Podcasts about 8B

Joe's Premium Subscription: www.standardgrain.comGrain Markets and Other Stuff Links —Apple PodcastsSpotifyTikTokYouTubeFutures and options trading involves risk of loss and is not suitable for everyone.

On this episode of Gov Tech Today, hosts Russell Lowery and Jennifer Saha step back from day-to-day procurement details to examine what California's public data reveals about technology spending trends. Jen shares analysis comparing FY 2024–25 to 2025–26 across major contract vehicles, finding a sharp decline in IT services: about $3.8B down to $2.59B, nearly a 30% drop, with notable decreases in interagency agreements (down roughly 50%), formal competitive bids, and non-competitive bids. At the same time, IT goods spending is steadier and slightly up—from about $2.3B to $2.7B—suggesting continued purchases of software, hardware, and cloud while agencies reduce reliance on long-term vendor-managed services. They discuss policy changes enabling implementation services through the Software Licensing Program and consider political pressures, budget constraints, and potential pent-up demand ahead of the next administration. 00:00 Show Intro 00:14 Why Spend Is Down 02:28 Services Spending Drop 05:07 Interagency Declines 06:33 Competitive Bid Slowdown 09:09 SLP Services Surge 11:49 Goods Mostly Flat 13:51 What Counts As Goods 16:26 Big Picture Outlook 19:21 Wrap Up And Contact  

(0:00) Michael Kratsios joins the show! (01:56) Is this administration anti-science? The Nature poll, DEI grants, and $8B down the drain  (8:16) Climate science cuts: RCP 8.5 gets pulled and the "climate emergency" narrative collapses (13:45) $47B at NIH, Eroom's Law, and golden tickets: has American science stagnated?  (22:24) Big bold bets: Genesis Mission, quantum by 2028, fusion by 2035, boots on the moon in '28  (34:04) The great race with China: $33B to $670B, and 7 out of 10 STEM PhDs aren't American  (44:09) Fauci did more damage to science than anyone in modern history, and NIH's median researcher is 71 Follow the besties:  https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg   Follow on X: https://x.com/theallinpod   Follow on Instagram: https://www.instagram.com/theallinpod   Follow on TikTok: https://www.tiktok.com/@allin   Follow on LinkedIn:  https://www.linkedin.com/company/allinpod   Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg   Intro Video Credit: https://x.com/TheZachEffect #allin #tech #news  

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

Hey this is Alex, welcome to... the chillest week in AI, since ... a long time. Chill, if you consider Moderna and MERK announcing a cancer vaccine and surging 115% in a day, a chill week. This week, the only two model drops we really saw came from the excellent Z.ai folks, they announced GLM 5.3, API only for now, and an amazing tiny release of Qwen 3.89 27B. In other big AI news, OpenAI announced they are pausing RL efforts (Reinforcement Learning) to focus on security and alignment post the scary AI Swarms hacking incident, dedicating up to 20% of compute towards reviewing agent thinking processes, and Stripe buying OpenRouter for a reported $8B! Sometimes the chill weeks are actually good, we're able to chat about how we use AI, what changed for us, and give our guests a bit of breathing room. This week, I invited Francesco from CUA to talk about computer use in open source + their new history plugin, Bin from HeyGen to talk about HyperFrames, a way for your agents to create videos and a breaking news guest, Jeff Huber from Chroma jumped on to talk about their new Foundations release, a unified memory for your agents! This was a great episode, I hope you'll like it, it's up here on Substack and everywhere you get your pod (Spotify, Youtube, Apple Podcasts). ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Are we being fed slop again? (Is Claude dumb again?)Before we get to releases, this week on the show, I complained, again, that I feel my AI's are degrading. If this feels like de-ja-vu to you, it's because the same happened a year ago in September 2025 (and Anthropic admitting this 2 weeks later), and ... now this happens with Fable?You see, I use pretty much the same prompts, every week, preparing for the show. This is partly my way to evaluate new models and compare to existing and previous ones while also bringing you the best researched weekly show in AI. Well, this week, one after another, Claude Fable, which is... like the best intelligence, gave me such poor output, that I couldn't believe what I'm seeing. First, literally ignoring instructions that say “hey, show me all the items I've collected and let me pick the most important ones”, Fable instead sent all of them to my research pipeline, without showing me. This has worked, consistently, without fail, for the past... year? maybe more! This worked with open source models, worked with GPT, and now Fable, a Mythos Level LLM, is doing the most basic dumb s**t possible, ignoring the main reason I even have this workflow. And this wasn't just a fluke either, when asked to create a run of show document, and given an example, Fable produced this... whatever this is. This is the same document and same format that Fable produced for me during AI Engineer which got me thinking “ok, this is AGI”, and here, given an example, I got a completely unusable artifact, despite direct instructions, structure and example! I got to say, given that privately this week, Anthropic disclosed that they have passed $65B in revenue, which is absolutely insane, this doesn't add up. So I figured, ok Alex, maybe this is your prompts or skills. But no, LDJ came in with some charts that show degradation, one from MarginLab.ai that shows significant lowering on number of tool calls and average runtime recently (this is for Opus 5) and And another chart from modelverify.ai model drift monitor showing drift scores.Do we have anoher Claude Gate on our hands? Is your Fable/Opus behaving weird lately? Or did you completely switched away to other models? OpenAI pausing RL and focusing on safetyLook, when we covered the HF hacking incident and then the pacing the frontier letter, I didn't imagine that results will come this fast, but this week, OpenAI publicly announced that they are pausing RL training, which is the last step of models, until they get their sandboxes in order and align the models better. We all agreed on stage that this is likely a very good move, and Peter was really awe-struck at the 20% dedication of resources towards reviewing thought processes of models. Is this a good enough response to the scary hacking incident? we'll see, but I think this is the right move from OpenAI, and still, waiting for the full postmortem on the OpenAI security incident. Open Source LLMsQwen3.8-27B ties GPT-5.6 Luna and runs on a 4090 (X, HF, Announcement)Following the release of their flagship, Alibaba dropped a model that became a community darling overnight, Qwen 3.8 with just 27B parameters. This “tiny” model scores 52 on the Artificial Analysis Intelligence Index, same score as GPT 5.6 Luna at Max reasoning and 51 on Agentic index, beating Opus 4.8 MaxAll while running at around 68t/s on a 4090 GPU, and around 40 on max via MLX, hell it even does 11t/s on Xenova's WebGPU kernels right in the browser! This model exploded on the HuggingFace hub, with tons of quants, over 152 fine-tunes, it was downloaded over 10M times overall

TikTok Shop USA sales are booming. There are some new regulations for packaging if you're selling in Europe, and the most-requested tool for MCP for Helium 10 is now available. These stories and more on today's weekly buzz!   We're back with another episode of the Weekly Buzz with Helium 10's VP of Education and Strategy, Bradley Sutton. Every week, we cover the latest breaking news in the Amazon, TikTok Shop, Walmart, and E-commerce space, talk about Helium 10's newest features, and provide a training tip for the week for serious sellers of any level.   TikTok Shop's U.S. GMV Nearly Doubles to $11.8B in H1 2026 https://www.netinfluencer.com/tiktok-shop-us-gmv-nearly-doubles-to-11-8b-usd-in-h1-2026/ Why China's traders, e-commerce merchants feel boxed in by new EU packaging rules https://www.scmp.com/economy/policy/article/3363746/why-chinas-traders-e-commerce-merchants-feel-boxed-new-eu-packaging-rules Most Asked For Helium 10 MCP Update Helium 10 has launched Black Box Niche for MCP, giving sellers the X-ray-style data they've been asking for directly inside Claude. Instead of researching keywords one at a time with the Chrome extension, users can analyze multiple niches at once and compare metrics like search volume, top-product revenue, number of listings generating over $5,000 in sales, and how many top products have low review counts. The feature makes it much faster to compare market opportunities and competition across multiple keywords using the Helium 10 MCP.   How Amazon's AI shopping assistant makes hundreds of millions of products feel personal https://www.aboutamazon.com/news/retail/alexa-for-shopping-learn-and-be-curious-podcast Optimize your listings with built-in Seller Central tools https://sellercentral.amazon.com/seller-news/articles/QVRWUERLSUtYMERFUiNHR1ZKWFZETkRTSFhYQVo1 Amazon Expands Locker Network to more than 750 U.S. College Locations https://press.aboutamazon.com/retail/2026/8/amazon-expands-locker-network-to-more-than-750-u-s-college-locations In episode 545 of the AM/PM Podcast and Weekly Buzz, Bradley talks about: 00:00 -  Introduction 00:43 - TikTok Shop US BOOMING 03:13 - New EU Packaging Regulations 05:11 - Most Asked For MCP Update 09:13 - Amazon's Alexa Predictions 14:36 - Research Dead Amazon Listings For Opportunity 17:55 - Amazon Caters To College Students

Hosts Andy and Tom talk about a rare Cincinnati unicorn – a startup company worth at least $1B – that abruptly ceased operations. In other news, one of Cincinnati's largest private companies is expanding its downtown headquarters, and bringing back the skywalk; Amazon touts its local impact; P&G is making a $3.8B acquisition; and a James Beard-nominated chef is opening a new restaurant.Interview starts at (23:43) Dave Jenike started at the Cincinnati Zoo as an intern. Now, he's its CEO. He was also part of the duo responsible for its transformation over the last two decades. He views his job as the zoo's chief storyteller, and getting people inspired by nature. He talks about what's new and coming at the Cincinnati Zoo.https://www.bizjournals.com/cincinnati/news/2026/08/07/80-acres-how-why-closed-henry-gordon-smith-layoffs.htmlhttps://www.bizjournals.com/cincinnati/news/2026/08/03/divisions-maintenance-group-jobs-downtown-skywalk.htmlhttps://www.bizjournals.com/cincinnati/news/2026/08/04/jimmie-lou-reopen-pendleton-jeff-harris-nolia.html

-Taking vibe-coding a step further, Naive claims its infra can automate most of the work in setting up and running a business. -Defense tech Hadrian raises $1.37B at $8B valuation -Athens-based Omilia, which has been working on automating voice calls and customer support since 2002, throwing AI at every process can be wasteful.  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Google shook up its AI leadership, kicking Demis Hassabis upstairs while Jeff Dean and Sanjay Ghemawat left to found Discovery Loop. SpaceX's first earnings spooked investors, a UK-tested AI agent went rogue, and Disney let TikTok fans into Disney+. Links Google just announced a major shakeup of its top AI leadership (The Verge) SpaceX reports Q2 revenue up 92% YoY to $7.8B, vs. $6.81B est., AI operating loss of $1.26B, vs. $2.39B est., says capex in Q3 and Q4 will remain similar to Q2 (Bloomberg) An AI agent went rogue during UK safety tests, creating fake identities and launching social engineering attacks unprompted (The Decoder) Disney announces a global deal with TikTok to bring "thoughtfully curated" fan-created short-form videos based on Disney's IP to Disney+'s vertical Verts feed (The New York Times) Subscribe to the ad-free feed.

August 5, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: P&G acquires supplement maker Thorne for $3.8B, adding the practitioner-trusted brand to a portfolio that includes Metamucil and Wonderbelly Cleveland Clinic launches the first long-term drone prescription delivery program at a US health system, partnering with Zipline as healthcare competes on convenience World1 launches microsports, 30-second mobile-first competitions backed by Mark Cuban, betting short-form content will reshape how younger audiences consume sports More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe  Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider  Reach out → insider@fitt.co

★朝日新聞のデジタル版は、同居のご家族4人まで1つのログインIDでご利用いただけます。ただし、ベーシックコース、スタンダードコースをご利用の方は、お申し込みいただいたご本人のみのご利用となります。 https://support.asahi.com/hc/ja/articles/28232676086551-%E5%AE%B6%E6%97%8F%E3%82%82%E5%90%8C%E3%81%98ID%E3%81%A7%E5%88%A9%E7%94%A8%E3%81%A7%E3%81%8D%E3%81%BE%E3%81%99%E3%81%8B ★おたより、お待ちしています！朝日新聞アプリで神田大介をフォロー https://bit.ly/4k4ZKwA ※たんたんさん主催！「朝リス課外活動」 https://discord.gg/fxZCQySZe ※メディアトークのdiscord https://discord.gg/TU8c9qtzvw ※番組中で紹介したポッドキャストは「ののラジオ～名作文学を朗読で～」です 【関連記事】体育館のクーラー設置率、東京は9割超、最下位は0.8%　公立小中https://www.asahi.com/articles/ASV7S31BFV7SULLI00XM.html?iref=omny 「声の権利」保護、法務省検討会が明記　AI使った権利侵害に歯止めhttps://www.asahi.com/articles/ASV7W1TG8V7WUTIL038M.html?iref=omny 【おねがい】朝日新聞ポッドキャストは、みなさまからの購読料で配信しています。番組継続のため、会員登録をお願いします！https://t.asahi.com/wqin 【番組内容】みなさまのお盆の思い出を教えてください。 【出演・スタッフ】神田大介（MC、音源編集） https://bit.ly/4k4ZKwA 【朝ポキ情報】アプリで記者と対話 http://t.asahi.com/won1 交流はdiscord https://bit.ly/asapoki_discord おたよりフォーム https://bit.ly/asapoki_otayori 朝ポキTV https://www.youtube.com/@asapoki_officialメルマガ https://bit.ly/asapoki_newsletter 広告ご検討の企業様は http://t.asahi.com/asapokiguide 番組検索ツール https://bit.ly/asapoki_cast 最新情報はX https://bit.ly/asapoki_twitter 番組カレンダー https://bit.ly/asapki_calendar 全話あります公式サイト https://bit.ly/asapoki_lp See omnystudio.com/listener for privacy information.

Day 5 of 50 Days for Freedom, hosted from @Swan after Cory's handle hit tech trouble. Swan's buy fee sits at 50 basis points through Labor Day. Framing doc: swan.com/battle. Cory on disagreeing well. Three summers of shows with Vlad Costea despite splitting on layer twos and drivechain. A lot of people we think we oppose actually love Bitcoin, and privacy is common ground. UK digital ID scrapped, sort of. Suz reports the £1.8B scheme killed after a 2.9M-signature petition and cross-party opposition. Her warning: canceling a brand name is not abandoning the architecture. The back door is already open. GOV.UK One Login covers 122 services, with all central government services slated to join by 2027. Age verification, employment checks, and the Online Safety Act converge on the same result. America's version, differently packaged. No single federal portal yet, but Real ID, mobile driver's licenses, and digital age checks add up. Federalism is a partial brake. Panel consensus: very close. Fear plus convenience is the playbook. 9/11, COVID, now the FATF travel rule reframed as national security. Suz: "you can make a scared man do anything." Bitcoin's answer is separating money from the identity gateway. Fourth Turning, with an exit. Brady's case: institutions are collapsing on schedule, but this cycle has Bitcoin and Nostr already built. Freedom tech that math makes un-co-optable. Cory's version: ten million US Bitcoiners, the race to avoid the war. Education is the whole mission. Lyn Alden's Seven Misconceptions, Vijay's 2018 Bullish Case article, Yan Pritzker's Inventing Bitcoin, and the Bitcoin Season documentary. Cory: understanding earns you the right to own more. ETF buyers who skipped it get lettuce hands. Swan versus Coinbase, box by box. Swan Sovereign, Swan Vault multisig, Swan Safe Plus with live video withdrawal confirmation, buy fees under Coinbase's advanced exchange, and Swan covering network fees. Plus Steve's scarcity chart: 60 million millionaires, 21 million coins. Agentic commerce wants Bitcoin. Scott's Machine Economy project, Buzz, Lightning Labs' Wavelength, and HTTP 402 finally getting built. An agent needs only a keypair. Cory also set the BIP110 policy: not daily here, but a moderated debate is coming.

Ben and his co-founder had no product, no law enforcement background, and no pitch—just an offer to help detectives solve cases. One commander took a chance, handed them background checks, and said "let's see what you can do." They worked out of that police department every day for 18 months. Peregrine just raised $250M at a $6.8B valuation.In this episode, Ben breaks down how researching every police captain in the Bay Area landed their first design partner, why working as free crime analysts for 18 months was "the purest form of method acting," how forward-deployed engineers drove them from $1M to $3M to $10M ARR, and the 120% RFP prep that won a contract written for a billion-dollar competitor.Why You Should ListenWhy doing your customer's job is the fastest path to product market fit.How two founders with no product convinced a police department to let them in.Why over-investing in deployment became a growth engine, not a margin problem.How obsessive research wins enterprise deals when you have zero credibility.Keywords startup podcast, startup podcast for founders, product market fit, finding pmf, Peregrine, govtech, public safety technology, forward deployed engineers, selling to government, enterprise sales, data integration, design partners, Ben RudolphChapters00:00:00 Intro00:01:07 The Moment of True Product Market Fit00:11:51 Getting a Police Department to Say Yes00:16:45 Slow Growth and Word of Mouth00:19:49 Forward-Deployed Engineers Before They Were Cool00:27:34 Cracking Government Go-To-Market00:34:08 Winning an RFP Written for Someone ElseSend me a message to let me know what you think!

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

Agentic AI is changing the threat landscape. We've moved beyond chatbots to autonomous systems that navigate files and take actions, expanding the attack surface. Recent incidents highlight the risk. Hugging Face suffered a breach where an AI agent became the entry point into production systems, showing that developer tools are now critical infrastructure. OpenAI's GPT-5.6 accidentally deleted user data, including a production database, after misconfiguring an environment variable—no malicious intent, just real consequences, underscoring the need for mandatory sandboxing. Meanwhile, SpaceX's Grok Build tool was found defaulting to collecting user repositories and sending them to internal cloud storage without clear consent, a reminder of “default-on” data exfiltration risks. A newer threat, Agent Data Injection, manipulates trusted metadata (like IDs or fields) with crafted inputs to mislead agents into unintended actions. A strong counterexample is the 1Password–Claude integration, which requires biometric approval before accessing credentials, keeping secrets out of the AI entirely. Human-in-the-loop controls should be standard.Legacy systems remain a major risk. Windows, OpenSSL, and WordPress continue to produce serious vulnerabilities. LegacyHive allows privilege escalation in Windows by loading another user's registry hive. HollowByte exploits a small crafted TLS payload in OpenSSL to trigger memory over-allocation and denial of service. The wp2shell chain enables unauthenticated remote code execution in WordPress core (versions 6.9–7.0), challenging the assumption that only plugins are risky. At the same time, Windows 10 is reaching end-of-life, yet roughly 17% of devices still run it, especially in cost-sensitive sectors like healthcare and small business. As Windows 11 patches are released, they effectively expose underlying flaws that attackers can reuse against unsupported systems.Trust itself is increasingly being exploited. Attackers are hiding inside legitimate platforms instead of building new infrastructure. HollowGraph malware uses compromised Microsoft 365 calendars as command-and-control channels, embedding instructions in far-future events and routing through normal Graph API traffic. A Norwegian transit test revealed electric buses could be remotely disabled via foreign SIM cards, highlighting risks in over-the-air control systems across transportation sectors. Ransomware groups are also evolving: JadePuffer's ENCFORGE targets AI model artifacts, treating trained models as high-value assets. Researchers have already used GPT models to build exploit chains, signaling AI's shift into offensive security roles.Governance gaps are compounding the problem. A ProPublica report found Microsoft used China-based engineers to support U.S. Department of Defense cloud systems via low-paid American intermediaries who relayed code without understanding it—technically compliant, but operationally risky. The UK scrapped its £1.8B digital ID program after execution failures. In another case, a single typo in a police report, combined with automated license plate recognition, led to a wrongful arrest, showing how systems can enforce human error without validation.The common thread is misplaced confidence—in AI agents, legacy systems, and formal compliance. Practical steps are clear: require human approval for sensitive AI actions, verify data access beyond contractual assurances, sandbox all agents, treat AI models as critical assets with backups, and update detection strategies to monitor abuse within trusted platforms, not just external threats.Trust, increasingly, is the vulnerability.

Mark Carney announced approximately $900 million in new military support for Ukraine at the NATO summit on July 7.According to the Prime Minister's Office, the package includes:• $475 million for ammunition• Approximately $400 million for 35 Canadian-made armoured vehicles• $50 million for "critical technology"But the numbers add up to $925 million.The government never specified the currency. The written PMO readout doesn't match the remarks made at the podium regarding air defence. And Canada's Department of Finance has previously redacted details of federal aid to Ukraine.In this episode, I break down the math, examine the inconsistencies, and explain what a real paper trail for nearly a billion dollars of public spending should look like.Sources• PMO breakdown (via Kyiv Independent)https://kyivindependent.com/canada-pledges-900-million-to-ukraine-for-vehicles-and-ammo-but-no-air-defense/• Epoch Times ($475M / $400M / $50M breakdown; $2.8B 2026 commitment)https://www.theepochtimes.com/world/canada-to-provide-ukraine-900m-for-ammo-armoured-vehicles-carney-6058644• Ukrainska Pravda (currency unspecified)https://www.pravda.com.ua/eng/news/2026/07/07/8042849/• Kyiv Post (podium remarks on air defence)https://www.kyivpost.com/post/79782• Euromaidan Presshttps://euromaidanpress.com/2026/07/07/canada-announces-900-million-military-aid-package/• Juno News ($25.5B total aid; Department of Finance redactions)https://www.junonews.com/p/pm-carney-pledges-900m-aid-package• LIGA.net ("over $925 million"; Development Support Recovery Bank)https://news.liga.net/en/war/news/official-canada-has-announced-a-new-aid-package-for-ukraine-worth-over-900-millionChapters00:00 Introduction00:30 The announcement02:30 Which currency?04:00 The vehicle math05:30 The mismatch07:00 The redactions08:00 What accountability looks like12:00 The word everybody's using13:30 Who gets the money?15:30 Your household's share17:30 Final thoughts#CanadianPolitics #GovernmentAccountability #UkraineAid #NATOSummit #MarkCarney #ForeignAid #DefenseSpending #Taxpayers #CanadaBuy me a coffee! - https://buymeacoffee.com/kelsisherenLet's connect!Substack: https://substack.com/@kelsisherenRumble - https://rumble.com/user/TheKelsiSherenPerspectiveInstagram - https://www.instagram.com/thekelsisherenperspective?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw%3D%3DX: https://x.com/KelsisherenSUPPORT OUR PEOPLE - - - - - - - - - - - -Ketone IQ- 30% off with code KELSI - https://ketone.com/KELSIGood Livin - 20% off with code KELSI - https://www.itsgoodlivin.com/?ref=KELSIBrass & Unity - 20% off with code UNITY - http://www.brassandunity.com

Michael Saylor's "never sell" era is OVER. Strategy sold 3,588 BTC (~$216M) at a 20% loss to cover $1.8B in dividend obligations — and Canadian pensions are holding close to $1B of MSTR stock. On June 29, Strategy's board adopted its "Digital Credit Capital Framework": $1B stock buybacks, a 12% STRC dividend, and a $1.25B "BTC Monetization Program" authorizing Bitcoin sales "when strategic." CEO Phong Le calls it "evolving from one-way capital issuance to active capital management." The mNAV premium that financed five years of buying has collapsed from 2.66x to around 1x — and one in three Bitcoin treasury companies now trades below the value of its coins. In this episode of the Canadian Bitcoiners Podcast:- Strategy's pivot: the mNAV death spiral, coins sold at a 20% realized loss, insider selling, and JPMorgan's $2.8B-$11.6B index-exclusion warning- The Canada connection: CPPIB, AIMCo, National Bank, RBC and HOOPP hold ~$1B of MSTR — your pension bought the wrapper trade- A quantum-proof recovery tool that works for everyone except Satoshi's 1.1M BTC- OkoBot: malware that fakes your Ledger/Trezor recovery screen- Global hashrate is shrinking — but Pakistan is up 733%- CleanSpark signs a $6.6B, 20-year AI data center lease- Canada bans crypto political donations under Bill C-25- Clown World North: Stan Cho's $16,203 hotel bill, Canada Post's $30.8M in bonuses against a $1.57B loss, and $159,800 in flight catering- The BIS confirms Canada's housing crash is the biggest on record as 56,400 Canadians leave in a year The leverage cycle is unwinding — the conviction cohort isn't. Long-term holders just hit a record 14.85M BTC. ETF outflows and treasury-company stress are paper Bitcoin changing hands; the base layer doesn't care. Hold your own keys. — Canadian Bitcoiners Podcast- Website: https://canadianbitcoiners.com- X: @CanadianBTCPod- Subscribe & turn on notifications ————————————————————————————————SPONSORS

July 21, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Samsung Biologics acquires PolyPeptide Group for $1.8B, expanding into peptide manufacturing as GLP-1 demand makes production capacity the real bottleneck Vital Signals unveils Signal Ring, a cuffless smart ring measuring blood pressure continuously, with preorders at $399 ahead of an October launch LeBron James says Nike's path forward starts with reconnecting to the communities and young consumers that built the brand amid slowing growth More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

Michael Saylor's "never sell" era is OVER. Strategy sold 3,588 BTC (~$216M) at a 20% loss to cover $1.8B in dividend obligations — and Canadian pensions are holding close to $1B of MSTR stock. On June 29, Strategy's board adopted its "Digital Credit Capital Framework": $1B stock buybacks, a 12% STRC dividend, and a $1.25B "BTC Monetization Program" authorizing Bitcoin sales "when strategic." CEO Phong Le calls it "evolving from one-way capital issuance to active capital management." The mNAV premium that financed five years of buying has collapsed from 2.66x to around 1x — and one in three Bitcoin treasury companies now trades below the value of its coins. In this episode of the Canadian Bitcoiners Podcast:- Strategy's pivot: the mNAV death spiral, coins sold at a 20% realized loss, insider selling, and JPMorgan's $2.8B-$11.6B index-exclusion warning- The Canada connection: CPPIB, AIMCo, National Bank, RBC and HOOPP hold ~$1B of MSTR — your pension bought the wrapper trade- A quantum-proof recovery tool that works for everyone except Satoshi's 1.1M BTC- OkoBot: malware that fakes your Ledger/Trezor recovery screen- Global hashrate is shrinking — but Pakistan is up 733%- CleanSpark signs a $6.6B, 20-year AI data center lease- Canada bans crypto political donations under Bill C-25- Clown World North: Stan Cho's $16,203 hotel bill, Canada Post's $30.8M in bonuses against a $1.57B loss, and $159,800 in flight catering- The BIS confirms Canada's housing crash is the biggest on record as 56,400 Canadians leave in a year The leverage cycle is unwinding — the conviction cohort isn't. Long-term holders just hit a record 14.85M BTC. ETF outflows and treasury-company stress are paper Bitcoin changing hands; the base layer doesn't care. Hold your own keys. — Canadian Bitcoiners Podcast- Website: https://canadianbitcoiners.com- X: @CanadianBTCPod- Subscribe & turn on notifications ————————————————————————————————SPONSORS

With just 105 days until the 2026 midterms, Chuck Todd and Chris Cillizza use their last Super Tuesday before the sub-100-day mark to take stock of what they think they know. The headline: with Trump's approval stuck below 40, gas back over $4, an Iran conflict reigniting, and an ICE controversy dominating the news, nearly every fundamental points toward Republicans losing both chambers — and the guys argue the only thing standing between Democrats and a monster year is the Democrats themselves. They dig deep into the polling weeds, unpacking why the generic ballot has quietly become more of a party-ID number, why you can't compare it across cycles anymore, why independents are breaking hard left, and what to make of the model giving Democrats a 62% shot at the House while Republicans hold a 59% edge in the Senate. From there it's onto the races reshaping the map: South Carolina, where Trump ally Russell Frye jumps into the special and Lindsey Graham's appointed sister surprises everyone by running for a full term — a rare bit of GOP pragmatism that hands John Thune a much-needed win — and Maine, where Troy Jackson is set to be coronated at the Democratic convention and the ICE story has erased Susan Collins's post-Platner breathing room. Chuck and Chris also weigh the stalled Todd Blanche confirmation, the $400 million Qatari plane suddenly deemed too unsafe to fly, and why character and temperament remain the whole ballgame. Then they close on LeBron's looming decision — Heat, Cavs, or Sixers — and Chuck plugs the summer slate across the "Chuck Todd expanded universe." Timeline: 00:00 105 days out from the midterms 02:07 Why August, not July, is when politics really kicks off 03:01 Why campaigns no longer wait for Labor Day to run their hits 04:34 Trump's approval is stuck below 40 a recipe for losing both chambers 05:44 ICE story erases whatever Collins got from the Platner disaster 06:49 Democrats are more motivated 07:23 Arizona Republicans line up to nominate a candidate who can't win 08:08 Trump's primetime speech relitigating 2020 09:26 GOP candidates on the trail are running as far from Trump as possible 10:50 Trump is too lazy to be a dictator 11:54 On 2020, Trump is on an island — even his own party won't follow 14:14 The history: 20 of the last 22 midterms punish the president's party 15:05 The generic ballot vs. 2018 and 2010, for reference 15:51 Why the generic ballot is now really a party-ID number 17:31 Trust the trend lines, not the raw numbers 19:59 The StateNavigate Georgia poll that floored them 20:49 Why Democrats peak in summer polling 22:19 A smarter "informed ballot" experiment using Google search results 24:36 Democrats 62% to win the House, Republicans 59% to hold the Senate 25:20 Why the Senate number is lower than it should be 26:54 Iowa as the 50-yard line for a wave 27:42 The daunting Senate map for Dems 30:04 Why the 62% House number surprised Chris 31:52 The one thing that changed all summer — a possible new forever war 32:56 The math favors the GOP: Democrats are playing a road game 34:18 No swing districts left, and a country sorted to the bone 36:03 South Carolina: Trump ally Russell Frye jumps into the special 36:56 Graham's appointed sister surprises by running for a full term 37:30 A rare case of Trump and the GOP doing the pragmatic thing 41:13 Troy Jackson set for a coronation 42:53 The stalled Todd Blanche confirmation & the $1.8B slush fund 43:15 Why the ICE story is a real problem for Collins 44:02 Ambition as a drug — Homan, Bovino, Noem & the $50k question 45:09 The Odyssey movie and how online discourse isn't real life 47:07 The $400M Qatari plane is suddenly too unsafe to fly 48:07 Character and temperament as the whole ballgame 50:20 Quotes that aged badly 52:05 Trump as Grover Cleveland 53:34 LeBron's decision: Heat, Cavs, or Sixers 56:20 Miami and Denver as the best physical fits for a 40-year-old LeBron 57:34 The Hall of Fame "five rings table" and the chase for a fifth title 1:00:17 What the prediction markets say See omnystudio.com/listener for privacy information.

July 17, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Eli Lilly acquires AtaiBeckley for up to $3.8B, the largest psychedelic medicine deal yet, gaining an intranasal DMT therapy in Phase 3 for depression Wonder closes $650M Series D at $9B valuation, building a vertically integrated food platform spanning restaurants, delivery, and AI personalization Hemispheric emerges from stealth with $52M and Descartes, a foundation AI model trained on EEG data to diagnose depression, PTSD, and Alzheimer's Today's episode is brought to you by AIIR — a modern communications and experiential agency for health, wellness, fitness, and performance brands. From earned media to events and creator-led campaigns, AIIR helps companies sharpen their story, earn attention, and build trust that compounds. Visit https://aiir.agency to learn more. More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe  Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider  Reach out → insider@fitt.co 

On today's episode, Yaron Werber, John Maraganore, Sam Fazeli, and Matt Gline open with a discussion of biotech market volatility, with Matt noting that it often seems driven more by opaque factor dynamics than company-specific fundamentals. The group then turns to Eli Lilly's continued “Amazonification” and reinventing the pharma business as it acquires a variety of different companies, including this week's $2.8B upfront acquisition of Atai Beckley, a psychedelic-focused company. The conversation turns to BioCentury's reporting on Asia-to-West NewCos, prompting a debate about whether China is uniquely changing the market or simply reflects broader shifts in cheaper, faster development. On policy, the group covers BIO's response to the OMB proposal that could inject political review into federal grant decisions. The co-hosts also debate Kalshi's move to create prediction markets around clinical trial and regulatory outcomes. On pipeline updates, the group discusses Merck's approval of Lipfendra, the first oral PCSK9 drug, and the broader class implications. In CNS, Biogen and Ionis' diranersen tau ASO data spark a discussion of aconfusing dose response, ASO tolerability, tau as a target in Alzheimer's disease, and the promise of alternative modalities from Arrowhead and Alnylam. The episode closes with M&A and financing, including AstraZeneca's licensing deal with Dizal for the EGFR exon 20 inhibitor sunvozertinib andErasca's RAF data plus its $500M financing, which Sam views as a strong market signal despite a volatile biotech backdrop. This episode aired on July 17, 2026.

Thursday, July 16, 2026 Today, Todd Blanche and Jay Clayton testify in their respective confirmation hearings on Capitol Hill; Trump says ICE should continue traffic stops less than one day after DHS suspended the practice; Donald threatens to destroy bridges and civilian power plants in Iran; ICE admits it has a trove of documents about being stationed at polling places; plus Allison and Dana deliver your Good News. Thank You, HoneyLove Save 20% Off Honeylove by going to honeylove.com/DAILYBEANS #honeylovepod  #sponsored The Daily Beans is proud to partner with Miles Taylor and our friends at DEFIANCE.org For a limited time, members of the Daily Beans community can receive a FREE 3-month full membership to DEFIANCE.org and gain access to one of the fastest-growing pro-democracy movements in America. Join here: https://www.defiance.org/beans Join The Daily Beans and give a gift today to ensure The Trevor Project can continue its crucial work in the face of continued challenges. Donate to The Trevor Project - Daily Beans Podcast Guest: Adam KlasfeldAll Rise News@allrisenews|Bluesky, @klasfeldreports.com|BlueSky, @KlasfeldReports|Twitter, @senecaprojectus - Instagram The Latest Breakdown:BREAKING: EXCLUSIVE: FBI Admits it has Epstein Files Training Videos StoriesICE reverses, admits it may have trove of documents on agents at polling places | Democracy Docket Trump: ICE should continue traffic stops after recent shootings, seeming to contradict new policy | NBC News AG nominee Blanche told top Democrat he ‘made a mistake' with $1.8B anti-weaponization fund | Courthouse News Service Trump threatens to bomb bridges and power plants unless Iran resumes talks | BBC NewsGood Troubleimmigrantjustice.org - know-your-rights in an ice encounter →GoodTroubleLivesOn.org/ July 17-19 →Urge Democrats to Oppose and Stop Trump's Crypto Corruption | Indivisible Guide →Defiance.org/beans →Show up for our Libraries - action.ala.org →How to help those impacted by the Venezuela earthquakes|AP →Oppose House Amendment to Defund the Peace Corps! →Stand With Minnesota →ICE List  →iceout.org Good NewsVolunteer Opportunities, Events, and Petitions Near Me · John Lewis Actions on Mobilize Altadena Cookie Co dana-goldbergs-southwest-funnyfest Oct 9 -Email Dana@DanaGoldberg.com for sponsorship informationTour - DANA GOLDBERGTickets for Dana Goldberg: Outrageous - Sep 23 - Den Theater - Chicago  →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” Subscribe to the MSW YouTube Channel - MSW Media - YouTube Our Donation Links The Trevor Project - trevorproject.org/beans Blue Wave California - https://secure.actblue.com/donate/msw-bwc Donate to Public Citizen - https://citizen.org/beans/ Donate to It Gets Better / The Daily Beans Fundraiser Pathways to Citizenship link to MATCH Allison's Donationhttps://crm.bloomerang.co/HostedDonation?ApiKey=pub_86ff5236-dd26-11ec-b5ee-066e3d38bc77&WidgetId=6388736 Join Dana and The Daily Beans in support of Human Rights Campaign http://onecau.se/_ekes71National Security Counselors - Donate, ActBlue.com/donate/msw-bwc, WhistleblowerAid.org/beans Dr. Allison Gill - The Breakdown | Allison Gill, Mueller, She Wrote @muellershewrote.com - Bluesky, MSW & The Daily Beans Podcast @muellershewrote - Instagram, MSW Media - YouTube →Federal workers - email AG - fedoath@pm.me Dana Goldberg - Dana is on Patreon! At Dana's Dugout, @dgcomedy - Bluesky, @dgcomedy - IG, Dana Goldberg - Facebook,  DanaGoldberg.com More from MSW Media - Shows - MSW Media, Cleanup On Aisle 45 pod, The Breakdown | Allison Gill Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Uber agreed to acquire Delivery Hero for ~$14.8B, expanding into 99 markets. Thinking Machines released its first open-weight model, Inkling, SpaceXAI open-sourced Grok Build after a data-upload backlash, and sources detailed xAI's chaotic race to catch Claude under new leadership. Uber agrees to acquire Delivery Hero in a deal that values the German food delivery company at ~$14.8B, offering €41.50 per share and buying Prosus' 16.8% stake (Bloomberg) Thinking Machines Lab debuts Inkling, an open-weight MoE model with 975B total and 41B active parameters, trained to be broad rather than optimized for one area (Thinking Machines Lab) Thinking Machines Lab debuts Inkling, an open-weight MoE model with 975B total and 41B active parameters, trained to be broad rather than optimized for one area (WSJ) SpaceXAI open-sources Grok Build under an Apache 2.0 license, after the tool uploaded user repositories to SpaceXAI's Google Cloud bucket, causing a backlash (Simon Willison) SpaceXAI open-sources Grok Build under an Apache 2.0 license, after the tool uploaded user repositories to SpaceXAI's Google Cloud bucket, causing a backlash (The Decoder) Sources detail how xAI has been slowed down by internal chaos as Musk pushed for Grok to match Claude, amid signs it is turning a corner under Michael Nicolls (Bloomberg) Sources: Apple is preparing new iPads, including an iPad mini with an OLED screen by October and refreshed entry-level iPads and iPad Airs for 2027 (Bloomberg) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

On this episode of Run the Numbers, CJ breaks down Cumberland Farms' IPO filing and the surprising business behind it.—SPONSORS:RightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comPulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetricsRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer cut their close in half, took audit findings from seven to zero, and cut back-office costs by 70% in six months. You pay for outcomes, not seats. See it at https://www.maximor.ai/Brex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAnrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtn—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and Intro2:57 Key metrics4:28 Europe now out-earns America5:01 $5.8B debt at 8x leverage6:20 TLDR: snacks, gas, borrowed money7:58 Fuel vs. inside: the gross profit photo finish8:58 COCO, CONCO, and OTHER explained10:57 Sponsors — Maximor | Brex | Anrok14:02 Why scale matters: four reasons16:02 The US convenience store market17:42 EVs: long the birth rate18:05 Back to the debt19:10 The sale leaseback20:24 Sponsors — RightRev | Pulley | Rillet23:26 Does the deleveraging math work?24:27 The growth thesis25:06 600–700 stores become Cumberland Farms26:16 The chicken fryer ROI27:00 Coffee and loyalty: 6M members in 13 months28:15 Red flag 1: five-for-five COSO failures28:41 Red flag 2: entire C-suite is new29:04 Red flag 3: lending to their own parent29:42 Red flag 4: Big Tobacco funds loyalty29:57 Red flag 5: foreign issuer, Cayman charter30:10 Red flag 6: one supplier is 31% of costs30:32 Red flag 7: balance sheet is stale30:45 Cap table: TDR Capital and the Issa brothers31:55 Casey's vs. ARCO33:47 Goldman got bumped34:08 Massachusetts banned the pump clip until 201534:57 No health insurance disclosure for 16K US workers35:30 Robert Swan (ex-Intel CEO) is on the board36:08 Credits

In "Private Fleets Rescue Freight Brokers in 2026", Joe Lynch and Russell Jones, CEO & Co-founder of Private Fleet Net Zero, discuss how unlocking empty private backhauls provides brokers with discounted, high-quality capacity to combat fraud and skyrocketing liability. About Russ Jones Russell Jones co-founded Private Fleet Net Zero to help the 45% of trucks that are in Private Fleets with usually empty backhauls find loads from $50B+ of 3PL freight spend, leveraging his leadership of Cargo Chief, which enables 1,200+ 3PL buyers with $8B+ of spend to buy transportation capacity more profitably. Previously, Mr. Jones co-founded and led two cloud-based physical security firms.  He was also the founding CEO of Clearvox Communications, which pioneered the market for cellular phone headsets, which he sold to Plantronics.  Beforehand at Adaptec, Mr. Jones doubled a $50M channel products business to $100M. Mr. Jones has been awarded 10 patents, and holds a BSBA with highest honors from Boston University and an MBA from the Harvard Business School. About Private Fleet Net Zero Private Fleet Net Zero, PFNZ, is uniquely aggregating 10,000s of trucks with 1,000s of lanes of underutilized, underpriced, theft-free and superior private and dedicated fleet trucking capacity and matching via multi patent-pending technologies and artificial intelligence to $10Bs of freight spend registered on our cloud-based platform, while generating a compelling client ROI. Our network is quickly and efficiently growing both fleets and 3PLs on PFNZ, which is on a path to save 30M+ tree equivalents. Key Takeaways: Private Fleets Rescue Freight Brokers in 2026 In "Private Fleets Rescue Freight Brokers in 2026", Joe Lynch and Russell Jones, CEO & Co-founder of Private Fleet Net Zero, discuss how unlocking empty private backhauls provides brokers with discounted, high-quality capacity to combat fraud and skyrocketing liability. Massive Fleet Scale: Private Fleet Net Zero (PFNZ) has rapidly aggregated 80,000 trucks and 40,000 lanes of coverage, using patent-pending AI to match this massive pool of underutilized capacity with tens of billions of dollars in registered freight spend. The $150B Backhaul Waste: Private fleets (where the cargo owner owns the asset, like Walmart or Sherwin-Williams) make up 45% of all trucks on the highway, yet they run empty 80% of the time on their backhauls, leaving a $150 billion pool of premium capacity sitting idle. Pure Profit for Fleets: Because the primary "front haul" already covers the driver's salary, equipment, insurance, and core fuel costs, any backhaul revenue captured through PFNZ represents a 95% pure profit margin for the fleet owner. Quadrupled Broker Margins: Brokers can secure this premium capacity at a 25% discount to the market rate. In a tight market where a typical gross profit might only be $150 on an $1,150 load, cutting carrier costs from $1,000 to $750 can effectively triple or quadruple a broker's net margins. Eliminating Fraud and Liability: Shifting to private fleets bypasses the modern plague of cargo theft, cyber fraud, and "chameleon carriers" who hide bad histories under new DOT numbers. Furthermore, because private fleets have newer equipment and a third less accidents, they shield brokers from catastrophic multi-million dollar "nuclear verdicts" tied to carrier safety under the recent Montgomery ruling. Combating the 2026 Capacity Crunch: Massive federal enforcement of English language proficiency rules is projected to strip 25% of for-hire drivers (400,000 to 600,000 drivers) off the road. PFNZ rescues brokers by giving them an automated "outsourced recruiting team" to tap into stable private capacity that was previously heavily monopolized by the top 10 mega-brokerages. Seamless Integration & Sustainability: PFNZ connects to a broker's TMS within weeks via APIs, reports, or an AI bot to automatically map buying patterns. By eliminating empty miles, the platform is on track to save over 45 million tree equivalents in CO2, giving public companies and shippers a verifiable decarbonization story for SEC and board reporting. Learn More About Private Fleets Rescue Freight Brokers in 2026 Russ Jones | Linkedin Private Fleet Net Zero | Linkedin Private Fleet Net Zero Private Fleet Net Zero: The Deadhead is Dead with Russ Jones The Broken Safety System Threatening Shippers and Brokers with Chris Burroughs What is Blue Ocean Strategy | About Blue Ocean Strategy The Logistics of Logistics Podcast If you enjoy the podcast, please leave a positive review, subscribe, and share it with your friends and colleagues. The Logistics of Logistics Podcast: Google, Apple, Castbox, Spotify, Stitcher, PlayerFM, Tunein, Podbean, Owltail, Libsyn, Overcast Check out The Logistics of Logistics on Youtube

In this week's episode of the Coin Stories News Block powered exclusively by Ledn, we cover these major headlines related to Bitcoin, macroeconomics, and global finance: Trump reported $1.4 billion in crypto income — more than Coinbase earned all year The Trump token collapsed 97% — here's who actually made money Strategy sold 3,588 Bitcoin this morning in its biggest sale yet Bear market data is flashing a signal we've only seen five times since 2011 Saylor says Bitcoin evolves by not changing — Lyn Alden weighs in tomorrow ---- The News Block is powered exclusively by Ledn – the global leader in Bitcoin-backed loans, issuing over $10 billion in loans since 2018, and they were the first to offer proof of reserves. With Ledn, you get custody loans, no credit checks, no monthly payments, and more. My followers get .25% off their first loan. Learn more at www.ledn.io/natalie  ---- Order Natalie's new book "Bitcoin is For Everyone," a simple introduction to Bitcoin and what's broken in our current financial system: https://amzn.to/3WzFzfU  ---- Read every story in the News Block with visuals and charts! Join our mailing list and subscribe to our free Bitcoin newsletter: https://thenewsblock.substack.com  —- References mentioned in the episode: Trump Reports More Than $1.4B in Crypto Income for 2025 Trump Insists There's Nothing Wrong With His Big Crypto Gains Trump Pocketed More Than $1B From Crypto Ties as Industry Headed Toward Slump Trump Says There's 'Nothing Wrong' With Family's Crypto Windfall Trump's Crypto Token Buyers Are Down $3.8B, Blockchain Data Shows Nearly 1 Million Wallets Are Down $3.8B on Trump's Memecoin Trump Financial Disclosures Show Hundreds of Millions in Crypto-Related Income Trump's Memecoin Investors Are Down Billions Clarity and Congress's Summer Break — State of Crypto River's Chart on 5-Year Crypto Performance Bitcoin Magazine on Trump's Crypto Disclosure Bitcoin Long-Term Holders Have Returned to Accumulation, Glassnode Says Barstool's Portnoy Plans to Hold Bitcoin Down to $0 After Timing It Wrong Every Time Michael Saylor: "Bitcoin Evolves by Not Changing" DurdenBTC: ~45% of Long-Term Holder Supply in Loss DurdenBTC: Bitcoin Supply-in-Profit at Historical Lows Bitfinex: Long-Term Holder Supply in Loss  ---- Upcoming Events: The best time to plan for Bitcoin 2027 is right now. Early bird tickets are live — grab the lowest pricing available and use code HODL for 10% off: https://tickets.b.tc/event/bitcoin-2027?promoCodeTask=apply&promoCodeInput=HODL  ---- This podcast is for educational purposes and should not be construed as official investment advice. ---- VALUE FOR VALUE — SUPPORT NATALIE'S SHOWS Strike ID https://strike.me/coinstoriesnat/ Cash App $CoinStories #money #Bitcoin #investing

Multifamily investors are hurting — foreclosures, capital calls, and wiped-out equity are dominating the headlines and social media feeds. So is the investment thesis actually dead? Spencer Gray and Griffin Haddad tackle the question head-on this week, plus break down Gray Capital's newest offering: Century Apartments in West Lafayette, Indiana.In this episode:• Introducing Century — a new multifamily investment opportunity located inside the Purdue Research Park in West Lafayette, IN, directly adjacent to SK hynix's $3.8B semiconductor facility• A look inside Gray Capital's AI-powered deal room — including a multi-agent simulation that stress-tests every assumption in the business plan• Breaking down a viral LinkedIn post on real losses happening across the multifamily industry — and why context matters• Yardi Matrix's Summer 2026 Outlook: new supply is projected to drop nearly 40% by 2027–2028• RentCafe's $1,500 rent budget study — how far your money goes in 200+ US cities• Explore the Century investment opportunity and interactive deal room: https://GrayCapitalLLC.com

In episode 254 of the 6G podcast, Anshel Sag and Mike Danow discuss major telecom and space-industry developments. Sag covers T-Mobile forcing customers off older plans with roughly $4-per-line increases and potential competitive fallout, plus a Verizon–BT 50/50 joint venture combining international enterprise operations serving 3,000 customers in 180 countries with about $4B in revenue, and renewed reporting about a rumored SpaceX/xAI handheld AI device that Elon Musk denies. Danow focuses on spectrum and satellite trends: the FCC's planned upper C-band auction next summer for 160 MHz nationwide with commercial use delayed until 2030–2031, Rocket Lab's announced $8B acquisition of Iridium emphasizing L-band spectrum for direct-to-device services, and the FCC approving Grain Management's plan to lease divested 800 MHz spectrum for D2D, with interest hinted from AST SpaceMobile and results due by November 5.00:00 Welcome and Catch Up01:27 T-Mobile Plan Price Hike04:43 FCC Upper C Band Auction07:42 Verizon BT Enterprise JV11:17 Rocket Lab Buys Iridium17:09 SpaceX AI Device Rumor19:36 Grain 800 MHz D2D Leasing26:21 Wrap Up and Holiday

On this episode of CoinDesk's Public Keys from the New York Stock Exchange, host Jennifer Sanasie is joined by CoinDesk Indices and Data to break down nearly $1.8 billion in weekly Bitcoin ETF outflows, Strategy's new capital plan, and whether the digital asset treasury narrative is back. SharpLink CEO Joseph Chalom joins to unpack the Ethereum Foundation's funding crisis, the launch of ETHlabs, and the company's $75 million raise, as he makes the case for an institutional supercycle in ETH. In this week's 10X, Kaizen founder Brian Jung breaks down his MicroStrategy short. Moody's Ratings Managing Director and Global Head of Digital Economy Fabian Astic explains how the firm is embedding credit ratings into tokenized securities on Solana and unveils the first-ever credit rating methodology for stablecoins. Plus, Midnight Foundation President Fahmi Syed details the partnership with Bank of England-regulated Monument Bank and why privacy is becoming the missing piece for institutional adoption. - This episode of Public Keys is brought to you by Kraken Pro. For more: https://pro.kraken.com/ - Learn more at https://www.bullish.com/. - To get market moving news delivered daily, download CoinDesk's mobile app: https://linktr.ee/coindeskapp. - Timecodes: 00:00 Welcome to Public Keys 00:52 BTC ETFs See $1.8B in Weekly Outflows 02:57 Strategy's Capital Plan and Bitcoin's Week 04:12 Is the Digital Asset Treasury Narrative Back? 06:37 Ethereum Foundation Departures and ETHlabs 07:06 SharpLink CEO Joseph Chalom Joins 08:15 Ethereum's Funding Crisis and the ETH Bull Case 10:25 Inside SharpLink's $75M Raise 13:36 ETH's Institutional Super Cycle and Price Outlook 15:19 Will the Clarity Act Pass This Year? 17:45 10X: Brian Jung's Strategy Short 19:16 Moody's Ratings Brings Credit Ratings On-Chain 19:46 Fabian Astic on the First Stablecoin Credit Rating 21:36 Do Stablecoins Need Ratings After the Genius Act? 23:17 Why launch token ratings on Solana and Canton first? 25:36 Collateral Mobility and $255T in Trapped Liquidity 28:46 Is Privacy the Missing Piece for Institutions? 29:02 Midnight's Fahmi Syed on the Monument Bank Deal 33:46 The Collateral Warehouse and Global Expansion 36:38 Thanks for Watching

In this episode of Run the Numbers, CJ breaks down Lime's S-1 as the scooter company heads toward the public markets. He gets into the $1.8B valuation, Wayne Ting's turnaround playbook, Lime's unit economics, the post-Covid recovery, and the debt wall that may be forcing the IPO window open.—SPONSORS:SpendHound cuts your SaaS and AI spend by up to 30% using real pricing benchmarks across 10,000 vendors, so you always know what fair pricing looks like before your next renewal. Rated #1 on G2 in SaaS spend management, it's free forever for teams up to 1,000 employees. Sign up by June 12th and get $500 just for getting started. Go to https://www.spendhound.com/cjBrex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartups—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:SpaceX S1 Breakdownhttps://youtu.be/AARjbTO8FKo—TIMESTAMPS:0:00 Intro1:48 What Lime does2:47 Phoenix rising from the ashes3:53 Revenue: $887M, 29% growth6:38 Gross margin and the depreciation trick7:53 Adjusted EBITDA: $218M, up 42%8:27 Why the net loss got bigger9:54 Sponsors — SpendHound | Brex | Aleph13:08 Revenue per vehicle per day13:42 Marketing: only 2% of revenue14:01 Subscription mix: 28% and rising14:25 The debt: $821M but most converts15:37 The turnaround story16:42 What Wayne found taking over17:31 The three numbers he locked in on18:05 The warehouse overhaul19:31 Sponsors — RightRev | Rillet | EY22:33 The debt deep dive23:20 Why the net loss widened24:27 Post-IPO: nearly debt free25:07 Why more scooters make each scooter worth more28:43 How a scooter pays for itself30:43 The swappable battery32:39 Is this a real moat?34:14 Red flag 1: going concern35:11 Red flag 2: depreciation change before IPO36:11 Red flag 3: permits can disappear36:32 Red flag 4: Uber is everything37:24 Red flag 5: vehicles catch fire37:56 Valuation: under 2x revenue39:55 Comps: between Lyft and Uber40:55 Bull vs. bear41:52 Legal name is Neutron Holdings42:47 Credits

In this episode of Building Billions, I sit down with JD Ross, founder of Opendoor, partner at Atomic Ventures, and founder of WithCoverage, to break down how he’s taken companies from idea to billions in enterprise value, including scaling Opendoor from first revenue to an $8B public company in under six years. We get into what actually drives growth at scale, how to understand your business equation, why companies break as they expand, and what it really takes to build and lead teams beyond the founder, while also unpacking the massive opportunity in “boring” industries, AI-enabled operators, and the $5T+ small business wealth transfer that’s reshaping where the next generation of billion-dollar companies will be built.Support the show: http://cardoneventures.comSee omnystudio.com/listener for privacy information.

In this episode of The Capital Raiser Show, Richard C. Wilson sits down with entrepreneur, investor, and 10X founder Grant Cardone for a high-energy fireside chat on scaling businesses, raising capital, multifamily real estate, Bitcoin, social media leverage, and building long-term wealth. Grant shares the mindset and strategies behind building a $5B+ real estate portfolio, raising over $1.8B in equity, using social media to attract investors at scale, and why he believes most traditional investment structures are fundamentally flawed. The conversation dives into real estate cycles, branding, investor psychology, forced appreciation, Bitcoin treasury strategy, scaling through audience building, and how high-performing entrepreneurs continually reinvent themselves to operate at larger levels. Topics covered include: • Building and scaling a $5B+ real estate portfolio • Raising capital outside traditional Wall Street channels • Why social media is a massive investor acquisition tool • Long-term real estate investing and 10-year holds • Bitcoin, cash flow, and treasury strategy • Branding multifamily assets for scale • The psychology of growth and thinking bigger • Why successful people must leave comfort repeatedly • Investor relations, scale, and building loyal communities • High-velocity decision making and deal flow The Capital Raiser Show brings together billionaire investors, family offices, founders, operators, and elite entrepreneurs to discuss capital raising, scaling, investing, and strategic growth. Subscribe for more interviews with top investors, billionaires, family offices, and industry leaders.

Clayton as DNI, DOJ/Trump's $1.8B slush fund lives, Trump vs Platner, and Iran and inflation. It has been a wild, weird, harrowing week — Iran, ICE in Minneapolis, a UFC fight at the White House, the Knicks in the playoffs, and a president who keeps telling you out loud what he plans to do next. In this special Friday pop-media episode, Paul Rieckhoff brings you his weekly conversation from MS Now and breaks down what he's calling Trump's Plan A, Plan B, and Plan C: weaponize the National Guard, weaponize ICE, and weaponize the ballot box. It's not speculation. Trump has said it. Steve Bannon has said it. The reporting backs it up. And Congress — by Paul's read — has stopped exactly nothing. This is a no-BS briefing for the angry middle. Paul connects the dots between the resurrected payout scheme for January 6th defendants, the ICE escalation in blue cities, the Iran war driving gas prices through the roof, and the coming primary fights from Maine to Nebraska to Montana where independent veterans are stepping up where Democrats can't. He's blunt about the Democratic brand problem, blunt about the Republican capitulation, and clear about where the circuit breaker actually lives: election integrity, the courts, Congress, and an angry middle that refuses to check out for the summer. -WATCH full video of this episode here. -Join Noble Mobile today and get a $100 bonus when you stay a member for 2 months! -Join IVA and stand up to Trump's Forever Wars. -Learn more about Paul's work to elect a new generation of independent leaders with Independent Veterans of America. -Learn more about American Veterans for Ukraine here. -Remember Independent is an Attitude. -Learn more about The Headstrong Project for Veterans, Tragedy Assistance Program for Survivors (TAPS), and Department of Veterans Affairs resources in your area. Seeking support is not a sign of weakness. It's a show of strength. If you or a loved one are in immediate crisis, dial 988 and press 1, or text 838255. Connect with Independent Americans: Subscribe on YouTube, Spotify, Apple Podcasts, and all podcast platforms Read more at Substack Support ad-free episodes at Patreon  Connect: Instagram  • X/Twitter • BlueSky • Facebook  Follow on social: @PaulRieckhoff on X, Instagram, Threads, and Bluesky   -Join the movement. Hook into our exclusive Patreon community of Independent Americans. Get extra content, connect with guests, meet other Independent Americans, attend events, get merch discounts, and support this show that speaks truth to power.  -And get cool IA and Righteous hats, t-shirts and other merch now in time for the new year.  Independent Americans is powered by veteran-owned and led Righteous Media.  And now part of the BLEAV network!  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Reviewing a string of fascist failures; the $1.8B slush fund, Congress not supporting the Iran war, the Senate not passing the Save Act, a Judge undoing the naricisstic rebrand of the Kennedy Center, artists bailing on the White House's Fyre Fest, and a new lawsuit brought by 35 former Federal Judges. Also, reading up on dueling 250th celebrations for America (one congressionally approved, one rogue), learning why Albanians are protesting the aquisition and development of a protected island, and seeing how our tax dollars are paying for petty websites targeting Americans. Then, a look at the hunger strike, protests, and ongoing allegations of inhumane treatment at Delaney Hall, a for-profit Detention Center in New Jersey. Check your voter registration, find your polling location, or contact your representatives via USA.GOV, VOTE.GOV, and/or the "5 Calls" app. All opinions are personal and not representative of any outside company, person, or agenda. This podcast is hosted by a United States citizen, born and raised in a military family that is proud of this country's commitment to free speech. Information shared is for entertainment purposes only and is cited via published articles, legal documents, press releases, government websites, executive orders, public videos, news reports, and/or direct quotes and statements, and all may be paraphrased for brevity, presented satirically, and in layman's terms.“I love America more than any other country in the world and, exactly for this reason, I insist on the right to criticize her perpetually.” - James BaldwinWanna support this independent pod? Links below:Patreon - https://www.patreon.com/cw/BBDBBuyMeACoffee - https://www.buymeacoffee.com/BBDBVenmo @TYBBDB Hosted on Acast. See acast.com/privacy for more information.

The House passed a resolution to limit President Trump's Iran war powers.  The Senate is set to debate ICE funding, as Trump won't commit to killing the $1.8B anti-weaponization fund.  The suspect in a 15-hour hostage standoff in California was killed.  The President touts progress on the Reflecting Pool and the building of the UFC stage on the south lawn.  Plus, a stranger helps a family who had their van stolen on vacation.  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Berkshire Hathaway announces plans to buy homebuilder Taylor Morrison for $6.8B, we talk with CEO Sheryl Palmer about why the company agreed to be bought. Then the CEO of Related Digital, investing $16B in a Michigan data center, developed for Oracle as part of its Stargate project. Plus, the CEO of Hilton on the consumer and state of the travel industry. And Bristol Myers Squibb, unveiling new lung cancer drug data, its CEO joins us the from the ASCO conference. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

May 29, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Noom launches at-home biomarker kits measuring 17 markers with microneedle collection, finding 70% of users have high LDL cholesterol despite appearing healthy Scientists report single gene-editing infusion lowering LDL cholesterol by 62% with results maintained 18+ months, potentially replacing years of daily medication Retro Biosciences reaches $1.8B valuation backed by Sam Altman, entering human trials testing cellular cleanup process to combat neurodegeneration and age-related disease More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

Wednesday, May 27th, 2026 Today, South Carolina Senate Republicans reject Trump's redistricting bid in the state; a federal court blocks Alabama's new Republican map; Governor Mikie Sherrill demanded access to an ICE facility amid an ongoing hunger strike; the Supreme Court shoots down Florida's bid to stop other states from issuing driver's licenses to immigrant truckers; France bans Israel's Ben-Gvir after unspeakable treatment of flotilla detainees; Jim Acosta files a claim against the $1.8B slush fund; an American journalist is charged with failing to register as a foreign agent of China; a judicial panel upholds the reprimand of a federal judge caught having sex in chambers with a high ranking police officer; the Congressional Black Caucus urges companies to oppose Republican redistricting; CBS won't limit access to Colbert's public access show; and Allison delivers your Good News. Thank You, Fast Growing Trees Get 20% off your first purchase  FastGrowingTrees.com/dailybeans Thank You, LumiGummies Go to LumiGummies.com and use code DAILYBEANS for 30% off your order. Guest: Jim AcostaI'd Like to Apply for the Anti-Weaponization FundThe Jim Acosta Show | SubstackJim Acosta - YouTube@jimacosta.bsky.social - BlueskyJim Acosta (@jimacosta) - InstagramJim Acosta (@Acosta) - Twitter The Latest Breakdown:Don't Be Fooled: Trump Is Dying and Losing StoriesSouth Carolina's Trump-backed redistricting push fails in the state Senate amid GOP opposition | NBC News Federal court blocks Alabama from using GOP-drawn congressional map | NBC News Gov. Sherrill Demands Access to ICE Facility as Hunger Strike Widens | NYT Supreme Court rejects Florida's attempt to sue California and Washington over immigrant truck drivers | CBS News France bans Israeli minister Itamar Ben-Gvir after 'unspeakable' flotilla detainee taunts | AP News American journalist charged with serving as unregistered agent for China | POLITICO Panel upholds US judge's private reprimand for affair with police officer | Reuters Congressional Black Caucus urges companies to oppose Republican redistricting | PBS News After Stephen Colbert's viral talk show parody, CBS backs down from copyright action | NPR Good Trouble Contact - Senator Andy Kim Office of New Jersey Governor Mikie Sherrill Beans Talk - YouTube, Beans Talk - audio feed →Form WTAF-8647 →Recall Gov. Jeff Landry - Louisianadeservesbetter.com →STOP the deportation proceedings against Mohsen Mahdawi - Action Network →SusanRogan - how-to-help-win-the-midterms →detentionwatchnetwork.org →FieldTeam6.org →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible, Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List  →iceout.org Good NewsWounded Warrior Umpire Academy Uncle Sammy - YouTube Kat Abughazaleh (@kabughazaleh) - Instagram →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” Subscribe to the MSW YouTube Channel - MSW Media - YouTube Harry Dunn is running for CongressHarry Dunn for Maryland Our Donation Links Blue Wave California - bluewavecalifornia.org/concert The Daily Beans is donating $10,000 and invites you to give what you can to support their life-affirming work - Donate to It Gets Better / The Daily Beans Fundraiser   Pathways to Citizenship link to MATCH Allison's Donationhttps://crm.bloomerang.co/HostedDonation?ApiKey=pub_86ff5236-dd26-11ec-b5ee-066e3d38bc77&WidgetId=6388736 Join Dana and The Daily Beans in support of Human Rights Campaign http://onecau.se/_ekes71 More Donation LinksNational Security Counselors - Donate, ActBlue.com/donate/msw-bwc, WhistleblowerAid.org/beans Dr. Allison Gill - The Breakdown | Allison Gill, Mueller, She Wrote @muellershewrote.com - Bluesky, MSW & The Daily Beans Podcast @muellershewrote - Instagram, MSW Media - YouTube →Federal workers - email AG at fedoath@pm.me and let me know what you're going to do, or just vent. I'm always here to listen.  Dana Goldberg - Dana is on Patreon! At Dana's Dugout, @dgcomedy - Bluesky, @dgcomedy - IG, Dana Goldberg - Facebook,  DanaGoldberg.com More from MSW Media - Shows - MSW Media, Cleanup On Aisle 45 pod, The Breakdown | Allison Gill Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A former Capitol Police Officer wins her defamation suit against Blaze Media for saying she was the DC Pipe Bomber. The justice department proudly removed the January 6th press releases from their website. Harry sues to dissolve the $1.8B slush fund, An appeals court allows Rep. Jamie Raskin to file an amicus brief against dismissing the seditious conspiracy charges against the Oath Keepers and the Proud Boys. Allison Gillhttps://muellershewrote.substack.com/https://bsky.app/profile/muellershewrote.comHarry DunnHarry Dunn | Substack@libradunn1.bsky.social on BlueskyWant to support this podcast and get it ad-free and early?Go to: https://www.patreon.com/aisle45podTell us about yourself and what you like about the show - http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=short Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Allison speaks with former Capitol Police Officer Harry Dunn and his lawyer Brendan Ballou about their lawsuit to dissolve the $1.8B slush fund for insurrectionists. Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Department of Justice has established a $1.8B “anti-weaponization fund” after Trump dropped his lawsuit against the IRS. A former federal prosecutor has been indicted for sending herself copies of Volume II of Jack Smith's final report. A federal judge has dismissed the charges against Kilmar Ábrego García on vindictive and selective prosecution grounds. The Justice Department has dropped all remaining charges against the Broadview 6 after a grand jury transcript showed gross misconduct. Plus listener questions. Do you have questions for the pod or something for HITMEINTHEHEADWITHABAT?  TACO, NACHO, and More Trump Acronyms - by Carlos Greaves Check out other MSW Media podcastshttps://mswmedia.com/shows/ Follow AGMueller, She Wrote SubstackMueller She Wrote on Blueskyhttps://twitter.com/MuellerSheWrotehttps://twitter.com/dailybeanspodMore from Andrew McCabeThe Real McCabe on Substack@therealmccabe.com on BlueskyThe Threat: How the FBI Protects America in the Age of Terror and Trump This Show is Available Ad-Free And Early For Patreon and Supercast Supporters at https://patreon.com/thedailybeansOr when you Subscribe on Apple Podcastshttps://apple.co/3YNpW3P Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today's Headlines: The Department of Homeland Security is threatening to cut customs staffing at major international airports in sanctuary cities like New York, LA, Chicago, and DC — which would affect not just tourism but cargo shipments and the broader economy — because DHS Secretary Markwayne Mullin apparently thinks international trade can just reroute to Florida. Trump, meanwhile, said he's "in no hurry" to end the Iran war while also telling reporters he might skip his own son's destination wedding in the Bahamas because of "this thing called Iran," which is either a convenient excuse or the most relatable thing he's ever said. The Traitor Fund — formerly known as Trump's $1.776 billion slush fund — is getting wilder by the day: Proud Boys leader Enrique Tarrio wants $2-5 million, Mike Lindell is asking for $400 million, a January 6th rioter who compared herself to Jesus during sentencing wants $10 million, George Santos might file, and the couple who pointed guns at BLM protesters is reportedly interested. Acting AG Todd Blanche went to Congress to lobby Republicans not to block the fund — and offered to cut them in on it, specifically noting that senators whose records were secretly subpoenaed are eligible to file claims, which is a sentence that exists. The Senate responded by going home for a few weeks rather than dealing with it, which is technically a dereliction of their constitutional duty to approve funding, but here we are. Mortgage rates also hit a nine-month high this week at 6.51%, Trump is claiming he can build his DC arch without congressional approval, and a design commission approved the arch's look, which is one of many steps still required — Congress included, no matter what Donald says. On the Epstein files, Jeffrey Epstein's former personal assistant Sarah Kellen testified before the House Oversight Committee in a session described as deeply informative and genuinely harrowing — revealing she was recruited at 21, sexually and psychologically abused by Epstein for over a decade, and was "being paid in part to be raped." She also told the committee that the federal government included her name in Epstein's 2008 nonprosecution agreement without ever speaking to her, effectively branding her a criminal in a secret deal made with her own abuser. And finally, last night was Stephen Colbert's last show, which aired after this episode was recorded. Resources/Articles mentioned: The Atlantic: Homeland Security's Plan to Squeeze International Flights NYT: Trump Says He Will ‘Try and Make' Son Don Jr.'s Wedding, but Timing ‘Not Good' CBS News: Trump says Netanyahu will do "whatever I want" on Iran, and he's "in no hurry" to make a deal The Independent: Jan 6ers and other Trump allies already lining up to get their hands on slice of his $1.8B ‘slush fund' X - Paula Reid: https://x.com/PaulaReidCNN/status/2056842557334904940 USA Today: Fuming at Trump over 'slush fund,' Senate GOP skips town without passing ICE bill WaPo: Trump officials say they can build 250-foot arch without Congress's approval WSJ: Mortgage Rates Hit a Nine-Month High in Blow to Prime Buying Season ABC News: Former Jeffrey Epstein assistant tells House Oversight Committee he abused her for years AP News: Stephen Colbert is saying goodbye to 'The Late Show.' How it ends is still a secret Subscribe to the Betches News Room and join the Morning Announcements group chat. Go to: betchesnews.substack.com Morning Announcements is produced by Sami Sage and edited by Grace Hernandez-Johnson Learn more about your ad choices. Visit megaphone.fm/adchoices

Quantum computing stocks surged after the US announced $2B in grants with equity stakes. Spotify jumped 13% on 2030 guidance targeting $100B in revenue. Anthropic expects $10.9B in Q2 revenue and its first-ever operating profit, while Trump pulled back an AI executive order after calls with Musk and Zuckerberg. Shares of quantum computing companies surged Thursday after the US government announced grants with equity stakes: D-Wave closed up 33%, Rigetti 30%, IBM 12% (CNBC) Spotify closed up 13% on Thursday after announcing new features and 2030 guidance, forecasting a compound annual growth rate in the mid-teens (CNBC) Workday reports Q1 revenue up 13% YoY to $2.54B vs. $2.52B est., and lifts its full-year forecast, saying its AI strategy is working; WDAY jumps 9%+ after hours (CNBC) Sources: Trump delayed signing the AI EO because "he just hates regulation"; there were questions about the EO giving the Treasury Department a leading role (Axios) Investor disclosures: Anthropic says it expects to generate $10.9B in revenue in Q2, up 127% from $4.8B in Q1, and turn a $559M operating profit, its first ever (WSJ) Longreads In more than two-thirds of the world's countries, birthrates have fallen below replacement, and researchers increasingly point the finger at smartphones and social media (FT) Learn more about your ad choices. Visit megaphone.fm/adchoices

In part two of Red Eye Radio with Gary McNamara and Eric Harley, Trump approves more troops to be sent to Poland / Trump's $1.8B compensation fund for wrongful Biden DOJ prosecutions / Jeff Bezos says there will be a labor shortage as a result of A.I. / The Wall Street Journals's opinion piece "The Panic Industry's New Target" / The new movie about the Battle Of The Buldge "Lucky Strike" For more talk on the issues that matter to you, listen on radio stations across America Monday-Friday 12am-5am CT (1am-6am ET and 10pm-3am PT), download the RED EYE RADIO SHOW app, asking your smart speaker, or listening at RedEyeRadioShow.com. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Trump Plummets in Polls with Independents, $5 Gas, and War With Iran He Can't Spin. We're Not The Spoilers! The Surging Knicks. $1.8B for Insurrectionists and Violent Proud Boy Extremists? Is Cuba Next? Memorial Day Pain is a Reminder. The Real Spoilers are the Parties. Thank you, Stephen Colbert.  Trump is in the 20s with independents, gas is over five bucks a gallon in some states, Memorial Day is about to remind Americans of the human cost of his Iran war, and Cuba is looming over the horizon. Meanwhile the administration is funneling what looks like $1.8 billion in taxpayer money to January 6 insurrectionists and violent Proud Boy extremists — including a reported $2-to-5 million ask from Enrique Tarrio. Paul Rieckhoff calls it Plan C: if the military won't invoke the Insurrection Act and ICE can't carry the water, you green-light the angry mob and promise pardons in advance. This is what corruption looks like, and it's the most immediate threat to free and fair elections this fall. In this special quick-fire episode, Paul takes you out of the studio and into the streets of Midtown Manhattan with highlights from two back-to-back media hits — one left of center on MSNOW, one right of center on NewsNation — same independent read on both. He breaks down why ten thousand Americans are walking out of the Republican and Democratic parties every single week, why Thomas Massie just got elevated by Trump's attacks, why the Democrats are the actual spoilers in Senate races in Nebraska, Montana, and South Dakota, and why the rigged two-party system is finally cracking. Plus a Memorial Day shout-out to Stephen Colbert on his last show, and yes — a Knicks pick. -WATCH full video of this episode here. -Join IVA and stand up to Trump's Forever Wars. -Learn more about Paul's work to elect a new generation of independent leaders with Independent Veterans of America. -Learn more about American Veterans for Ukraine here. -Remember Independent is an Attitude. -Learn more about The Headstrong Project for Veterans, Tragedy Assistance Program for Survivors (TAPS), and Department of Veterans Affairs resources in your area. Seeking support is not a sign of weakness. It's a show of strength. If you or a loved one are in immediate crisis, dial 988 and press 1, or text 838255. Connect with Independent Americans: Subscribe on YouTube, Spotify, Apple Podcasts, and all podcast platforms Read more at Substack Support ad-free episodes at Patreon  Connect: Instagram  • X/Twitter • BlueSky • Facebook  Follow on social: @PaulRieckhoff on X, Instagram, Threads, and Bluesky -Join the movement. Hook into our exclusive Patreon community of Independent Americans. Get extra content, connect with guests, meet other Independent Americans, attend events, get merch discounts, and support this show that speaks truth to power.  -And get cool IA and Righteous hats, t-shirts and other merch now in time for the new year.  Independent Americans is powered by veteran-owned and led Righteous Media.  And now part of the BLEAV network!  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Tuesday, May 19th, 2026 Today, Trump has unilaterally dropped his $10B lawsuit against the IRS and has set up a $1.7B slush fund to pay his criminal co-conspirators; the district attorney in Hennepin County Minnesota has charged ICE officer Christian Castro with assault and lying in the shooting of Julio Cesar Sosa-Celis; a jury has dismissed Elon Musk's claims against Open AI CEO Sam Altman; the House Oversight Committee will interview one of the prison guards on duty when Epstein died; and Allison and Dana deliver your Good News. Thank You, IQBAR Text DAILYBEANS to 64000 to get 20% off all IQBAR products, plus FREE shipping. Message and data rates may apply.  California Rising - It was a powerful night to launch the fight to win back the House! The show is over but you can still help us reach our fundraising goal! bluewavecalifornia.org/concert Guest: Adam KlasfeldAll Rise News@allrisenews|Bluesky, @klasfeldreports.com|BlueSky, @KlasfeldReports|Twitter, @senecaprojectus - Instagram The Latest Breakdown:Retired Judge Blasts Trump's $1.7B Slush Fund for Allies | The Breakdown StoriesLive updates: Three killed, two suspects dead in shooting at San Diego mosque | NBC 7 San Diego DOJ sets up $1.8B ‘anti-weaponization' fund after Trump drops IRS lawsuit | NBC News House Oversight Committee to interview prison guard on duty when Jeffrey Epstein died | ABC7 New York Minnesota county charges ICE officer in shooting during immigration crackdown | PBS News Jury dismisses all claims in Elon Musk's lawsuit against OpenAI CEO Sam Altman | NPR Good Trouble The next protest here opposing the "deathstar" 'Stratos' data center is by Indivisible, Saturday, 23 May at the Utah State Capitol, 11 am. Dump Data Centers · Indivisible →STOP the deportation proceedings against Mohsen Mahdawi - Action Network →SusanRogan - how-to-help-win-the-midterms →detentionwatchnetwork.org →FieldTeam6.org →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible, Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List  →iceout.org Good NewsV Spehar (@underthedesknews) - Instagram LouisianaDeservesBetter.comgeauxvote.com/ElectionsAndVoting.html No Detention Centers in Michigan Conserve Ohio →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” Subscribe to the MSW YouTube Channel - MSW Media - YouTube Harry Dunn is running for CongressHarry Dunn for Maryland Our Donation Links The Daily Beans is donating $10,000 and invites you to give what you can to support their life-affirming work - Donate to It Gets Better / The Daily Beans Fundraiser The Daily beans is donating $10,000 and invites you to give what you can to support their life-affirming work - Donate to It Gets Better / The Daily Beans Fundraiser Pathways to Citizenship link to MATCH Allison's Donationhttps://crm.bloomerang.co/HostedDonation?ApiKey=pub_86ff5236-dd26-11ec-b5ee-066e3d38bc77&WidgetId=6388736 Join Dana and The Daily Beans in support of Human Rights Campaign http://onecau.se/_ekes71 More Donation LinksNational Security Counselors - Donate, ActBlue.com/donate/msw-bwc, WhistleblowerAid.org/beans Dr. Allison Gill - The Breakdown | Allison Gill, Mueller, She Wrote @muellershewrote.com - Bluesky, MSW & The Daily Beans Podcast @muellershewrote - Instagram, MSW Media - YouTube →Federal workers - email AG at fedoath@pm.me and let me know what you're going to do, or just vent. I'm always here to listen.  Dana Goldberg - Dana is on Patreon! At Dana's Dugout, @dgcomedy - Bluesky, @dgcomedy - IG, Dana Goldberg - Facebook,  DanaGoldberg.com More from MSW Media - Shows - MSW Media, Cleanup On Aisle 45 pod, The Breakdown | Allison Gill Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today's Headlines: Three people were killed in a shooting at the Islamic Center of San Diego — the city's largest mosque — by two teenage gunmen who died of self-inflicted wounds, with a security guard preventing the attack from being significantly worse. Notably, one shooter's mother had called police two hours earlier as a runaway juvenile report, telling them her son might be suicidal and had taken three of her weapons and her car. On the grift beat, the DOJ officially created the "Antiweaponization Fund" — a $1.776 billion taxpayer-funded slush fund that will pay unlimited, untaxable claims to anyone who says they were persecuted by the Biden administration, administered by a secret five-member board appointed by Todd Blanche, Trump's former personal criminal attorney. This is the "concession" Trump made in exchange for dropping his $10 billion IRS lawsuit — while still personally pocketing $230 million and getting all his family's tax audits dropped. Meanwhile, 60 Minutes reported that nine interconnected Polymarket accounts made $2.4 million betting on specific military actions during the Iran war with a 98% win rate across 80 bets, including the exact dates of the first strikes and the ceasefire announcement, which raises some extremely interesting questions about who's behind those accounts. Trump also announced he's pausing his planned Iran strike — which he said was scheduled for today — at the request of Gulf state leaders, while simultaneously instructing Hegseth to have a "full, large scale assault" ready at a moment's notice, because that's what de-escalation looks like now. Speaking of Hegseth, he flew to Kentucky on taxpayer — sorry, "personal" — time to campaign against Thomas Massie ahead of today's primary. In dictator vibes news, Putin arrived in Beijing today to meet with Xi Jinping four days after Trump's visit. North America's electric grid watchdog issued its strongest-ever warning that data centers are pushing the grid toward blackouts and water shortages, and Trump's reflecting pool paint job is now also reportedly toxic to the workers applying it and the public breathing it in. And finally, Elon Musk lost his OpenAI trial in under two hours — on a technicality about waiting too long to file — and his attorney responded by comparing the loss to battles during the Revolutionary War, which is completely normal legal analysis. Resources/Articles mentioned: AP News: Teenage gunmen open fire on San Diego mosque, killing 3 men and then themselves AP News: Justice Department announces nearly $1.8B fund to compensate Trump allies in a deal to drop IRS suit CBS News: Suspected insider accounts net $2.4 million on Polymarket Iran war bets with 98% win rate, firm finds Axios: Trump says he's pausing plan to attack Iran The Hill: Pentagon says Hegseth campaigning against Massie in ‘personal capacity' The Guardian: Xi prepares to welcome Putin to China four days after hosting Trump QZ: How AI data centers create cascading power outages The Guardian: Workers racing to turn reflecting pool blue for Trump may be at risk, union warns | Washington DC Wired: Elon Musk Loses Landmark Lawsuit Against OpenAI Subscribe to the Betches News Room and join the Morning Announcements group chat. Go to: betchesnews.substack.com Morning Announcements is produced by Sami Sage and edited by Grace Hernandez-Johnson Learn more about your ad choices. Visit megaphone.fm/adchoices

This episode is brought to you by Audible, WHOOP and Strong Coffee Company.  What really created Tesla's explosive growth? Was it Elon Musk, innovation, timing… or was there actually a repeatable formula behind it all? In this episode of Ever Forward Radio, former Tesla President Jon McNeill breaks down the exact framework used to scale Tesla from $1.8 billion to nearly $20 billion in revenue in just 30 months. Drawing from his new book, The Algorithm, Jon explains how companies like Tesla, SpaceX, Lululemon, and others use systems thinking, customer obsession, speed, curiosity, and innovation to create hypergrowth. But this conversation goes far beyond business. Chase and Jon explore how the same principles can be applied to your personal life, fitness, mindset, relationships, habits, and purpose. They discuss the danger of comfort and convenience, why most startups fail even with funding, how to build a mission-driven life, and the hidden cost of scaling too fast. Jon also shares behind-the-scenes stories from Tesla, lessons from working alongside Elon Musk, the importance of values and intentionality, and why curiosity may be the greatest superpower for growth. If you want to learn how to think bigger, move faster, simplify your life, and build something meaningful — this episode is for you ----- 00:00 — Tesla's Hypergrowth Story Begins 00:02 — The Mobile Service Breakthrough 00:12 — Tesla's Parking Lot "Triage" System 00:26 — The Algorithm Explained in 30 Seconds 00:43 — How Tesla Reduced Car Buying From 64 Clicks to 13 01:01 — Intro & Audible Sponsor 01:58 — How Tesla Scaled From $1.8B to $20B in 30 Months 02:35 — Is Hypergrowth Just Controlled Chaos? 03:14 — Growth vs Innovation: Which Comes First? 04:20 — "Creative Dissatisfaction" Inside Tesla 04:42 — Why "Done Is Better Than Perfect" 05:46 — How Tesla Used Customer Feedback Loops 07:23 — The Service Problem That Nearly Broke Tesla 08:53 — Creating Tesla's Mobile Service Model 10:12 — Why Convenience Changes Everything 12:08 — Step 1 of The Algorithm: Question Assumptions 14:03 — Consumer Friction & Asking Better Questions 16:14 — Why Tesla Put Stores Next to Apple & Lululemon 17:04 — Elon Musk's "Domino's Pizza" Car Buying Challenge 18:06 — Eliminating Unnecessary Loan Paperwork 19:20 — How Tesla Made Buying a Car Feel Like Ordering Pizza 20:18 — Convenience vs Character 21:36 — Fitness, Discipline & GLP-1s 23:01 — The Power of Intentionality 24:08 — Building Systems That Scale 25:38 — Learning From Hospitals & Emergency Rooms 27:20 — Curiosity as a Superpower 27:58 — Breaking Down The Algorithm Step-by-Step 29:12 — Why Speed & Quality Must Work Together 30:00 — Lessons From Olympic Cross-Country Skiers 31:20 — Using Speed to Expose Weaknesses 32:03 — How Toyota Forced Tesla to Improve Faster 32:43 — Turning Customers Into Tesla Evangelists 35:19 — Why Tesla Owners Became Obsessed With the Brand 37:14 — Strong Coffee Sponsor Break 37:27 — Knowing When to Pivot vs Keep Pushing 39:12 — Why "Good Enough" Is Dangerous 39:33 — Steve Jobs & The Simplicity Principle 42:04 — How Lululemon Cut Production From 1 Year to 8 Weeks 45:39 — Elon Musk's 10X Thinking 47:02 — Finding People Who Challenge You 49:33 — The Importance of Shared Values 51:26 — Tesla's Core Value: Customer Obsession 52:32 — Values vs Goals 52:51 — Productive Pressure vs Destructive Stress 54:46 — When Hypergrowth Becomes Dangerous 56:11 — Jon McNeill's Daily Habits & Routines 58:02 — How Family & Values Shape Success 59:13 — Why Company Values Matter 01:01:35 — What Surprised Jon While Writing The Book 01:03:38 — Why Tesla Almost Failed 01:04:42 — The #1 Trait of Successful People 01:05:50 — Why Most Startups Die 01:06:19 — How to Scale Your Life Like a Company 01:07:00 — The Moral Responsibility of Growth 01:07:22 — What "Ever Forward" Means to Jon McNeill 01:08:36 — Where to Find Jon & The Algorithm Book ----- Episode resources: Get Jon's new book The Algorithm Get his audiobook for FREE with your 30-day trial of Audible at https://www.AudibleTrial.com/everforward  Track your sleep, training, recovery and so much more with the WHOOP physical activity tracker  Save 15% on my favorite at-home coffee with code CHASE at https://www.StrongCoffeeCompany.com/chase  Watch and subscribe on YouTube