec un super VC cette semaine !Martin Mignot, Partner chez Index Ventures, le fonds qui a accompagné dès leurs débuts certains des plus beaux succès de la tech comme Facebook, Revolut ou Slack. Ils viennent de publier un guide de 123 pages pour aider les fondateurs européens à conquérir l'Amérique – en s'appuyant sur l'analyse de plus de 500 startups, des enquêtes terrain, et des interviews de fondateurs. Fait marquant : ¾ d'entre eux citent les clients – pas le capital – comme moteur principal de leur expansion. Martin est au cœur de cette transformation, alors on va parler de cette nouvelle géographie de l'ambition tech, du rôle des VCs, et de sa vision pour l'écosystème européen. Épisodes cités :

At the EUVC Summit, Bernard Dalle (formerly of Index Ventures) and Thomas Kristensen (LGT Capital Partners) shared candid reflections on how to build a venture firm from the inside out. Instead of fixating on star hires and grand strategies, their talk emphasized the compounding power of cultural alignment, junior talent development, and early operational investment.Drawing on first-hand experience, they unpack what it takes to build enduring institutions—where team, trust, and time matter more than titles.Whether you're raising your first fund or scaling your platform team, this conversation offers timeless lessons from one of Europe's most respected firms.Here's what's covered:00:45 Betting on People: Why hiring for cultural fit beats chasing CVs02:20 Long-Term Talent Playbooks: Junior hires, long runway, big impact03:50 Under-hiring on Purpose: Why Index rarely hired GPs straight out05:10 The Operations Edge: Building support teams early pays dividends07:00 The Index Blueprint: Early days with David, Pascal, and a deep ops bench08:30 Institutional Memory: Capturing partner insights across the portfolio

Martin Mignot s'installe dans le fauteuil de tech 45' cette semaine ! Ce frenchie est une star du VC mondial, Partner chez Index Ventures, il publie aujourd'hui un guide à destination des fondateurs. "Winning in the US" s'adresse aux startups qui sont de plus en plus « born global » — misant très tôt sur une présence transatlantique, malgré les incertitudes économiques et politiques. C'est particulièrement vrai pour les startups européennes : 64 % d'entre elles se développent désormais aux États-Unis dès le stade pré-seed ou seed, contre 33 % il y a cinq ans. Ces 123 pages regorgent de témoignages et stratégies de fondateurs et d'opérateurs parmi les startups les plus emblématiques : Spotify, Revolut, Adyen, Pigment ou DeepL…Episode à suivre ce vendredi, en exclu pour tech 45'

Today on the pod, we're bringing you two of the liveliest panels from the 2025 Cerebral Valley AI Summit, held this week in London.Both panels — “The Autonomous Vehicle Rollout” and “Investing in 2030” — explore one of the major themes from the event: where AI is poised to show up next in our everyday lives, beyond the chatbot. Think voice, devices, and even your car.First up, we'll hear from Uber CEO, Dara Khosrowshahi, and Alex Kendall, Co-founder and CEO of Wayve, who are teaming up to bring self-driving cars to the UK.Then we turn to the investor perspective, with top European VCs — Philippe Botteri of Accel, Tom Hulme of Google Ventures, and Jan Hammer of Index Ventures — on where they see the biggest AI opportunities for founders in the years ahead.

Eléonore Crespo is the Co-Founder and CEO @ Pigment, one of Europe's fastest-growing companies. With Pigment, Eleonore has raised over $397M from the best in the world including ICONIQ, Greenoaks and IVP to name a few. Prior to Pigment, Eléonore was on the other side of the table as an investor with Index Ventures. In Today's Episode We Discuss:  [04:10] “I had 3 surgeries. That's when I knew I had to become a founder.” [06:50] Why Index Ventures isn't on her cap table [08:40] Eleonore's CIA-style co-founder hunt (she literally made a target list) [11:50] Co-CEOs: “We talk 3x a day. That's our superpower.” [13:30] The boutique coffee metaphor for product excellence [15:40] Yuri Milner's 4 traits of legendary founders (one is shocking) [17:30] “Hiring is everything. I hunt talent like a football scout.” [19:00] Wild Olympic Games story → led to hiring a top CFO [24:50] How she filters out title-chasers and political hires [29:30] “Too much process? I make teams list the dumbest ones.” [33:00] Her blunt answer on whether Europe can produce scale execs [35:00] Why she raised so much money… even when they didn't need it [38:50] Board power is real: “They can fire you. I've seen it.” [43:30] Rob Ward's counter-cyclical advice: double down during a downturn [44:50] “We closed a massive US deal… at 2am… while drenched in rain.” [47:10] Selling into the US as a European founder—her full playbook [50:20] The hardest part of being a CEO no one talks about [54:00] “Children remind you what happiness is.” [56:30] “I don't fast. That would make me unhappy.” On longevity culture [59:20] Why her husband knows nothing about Pigment [01:04:20] “Forget $50B. I want to build a $200B company.” Follow Eleonore Crespo LinkedIn: Eleonore Crespo Pigment: pigment.com Subscribe to 20VC for more conversations with the world's best founders and investors.  

Origins host Beezer Clarkson sits down with her colleague Nate Leung, fellow LP and Partner at Sapphire Partners, to riff on  her recent conversation with Nina Achadjian, Partner at Index Ventures. Together, Beezer and Nate walk through the steps Index took to become a franchise - the decisions they made and the mistakes they avoided, plus the firm's ability to pick excellent companies early. They discuss the edge GPs gain by investing with a broader purpose, as well as the LP POV on the need for distributions and consolidation in 2025.Learn more about Sapphire Partners: sapphireventures.com/sapphire-partnersLearn more about OpenLP: openlp.vcLearn more about Asylum Ventures: asylum.vcLearn more about Index Ventures: indexventures.comSubscribe to the OpenLP newsletter for a monthly roundup of the latest venture insights, including the newest Origins episodes, delivered straight to your inbox.CHAPTERS:(0:00) Welcome to Origins2:04-Keeping the Main Thing the Main Thing to Build a Franchise3:43-Liquidity and IPOs(4:45) 2025 - The Year of Reckoning(7:13) Let There Be More Distributions9:34-Finding the Great Companies Early

After nearly a decade at Index Ventures, where he backed standout fintech companies like Plaid, Persona, Lithic, and Pilot, Mark Goldberg left to launch Chemistry, an early-stage venture firm. Founded alongside Kristina Shen and Ethan Kurzweil, the $350 million fund is part of a growing trend in venture capital: seasoned investors breaking out from large platforms to build more focused, boutique outfits. Today on Equity, Mary Ann Azevedo caught up with Goldberg about what led him to make the move, what Chemistry is all about, and how the venture landscape has evolved over the past few years. Listen to the full episode to hear more about: The state of fintech, a sector Goldberg has long had his eye on—and why he sees “a lot more tech-fin than fintech” these days Why those waiting for a wave of fintech IPOs might be in for a long hold What he's watching for in 2025 and beyond, from the impact of AI on fraud to shifting deal activity, including a pickup in M&A and secondaries Equity will be back with our weekly news roundup on Friday, so don't miss it! Equity is TechCrunch's flagship podcast, produced by Theresa Loconsolo, and posts every Wednesday and Friday.  Subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. For the full episode transcript, for those who prefer reading over listening, check out our full archive of episodes here. Credits: Equity is produced by Theresa Loconsolo with editing by Kell. We'd also like to thank TechCrunch's audience development team. Thank you so much for listening, and we'll talk to you next time. Learn more about your ad choices. Visit megaphone.fm/adchoices

Nina Achadjian is a Partner at Index Ventures, where she invests across seed, venture, and growth stages in AI, enterprise software, and vertical SaaS. She sits down with Beezer Clarkson, LP at Sapphire Partners, and the two discuss Nina's predictions for M&A in 2025, the importance of product market fit and what Nina looks for in a new hire. Plus, the two dig into Index's recent IPO with ServiceTitan, and how they managed a high-profile exit in the difficult IPO market of 2024.Learn more about Sapphire Partners: sapphireventures.com/sapphire-partnersLearn more about OpenLP: openlp.vcLearn more about Asylum Ventures: asylum.vcLearn more about Top Tier Capital Partners: ttcp.comSubscribe to the OpenLP newsletter for a monthly roundup of the latest venture insights, including the newest Origins episodes, delivered straight to your inbox.CHAPTERS:(0:00) Welcome to Origins(1:49) The Trading Floors of London and New York4:54-The ServiceTitan IPO(15:22) The Exit Window for SaaS Companies in 2025(17:45) Predictions & AI(25:10) More M&A vs. IPO(30:34) Investment Lessons Learned at Index(34:59) Hiring People Who Become Great Investors(39:03) Hive & the Armenian Tech Ecosystem

Wes Kao is an entrepreneur, coach, and advisor. She co-founded the live learning platform Maven, backed by First Round and a16z. Before Maven, Wes co-created the altMBA with best-selling author Seth Godin. Today, Wes teaches a popular course on executive communication and influence. Through her course and one-on-one coaching, she's helped thousands of operators, founders, and product leaders master the art of influence through clear, compelling communication. Known for her surgical writing style and no-BS frameworks, Wes returns to the pod to deliver a tactical master class on becoming a sharper, more persuasive communicator—at work, in meetings, and across your career.What you'll learn:1. The #1 communication mistake leaders make—and Wes's proven fix to instantly gain buy-in2. Wes's MOO (Most Obvious Objection) framework to consistently anticipate and overcome pushback in meetings3. How to master concise communication—including Wes's tactical approach for brevity without losing meaning4. The art of executive presence: actionable strategies for conveying confidence and clarity, even under pressure5. The “sales, then logistics” framework—and why your ideas keep getting ignored without it6. The power of “signposting”—and why executives skim your docs without it7. Exactly how to give feedback that works—Wes's “strategy, not self-expression” principle to drive behavior change without friction8. Practical ways to instantly improve your writing, emails, and Slack messages—simple techniques Wes teaches executives9. Managing up like a pro: Wes's clear, practical advice on earning trust, building credibility, and aligning with senior leaders10. Career accelerators: specific habits and tactics from Wes for growing your influence, advancing your career, and standing out11. Real-world communication examples—Wes breaks down real scenarios she's solved, providing step-by-step solutions you can copy today—Brought to you by:• WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs• Vanta—Automate compliance. Simplify security• Coda—The all-in-one collaborative workspace—Where to find Wes Kao:• LinkedIn: https://www.linkedin.com/in/weskao/• Website: https://www.weskao.com/• Maven course: https://maven.com/wes-kao/executive-communication-influence—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Wes Kao(05:34) Working with Wes(06:58) The importance of communication(10:44) Sales before logistics(18:20) Being concise(24:31) Books to help you become a better writer(27:30) Signposting and formatting(32:05) How to develop and practice your communication skills(40:41) Slack communication(42:23) Confidence in communication(50:17) The MOO framework(54:00) Staying calm in high-stakes conversations(57:36) Which tactic to start with(58:53) Effective tactics for managing up(01:04:53) Giving constructive feedback: strategy, not self-expression(01:09:39) Delegating effectively while maintaining high standards(01:16:36) The swipe file: collecting inspiration for better communication(01:19:59) Leveraging AI for better communication(01:22:01) Lightning round—Referenced:• Persuasive communication and managing up | Wes Kao (Maven, Seth Godin, Section4): https://www.lennysnewsletter.com/p/persuasive-communication-wes-kao• Making Meta | Andrew ‘Boz' Bosworth (CTO): https://www.lennysnewsletter.com/p/making-meta-andrew-boz-bosworth-cto• Communication is the job: https://boz.com/articles/communication-is-the-job• Maven: https://maven.com/• Sales, not logistics: https://newsletter.weskao.com/p/sales-not-logistics• How to be more concise: https://newsletter.weskao.com/p/how-to-be-concise• Signposting: How to reduce cognitive load for your reader: https://newsletter.weskao.com/p/sign-posting-how-to-reduce-cognitive• Airbnb's Vlad Loktev on embracing chaos, inquiry over advocacy, poking the bear, and “impact, impact, impact” (Partner at Index Ventures, Airbnb GM/VP Product): https://www.lennysnewsletter.com/p/impact-impact-impact-vlad-loktev• Tone and words: Use accurate language: https://newsletter.weskao.com/p/tone-and-words-use-accurate-language• Quote by Joan Didion: https://www.goodreads.com/quotes/264509-i-don-t-know-what-i-think-until-i-write-it• Strategy, not self-expression: How to decide what to say when giving feedback: https://newsletter.weskao.com/p/strategy-not-self-expression• Tobi Lütke's leadership playbook: Playing infinite games, operating from first principles, and maximizing human potential (founder and CEO of Shopify): https://www.lennysnewsletter.com/p/tobi-lutkes-leadership-playbook• The CEDAF framework: Delegating gets easier when you get better at explaining your ideas: https://newsletter.weskao.com/p/delegating-and-explaining• Swipe file: https://en.wikipedia.org/wiki/Swipe_file• Apple Notes: https://apps.apple.com/us/app/notes/id1110145109• Claude: https://claude.ai/new• ChatGPT: https://chatgpt.com/• Arianna Huffington's phone bed charging station (Oak): https://www.amazon.com/Arianna-Huffingtons-Phone-Charging-Station/dp/B079C5DBF4?th=1• The Harlan Coben Collection on Netflix: https://www.netflix.com/browse/genre/81180221• Oral-B Pro 1000 rechargeable electric toothbrush: https://www.amazon.com/dp/B003UKM9CO/• The Best Electric Toothbrush: https://www.nytimes.com/wirecutter/reviews/best-electric-toothbrush/• Glengarry Glen Ross on Prime Video: https://www.amazon.com/Glengarry-Glen-Ross-James-Foley/dp/B002NN5F7A• 1,000,000: https://www.lennysnewsletter.com/p/1000000—Recommended books:• On Writing Well: The Classic Guide to Writing Nonfiction: https://www.amazon.com/Writing-Well-Classic-Guide-Nonfiction/dp/0060891548/• Stein on Writing: A Master Editor of Some of the Most Successful Writers of Our Century Shares His Craft Techniques and Strategies: https://www.amazon.com/Stein-Writing-Successful-Techniques-Strategies/dp/0312254210/• On Writing: A Memoir of the Craft: https://www.amazon.com/Writing-Memoir-Craft-Stephen-King/dp/1982159375• Several Short Sentences About Writing: https://www.amazon.com/Several-Short-Sentences-About-Writing/dp/0307279413/• High Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884• Your Brain at Work: Strategies for Overcoming Distraction, Regaining Focus, and Working Smarter All Day Long: https://www.amazon.com/Your-Brain-Work-Revised-Updated/dp/0063003155/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Conversé con Arturo Sánchez, cofundador y CEO de Sofía Salud, una startup de seguros médicos todo en uno. Arturo fue uno de mis primeros invitados al podcast cuando Sofía tenía un enfoque B2C que no terminaba de despegar. Pero hoy, tras hacer un pivot hacia un modelo B2B dirigido a PYMEs, proyecta crecer más 200% este año.A la fecha, Sofía ha levantado $25M de inversionistas como Index Ventures, Kaszek y Ribbit Capital.-En Startupeable hacemos más gracias a  Notion, la plataforma todo en uno para organizar tu startup. Centraliza documentos, tareas y bases de datos en un solo lugar: el cerebro digital de tu negocio, ahora con IA para agilizar tu trabajo.Nos aliamos con Notion para regalarte 3 meses gratis del plan Plus y acceso ilimitado a su IA.

Josh Kamdjou is CEO and Founder of Sublime Security. Josh started Sublime after realizing just how easy it was for him to break into companies with phishing emails. He wanted to build a solution that better addressed the tailored environment of each organization such as historical data. Now the company has raised over $80 million from leading VCs such as IVP, Index Ventures, and Decibel. Before Sublime, Josh worked as a DoD hacker for 9 years.In the episode we discuss his emphasis on leveraging the attacker perspective, the fundamental difficulties of email security, his conviction in product-led growth, and more.Website: https://sublime.security/Sponsor: VulnCheck

On this episode of the Scouting For Growth podcast, Sabine VdL talks to Michael Lingelbach, CEO and co-founder of Hedra—a company that specializes in long-form generative video and agentic AI solutions. In just over a year, Michael and his team have seen explosive growth and raised backing from leading tech investors, including Index Ventures and Andreessen Horowitz. From marketing and social media campaigns to corporate training videos, Hedra’s technology is revolutionizing how we produce immersive, human-like content at scale. Michael and I discuss the power of agentic AI, the ethical dimensions of automated digital creation, and how he’s charting new paths for startups, enterprises, and content creators alike. KEY TAKEAWAYS We’re still very early in ‘generative media’. Stable Diffusion came out 2 years ago for images, video models have been maturing rapidly, but right now they’re focused on small fragments of content not cohesive brand storytelling. Building models that can not only generate compelling dialogue performances, and incorporate consistent identities and assets is a challenging research problem and something we’re pushing on. When people first think about generative AI they think about increasing the volume of content, but that typically isn’t a problem. The predominant concern of most marketers now is engagement. We live in a limited attention economy, so the focus now – in my opinion – is how to make really good content that’s going to hook people. For short-form content you’re usually trying to hook the viewer’s attention in the first 5-10 seconds as they’re scrolling through Tik Tok style feed. You want bright colours, a crazy character or a hook like “OMG you’re not going to believe what we’re going to talk about today!” With long-form content you’re optimising for retention. You still want the viewer to be engaged, but usually they want information or entertainment. We think the big opportunity is making it accessible for product/social marketing managers to have all this powerful technology to generate video and have a workflow were they’re working together with AI to make compelling content. That doesn’t require them to outsource to an external agency. We then get rapid feedback cycles rather than drawn out ones when you’re working with an external partner. BEST MOMENTS ‘We’re focused on bringing this technology from something that’s fun to play with to something that’s a strong part of an enterprise/brand marketing workflow.’ ‘Are you conveying information that makes the user feel like you’re conveying information that’s also usable for them? That’s the job of a content creator.’ ‘Video is the most natural form of communication; people have been talking to each other face-to-face for a long time!’ ‘Video is a massive market and it’s growing rapidly, it’s where most advertising, marketing information, learning and spending is shifting towards.’ ABOUT THE GUEST Michael Lingelbach is the CEO and co-founder of Hedra. While pursuing his PhD at Stanford, Michael worked closely with world-renowned AI researchers and developed a deep interest in pushing the boundaries of long-form video generation. Seeing an opportunity to combine advanced visual models with natural, human-centered dialogue, he set out to create a platform that produces fully generated video and immersive, conversational virtual avatars. Under Michael’s leadership, Hedra attracted early backing from top-tier investors, including Index Ventures and Andreessen Horowitz. Since launching publicly in 2023, the company has grown its user base to over one million registered users, earning recognition from both independent creators and major enterprises. Hedra’s generative video technology now powers cutting-edge use cases ranging from marketing and social media content to more complex interactive experiences. ABOUT THE HOST Sabine is a corporate strategist turned entrepreneur. She is the CEO and Managing Partner of Alchemy Crew a venture lab that accelerates the curation, validation, & commercialization of new tech business models. Sabine is renowned within the insurance sector for building some of the most renowned tech startup accelerators around the world working with over 30 corporate insurers, accelerated over 100 startup ventures. Sabine is the co-editor of the bestseller The INSURTECH Book, a top 50 Women in Tech, a FinTech and InsurTech Influencer, an investor & multi-award winner. Twitter LinkedIn Instagram Facebook TikTok Email Website

Farhan Thawar is the head of engineering at Shopify, where he oversees more than 1,000 engineers and a platform that powers over 10% of all U.S. e-commerce. Before Shopify, he was VP of engineering at Pivotal Labs and Xtreme Labs, and co-founder of Helpful.com. In our conversation, Farhan shares:• Why choosing the harder path leads to better outcomes• How to create intensity within your org (without burnout)• Why every company should be embracing pair programming• How he hires without interviewing• How he built the world's largest internship program• His mission to create a “crafter's paradise” for engineers• Much more—Brought to you by:• DX—A platform for measuring and improving developer productivity• Persona—A global leader in digital identity verification• Vanta—Automate compliance. Simplify security—Find the transcript at: https://www.lennysnewsletter.com/p/how-shopify-builds-a-high-intensity-culture-farhan-thawer—Where to find Farhan Thawar:• X: https://x.com/fnthawar• LinkedIn: https://www.linkedin.com/in/fnthawar—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Farhan's background(05:38) Choosing the hard path(09:37) Getting comfortable with looking dumb(13:20) Lessons from working with visionaries(19:19) Creating intensity in organizations(22:06) The power of pair programming(29:18) Shopify's culture of intensity(37:18) Meeting Armageddon: revolutionizing company meetings(39:46) Reducing distractions(41:10) Deleting 1M+ lines of code(49:05) Three buckets of building(57:45) Remote work and trust battery(01:00:29) Finding stability in uncomfortable times(01:03:14) Hiring philosophy(01:11:41) Internship programs and co-op systems(01:15:32) Lessons from managing 120 direct reports(01:20:40) Failure corner(01:27:46) Lightning round and closing thoughts—Referenced:• The Steve Jobs quote about connecting dots: https://www.goodreads.com/quotes/463176-you-can-t-connect-the-dots-looking-forward-you-can-only• Shopify: https://www.shopify.com/• GitHub: https://github.com/• Farhan's “questions to ask” framework: https://x.com/fnthawar/status/1514193402828574721• Palantir: https://www.palantir.com/• Joe Liemandt: https://www.linkedin.com/in/liemandt• Chamath Palihapitya: https://en.wikipedia.org/wiki/Chamath_Palihapitiya• Xtreme Labs: https://www.xtremelabs.io• Parkinson's law: https://www.verywellmind.com/what-is-parkinsons-law-6674423• Pair programming: https://dev.to/documatic/pair-programming-best-practices-and-tools-154j• Cody Fauser on LinkedIn: https://www.linkedin.com/in/codyfauser• How Shopify builds product: https://www.lennysnewsletter.com/p/how-shopify-builds-product• Chaos Monkey: We look at Shopify's new ‘culture of focus': https://www.siliconrepublic.com/careers/shopify-chaos-monkey-meetings-culture-deann-evans• Empowering devs with AI: How Shopify made GitHub Copilot core to its culture: https://www.youtube.com/watch?v=wVKBwcm5dbw&t=2318s• Tobi Lütke of Shopify: Powering a Team with a ‘Trust Battery': https://www.nytimes.com/2016/04/24/business/tobi-lutke-of-shopify-powering-a-team-with-a-trust-battery.html• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• Stop Being Deceived by Interviews When You're Hiring: https://www.forbes.com/sites/forbesleadershipforum/2012/02/07/stop-being-deceived-by-interviews-when-youre-hiring/• Shopify's made the Life Story a major part of their interview: https://news.ycombinator.com/item?id=39294140• Internships at Shopify: https://internships.shopify.com• Nick Adams on LinkedIn: https://www.linkedin.com/in/nick-adams-32b28139• React Native: https://reactnative.dev• Swift: https://www.swift.org• Acquired podcast: The Mark Zuckerberg interview: https://www.acquired.fm/episodes/the-mark-zuckerberg-interview• The Power of Performance Reviews: Use This System to Become a Better Manager: https://review.firstround.com/the-power-of-performance-reviews-use-this-system-to-become-a-better-manager• Airbnb's Vlad Loktev on embracing chaos, inquiry over advocacy, poking the bear, and “impact, impact, impact” (Partner at Index Ventures, Airbnb GM/VP Product): https://www.lennysnewsletter.com/p/impact-impact-impact-vlad-loktev• The Secret to a Great Planning Process—Lessons from Airbnb and Eventbrite: https://review.firstround.com/the-secret-to-a-great-planning-process-lessons-from-airbnb-and-eventbrite• How to do great work: https://www.paulgraham.com/greatwork.html• Challengers on Prime: https://www.amazon.com/Challengers-Luca-Guadagnino/dp/B0CX5MZ9M4• Halt and Catch Fire on Prime: https://www.amazon.com/Halt-Catch-Fire-Season-1/dp/B0CKXZNT96• Meta Ray-Bans: https://www.meta.com/smart-glasses/shop-all• Making Meta | Andrew ‘Boz' Bosworth (CTO): https://www.lennysnewsletter.com/p/making-meta-andrew-boz-bosworth-cto—Recommended books:• The Undoing Project: A Friendship That Changed Our Minds: https://www.amazon.com/Undoing-Project-Friendship-Changed-Minds/dp/0393254593• Range: Why Generalists Triumph in a Specialized World: https://www.amazon.com/Range-Generalists-Triumph-Specialized-World/dp/0735214484• Manna: Two Visions of Humanity's Future: https://www.amazon.com/Manna-Two-Visions-Humanitys-Future-ebook/dp/B007HQH67U• Business Adventures: Twelve Classic Tales from the World of Wall Street: https://www.amazon.com/Business-Adventures-Twelve-Classic-Street/dp/1504000021—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Mark Goldberg is a Managing Partner and Co-Founder at Chemistry, a $350M fund announced just yesterday with the mission to lead the best seed and Series A rounds. Before Chemistry, Mark was a Partner at Index Ventures, where he led early stage investments in Plaid, Bridge, Pilot, Anrok and Persona. Prior to Index Ventures, Mark was one of the first business hires at Dropbox. In Today's Episode with Mark Goldberg We Discuss: 1. The Truth About Multi-Stage Firms: Why are portfolio services there to help the investing partners and not the founders? What are the most broken elements within a multi-stage firm? How does decision-making break down in large partnerships? When is the right time to work with multi-stage firms? When is not? 2. From Boutique High Margins to Commoditised Low Margins:  With the immense amount of cash that has entered VC, will returns simply get worse? Who will be the winners in the next 10 years of venture? Who will be the losers? What can they do today to change this? What element of the future of venture are not enough people spending time on? 3. Lessons from Leading Unicorn Company Rounds: What happens to all the unicorns with insanely high prices they cannot grow into? What has been Mark's biggest hit? What did he learn? What has been his biggest miss? How did that change his go-forward approach? Does Mark agree that 90% of VC do not add value?    

Vlad Loktev spent 10 years at Airbnb, where he started as an IC PM and quickly advanced to lead the core Airbnb marketplace business and then GM the entire homes business, managing over 1,000 people and reporting directly to CEO Brian Chesky. He recently left Airbnb and joined Index Ventures as their newest partner. Vlad was my manager at Airbnb for many years, and is the person I credit most for teaching me how to be a great product manager. Prior to Airbnb, Vlad spent a year at Zynga, where he helped grow Words with Friends to over 14 million daily active users. In our conversation, Vlad shares:• Insight into Brian Chesky's leadership style• Why success as a PM is all about impact, impact, impact• Why chaos can be good• Why as a leader it's OK to let some fires burn• Why you should learn to “poke the bear”• Balancing product release speed with quality• Lessons on prioritization, decision-making, and organizational design• Advice for founders on building company culture• Much more—Brought to you by:• Pendo—The only all-in-one product experience platform for any type of application• Vanta—Automate compliance. Simplify security• Eppo—Run reliable, impactful experiments—Find the transcript and show notes at: https://www.lennysnewsletter.com/p/impact-impact-impact-vlad-loktev—Where to find Vlad Loktev:• X: https://x.com/vladimirloktev• LinkedIn: https://www.linkedin.com/in/vladimirloktev/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Vlad's background(02:54) Reflecting on transformative years at Airbnb(04:28) Skills and mindsets for success(11:03) Impact-driven mindset(13:16) Saying no and inquiry before advocacy (17:54) “Poking the bear”(22:46) Psychological tools for leadership(30:08) Building and scaling teams(36:12) Letting fires burn(47:34) Embracing chaos(54:40) The unsell email strategy(01:02:01) Finding your place in an organization(01:05:38) The importance of company culture(01:13:16) Airbnb's unique approach to product management(01:26:41) Failure corner(01:31:32) Lightning round and final thoughts—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe