Podcasts about greenoaks

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

Welcome back to the Alt Goes Mainstream podcast.Today's podcast takes us to the heart of London, where we sat down with Maggie Fanari, the CEO of J Rothschild Capital Management Limited, manager of RIT Capital Partners plc. RIT blends a rich heritage with a modern approach to both asset allocation and private markets. Lord Jacob Rothschild founded Rothschild Investment Trust in 1971. RIT listed on the London Stock Exchange with total assets of £280M. Today, the firm stands tall as one of the UK's largest investment trusts with over £4.7B of total assets.The firm's permanent capital and family office heritage have enabled the firm to think long-term, according to Maggie. “Permanent capital is a privilege,” she said.Maggie has brought an institutional allocator's background to RIT. She joined as CEO of RIT from Ontario Teachers' Pension Plan in 2024, where she was Senior Managing Director, Global Group Head of High Conviction Equities at OTPP, which has a global mandate to invest in public and private companies.Maggie and I had a fascinating discussion about how the firm invests across public and private markets, balancing both top-down portfolio construction and bottom-up asset selection. We covered:How RIT has aimed to compound wealth over time.Why top-down portfolio construction and bottom-up asset allocation are equally important.How can investors capture as much growth, limit market volatility, and compound growth over a long period of time?How RIT finds unique and different managers in private markets, which includes some of the top investors in the world.What market structure changes mean for investing across public and private markets?How to invest when the world order has changed.Taking a family office mindset and applying that investment mindset for investors in RIT.Why permanent capital is a privilege.How to be early to a theme rather than chase the trend.Why RIT decided to invest in SpaceX, Anthropic, OpenAI, Databricks, and Epic Systems.Where do investors bucket RIT into their asset allocation?What is a manager's edge and how can they apply that edge with consistency?Why depth of network matters for private markets managers.Why RIT invested in firms like Thrive, Greenoaks, and Ribbit.BioMaggie Fanari is the CEO of J. Rothschild Capital Management Limited (JRCM) , investment manager for RIT Capital Partners plc. She is Chair of JRCM's Investment Committee.Maggie was previously Senior Managing Director, Global Group Head of High Conviction Equities at Ontario Teachers' Pension Plan, which has a global mandate to invest in public and private companies.At Ontario Teachers', she served as a member of many of the pension plan's investment committees. She was involved in the execution of investments across a variety of asset classes (private and public), including supporting the development and execution of the venture and growth business.Before joining Ontario Teachers', Maggie worked at KPMG and Scotia Capital. Maggie is a chartered accountant and a CFA charter holder. She also holds a BBA from the Schulich School of Business at York University and ICD.D certification from the Institute of Corporate Directors.Maggie served as a non-executive director on the Board of RIT Capital Partners plc from April 2019 to February 2024.Thanks, Maggie, for sharing your wisdom, expertise, and passion across public and private markets and your thoughtful perspectives from your experiences as an institutional investor.This podcast was recorded on 15 June 2026, and therefore all RIT data is provided as at 31/05/2026. Show Notes00:42 Meet Maggie Fanari03:44 Teachers' Pension Roots04:51 Top Down Meets Bottom Up05:50 Allocating In New Paradigm06:15 Diversification Returns07:02 Volatility Creates Opportunity07:22 What Makes RIT Unique08:08 Compounding With Downside09:41 Brand Opens Doors10:05 Backing Emerging Managers11:57 Co-Invest Importance12:36 Returns And Realizations13:22 Great Co-Investor Playbook15:02 Building AI Theme Exposure16:06 Sourcing Deals Like SpaceX16:37 Public Private Value Split20:09 Public Themes And Sovereignty20:58 Moats And Terminal Value24:06 Permanent Capital Edge25:07 Oversubscribed Fund Access26:46 Underwriting And Discipline27:17 Why AI Needs Capital27:49 Anthropic Growth Math28:15 Databricks Scale Comparison28:41 Can Funds Get Bigger30:22 FOMO And Chasing30:47 Portfolio Allocation Guardrails31:47 Permanent Capital Advantage32:13 Right Sized Private Exposure32:51 Liquidity And Realizations33:28 Owning Winners At Scale34:11 Private To Public Hold34:41 Re Underwriting Post IPO35:38 Retail Investor Impact36:20 Public Market Liquidity Needs37:57 Why Investment Trusts Work38:56 Discounts As Margin Safety40:08 How Shareholders Allocate41:03 Sentiment Shifts In Cycles42:02 What Makes Great Managers43:14 Manager Edge Examples45:02 AI And Finding Leaders46:56 Consolidation And Differentiation47:51 Being A Great LP Partner48:50 Macro Lens As Edge49:42 Private Signals Inform Public50:48 Culture One Team One NAV51:28 Risk And Scenario Analysis52:59 Multipolar World Investing54:14 Geopolitics In Diligence55:10 Permanent Capital Best Of BothA Word from Our Sponsor, UltimusThis episode of Alt Goes Mainstream is brought to you by Ultimus, the full-service fund administrator and transfer agent powering asset managers in private and public markets. As alts go mainstream, you need real expertise to handle complex fund structures, connect with key distribution partners, and handle sophisticated compliance, reporting, and transparency demands.That's Ultimus: high-tech, high-touch solutions for over 450 clients and 2,500 funds with $775B in assets under administration. Backed by an expert team of over 1,200 employees, they place client service at the core of their business, helping you navigate complexity during your fund structuring or launch and then supporting you through every stage of growth. Whether you're already in the market or thinking about entering private wealth, you can trust their team's deep expertise in retail alternatives to help you reach your goals.Learn more at ultimusfundsolutions.com or email info@ultimusfundsolutions.com.We thank Ultimus for their support of alts going mainstream.Editing and post-production work for this episode was provided by The Podcast Consultant.

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    

Clay Bavor is the Co-Founder of Sierra, one of the world's fastest-growing enterprise AI companies. Sierra is valued at approximately $15.8 billion, has raised more than $1.5BN from leading investors including Sequoia, Benchmark, Greenoaks, GV and Tiger Global, and today serves more than 40% of the Fortune 50. The company recently surpassed $150 ARR, making it one of the fastest-growing enterprise software businesses in history. AGENDA: 00:00 – Why Frontier AI Demand Will Be Unlimited 08:00 – Open Models vs Frontier Models: Who Actually Wins? 17:00 – China's AI Advantage & The Distillation Debate 20:30 – Inside Sierra: The AI Agents Running the Entire Company 24:00 – The $100,000 Token Budget Every Engineer Will Soon Need 29:00 – Building AI for 40% of the Fortune 50 37:00 – Why Forward-Deployed Engineers Are the Future of Enterprise AI 43:00 – Sierra's Unusual Board Meetings & Billion-Dollar Company Playbook 48:00 – The Four Values Behind a $16B Startup: Craftsmanship, Intensity & Family 56:00 – Clay Bavor's Hiring Philosophy, AI-First Teams & What's Coming Next  

OpenGolf tourney tomorrowChoking. Heimlich maneuverUS Bank Fees$12.50 per $50. That is 25% instantlySo $1000, is 20 * $12.50 = $250. + interest.Reinstate the SATMore than 1,100 University of California math and science professors are urging UC regents to reinstate college-entrance exams, saying that unprepared students are lowering academic standards and draining teaching resources.Today, more than 90% of schools don't mandate the exams, Feder said.60 minutesWelcome to real life Scott Pelley. New boss, new style. Work or walk. Recommendations: Bill Ackman Sara Frier Finance folks should know Codex (previously Excel)PanthalassaMarkets: Huge correction today.  Tech down 5%+ and S&P500 2.6%. The losses intensified after a robust jobs report raised new worries that the Federal Reserve may need to raise interest rates later this year to fight inflation.S&P 500 still up 27% and tech 40-60% YoY. Huge IPOs coming: SpaceXAnthropic OpenAICash. Think about your cash investments. Cash is nice Owning your home is nice. AI & DatacentersGoogle to raise $85 billion Anthropic IPOIn May, Anthropic raised $65 billion in new funding from investors including Greenoaks, Dragoneer, Altimeter Capital and Sequoia Capital, in a round that valued the company at $965 billion. At the same time, the company said its revenue run-rate had surpassed $47 billion, up from $9 billion at the end of 2025LLM usageGrok: no bueno.  Grok and Spreadsheets.  Oh my.Gemini. Good. Claude: BEST. BTW, OpenAI was suspiciously very negative on SpaceX. SpaceX Going public ~June12. Next Friday!? $75b raise at $1.75T valuation.  Float is ~4-5% of total shares $10-18b must be purchased by index funds. More coming out in next 6 months. Employee lockups. Cap table investors want liquidity.Great detail here from Alexandra  IPO EducationHire IB's.  Allocate to VIPs and whales. 5% to retail.Valuation Over-valued? Valuation is highly relative to time!!!?? $135 price. $300 price? Either way 10-20x in 10 years.  Not investment advice.AI OpportunitySpaceX is becoming an AI infrastructure play!!Another Rental of Compute from Google to SpaceX.  Anthropic and Google are now paying @SpaceX a combined $2.17 billon per month for compute capacity. That's a revenue run rate of $26 billion per year. BIG MONEY.Jamie Dimon Interview of Elon.   Elon and Dimon  Another link here from Why SpaceX public now. Play at 4:00min mark: Why fundraising. Embarking on significant growth phase. 100,000 satellites. BTW. Why are datacenters hard if already doing satellites. 100x more bandwidth and ½ latency for v3. He just said that Starlink will be highest bandwidth and lowest latency or ANYTHING!! AI Datacenters in space. Massive capital endeavor. Hard to build power in the US or on land. US usage is 500GW.  To double. Would need to 2x # of power plants. BUT if in space can go far beyond EarthManufacturing on the moon and building beyond 1000TW per year of AI Space ComputeDataCenters in SpaceEasier than their communication satellites. AI datacenter is EASYElections: Why does it take so long to count votes? Could take weeks? 

Another good month – investors are giddy. Oil – CRITICALLY LOW inventory (Inside Baseball). Fed governor admits inflation is hard to control. A major name says they are reducing stocks – but are they really? Announcing the Winner of the CTP for Salesforce (CRM). PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? PayPal.Donation.Button({ env:'production', hosted_button_id:'JJJHP2GDEJC7J', image: { src:'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt:'Donate with PayPal button', title:'PayPal - The safer, easier way to pay online!', } }).render('#donate-button'); Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Warm-Up - Another good month - investors are giddy - Oil - CRITICALLY LOW inventory (Inside Baseball) - Fed governor admits inflation is hard to control - A major name says they are reducing stocks - but are they really? - Announcing the Winner of the CTP for Salesforce Markets - Huge reversal in Software stocks - A few names on the move - and moving BIG! - SpaceX IPO - could drain markets - More AI valuations through the roof Pizza Mouth ! Reversal - Software stocks bounced this week on strong results from Snowflake and Okta, which both recorded their best days on record. - The results signal that investors may have been too quick to declare the end of software with the emergence of artificial intelligence. - Even as AI displaces certain tools and job functions, many software companies continue to show growth, assisted by their own AI products. - The iShares Expanded Tech-Software exchange-traded fund rose 8% this week and closed May up 21%, the best monthly performance for the ETF since October 2001. - With this month's rally, the iShares software ETF is only down 3.8% for the year, still badly trailing the Nasdaq, which has gained 18% in 2026. Snowflake - Amazon said Wednesday that its cloud division has landed a $6 billion spending commitment from Snowflake, which includes the use of the company's custom silicon and chips for artificial intelligence. - Snowflake's purchase of services and technology from Amazon Web Services will occur over five years, according to a press release about the agreement. - Snowflake intends to expand its use of Amazon's Graviton general-purpose chips, as well as cloud-based graphics processing units for AI. - Snowflake and Amazon are frenemies - they compete but also partner with each other. - Stock up 36% on this news DELL!!!!!!!!!!!! - Dell Technologies Inc. shares surged due to an outlook for annual sales that far surpassed expectations on demand for servers that power artificial intelligence work. - Revenue in the fiscal year ending in January 2027 will be about $167 billion, including $60 billion from the sale of AI servers, topping analysts' average estimate of $142.1 billion. - The company booked $24.4 billion in AI orders and generated $16.1 billion in AI server sales in the quarter ended May 1, with Chief Operating Officer Jeff Clarke saying “The AI opportunity shows no signs of slowing.” - The shares surged 33% to $420.91 at the close Friday in New York, the biggest single-day increase in the more than seven years since the hardware maker returned to the public markets after a five-year hiatus as a private firm. - Up 150% YTD More Dell - New XPS 13 at $699 targets price-sensitive market - Aims to compete with MacBook Neo, lower-end Windows devices - Launch amid global memory chip crunch to gain market share - WINING OVER JCD: -- 13.4-inch screen (very compact footprint) Options: 2K / 2.5K LCD (120Hz) OLED touchscreen (higher contrast)| - Very thin bezels ? almost edge?to?edge screen - Weighs 2.2 lbs - one of the lightes out there and a rival to Apple's Macbook Neo Infighting - OpenAI may release multi-chip AI software, challenging Nvidia's (NVDA) ecosystem advantage, according to The Information - Oh, and NVDA is now releasing a CPU for PCs that is aggrevating Intel and AMD Kaboom! - Blue Origin's New Glenn rocket exploded in a massive fireball while undergoing a test on a Florida launchpad, dealing a major setback to the company. - The explosion is the latest blow to New Glenn's reputation as a reliable alternative to SpaceX's Falcon 9, and Blue Origin's launch schedule is certain to suffer significant delays. - The incident will also affect Amazon's ambitions to build out its Leo satellite network and may delay Blue Origin's role in NASA's Artemis program, which aims to send humans back to the moon. - As important as it will be for Blue Origin to diagnose the cause of the rocket explosion, it could take many months to repair its launchpad in Florida. Taking Down - Really? - BlackRock Inc. is trimming its bet on stocks across its model-portfolio business as US equities surge to record highs following a strong earnings season. - The firm cut its overweight position in equities from 3% to 1%, triggering billions of dollars of flows between BlackRock's exchange-traded funds. - BlackRock remains confident in equities and will maintain positions that bet on growing corporate profits, artificial intelligence and government spending, but is rotating away from longer-dated US debt in favor of global fixed-income and liquid alternatives. Slight - SpaceX is targeting a valuation of at least $1.8 trillion in its initial public offering, according to people familiar with the matter. - The company is seeking to raise as much as $75 billion, which would make it the biggest IPO of all time, and is expected to start formal marketing of its IPO as soon as June 4. -SpaceX had $18.7 billion in revenue in 2025, and the company's pitch to investors shows its evolution into an AI services and infrastructure giant with a total addressable market of $28.5 trillion. - 3-5% of the shares will be floated (TIGHT) Strategy: keep supply constrained, which: supports price discovery maintains founder control creates early scarcity dynamics - - - SpaceX has reserved 5% of the shares ?in its planned initial public offering for certain employees and individuals selected by its executive officers, exempting them from post-IPO lock-up restrictions AND.. Even more Valuations - AI giant Anthropic is now worth more than OpenAI. - Anthropic announced a $65 billion Series H financing at a $965 billion valuation, a round led by Altimeter Capital, Dragoneer, Greenoaks and Sequoia Capital. - The financing puts its valuation above that of rival AI lab OpenAI. - The valuation has TRIPLED since February Let's GO! - Shares of LG Electronics surged as much as 24% after the company announced a series of automotive innovations built with technology from Alphabet Inc.'s Google. - The company said its new range of solutions is built on Android automotive operating systems. Its system can control multiple displays with different aspect ratios at the same time by using a single-on-chip, which is different from other conventional in-vehicle display systems, LG said. - But 24% on this news? - More reason that the KOSPI is moving higher No One Care - But... - Inflation has been above the 2% target for 5 years now - Minneapolis Federal Reserve President Neel Kashkari said Thursday that bringing down inflation in the U.S. remains his top priority, warning that consumer prices are still “much too high.”| - Speaking to CNBC's Kaori Enjoji at the Bank of Japan-IMES Conference, Kashkari said that the U.S. central bank would continue taking a “balanced approach” to its dual mandate of price stability and full employment. - 5 YEARS! ---- What that tells us is that the Fed is totally unable to do anything about inflation .... Are we the only ones that see that? Inside Baseball - From a colegie that will go un-named. --- Let's just say he is someone who knows what they are talking about and runs BIG money ----- This is what he said to me..... - Apparently, oil execs were opining with POTUS in meetings yesterday that oil inventories are at alarmingly low levels and oil prices could soon skyrocket (I might soften that language a bit but they know the oil biz better than me) if SoH does not open soon. - I ran a few numbers on total oil inventories including and excluding the SPR. - Total supplies are 10th percentile vs history (although that includes a period when the SPR ramped from 0 to 600mln barrels in the 1980's). - Today it is 4th percentile if you start from 1990 when the SPR was basically full. - The 4 week net and % draw the last 3 weeks are the largest draws of all time. - And not surprising the 1 week net and % draw of the SPR are also the 2 largest draws of all time the last 2 weeks. Surprised - No.... --- This is another story similar to what we saw a few months ago - Taiwan prosecutors suspect that three individuals smuggled at least one shipment of Nvidia Corp. AI chips to China after first exporting them to Japan. - The trio was detained for allegedly falsifying documents related to exports of Super Micro Computer Inc. servers containing advanced Nvidia chips, which the US has barred from sale to China without a license. - Taiwan authorities seized about 50 servers for which they accuse the trio of preparing fraudulent export documents, but at least one shipment had already gone through Taiwan customs and made it to Hong Kong. Under/Over? - Tesla will be somehow folder/merged or taken over by SpaceX in an all stock deal - Tesla market cap is $1.6 Trillion so that will be a tough one to take on as SpaceX is about equal in size. ---- If this happens, when ? Mini Retirement - Is this a THING? - A mini retirement is when you take a planned break from working, usually for a few months to a couple of years, instead of waiting until age 65+ to fully retire. - Tim Feerris popularized this... (4 day workweek dude) Step 1: Work & save aggressively 2–10+ years Build a specific “freedom fund” Step 2: Take time off 3 months to 2 years Travel, recharge, pursue interests, or experiment with new ideas Step 3: Return to work Same career… or pivot to something new Then repeat if desired.   Love the Show? Then how about a Donation? Announcing the THE CLOSEST TO THE PIN for SALESFORCE (CRM)   Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter

Today's top stories, with context, in just 15 minutes.On today's podcast:1) The US and Iran have reached a preliminary deal to extend a ceasefire by 60 days and discuss the future of Tehran’s nuclear program, according to a person with knowledge of the matter. Vice President JD Vance said the US and Iran are “going back and forth on a couple of language points,” including over issues relating to Tehran’s nuclear capabilities, and that Iran appears to be negotiating in good faith. The US Treasury Secretary reiterated President Trump’s three “red lines” — reopening the Strait of Hormuz, Iran surrendering highly enriched uranium and ending its nuclear program — remain in place.2) Dell shares soared in extended trading after the company gave an outlook for annual sales that far surpassed analysts’ estimates. Revenue in the fiscal year ending in January 2027 will be about $167 billion, including $60 billion from the sale of AI servers, according to the company. Dell’s server business has been viewed as an AI winner this year, sparking the stock more than 150% higher through Thursday’s close.3) Anthropic PBC raised $65 billion in a funding round that valued the artificial intelligence company at $965 billion including the new investment. The funding was led by Altimeter Capital, Dragoneer, Greenoaks and Sequoia Capital, with each of the lead investors putting in more than $2 billion. Alphabet Inc.'s Google and Amazon.com Inc. also invested in the round, with Google contributing several billion dollars and Amazon investing $5 billion.See omnystudio.com/listener for privacy information.

In today's Tech3 from Moneycontrol, we unpack Bajaj Finance's aggressive AI push and how it could reshape lending and operations. We also look at the rapid rise of India's micro-drama market, set for explosive growth. Plus, Snowflake's bet on simplifying enterprise AI adoption without large engineering teams. And finally, deeptech momentum continues as AI data centre startup Kluisz looks to raise fresh capital amid rising investor interest.

AI startups in 2025 have raised $122 billion, with the United States accounting for $104 billion of the total. In the second quarter, AI companies secured $50 billion, nearly half of all venture capital invested during that period. Major deals include Meta's $14.3 billion investment in Scale AI, Anduril's $2.5 billion round led by Founders Fund, and Safe Superintelligence's $2 billion from Greenoaks, Alphabet, and Andreessen Horowitz. The sector is seeing increased investment in infrastructure-heavy AI projects, supported by a proposed $92 billion federal funding package. Overall venture capital funding has declined to $101.5 billion in the second quarter due to an ongoing IPO drought, with major AI firms such as Databricks and OpenAI remaining private. Capital is increasingly concentrated among leading AI startups, making fundraising more difficult for smaller companies, while investment firms like SoftBank, Andreessen Horowitz, Tiger Global, Sequoia, and Lightspeed continue to lead in AI investments.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Peter Rahal is the Co‑Founder & CEO of David Protein, the highest protein‑to‑calorie ratio for any protein bar on the market. Peter has raised over $85M from Greenoaks, Dr. Peter Attia and Dr. Andrew Huberman with the latest round valuing the company at $725 million. The company is poised for over $100 million in first‑year revenue. Formerly, Peter co‑founded RXBAR in his mom's basement with a $10k start, growing it into a household brand and selling it to Kellogg for $600 million. poised for over $100 million first‑year revenue   Agenda for Today:  00:04 – The One Piece of Advice from My Father That Made $600M 00:07 – Selling Protein Bars from a CrossFit Gym to $2M in Year One 00:12 – Why Raising Money Early Would Have Killed RXBAR's Success 00:15 – Product vs Brand: What Every Brand Gets Wrong Today 00:17 – Why Red Bull is the Best Brand in the World? What Can We Learn From It?  00:20 – Are Brands the New Religion? How Status and Community Really Work 00:27 – The Boiled Cod Stunt: Brilliant Marketing or Massive Waste of Time? 00:35 – Selling RXBAR for $600M: Inside the Decision and the TAM Ceiling 00:40 – $100M Overnight: What Really Changes When You Get Rich 00:44 – The Hidden Costs of Success: Health, Relationships and Obsession 00:47 – Why Peter Doesn't Care What People Think… and Actually Likes Upsetting Them 00:53 – The $10B Plan for David: From Protein Bars to a Portfolio of Brands  

Send us a text[Original air date, April 9, 2024]  Miguel Armaza interviews Jack Zhang, CEO & Co-Founder of Airwallex, a global payments and treasury giant that moves $7 billion in monthly transaction volume and serves 100,000+ clients, including Brex, Rippling, and SHEIN.Founded in Melbourne in 2015, Airwallex was last valued at $6.2 billion and has raised over a billion dollars from Sequoia, DST, Square Peg, Greenoaks, Mastercard, Tencent, Salesforce, and many more.In this episode, we discuss:How investing in a coffee shop led him to co-found a global payments networkWhy Airwallex prioritizes people with great leadership skills who are also technicalThe impact of AI in financial servicesNew risks of AI-powered financial fraudPartnering with almost 100 banks around the world… and a lot more!Want more podcast episodes? Join me and follow Fintech Leaders today on Apple, Spotify, or your favorite podcast app for weekly conversations with today's global leaders that will dominate the 21st century in fintech, business, and beyond.Do you prefer a written summary? Check out the Fintech Leaders newsletter and join 80,000+ readers and listeners worldwide!Miguel Armaza is Co-Founder and General Partner of Gilgamesh Ventures, a seed-stage investment fund focused on fintech in the Americas. He also hosts and writes the Fintech Leaders podcast and newsletter.Miguel on LinkedIn: https://bit.ly/3nKha4ZMiguel on Twitter: https://bit.ly/2Jb5oBcFintech Leaders Newsletter: bit.ly/3jWIp

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.  

Today's guest is Neil Mehta, founder of Greenoaks Capital. In 2012, aged 27, Neil left D.E. Shaw to start Greenoaks with his friend Benny Peretz. One of their first investments was in Coupang, a South Korean e-commerce company led by founder Bom Kim. Neil was so convinced of Coupang's potential that he invested 40% of their initial $50 million fund into the company—a bet that eventually returned about $8 billion. Over its first 13 years, Greenoaks has backed legendary companies like Figma, Wiz, Carvana, Stripe, Discord, Rippling, and Toast—generating over $13 billion in gross profits with a 33% net IRR. Henry Kravis, one of Neil's early investors, describes him as "extremely disciplined" with "exceptional timing" who has "gone against the tide many times." Greenoaks operates with remarkable concentration: just 55 core companies across nearly $15 billion in assets, managed by only nine investment professionals. Their approach reflects their singular pursuit: finding companies that will become a meaningful part of the S&P 500. In our wide-ranging conversation, Neil shares this mission along with his framework for identifying exceptional founders, his concept of "jaw-dropping customer experiences," and how his grandfather's gun shop in India shaped his appreciation for builders of all kinds. Please enjoy my excellent conversation with Neil Mehta. Neil Mehta's Profile in Colossus Review. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Ramp. Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to Ramp.com/invest to sign up for free and get a $250 welcome bonus. – This episode is brought to you by Ridgeline. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Head to ridgelineapps.com to learn more about the platform. –  This episode is brought to you by AlphaSense. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Invest Like the Best listeners can get a free trial now at Alpha-Sense.com/Invest and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Welcome to Invest Like the Best (00:06:32) Connecting Craftsmanship to Career (00:07:45) The Concept of Jaw Dropping Customer Experience (JDCE) (00:09:48) Building a Successful Business: The Coupang Case Study (00:17:26) The Importance of Founders & Business Models (00:30:05) Greenoaks' Unique Approach to Venture Capital (00:37:54) A Memorable Encounter with Henry Kravis (00:40:52) Early Career and Lessons from Hong Kong (00:44:53) The Partnership with Benny (00:50:28) Navigating the Competitive Landscape (00:59:14) High Conviction Investments: TripActions, Rippling, and Carvana (01:07:00) Investment Strategy and Company Evaluation (01:13:23) Adventures in Emerging Markets (01:17:09) Challenges and Lessons Learned (01:26:16) Personal Values and Community Impact (01:32:16) The Kindest Thing Anyone Has Ever Done For Neil

We're opening in a celebratory mood this week with Wiz's big exit to Google, which if it holds will offer some much needed liquidity to venture firms and their LPs. It's a big win for several of Silicon Valley's heavy hitters, including Sequoia's Doug Leone, Index Ventures' Shardul Shah, and Greenoaks' Neil Mehta. IPOs are looking a little bit more uncertain, however. Plus, Deel's allleged corporate espionage at Rippling has all the makings of a great HR tech spy thriller. Next up, we touch on the new liberal "abundance" agenda, which has more than a few similarities to Marc Andreessen's "Time to Build" manifesto. In the second half of our show, Eric sits down with Browserbase CEO Paul Klein to discuss their tools for running headless browsers and their rapid growth over the past year.Produced by ⁠Christopher Gates⁠

We're opening in a celebratory mood this week with Wiz's big exit to Google, which if it holds will offer some much needed liquidity to venture firms and their LPs. It's a big win for several of Silicon Valley's heavy hitters, including Sequoia's Doug Leone, Index Ventures' Shardul Shah, and Greenoaks' Neil Mehta. IPOs are looking a little bit more uncertain, however. Plus, Deel's allleged corporate espionage at Rippling has all the makings of a great HR tech spy thriller. Next up, we touch on the new liberal "abundance" agenda, which has more than a few similarities to Marc Andreessen's "Time to Build" manifesto. In the second half of our show, Eric sits down with Browserbase CEO Paul Klein to discuss their tools for running headless browsers and their rapid growth over the past year.Produced by ⁠Christopher Gates⁠

Amitt Mahajan is the Co-Founder of Proof of Play. Proof of Play's mission is to create fun, accessible onchain games and, in the process, develop novel technology that makes onchain game development easier for everyone. Why you should listen Founded by leaders from Epic Games, Zynga, EA, Riot Games, Activision, Google, Facebook, Disney, and Warner Brothers, Proof of Play's mission is to create fun, accessible onchain games and, in the process, develop novel technology that makes onchain game development easier for everyone.  The company's flagship game, Pirate Nation, is a fully onchain RPG where players battle monsters, build their pirate crew, and compete to become number one on the captain leaderboard.  The team has raised $33M in funding and is supported by a16z, Greenoaks, and other investors. Pirate Nation is a fully onchain free-to-play pirate-themed roleplaying game (RPG). Pirate Nation is live on Proof of Play's Apex chain. Pirate Nation is a fully onchain game. This is a new type of game that's only possible using blockchain technology. The game and all of its functionality is running on the blockchain using hundreds of smart contracts. Onchain games have a lot of unique characteristics that distinguish them from traditional, centralized games: All player items, actions, and achievements are publicly viewable and auditable.  Onchain games are "forever games" and will continue to operate as long as the blockchain they are running on exists. It is possible for onchain games to be able to be remixed. Meaning players can add their own features or even create fully derivative works from the game. Onchain games are more secure and transparent. They are enforced with the same security that protects other assets on blockchains. The Proof of Play engine, which powers Pirate Nation, isn't just another gaming chain—it's a cutting-edge onchain game engine designed to enable developers to build games onchain faster than offchain. They are developing a powerful, decentralized foundation for the next generation of onchain games, starting with Pirate Nation. On November 14th, Proof of Play launched Pirate Nation Season 3, adding a refreshed crafting system, two new leaderboards, and an enhanced staking experience, deepening player engagement. Supporting links Stabull Finance Proof of Play Pirate Nation Andy on Twitter  Brave New Coin on Twitter Brave New Coin If you enjoyed the show please subscribe to the Crypto Conversation and give us a 5-star rating and a positive review in whatever podcast app you are using.

Chris Walti is a seasoned engineer and entrepreneur whose career spans across world-class companies like Tesla and innovative startups. His story is one of resilience, constant reinvention, and an unwavering passion for building, culminating in the establishment of Mytra. Mytra has attracted funding from top-tier investors like Greenoaks, Eclipse Ventures, 515 Ventures, and Promus Ventures.

Pedro Franceschi is the Co-Founder and CEO @ Brex, the AI-powered spend platform with tens of thousands of customers, including DoorDash, Coinbase, Robinhood and Roblox. Pedro has raised over $1.2BN for the company from the likes of Greenoaks, Ribbit, DST, Bond and YC. The latest reported valuation was $12.3BN. Before Brex, Pedro was the first person to “jailbreak” the iPhone 3G in Brazil and co-founded payments company Pagar.me with Dubugras when he was 15. In three years, Pedro scaled it to over 100 people and US$1.5 billion in transactions processed. In Today's Episode with Pedro Franceschi We Discuss: 1. The Challenge is in Your Own Head: Why does Pedro believe all founders underestimate their own mental health? When was Pedro most anxious/depressed in the Brex journey? Why? What have been the single biggest needle movers for increasing his own mental health? How does Pedro advise other founders struggling with their own mental health? 2. From a 13-Year-Old Hacker in Brazil to Billionaire in LA: How did Pedro come to make $200K on the internet when he was just 12? Does Pedro agree that the best founders always started entrepreneurial pursuits young? How does Pedro reflect on his own relationship to money today? How has it changed? Pedro has famously taken large secondaries, how did that impact his mindset? How does Pedro advise other founders and VCs when it comes to secondaries? 3. The Importance of the Idea: What Everyone Misunderstands: What does Pedro mean when he says everyone does not appreciate enough how important the idea selection process is? How does he advise founders entering this process? Why does Pedro believe it is not that easy for founder to just pivot to a new idea? How did YC almost miss out on investing in Brex, now a $12BN company, due to the original idea? 4. Brex vs Ramp: Who Wins: How does Pedro feel when I say, "Ramp have gotten ahead on marketing and visibility"? Why does Pedro believe that "Ramp is a marketing company"? What does he mean when he says "great products will win over time"? Why does Pedro fundamentally disagree with Ramp's positioning of the best companies focus on saving and their giving away their software for free? How does this market play out over time? Winner take all or gains split across several?  

Andrés Gómez es un joven abogado colombiano que se mudó a Chile para trabajar en tecnología y que gracias a ese paso, en el 2021 llegó a México para abrir la filial de una startup HRTECH de origen chileno en este país.Hoy es Country Manager de BUK, una compañía, que ofrece un sistema integrado para la gestión de recursos humanos en una sola plataforma. Esta empresa recibió una valoración de US$500 millones en febrero 2023, cuando anunció una segunda ronda de financiamiento por US$35 millones por parte de Base10 Partners y Greenoaks para potenciar su crecimiento a partir de la internacionalización, y después de haber recibido US$50 millones cuatro meses antes.Aunque Andrés es abogado de profesión, lleva más de 8 años en el mundo de los negocios con mayor énfasis en la parte comercial. Desde el 2014 ingresó al mundo de las startups en Chile. Primero colaboró con Lemontech, una compañía especializada en software para abogados. Después formó parte de Nivelat, una plataforma de microlearning.* Digitalización de Recursos Humanos: El episodio discute cómo la tecnología y la inteligencia artificial están transformando las funciones de recursos humanos, desde la nómina hasta la gestión de talento.* Historia de Andrés Gómez: La trayectoria profesional de Andrés Gómez, desde sus inicios en Chile hasta convertirse en Country Manager de Book en México, destacando su experiencia en startups y su enfoque en el crecimiento comercial.* Estrategias de Crecimiento de Book: La evolución de Book, una empresa de tecnología para recursos humanos, incluyendo su enfoque en la expansión internacional y el uso de inteligencia artificial para optimizar procesos internos.Para más información y contenidos exclusivos:* Blog / Newsletter: Cuentos Corporativos en Substack* Facebook: Cuentos Corporativos en Facebook* Instagram: Cuentos Corporativos en Instagram* X (Twitter): Cuentos Corporativos en X* Email: adolfo@cuentoscorporativos.com#DigitalizaciónRH #InteligenciaArtificial #RecursosHumanos #Startups #Tecnología #TransformaciónDigital #CuentosCorporativos #BookEnMéxico #Emprendimiento This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cuentoscorporativos.substack.com

From humble beginnings in Sunnyvale to leading a groundbreaking AI company, Varun Mohan's journey is a testament to resilience, innovation, and a relentless pursuit of impactful solutions.In this interview, Varun shares his story, insights on the evolution of technology, and the strategic pivots that led him from autonomous vehicles to the creation of Codeium, an AI code acceleration tool transforming the software development landscape.Codeium has attracted funding from top-tier investors like Kleiner Perkins, General Catalyst, Greenoaks, and Founder's Fund.

SmartRecruiters, announced its latest product release, packed with new features designed to streamline the recruitment process and empower hiring teams. This April, users can explore new functionalities within the SmartRecruiters' platform, including advanced AI capabilities, improved integration options, and significant user interface enhancements. The highlights include: https://hrtechfeed.com/smartrecruiters-announces-new-release/ ShiftMed, a leader in On-Demand workforce technology, announced today its acquisition of CareerStaff Unlimited from Genesis HealthCare (Genesis), a national post-acute care provider. CareerStaff is one of the healthcare industry's leading per diem and contract managed service providers (MSP). ShiftMed, in combination with CareerStaff, will provide services to Genesis at over 200 skilled nursing and 1,200 Powerback Rehabilitation locations, under a seven-year exclusive agreement. https://hrtechfeed.com/shiftmed-acquires-msp-to-expand-its-on-demand-nursing-platform/  Daxtra, the resume parsing software platform, has acquired PivotCX, a technology-based talent acquisition and HR communications hub. Based in Indianapolis, PivotCX has become part of the Daxtra Group, and its products will be rolled into the Daxtra suite of solutions. The combined solution will deliver a powerful platform for talent acquisition teams, providing customers the opportunity to direct high-conversion candidate engagement more easily. https://hrtechfeed.com/daxtra-acquires-pivotcx/ Crosschq, the world's first Hiring Intelligence platform purpose-built to increase Quality of Hire, today announced the availability of Crosschq Insights, a first-of-its-kind recruiting analytics and Quality of Hire engine. Insights empowers recruiting teams by allowing them to pull data from multiple sources into a single dashboard, offering a more comprehensive and controlled look at the hiring process. https://hrtechfeed.com/crosschq-launches-recruiting-analytics-and-quality-of-hire-engine/ HCM platform Rippling has raised $200mm in new financing, and signed agreements with investors to repurchase up to $590mm of equity from current employees, former employees, and early investors. The financing was led by Coatue with participation from Founders Fund, Greenoaks, and other existing investors. Dragoneer is joining the round as a new investor. https://hrtechfeed.com/rippling-raises-200-million-series-f/

Miguel Armaza interviews Jack Zhang, CEO & Co-Founder of Airwallex, a global payments and treasury giant that moves $7 billion in monthly transaction volume and serves 100,000+ clients, including Brex, Rippling, and SHEIN.Founded in Melbourne in 2015, Airwallex was last valued at $5.5 billion and has raised 900 million dollars from Sequoia, DST, Square Peg, Greenoaks, Mastercard, Tencent, Salesforce, and many more.In this episode, we discuss:How investing in a coffee shop led him to co-found a global payments networkWhy Airwallex prioritizes people with great leadership skills who are also technicalThe impact of AI in financial servicesNew risks of AI-powered financial fraudPartnering with almost 100 banks around the world… and a lot more!Want more podcast episodes? Join me and follow Fintech Leaders today on Apple, Spotify, or your favorite podcast app for weekly conversations with today's global leaders that will dominate the 21st century in fintech, business, and beyond.Do you prefer a written summary? Check out the Fintech Leaders newsletter and join 65,000+ readers and listeners worldwide!Miguel Armaza is Co-Founder and General Partner of Gilgamesh Ventures, a seed-stage investment fund focused on fintech in the Americas. He also hosts and writes the Fintech Leaders podcast and newsletter.Miguel on LinkedIn: https://bit.ly/3nKha4ZMiguel on Twitter: https://bit.ly/2Jb5oBcFintech Leaders Newsletter: bit.ly/3jWIp

Varun Mohan is the cofounder and CEO of Codeium, an AI code generation tool used by hundreds of thousands of developers. They recently announced their $65M Series B led by Kleiner Perkins with participation from Greenoaks and General Catalyst. He was previously at Nuro. He has a bachelors and masters degree from MIT. Varun's favorite book: The Idea Factory (Author: Jon Gertner)(00:00) Introduction and State of Play(03:03) What Generative AI Can Do Well(06:10) Introduction to Codeium(08:53) Handling Different Programming Languages(11:26) Model Architectures and Optimization(13:27) Interpreting and Trusting AI Decisions(18:33) Security and Privacy Considerations(20:07) Impact on Software Quality and Developers(21:50) Potential Obsolescence of Programming Languages(23:39) Handling Edge Cases(26:07) The Biggest Impact of Generative AI for Coding(28:27) Technological Breakthroughs in Generative AI(29:30) Rapid Fire Round--------Where to find Prateek Joshi: Newsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-91047b19 Twitter: https://twitter.com/prateekvjoshi 

Ryan Akkina is a member of the Global Investment Team at the MIT Investment Management Company (MITIMCo), which is responsible for managing MIT's endowment and pension plans. Ryan has invested in the likes of Sequoia, Kleiner Perkins, a16z, Greenoaks and Initialized to name a few. Ryan also leads many of MITIMCo's direct co-investments including most notably into Coupang and Rippling. Prior to joining MITIMCo, Ryan was a consultant at McKinsey & Company. In Today's Episode with Ryan Akkina We Discuss: 1. From Engineer to LP with MIT: How did Ryan make his way into the world of fund investing as an LP with MIT? Why did he turn down the chance to be a VC early in his career? What does Ryan know now that he wishes he had known when he started at MIT? 2. The Manager Evaluation Process for MIT: What does Ryan look for most when investing in new managers? How important is track record when evaluating a new manager? What is the biggest mistake Ryan has made in picking a manager? What did he not see that he wish he had seen? How did that change his process? 3. How MIT Builds Their Portfolio: How does MIT construct their portfolio from private to public to everything in between? What are the three different types of check sizes that MIT writes when investing in new managers? What are the most common reasons why MIT will not re-up with a manager? What are the single biggest reasons why great managers turn bad? 4. MIT: The Direct Investor: Why does MIT see so much opportunity in direct investing? How does MIT approach the direct investing process? How do they approach underwriting themselves vs working with their managers in the process? How do MIT think about the right number of direct deals to make up their portfolio? How do they approach check sizing on a per-company direct investment? What has been Ryan's biggest direct investing mistake? How did that change his approach and mindset? 5. LP Markets Today and Where We Go From Here: Are LPs open for business today? What type of firms will not struggle? Which will? How does Ryan view liquidity windows today? When will M&A and IPO markets open? What would Ryan most like to change about the world of LPs? Why does Ryan believe the LP incentive structure in terms of compensation is broken?

Peter Lacaillade is a Managing Director @ SCS Financial Services where he leads its private investment program where he oversees the firm's activities in private equity, opportunistic credit and private real assets. Peter has been an early backer of Thrive, Founders Fund, a16z, Greenoaks and 20VC. Before SCS, Peter was an Associate at HarbourVest Partners in its Secondary Group where he analyzed venture capital, growth equity and buyout investments. In Today's Episode with Peter Lacaillade We Discuss: 1. Becoming One of the Great LPs in Venture: How did Peter make his way into the world of fund investing as an LP? What does Peter know now that he wishes he had known when he started as an LP? Why does Peter believe now is the best time to be investing in newer, emerging managers? 2. How to Pick the Best Venture Managers: What are the commonalities in the best VCs Peter has invested in? How important is track record for Peter when evaluating managers? What mistakes has Peter made when it comes to manager selection? What did he learn? How do the best managers build relationships with their LPs? 3. Building a Portfolio That Can 5x: In a venture fund portfolio, what is the distribution between those that outperform, perform as planned and then underperform? How does Peter invest in both large franchises and emerging managers with a barbell approach? How much in established franchises and how much in emerging managers? Are managers actively marking down their portfolios in the last 18 months? Who has been the best at this and who has been the worst? How much should portfolios be marked down? How does Peter evaluate the compression of deployment timelines we saw in the last 18 months? 4. A Breakdown of the LP Landscape: Family Offices: What are the biggest dangers of having family offices as LPs? Why do multi-family offices tend to be better? Endowments: Are they really as stable as people think they are? What separates a good vs great endowment? Who stands out? Fund of Funds: Why does Peter think fund of funds deserve more credit? How should managers think about working with FoFs most effectively? What is the right level of concentration managers should have between these different LP profiles? What are the biggest mistakes emerging managers make when approaching LPs?

Andrés Gómez es un joven abogado colombiano que se mudó a Chile para trabajar en tecnología y que gracias a ese paso, en el 2021 llegó a México para abrir la filial de una startup HRTECH de origen chileno en este país.Hoy es Country Manager de BUK, una compañía, que ofrece un sistema integrado para la gestión de recursos humanos en una sola plataforma. Esta empresa recibió una valoración de US$500 millones en febrero 2023, cuando anunció una segunda ronda de financiamiento por US$35 millones por parte de Base10 Partners y Greenoaks para potenciar su crecimiento a partir de la internacionalización, y después de haber recibido US$50 millones cuatro meses antes.Aunque Andrés es abogado de profesión, lleva más de 8 años en el mundo de los negocios con mayor énfasis en la parte comercial. Desde el 2014 ingresó al mundo de las startups en Chile. Primero colaboró con Lemontech, una compañía especializada en software para abogados. Después formó parte de Nivelat, una plataforma de microlearning.Puedes escuchar Cuentos Corporativos en vivo a través de la señal de Radiomex. Todos los martes y jueves a las 8 pm, hora de la Ciudad de México.Suscríbete aquí El newsletter de Cuentos Corporativos. Recibirás todas las semanas información sobre nuestros episodios y eventos.¿Te gustaría proponer a un invitado? Hazlo aquí o contáctanos a través de contacto@cuentoscorporativos.com¿Te gusta Cuentos Corporativos? Apóyanos con tu reseña. Déjala aquíAyúdanos a mejorar. Dinos qué opinas de Cuentos Corporativos, respondiendo esta breve encuesta. Muchas gracias!www.cuentoscorporativos.comFacebook InstagramLinkedInTwitter Hosted on Acast. See acast.com/privacy for more information.

Today's guest is Hristo Borisov, cofounder and CEO at Payhawk, a financial system that combines credit cards, payments, expenses, and cash into one integrated experience. Hristo has raised over $236 million with Payhawk from Lightspeed Ventures, Greenoaks, QED investors, and many more. Today, Payhawk is valued at US$1 billion, making it the first Bulgarian startup to achieve Unicorn status. Prior to founding Payhawk, Hristo worked at Telerik as software engineer and a product manager. In this episode, we talk about his founding of Payhawk, the challenges he encountered as an entrepreneur, lessons learned from scaling Payhawk into 32 countries, and much more.

Ville Tuulos is the cofounder and CEO of Outerbounds, a platform to develop and deploy production-grade AI applications. They have raised $24M in funding so far from investors such as Foundation, Amplify, and Greenoaks. He was previously at Netflix and AdRoll. Prior to that, he was the cofounder of Bitdeli. In this episode, we cover a range of topics including: - Origin of the open source framework Metaflow - The founding of Outerbounds - AI compute clusters - Large foundation models vs smaller specialist models - Training compute-optimal LLMs - Industrial AI - Multimodal AI - Building LLMs through experimentation - How will the AI compute market shape up (chips, cloud services, infrastructure platforms) Ville's favorite book: Surely You're Joking, Mr. Feynman! (Author: Richard Feynman)--------Where to find Prateek Joshi: Newsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-91047b19 Twitter: https://twitter.com/prateekvjoshi 

Guillermo Rauch is the Founder and CEO @ Vercel, giving developers the frameworks, workflows, and infrastructure to build a faster, more personalized Web. To date, Guillermo has raised $312M from Accel, Bedrock, Greenoaks, GV and more. Prior to founding Vercel, Guillermo co-founded LearnBoost and Cloudup where he served the company as CTO through its acquisition by Automattic in 2013. In Today's Episode with Guillermo Rauch We Discuss: 1. From Argentina to SF: The Boy Making Money Online: How did Guillermo first get into computers and start making money online? Does Guillermo still believe the US and SF offers the same opportunities it did when he came? Did Guillermo feel the weight of responsibility of providing for his family at a young age? 2. Timing, Markets and Narrative Violations: Why does Guillermo believe it does not matter being first but being right? Why does Guillermo believe the most important thing for a company is market selection? Why does Guillermo believe it is crucial that founders and companies have "narrative violations"? 3. The Future of AI: What model will win in the future; open or closed? Where does the value accrue; startups or incumbents? How will the SaaS business model change in a world of AI? 4. Silicon Valley's Most Successful Angel You Did Not Know: What are some of Guillermo's biggest lessons from angel investing? What is his single biggest miss? How has it changed how he thinks? What have been his biggest hits? How did they impact how he thinks about what it takes to win?

In questo primo episodio dei Remote Addendum vi presentiamo l'ambientazione e le meccaniche di gioco di Haunted Green Oaks

3 giovani anziani del fu ospizio di Green Oaks, prematuramente passati a vita ectoplasmica, si trovano ad affrontare una terribile minaccia. La loro dimora ancestrale sta per essere trasformata in un locale all'ultima moda con discoteca, spa e altri moderni intrattenimenti. Guardare i cantieri è sempre un passatempo interessante ma l'inazione potrebbe avere conseguenze tragiche

Bitcoin is up slightly at $26,577 Eth is up slightly at $1,592 BNB is up slightly at $210 Coinbase considered buying FTX Europe to expand derivatives Peter Marton, head of NYDFS virtual currency unit is stepping down. Consensys to deprioritize Truffle and Ganache. Proof of Play raises $33M in seed led by Greenoaks & a16z. Learn more about your ad choices. Visit megaphone.fm/adchoices

Howie Liu is the Founder and CEO @ Airtable, the fastest way to build apps for your business. To date, Howie has raised over $1BN with Airtable with the last round valuing the company at $11BN and an investor base including Benchmark, Thrive, Caffeinated, Greenoaks and Coatue to name a few. In Todays Episode with Howie Liu We Discuss: 1. Scaling into Enterprise: What are the single biggest challenges when moving from PLG to enterprise? Why does Howie believe you have only truly hit enterprise when you sign $1M contracts? How long did it take for Airtable to sign their first $1M ARR contract? How can founders know when is the right time to scale into enterprise? How does the product need to change with the scaling? 2. Enterprises: Do They Really Love AI: Why does Howie believe that enterprises are not jumping on AI yet? When does enterprise interest turn into enterprise buying and purchasing? What are the single biggest barriers to enterprises buying AI solutions today? Post-purchase, what are the biggest implementation challenges for enterprises with AI? 3. The Changing Sales Process: Are we seeing the bundling of tools within large enterprises today? Which categories and vendors are most vulnerable? Which will survive the cuts? What do vendors need to do to prove to CFOs that they need to remain in their budget? How has the customer success process changed over the last year with tightening budgets? 4. Howie Liu: AMA: Airtable famously got Benchmark to lead their Series C, how did this come to be when they famously always only do Series A? Why does Howie believe that it is total BS to suggest post-PMF, everything is good? What does Howie know now that he wishes he had known when he started Airtable?

The Idiots talk spirits with Derek and Sonja Kassebaum from North Shore Distillery and they learn that risk is good! When does the Pringles movie come out?

Uri Kolodny is now on his third startup. He's now working on his biggest and boldest tech venture so far. His startup, Starkware, has attracted funding from top-tier investors like Alameda Research, Coatue, Greenoaks, and Tiger Global Management.

Pre-IPO Stock Market Update - Mar 17, 2023 | Tiger Global marks down pre-IPO stocks, eToro cap raise, Checkout launches debit card issuing, Tyler's Corner (Revolut, Rippling, SpaceX)00:34 | Tiger Global marked down one of its venture fund portfolio by 33%, or $23b- SoftBank Group marks down 30% Vision Fund 2 ($48b)- Preferred shares play an interesting role in this valuation processPreferred shares have a liquidation preference, meaning that the preferred shareholder is guaranteed some level of return before other shareholders lower on the cap table are paid out.02:12 | eToro secures $250M at a $3.5B valuation- The company was to go public via a SPAC but called off the deal in Jul 2022- Round was via Advanced Investment Agreement (AIA)- 2.8m total 2022 funded accounts; up 17% from 2021 and 180% from 2022- $631m total 2022 commissions; down 49% from 2021 and up 5% from 2020- $5.8 billion in assets under administration across 100 countries03:06 | Checkout.com launches virtual and physical card issuing- The company has been testing Checkout.com Issuing for a while, and millions of cards have already been created with the new service- Checkout.com customers now have an opportunity to earn interchange revenue- $40b valuation at it last primary financing round in Jan 2022 but issues a 409A in Dec 2022 at $11b04:32 | Tyler's Corner by Tyler Siconolfi- Revolut partnered with Comic Relief to make it easier for Revolut customers to donate to those living in poverty. Comic Relief is a poverty focused charity in England.- Rippling raised $500m in Series E financing in just 12 hours from Greenoaks, a long-time investor. The money was used to help Rippling's customers pay employees that did not receive paychecks due to SVB failure.- SpaceX CEO Elon Musk announced that they will perform their first orbital test flight of Starship in April and is waiting on FAA approval05:42 | Large capital raises- Stripe | $6.5b Series I, $56.5b valuation- Rippling | $500m Series E, $11.8b valuation- Adept | $350m Series B, $1.0b valuation- Prizeout | $160m Series C, $760m valuation- Paige | $20m Series D, $650m valuation06:27 | Pre-IPO stock market performance- Pre-IPO stocks were down 3.54% for the week vs the S&P 500 up by 1.36%. Not a great week- YTD pre-IPO stocks still trail the S&P by about 8.5%- OpenSea and Kraken are both up north of 20% … Stripe, Airtable, Epic Games, and Chime are all down over 20%- No big winners this week. Kraken led the pack up 0.43%. Brex was down 17% for the week. Deel was down 14%. Revolut down 6%.AG Dillon & Co venture capital funds...- AG Dillon SpaceX Pre-IPO Stock Fund = www.agdillon.com/spacex- AG Dillon Pre-IPO Equity Fund (top 15 pre-IPO stocks) = www.agdillon.com/top15Subscribe or follow...Youtube = https://www.youtube.com/channel/UCSpr_9yjBA7dhqnQexSu7LAApple Podcasts = https://podcasts.apple.com/us/podcast/this-week-in-pre-ipo-stocks/id1653598601Spotify Podcasts = https://open.spotify.com/show/2ryF1V6y712AsizaRjImOHInstagram = https://www.instagram.com/aarongdillon/Facebook = https://www.facebook.com/profile.php?id=100089996314705LinkedIn = https://www.linkedin.com/company/ag-dillon-co

In this week's Espresso, we cover updates from Buk, Minu, Datamart, and more!Outline of this episode:[0:28] – Plerk and Minu announce their merger [0:43] – Buk raises $35M to enter the Brazilian market[1:03] – Datamart raises a $6.3M round[1:23] – Comun secures $4.5M in a funding round[1:39] – LatamList featured articlesResources & people mentioned:Startups: Minu, Plerk, Buk, Datamart, ComunVC firms: Base10 Partners, Greenoaks, SoftBank Latin America, Moonvalley Capital, Banco Santander, Grupo Falabella, BICE, Costanoa Ventures, South Park Commons, FJ LabsPeople mentioned: Juan Pablo Capello, Gino Ferrand

Ariel Cohen is the Co-Founder and CEO @ Navan (formerly TripActions), the #1 travel management super-app used by over 8,000 companies. Ariel has raised over $2BN for Navan from some of the best including a16z, Zeev Ventures, Lightspeed, Greenoaks, and Elad Gil. Prior to TripActions, Ariel co-founded streamOnce, a business multimedia integration platform that was successfully acquired by Jive Software, where Ariel had previously served in a senior position following his time at Hewlett-Packard. In Today's Episode with Ariel Cohen We Discuss: 1.) Why Education is Outdated and Wisdom to People Entering the Working World: Why did Ariel not really attend many classes when he was a student? What would be his biggest advice to young people leaving school today? Where would he focus? Why does Ariel believe that traditional education is more outdated now than ever before? 2.) Why SAP and Salesforce Will Die: Why does Ariel believe that SAP and Salesforce have not innovated for a decade? Why does Ariel believe that Slack is a disaster inside of Salesforce? What are the single biggest advantages that startups have over these large incumbents? What can startups do to retain innovation and speed as they scale into becoming an incumbent? Why are the best founders willing to kill their own projects? 3.) Growing a Business 3x and Raising at a $9.2BN Valuation in COVID: How did Ariel grow the business 3x with all travel being banned? What were the tactics to blitz scaling during COVID? How did Ariel approach his investors for a new round in the middle of COVID? How did he get such a high price in the midst of a global pandemic? What is the bull case for how Navan can be a $40BN company? 4.) Margins Matter: Gaining Leverage Through Additional Margin: With Navan's 80% margin, they have 30% higher margins than other competitors, how do they have such high margins? With the additional 30%, how does Ariel plan to scale Navan's reach and use the margin to do so? How does OpenAI play a role in helping Navan increase its margin even further?

Mai avremmo pensato prima d'oggi di poter interpretare dei simpatici vecchini di una RSA. Green Oaks ci trascina nel nostro ineluttabile futuro, con una nota di dark humor, fantasy e mistero.Dalle menti di Roberto De Luca e della crew di Fumble, ecco un gioco che oseremmo definire... Atipico.Ve ne parlano L'Oste e Frank in questa puntata del nostro speciale natalizio con Tarantasia.Music by AudioCoffee from Pixabay

Puntata n 17!Puoi supportare il podcast offrendoci un Caffè qui su KO-FIhttps://ko-fi.com/boardgamesofferteHai anche tu esperienze di gioco legate a quello che abbiamo detto in puntata e ti va di raccontarcele?.. scrivici su Spreakerhttps://www.spreaker.com/show/esperienze-di-giocoAbbiamo straparlato di e citato, queste coseArk novahttps://amzn.to/3ge5F6MInis Big Boxhttps://amzn.to/3EeRsOUD&d Dadi Trasformabili Hasbrohttps://bit.ly/3gaFk9QBakuganhttps://amzn.to/3X2H7OCTgcom24 Parla di Inishttps://bit.ly/3AnIazbSpirit Islandhttps://amzn.to/3GsIctcMysterium Parkhttps://amzn.to/3AlG4Q0Il sesto Senso.Mysterium (gioco)https://amzn.to/3V5CfGQIl sesto senso (Film)https://amzn.to/3AndDBiGreen Oakshttps://amzn.to/3OfT5AyPartita a Green Oaks (LaGiocofamiglia Youtube)https://youtu.be/36XjPo5Dj8MFiltro Orecchie Cane Snapchathttps://bit.ly/3TKZ3uvGli anni 80 prepotentemente in una sola rivista…Cioèhttps://bit.ly/3hSo619Kim Jong Un mentre sfoggia le sue artihttps://bit.ly/3GpydVIFiona (e Shrek)https://bit.ly/3Ochy9ZAdriano Pappalardo - Ricominciamohttps://youtu.be/1Hcf2TxhhX4INTRO Otierre - La nuova realtàhttps://youtu.be/7DYMnYpDdT4OUTRO Frankie Hi-Nrg Omaggio, Tributo, Riconoscimento > https://youtu.be/esnMHQMkN2A

Miguel Armaza sits down with Hristo Borisov, CEO and Co-Founder of Payhawk, the first-ever Bulgarian unicorn where they've built an all-in-one finance platform serving businesses across 32+ countries in Europe and the US.Founded just four years ago, Payhawk now enjoys unicorn status and has raised equity from Greenoaks, QED, Lightspeed, Endevor, Hubstpot, and many more.In this episode, we discuss:Scaling to unicorn status – Hristo shares hacks and techniques that helped them scale from zero to oneThe growing Bulgarian startup ecosystem and why it pushed them to build a global company from day onePayhawk's unique model for building and shipping products extremely fast, while keeping everyone at the company laser-focusedEarly mistakes and challenges as a fintech founder and why you should always overinvest in talent… and a lot more!Want more podcast episodes? Join me and follow Fintech Leaders today on Apple, Spotify, or your favorite podcast app for weekly conversations with today's global leaders that will dominate the 21st century in fintech, business, and beyond.Do you prefer a written summary, instead? Check out the Fintech Leaders newsletter and join 46,000+ readers and listeners around the world!Miguel Armaza is Co-Founder & Managing General Partner of Gilgamesh Ventures, a seed-stage investment fund focused on fintech in the Americas. He also hosts and writes the Fintech Leaders podcast and newsletter.Miguel on LinkedIn: https://bit.ly/3nKha4ZMiguel on Twitter: https://bit.ly/2Jb5oBcFintech Leaders Newsletter: bit.ly/3jWIpqp

Parker Conrad is the Founder & CEO @ Rippling, the company that lets you easily manage your employees' payroll, benefits, expenses, devices, apps & more—in one place. To date, Parker has raised over $697M for Rippling from some of the best including Sequoia, Founders Fund, Greenoaks, Bedrock, Kleiner Perkins and Initialized to name a few. Prior to founding Rippling, Parker was the Co-Founder and CEO @ Zenefits and if that was not enough, Parker is also a prominent angel having invested in the likes of Census, Pulley and then also AgentSync and TrueNorth, alongside 20VC Fund. In Today's Episode with Parker Conrad: 1.) Entry in Startups and Zenefits: How did Parker make his way into the world of startups? How did Parker end up being kicked out of his own company, Zenefits? How did he respond? How did that experience of being kicked out of Zenefits inspire him to build Rippling? 2.) Parker Conrad: The Leader: How does Parker define "high performance"? How would Parker describe his leadership style today? Why does Parker fundamentally disagree that with speed comes a trade-off in quality? How does Parker ensure Rippling does all things fast and to the best of its ability? How would Parker break down his decision-making framework today? How does he decide what to prioritize vs not? How does he decide what to delegate vs not? What are Parker's biggest insecurities in leadership today? How have they changed over time? What does Parker do to combat and mitigate them? 3.) Rippling: The Compound Startup How does Parker define a compound startup? What types of business do this verticalized approach work for vs not work for? What does Parker believe are the 4 core benefits of this approach? What are the single biggest challenges of building a compound startup? 4.) Rippling: The Economics: How does this compound startup approach impact ability to cross-sell? How much net new ARR today comes from cross-sell? What have been some of Rippling's biggest lessons on what it takes to do cross-sell so effectively? How do the margin profiles differ across their different products? How have the margin profiles changed over time? Why does Parker not believe that most startup margins are accurate? How does the compound startup approach change the amount invested in R&D? How does that impact the fundraising requirements of the business? 5.) Rippling: The Partner Ecosystem: How does Rippling think about building out the best partner ecosystem? What will it take for that to work? Why do Rippling want to introduce services that compete with their own products? Why do they not only build their own? How do the margins differ when comparing revenue share on partner products vs Rippling products? What are the single biggest barriers to this partner ecosystem working?

Henrique Dubugras is the Founder and CEO @ Brex, the company re-imagining financial systems so every growing company can realize its full potential. To date, Henrique has raised over $1.1BN for Brex from some of the best including Ribbit, Greenoaks, DST, IVP, Caffeinated Capital and Elad Gil to name a few. Henrique is also a board member at Mercado Libre. Prior to co-founding Brex, Henrique co-founded Pagar.me, there he scaled the company to $15BN in GMV and over 100 people before selling the company in 2016. In Today's Episode with Henrique Dubugras You Will Learn: 1.) The Founding of Brex: What was the founding a-ha moment for Henrique and Pedro with Brex? What advice did Evan Spiegel give Henrique when it comes to being a great CEO? 2.) Hiring: The Trials and Tribulations What have been Henrique's biggest hiring mistakes? How do founders know when they are ready to bring in the seasoned exec vs the younger jack of all trades candidate? What have been Henrique's biggest lessons in what it takes to hire true A* talent? Where does Henrique see other founders make big hiring mistakes? 3.) Product Expansion and Marketing: How does Henrique assess when is the right time to release a second product? What have been Henrique's biggest mistakes and lessons when it comes to product marketing? How can one retain the simplicity of product messaging with scaling the product? Brex expanded the product too far, too fast. How did they walk it back so successfully? 4.) Henrique: The Leader How does Henrique approach his own relationship to money today? How has it changed over time? What luxury expenditure has Henrique made over the last 12 months that he feels is worth it? How does Henrique think about ego management? What does he do to keep his in check? Item's Mentioned In Today's Episode with Henrique Dubugras Henrique's Favourite Book: The Innovator's Solution: Creating and Sustaining Successful Growth

Hasura raises $100 million to expand SaaS product, becomes a unicorn Hasura, which provides software for developers to connect disparate sources of data, has secured $100 million in funding in a round led by Greenoaks with participation from existing investors Nexus Venture Partners, Lightspeed Venture Partners and Vertex Ventures, the company said in a press release. The Series C round brings the total capital raised by Hasura to $136.5 million and the company's valuation to $1 billion. Hasura plans to use the funding to accelerate research and development and expand go-to-market activities globally for the company's GraphQL Engine, which makes it fast and easy for even those with zero GraphQL expertise to compose a GraphQL API from existing APIs and databases. Vymo raises $22 million in series C funding Vymo, an Intelligent Sales Engagement platform for Financial Institutions, has raised $22 million in Series C funding, led by Bertelsmann India Investments with existing investors Emergence Capital and Sequoia Capital participating. As part of the financing, BII's Rohit Sood will be joining the company's board. In 2021 Vymo saw over 20 percent quarterly growth, 142 percent net retention rate, zero logo churn, entry into the US with wins like Berkshire Hathaway, and onboarding some of the largest Insurers in Japan, co-founder and CEO Yamini Bhat said in a press release yesterday. The latest funding will help Vymo go after a market opportunity to provide sales intelligence estimated at $10 billion. Wipro Ventures invests in vFunction in cloud services partnership Wipro, India's fourth-biggest IT services provider, has formed a joint go-to-market partnership with vFunction, a Palo Alto-based startup that has developed a technology platform for modernising Java applications and accelerating migration to the cloud, the Bangalore company said in a press release yesterday. The partnership will strengthen Wipro's cloud services business. In conjunction with this partnership, Wipro Ventures, the corporate investment arm of Wipro, has invested in vFunction's Series A funding round to deepen the strategic partnership. Wipro didn't provide financial details. UMG launches Def Jam India to take Indian rap to other markets Universal Music Group has launched Def Jam India, a new label division within India and South Asia dedicated to representing the best hip-hop and rap talent from the region, the Dutch-American music corporation said in a press release yesterday. Def Jam India will follow the blueprint of the iconic Def Jam Recordings label, which has led and influenced the cutting-edge in hip-hop and urban culture for more than 35 years. UMG aims to tap the growing popularity of hip-hop as a genre in India and take Indian hip hop to other markets. Artists it has signed on already include Dino James and Fotty Seven. Theme music courtesy Free Music & Sounds: https://soundcloud.com/freemusicandsounds

Clover Health wants to improve physician performance by giving them access to cutting edge AI technology. But the company chose to enter the market not as a technology vendor but as an insurer, disrupting traditional payment structures and care navigation technology in one fell swoop. On today's HIMSSCast, Clover Health's Andrew Toy joins host Jonah Comstock to talk about his story and his own particular take on solving the healthcare cost crisis in America.Talking points:What Clover does and how its different from other payers and other startupsClover Assistant, Clover's provider-facing tech stack, and how it fits into their modelGetting away from the idea of networksWhat does value-based care mean for CloverWho holds the risk? And why it should be the insurersWhy the incentive alignment argument for value-based care is more complicated than people thinkIncentivizing doctors by giving them more powerful toolsHow health systems should fit into the value-based care landscapeWhy Clover launched as a payer and not a technology vendorWhy Clover built its Assistant outside of the EHRFixing healthcare means fixing healthcare for everyoneHow can innovation in insurance push through incumbent players?More about this episode:Medicare Advantage insurtech startup Clover Health raises $500MClover Health will join the public market by merging with Social Capital SPACClover Health's new subsidiary will rely on members, machine learning to fuel drug developmentClover Health laying off 25 percent of staff as it seeks new healthcare expertiseClover Health gets $130M from Greenoaks, Google Ventures, othersClover Health planning expansion into 101 new marketsClover Health taps MedArrive to vaccinate its homebound MA membersWalmart partners with Clover Health to offer Medicare Advantage plans

Will Shu is the Founder & CEO @ Deliveroo, the company that provides your favorite restaurants and takeaways, delivered to your door. Prior to their IPO earlier this year, Will raised over $1.7BN for the company from some of the best including Accel, Index, General Catalyst, Greenoaks, and more. Before Deliveroo, Will worked in finance as an analyst with SAC Capital, ESO Capital, and Morgan Stanley in New York and London. Fun fact, Will still enjoys regularly delivering food orders on his bike. In Today's Episode with Will Shu You Will Learn: 1.) How Will made his way from hedge funds and Morgan Stanley to changing the world of food and delivery with Deliveroo? Why did Deliveroo not work the first time Will started it? 2.) Restaurant + Customer Acquisition: How did Will acquire the first restaurants to the platform? What did that education process look like for them? What do the restaurants care about? How did Will acquire the first customers? How has that changed over time? What matters to customers; speed, selection or price? How does this change by geography and country? 3.) New Markets: How do Deliveroo select new markets to enter? What makes one more attractive than another? From a resource perspective, what does it take to open a new market? What have been some of the biggest lessons on zone maturity and time to breakeven? Why does Deliveroo not track driver efficiency on a number of drops basis? What is the right mechanism to measure driver efficiency? 4.) Competition: How did Deliveroo come late to markets like France and end up winning them? What was it like competing against Uber with Eats? How important is restaurant exclusivity to Deliveroo retaining its position? What would Will have done differently with regards to competition, with the benefit of hindsight? 5.) Quick commerce: What does Will make of the unprecedented rise of quick commerce? Will we see many winners on a per market basis or will this be a consolidatory environment? What do many of the new entrants mistake or not understand? Why is the vertical ownership of the supply chain such a superior model to working with grocery partners? Item's Mentioned In Today's Episode with Will Shu Will's Favourite Book: From Third World to First: Singapore and the Asian Economic Boom

Tata Consultancy Services, India's biggest software services company, kicked off the fiscal second quarter earnings season on Friday. It reported revenues of $6.33 billion for the three months ended September 30. Revenues grew 15.5 percent over the same period one year ago, in constant currency, the company said in a press release after the close of Mumbai trading on Friday. Profits rose 14 percent to $1.3 billion. And in our tech conversation, we speak with Silicon Valley serial entrepreneur Jahangir Mohammed, founder and CEO of Twin Health (2:30). Tata Consultancy Services, India's biggest software services company, kicked off fiscal second-quarter earnings season on Friday, reporting revenues of $6.33 billion for the three months ended September 30. Revenues grew 15.5 percent over the same period one year ago, in constant currency, the company said in a press release after the close of Mumbai trading on Friday. Profits rose 14 percent to $1.3 billion. During the quarter, the company won large orders from customers including Swiss Re, ZF, Transport for London, Cordis, Carrefour Belgium and MTN South Africa. The company ended the quarter with nearly 529,000 employees. Apple is appealing the September ruling in the lawsuit that was brought against it by Epic Games, in which a California judge ruled that the iPhone maker can't prevent developers from including payment systems outside the App Store, The Verge reports. That ruling was part of a verdict that Apple had called a ‘resounding victory' for itself. The ruling was set to become effective in December, but with the appeal, it will likely get pushed back, according to The Verge. Tiger Global, a New York-based hedge fund, venture capital and private equity company, is in advanced talks to lead a $100 million funding round at Slice, a Bangalore fintech startup, TechCrunch reports. Other investors could include Insight Partners, Ribbit Capital and Greenoaks, according to the report.Slice has previously raised around $30 million and was valued at under $200 million in a round earlier this year, according to TechCrunch. The company, which is looking to expand India's credit card market, counts Blume Ventures, Gunosy Capital and Better Capital among its investors. Ola Electric, the electric vehicle unit of ride-hailing company Ola, has raised $200 million in an investment that values the business at more than $5 billion, according to a Press Trust of India report that appeared in Economic Times. The new investment comes days after the company raised $200 million at a post-money valuation of $3 billion. The latest funding round saw the participation of existing investors and some US-based bluechip tech funds, according to ET. Jahangir Mohammed is your quintessential serial tech entrepreneur, with a finely honed knack for identifying problems that offered large market potential if they could be solved with the right technology-led solutions. Twenty-five years after he left India for the US, he sold his second company, Jasper Technologies, a Silicon Valley-based internet-of-things company, to Cisco for $1.4 billion. It was then, that his “life's work” started, he told me in a zoom call over the weekend. His latest venture is Twin Health, a medical services business that combines the latest in AI and medical sciences to treat and even reverse diseases like diabetes in many cases. As the name suggests, part of the solution is around developing a digital twin of each patient's metabolism.

Sébastien nous fait faire un voyage dans le temps sur cet épisode. Il reprend avec nous toute l'histoire d'Agicap, que ce soit le développement produit, les intégrations permettant d'agréger des sources de données variées, l'internationalisation de cette solution de gestion de trésorerie qui devient un véritable cockpit financier de la PME. Gabrielle revient sur les débuts de la relation de BlackFin avec Agicap notamment les premières discussions avec la start-up Lyonnaise qui lui donne de nombreux signaux très positifs et qui décide le fonds de VC à investir dans ce tour de 'seed'. Philippe, quant à lui, revient sur les convictions de Partech sur ce segment de marché et son potentiel extraordinaire tant au niveau du développement produit que de l'internationalisation. Nous découvrons également les secrets des différents tours de table et notamment le tout dernier annoncé récemment dans lequel est rentré l'investisseur américain Greenoaks. Autres éléments que nous abordons, la gestion de l'hypercroissance, l'accompagnement des VC, le Japon, ou encore la consolidation.  On repart avec trois livres à lire :  From impossible to inevitable d'Aaron Ross et Jason Lemkin Vous allez commettre une terrible erreur d'Olivier Sibony Nagori de Ryoko Sekiguchi  Bonne écoute à tous ! Pour contacter Agicap : site / LinkedIn. Le contact de Sébastien Beyet : LinkedIn, Philippe Collombel : LinkedIn et Gabrielle Thomas : LinkedIn.   Pour soutenir Finscale : S'abonner au podcast pour écouter le prochain épisode Mettre 5 étoiles sur Apple podcast pour aider d'autres personnes à découvrir ce podcast  Belle écoute et à la semaine prochaine !

Parker Conrad is the Founder and CEO @ Rippling, the employee management platform allowing you to manage your employees' payroll, benefits, devices and more—in one place. To date, Parker has raised over $197M for Rippling from the likes of Founders Fund, Kleiner Perkins, Initialized, Bedrock, Greenoaks and Coatue. Prior to founding Rippling, Parker was the Co-Founder and CEO @ Zenefits and if that was not enough, Parker is also a prominent angel having invested in the likes of Census, Pulley and then also AgentSync and TrueNorth, alongside 20VC Fund. In Today’s Episode with Parker Conrad You Will Learn: 1.) How did Parker make his way into the world of technology and startups? What was the founding a-ha moment for Parker with Rippling? How did his journey with Zenefits change or alter his leadership style today with Rippling? 2.) Why does Parker believe that the conventional advice of focus, focus, focus is BS? What does Parker mean when he states, "The Compound Startup"? How does the approach of the compound startup differ from traditional approaches of product and company building? What are the core benefits of using the compound startup approach? 3.) How does Parker think about providing sufficient product quality with an increasing breadth of product offering, entailed within a compound startup? In what way does pricing differ when comparing compound startups to traditional startups? How can compound startups optimise their pricing on a bundle basis? What has Slack and Microsoft taught us about this? 4.) Why does Parker disagree with the conventional analogy of the VC founder relationship being a marriage? Why does Parker refer to it more as a "General Contractor" relationship for a house? What can founders do to sufficiently protect themselves from overarching VCs? What can VCs do to be the very best partners to the founders they work with? 5.) How does Parker evaluate his relationship to money today? How has it changed over time? What does Parker know now that he wishes he had known at the start of his founding of Rippling? What have been Parker's biggest lessons on talent acquisition? Why did Parker decide to bring on a COO when he did? How has it changed his role? Item’s Mentioned In Today’s Episode with Parker Conrad Parker’s Favourite Book: Matilda by Roald Dahl As always you can follow Harry and The Twenty Minute VC on Twitter here!