Podcasts about 70b

Grant Cardone sits down with the co-founder of Robinhood to break down how he went from an immigrant upbringing and failed startups to building a $70B company.  They cover what it really takes to build with no money, how to think differently than the market, and why betting on yourself beats playing it safe.  This is a masterclass in long-term thinking, resilience, and building at scale.  

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

Send us Fan MailConsumer and retail consulting used to be one market. Now it's 4.Each lane pulls a different lever – growth, margin, or talent. Firms that used to compete on "we do consumer and retail" are getting picked apart by specialists who can prove which lever they actually move.In this episode of The Briefing, Japheth sits down with Namaan to break down the 4 lanes reshaping a $70B market, why AI stopped being enough on its own, and why private equity's longer hold periods are turning growth and profitability into the same conversation.If you're a firm leader trying to protect market position, win the talent war, or defend margin, this one's for you.Resources:Go deeper on the data:Want to see which firms are already winning in each lane? Check out our 2026 Consumer & Retail rankingWant to hear it straight from firms already competing in these lanes? Watch our Consumer & Retail panelGo act on it:If your firm doesn't have a 10-second answer to "what do we solve better than anyone else," grab time with Namaan – MC works with firms to shape how that answer gets told to candidates and PE sponsorsIf you're a candidate trying to figure out which lane to network into, Black Belt helps you match your profile and story to the right one, and you can see who's actually hiring on our job boardConnect With Management ConsultedCreate a free MC account or download the MC app (Apple, Android) to start your prep todaySchedule a free 15min consultation with the MC TeamWatch the video version of the podcast on YouTubeFollow us on LinkedIn, Instagram, and TikTokJoin an upcoming live event – case interviews demos, expert panels, and more

Ted returns from his Brisbane trip to rejoin Pav as digital asset markets settle into a quiet lull. With global sporting events wrapping up and risk markets catching their breath, they discuss whether this boring sideways action signals that crypto is entering its classic bottoming phase. In this episode, they break down Bitcoin's recent slide following Middle East geopolitical tensions and explain why boring markets offer ideal DCA conditions for long-term investors. They contrast Bitcoin's holding power against tech stocks, noting SpaceX's 46% post-IPO drop and how cheap AI models like Alibaba's Qwen 3 are threatening big tech valuations. Finally, they cover BlackRock's $70B tokenization expansion, PancakeSwap's surprising $1B revenue milestone, and a sobering $1.5B retail liquidation event in South Korea.   You'll hear:  00:00 Ted returns from traveling as the guys reflect on market apathy during major global sporting events 02:00 Why sideways price action following geopolitical friction in the Middle East offers a calm entry point for multi-year horizons 03:36 SpaceX & the AI valuation threat 09:36 Balancing meme coin price gains against upcoming team and investor token unlock dates   13:36 How TradFi giants are preparing to bring private credit and treasuries directly onto the blockchain   17:00 Ted pulls up a chart/table showing the top 10 revenue-generating crypto apps and which app outpaced Hyperliquid 21:40 South Korea's $1.5B margin wipeout, a cautionary breakdown … and much more! Check out the chart Ted used for this episode here: https://experts.bitwiseinvestments.com/cio-memos/the-five-most-important-crypto-charts-from-q2 Want to see what we're looking at every episode? Watch the YouTube version of the podcast here. Ready to start? Get $10 of FREE Bitcoin on Swyftx when you sign up and verify:  https://trade.swyftx.com.au/register/?promoRef=tappingintocrypto10btc  To get the latest updates, hit subscribe and follow us over on the gram @tappingintocrypto or X @tappingintocrypto If you can't wait to learn more, check out these blogs from our friends over at Swyftx. This podcast provides general market commentary and is for educational and entertainment purposes only. It is NOT financial advice. We are NOT licensed financial advisors. Investing in cryptocurrency carries risk. You should always conduct your own research and seek independent financial advice before making any investment decisions. Please read Swyftx's Terms and Conditions and Risk Disclosure statement before investing.

It's News Day Tuesday on The Majority Report On today's program: ICE murders another person in cold blood, this time a 26-year-old father was shot in the head in Biddeford, Maine. In the wake of the murder, protestors swarm Susan Collins' office. Collins voted to provide a additional $70B in funds to ICE earlier this year. Elizabeth Ginexi, former NIH program official for 22 years and publisher of an eponymous Substack newsletter, joins to discuss how a little-noticed OMB rule could gut NIH and community funding. Minnesota Lt. Governor Peggy Flanagan joins to talk about her campaign for U.S. Senate. In the Fun Half: Italian American New Yorker's feelings are hurt over getting left off an immigrant enclave map of the city. PBD tries to capitalize on this "outrage" as a way to attack Mayor Mamdani. Marco Rubio makes his case for dismantling the Internation Criminal Court. Haley Stevens backed by tens of millions of dollars releases attack ads that label Abdul El-Sayed as sexist and mislead audiences into thinking that Obama has endorsed Stevens. Meta's CTO tries to sell Meta glasses as a way to remember people's names. Clavicular is surprised by the backlash he has received while visiting Israel over the footage from January where he is seen singing "Heil Hitler" by Kanye West. All that and more. Tell your Senators to block the NDAA until it is stripped of the proposal to integrate U.S. and Israeli militaries. Join Emma for a virtual DSA event: Workers Deserve More: DSA's 2026-27 Program Launch on Tuesday, July 14 To connect and organize with your local ICE rapid response team visit ICERRT.com The Congress switchboard number is (202) 224-3121. You can use this number to connect with either the U.S. Senate or the House of Representatives. Follow us on TikTok here: https://www.tiktok.com/@majorityreportfm Check us out on Twitch here: https://www.twitch.tv/themajorityreport Find our Rumble stream here: https://rumble.com/user/majorityreport Check out our alt YouTube channel here: https://www.youtube.com/majorityreportlive Gift a Majority Report subscription here: https://fans.fm/majority/gift Subscribe to the AM Quickie newsletter here: https://am-quickie.ghost.io/ Join the Majority Report Discord! https://majoritydiscord.com/ Get all your MR merch at our store: https://shop.majorityreportradio.com/ Get the free Majority Report App!: https://majority.fm/app Go to https://JustCoffee.coop and use coupon code majority to get 10% off your purchase Check out today's sponsors: COZY EARTH: Go to cozyearth.com/MAJORITYREPORT for an exclusive 20% off. FACTOR: Go to FactorMeals.com/majority50off and use code majority50off to get 50% off and free daily greens per box, with new subscription only, while supplies last until September 27, 2026. SUNSET LAKE CBD: Use coupon code "Left Is Best" (all one word) for 20% off of your entire order at SunsetLakeCBD.com.  Follow the Majority Report crew on Twitter: @SamSeder @EmmaVigeland @MattLech On Instagram: @MrBryanVokey Check out Matt's show, Left Reckoning, on YouTube, and subscribe on Patreon! https://www.patreon.com/leftreckoning Check out Matt Binder's YouTube channel: https://www.youtube.com/mattbinder Subscribe to Brandon's show The Discourse on Patreon! https://www.patreon.com/ExpandTheDiscourse Check out Ava Raiza's music here! https://avaraiza.bandcamp.

The U.S. and Iran have agreed to a peace deal, set to be signed on Friday. Vice President JD Vance shares details of the deal, including flows through the Strait of Hormuz, Iran's nuclear program, and the resulting shifts to energy prices. As SpaceX starts its first full day of trading on the public markets, Ron Baron, with about half of his $70B+ fund's portfolio in SpaceX and Tesla, says he's still betting on Elon Musk. Baron addresses valuation concerns and explains his ongoing optimism regarding the world's first trillionaire.    Leslie Picker Ron Baron  Vice President JD Vance   Leslie Picker - 10:01 Ron Baron - 18:26 Vice President JD Vance - 44:25   In this episode: Leslie Picker, @LesliePicker Robert Frank, @robtfrank Brian Sullivan, @SullyCNBC Becky Quick, @BeckyQuick Cameron Costa, @CameronCostaNY Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AlabamaThe state appeals to 11th Circuit court over judge halting an executionAL Congressional Delegates approve $70B funding for ICE and Border PatrolSen. Britt warns Iranian leaders to take a deal from Trump to end the warMobile pastor urges city to not use taxpayer money to promote Pride monththe new Financial oversight committee within ALGOP has its first meetingNationalPresident Trump signs EO that prohibits USPS from delivering mail in ballots in states denying access to names on their voter rollsTrump also reveals that US military have smuggled out oil tankers in HormuzActBlue CEO pleads the 5th amendment in house committee hearingBill Gates sits for closed door deposition in DC re: Jeffrey EpsteinOversight committee to subpoena Todd Blancher after NY Times report details White House coverup over Jeffrey Epstein and Trump

Today's Headlines: Iran shot down a US Army helicopter yesterday, the US responded with strikes on Iranian air defense systems, both crew members are stable, and stock futures dropped immediately — so the ceasefire is going great. Meanwhile, the World Cup starts tomorrow and the Trump administration is already making it a disaster: the best male referee in Africa was denied entry despite a valid visa, the Iraqi team's vice captain was detained for seven hours at O'Hare, the team photographer was turned away entirely, and Trump is preemptively blaming Europe for any Ebola outbreaks despite zero confirmed cases there. Meanwhile, the House voted to give ICE and Border Patrol $70 billion more for immigration enforcement — $38 billion to ICE, $26 billion to Border Patrol, and a breezy $5 billion for "unforeseen costs." Anthropic's cofounder published a blog post asking leading AI labs to consider pausing frontier AI development, comparing it to nuclear nonproliferation — the response was a collective "no," with some calling it self-serving given everyone's upcoming IPOs — and this comes as Anthropic is reportedly preparing to release Claude Fable 5, a model it deemed too dangerous for public release just six months ago. Epstein assistant Lesley Groff testified before the House Oversight Committee claiming she "never saw anything improper" after two decades of keeping Epstein's entire schedule, which the committee found highly inconsistent. Tom Steyer conceded the California governor's race, Trump kept pushing election fraud conspiracies about California to the point that a congressman reported a friend canceling their voter registration over Spencer Pratt, and Ken Paxton's own former impeachment attorney endorsed Democrat James Talarico in the Texas Senate race, saying Paxton is too focused on appeasing Trump to be a good senator. And finally, NASA announced the Artemis III crew of four astronauts who will orbit Earth practicing lunar lander docking in preparation for a 2028 moon landing — assuming Blue Origin delivers its lander on time, which is uncertain after one of its rockets exploded during a test. Resources/Articles mentioned: AP News: US and Iran launch airstrikes after Trump blamed Tehran for downing Army helicopter CNBC: Stock futures slip after U.S. launches ‘self-defense strikes' against Iran: Live updates NYT: U.S. Denies Entry to World Cup Referee From Somalia NYT: Iraq World Cup star Aymen Hussein questioned for ‘seven hours' by U.S. immigration officials Axios: Scoop: Trump admin pre-blames Europe for any World Cup Ebola AP News: House passes $70B bill to fund immigration enforcement for 3 years, sending to Trump MS Now: Longtime Epstein assistant denies knowledge of his crimes to House Oversight Committee Business Insider: What smart people are saying about Anthropic suggesting a global AI pause WSJ: Anthropic Releases Fable 5, a ‘Mythos-Class' AI Model With Guardrails WaPo: Maine Senate primary election live results: Graham Platner runs X: X | Ro Kanna  AP News: Ken Paxton's attorney in his impeachment trial endorses James Talarico in US Senate race AP News: NASA unveils Artemis III astronauts to test technology for a future moon landing 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

In part one of Red Eye Radio with Gary McNamara and Eric Harley, voters in Maine have spoken. Liberal upstart Graham Platner won Maine's Democratic Senate primary on Tuesday night, defeating Maine Gov. Janet Mills weeks after the more establishment-backed pick ended her campaign. The Associated Press called the race soon after polls in Maine closed at 8 p.m., where voters in the state are not only weighing in on one of the nation's most significant Senate races but also on a comeback attempt for a controversial former governor and a contest to decide the next governor that features several famous names. Also a Collin County jury has sentenced Karmelo Anthony to 35 years in prison after he was found guilty of murder in the fatal stabbing of 17-year-old Memorial High School student Austin Metcalf during a high school track meet in Frisco, Texas / Republican Sen. Lindsey Graham squeaked out a win Tuesday night in a crowded primary race for the Republican nomination in South Carolina / During a House Judiciary Committee hearing attacking the Southern Poverty Law Center, Democratic Rep. Jasmine Crockett of Texas gets a gentle rebuke from the niece of MLK / President Trump locks in ICE funding through end of presidency after the House passes $70B package / Social Security Administration projected to run out reserves in 7 years / and "Pet Talk" with Gary and Eric. 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

04:03 $70B immigration bill passes the senate cementing a huge victory for Trump15:05 Paramedics convictions reversed in the death of Elijah McClain 29:12 Father who killed daughter's alleged molester has charges dismissedLEO Round Table (law enforcement talk show)Season 11, Episode 113 (2,686) filmed on 06/08/20261. https://www.tampafp.com/dawn-victory-for-border-security-senate-passes-70b-immigration-funding-bill-crushing-left-delays/2. https://www.cnn.com/2026/06/04/us/elijah-mcclain-paramedics-convictions-reversed3. https://arktimes.com/arkansas-blog/2026/06/04/judge-dismisses-criminal-charges-against-aaron-spencer-the-dad-who-killed-daughters-alleged-molesterShow Panelists and Personalities:Chip DeBlock (Host and retired police detective)Chief Joel F. Shults, Ed.D. (retired chief and author)Sponsors:Galls - Proud to serve America's public safety professionalshttps://www.galls.com/leoUse 15% OFF Code: RADIO15Compliant Technologies - Cutting-edge non-lethal tools to empower and protect those who servehttps://www.complianttechnologies.net/The International Firearm Specialist Academy - The New Standard for Firearm Knowledgehttps://www.gunlearn.com/MyMedicare.live - save money in Medicare insurance options from the expertshttp://www.mymedicare.live/Related Events, Organizations and Books:Force Science Training and Conference Information:Get Ready—Early Registration for Force Science 2026 Conference​September 22 - 24, 2026 Austin Metro, TXSave $100!Use Code: earlybird26Also,Connect with Von Kliem on LinkedIn:linkedin.com/in/vonkliemconsultingAsk for the discount code for 15% off online FS courses which can be found at:https://www.forcescience.com/online-courses/Retired DEA Agent Robert Mazur's works:Interview of Bryan Cranston about him playing Agent Robert Mazur in THE INFILTRATOR filmhttps://vimeo.com/channels/1021727Trailer for the new book, THE BETRAYALhttps://www.robertmazur.com/wp-content/uploads/2023/05/The-Betrayal-trailer-reMix2.mp4Everything on Robert Mazurhttps://www.robertmazur.com/The Wounded Blue - Lt. Randy Sutton's charityhttps://thewoundedblue.org/Rescuing 911: The Fight For America's Safety - by Lt. Randy Sutton (Pre-Order)https://rescuing911.org/Books by panelist and retired Lt. Randy Sutton:https://www.amazon.com/Randy-Sutton/e/B001IR1MQU%3Fref=dbs_a_mng_rwt_scns_shareThey're Lying: The Media, The Left, and The Death of George Floyd - by Liz Collin (Lt. Bob Kroll's wife)https://thelieexposed.com/Lt. Col. Dave Grossman - Books, Newsletter, Presentations, Shop, Sheepdogshttps://grossmanontruth.com/Sheriff David Clarke - Videos, Commentary, Podcast, Shop, Newsletterhttps://americassheriff.com/Content Partners:Red Voice Media - Real News, Real Reportinghttps://www.redvoicemedia.com/shows/leo/ThisIsButter - One of the BEST law enforcement video channelshttps://rumble.com/user/ThisIsButterThe Free Press - LEO Round Table is in their Cops and Crimes section 5 days a weekhttps://www.tampafp.com/https://www.tampafp.com/category/cops-and-crime/Video Show Schedule On All Outlets:http://leoroundtable.com/home/syndication/Syndicated Radio Schedule:http://leoroundtable.com/radio/syndicated-radio-stations/

Further Reading:Proposition 2 1/2 overrides & exclusions - LINK (Mass.gov)Southampton officials feel relief after passed tax override vote - LINKHadley residents approve tax override questions - LINKSouth Hadley to vote on $3.5M tax override in September - LINKA city divided: Override battle heads to ballot box in Easthampton - LINKSenate passes $70B bill to fund immigration enforcement, without limits on Trump ‘anti-weaponization' fund - LINKIn an interview with Maine Public, Graham Platner denies being physically threatening - LINKEx-girlfriend at the center of Graham Platner's latest scandal says she was ‘set up.' He says she's a political operative. - LINKThe End Of ‘Destiny 2': All Expansions Canceled, Maintenance Mode Incoming - LINK Beginning Music: Glenn Gould - Goldberg Variation #5Ending Music: Destiny 2 - Deep Stone Lullaby Remember to Register to vote! Mass Residents should go to: https://www.sec.state.ma.us/ele/For more Civil Politics visit our website, civilpoliticsradio.com!If you want to get alerted to new episodes on social media, follow our Bluesky: @CivilPoliticsRadio.comDon't miss another episode - subscribe to our podcast (iTunes, Google Play, Spotify, and more!)This podcast is a member of the Planetside Podcast Network. Visit PlanetsidePodcasts.com to find other Planetside Productions!

AlabamaSCOTUS clears the way for AL to use 2023 congressional map in midtermsGovernor Ivey praises state leaders who fought for 2023 map to prevailAnother superseding indictment is issued against the SPLC by DOJHoover family releases video statement on son who went missing in JapanBirmingham state lawmaker is legally challenging the ballot qualifications of her winning primary challengerNationalSenate votes to start debate on $70B funding bill for ICE and Border PatrolHouse passes war powers resolution re: Iran war, now heads to SenateRetired Army Colonel says war needs to end fast or major problems hit USMerging US Military programs with Israel is bad idea says author Ben Freeman

Shoot us a Text.Episode #1350: Today we're talking about Stellantis betting big on affordable vehicles and platform consolidation, NADA helping dealers put families on the road through a new national partnership, and Bojangles turning EV charging into a side of biscuits with its new “charge-and-dine” concept.Stellantis is laying out its “FaSTLAne 2030” plan, and for dealers, the headline is simple: more affordable metal is coming. The automaker says North America will get nine vehicles under $40,000 by 2030, with two slipping under the $30,000 mark.Stellantis' five-year, $70B plan sends 70% of global investment toward Jeep, Ram, Peugeot, Fiat and Pro One.In North America, Stellantis is targeting 25% revenue growth, 35% volume growth and 11 all-new vehicles.The company expects U.S. factory utilization to reach 80% by 2030, helped by increased domestic production.Globally, Stellantis plans to simplify 50% of its vehicles around three core platforms, including the new “STLA One” architecture designed to boost efficiency, lower costs and increase shared components across brands.CEO Antonio Filosa said, “FaSTLAne 2030 is the result of months of disciplined work across the company.”NADA and Vehicles for Change are teaming up nationally to help dealers put more families on the road to stability. The new partnership gives dealers a turnkey way to donate vehicles and support low-income families needing reliable transportation for work, childcare and daily life.NADA and Vehicles for Change will officially launch a national dealer partnership on May 27 in Pennsylvania.#1 Cochran Buick GMC will donate two vehicles to local families during the kickoff event as an example for dealers nationwide.The program includes a dealer “playbook” with step-by-step guidance for stores wanting to participate in their own communities.Vehicles for Change says it has already helped more than 8,200 families gain affordable transportation through its Keys to Independence program.NADA Chairman Rob Cochran said, “This event demonstrates the powerful impact dealers can have.”Bojangles is entering the EV charging game, turning fried chicken stops into charging stops. The chain just launched its first EV charging station in Savannah, Georgia, pitching a new “charge-and-dine” experience as it looks to expand chargers nationwide.The company partnered with XLR8 America and Energy and Environmental Design Services to bring level 2 and level 3 chargers to future locations.Bojangles says the goal is to transform charging downtime into a hospitality experience built around food, comfort and convenience.The company says its charging network is designed for more than 97% uptime as EV adoption continues to grow.CIO Richard Del Valle said, “This is about more than charging vehicles. It's about redefining the stop along the way.”“At XLR8 America, our philosophy is simple: charge where you park, not park where you charge,” XLR8 America CEO Frank O'Connor said. “Bojangles gets that. When a driver pulls in for a Bo-Berry Biscuit and the battery tops off while they dine, that's not a coincidence — that's the charge-and-dine experience made real.”Join Paul J Daly and Kyle Mountsier every morning for the Automotive State of the Union podcast  as they connect the dots across car dealerships, retail trends, emerging tech like AI, and cultural shifts—bringing clarity, speed, and people-first insight to automotive leaders navigating a rapidly changing industry.Get the Daily Push Back email at https://www.asotu.com/JOIN the conversation on LinkedIn at: https://www.linkedin.com/company/asotu/

Podcasting 2.0 April 24th 2026 Episode 258 - "Perceptron" Dave and Adam walk through the entire technical process of building an industry resource for auto-flagging spam and slop podcasts. It's a doozy! Shownotes ----------------------------------------------------------------------------------------------------------------------------------------- Booking Tag ----------------------------------------------------------------------------------------------------------------------------------------- DeepSeek V4 Released — Open Source AI From China DeepSeek V4 released April 24 — two variants: V4-Pro (1.6T params) and V4-Flash (284B params, 13B active) Open-weight MIT license, 1 million token context window Trained on domestic Chinese chips (Huawei Ascend, Cambricon) — not Nvidia V4-Flash: $0.14/M input tokens — cheapest frontier-class model available White House OSTP accusing China of 'industrial-scale' distillation of US AI models Anthropic caught DeepSeek using 24,000 fake accounts and 16M exchanges to distill Claude Our own testing: DeepSeek V3.1 on Together.ai is 4-10x faster and half the cost of Llama 3.3 70B for production tasks CNBC: DeepSeek V4 Preview CNN: White House Accuses China Copying American AI ----------------------------------------------------------------------------------------------------------------------------------------- Anthropic Claude Code Pricing Drama Anthropic quietly removed Claude Code from $20/month Pro plan on April 21 Developer backlash immediate — reversed within hours Head of Growth: 'a test on 2% of new signups' — but page changes were global Internal data: heavy users cost Anthropic $60-$150/month on a $20 subscription GitHub also suspended flat-rate Copilot signups, removed Claude Opus from $10 tier The AI subscription model is structurally broken — WeWork/Uber playbook StartupFortune article Anthropic postmortem (April 23) ----------------------------------------------------------------------------------------------------------------------------------------- Microsoft Voluntary Buyout — 7% of US Workforce ----------------------------------------------------------------------------------------------------------------------------------------- Topics for Today Podping / namespace updates? Feed cloning follow-up from 257 — any new developments? Sovereign Feeds update — feed editing experience Last Modified 04/24/2026 14:38:32 by Freedom Controller

Podcasting 2.0 April 24th 2026 Episode 258 - "Perceptron" Dave and Adam walk through the entire technical process of building an industry resource for auto-flagging spam and slop podcasts. It's a doozy! Shownotes ----------------------------------------------------------------------------------------------------------------------------------------- Booking Tag ----------------------------------------------------------------------------------------------------------------------------------------- DeepSeek V4 Released — Open Source AI From China DeepSeek V4 released April 24 — two variants: V4-Pro (1.6T params) and V4-Flash (284B params, 13B active) Open-weight MIT license, 1 million token context window Trained on domestic Chinese chips (Huawei Ascend, Cambricon) — not Nvidia V4-Flash: $0.14/M input tokens — cheapest frontier-class model available White House OSTP accusing China of 'industrial-scale' distillation of US AI models Anthropic caught DeepSeek using 24,000 fake accounts and 16M exchanges to distill Claude Our own testing: DeepSeek V3.1 on Together.ai is 4-10x faster and half the cost of Llama 3.3 70B for production tasks CNBC: DeepSeek V4 Preview CNN: White House Accuses China Copying American AI ----------------------------------------------------------------------------------------------------------------------------------------- Anthropic Claude Code Pricing Drama Anthropic quietly removed Claude Code from $20/month Pro plan on April 21 Developer backlash immediate — reversed within hours Head of Growth: 'a test on 2% of new signups' — but page changes were global Internal data: heavy users cost Anthropic $60-$150/month on a $20 subscription GitHub also suspended flat-rate Copilot signups, removed Claude Opus from $10 tier The AI subscription model is structurally broken — WeWork/Uber playbook StartupFortune article Anthropic postmortem (April 23) ----------------------------------------------------------------------------------------------------------------------------------------- Microsoft Voluntary Buyout — 7% of US Workforce ----------------------------------------------------------------------------------------------------------------------------------------- Topics for Today Podping / namespace updates? Feed cloning follow-up from 257 — any new developments? Sovereign Feeds update — feed editing experience Last Modified 04/24/2026 14:38:32 by Freedom Controller

Pat Gelsinger is the former CEO of Intel and longtime Silicon Valley leader.  He joins Auren to unpack the forces reshaping technology, geopolitics and leadership.After more than 30 years at Intel (including serving as its first CTO) and nearly a decade as CEO of VMware, Pat now sits at the intersection of deep tech and purpose-driven innovation as executive chair and head of technology at Gloo and general partner at Playground Global.In this episode of Summation, Pat and Auren discuss:Why the US now imports more from Taiwan than from China -- and what that means for national securityThe case for a U.S. sovereign wealth fund to counter China's tech investments in semiconductors, energy, and rare earth mineralsWhat happened when Intel gave $70B back to shareholders instead of building fabs, and why tech companies need technologist CEOsAndy Grove's 35-year mentoring relationship with Pat, starting with a cold call that changed his careerYou can find Auren Hoffman on X at @auren and Pat Gelsinger on X at @PGelsinger

I. Mary's Act II. Jesus' Defense III. Our Benefit Scripture Reading: John 12 Text: Mark 14:1-11 Psalter Numbers: 102C, 70B, 62D, 136A

Brian Szytel recaps a rebound day in markets with broad gains (Dow +238, S&P +0.8%, Nasdaq +1.3%) amid headline-driven volatility tied to Iran and renewed tariff discussion. He notes Secretary Bessent's comments on Section 122 potentially moving tariffs from 10% to 15%, which would still mean $65–$70B less in taxes than under IEPA, helping especially smaller and mid-sized businesses. Key market watchpoints are oil and shipping through the Strait of Hormuz and bond yields, which rose with higher energy and inflation expectations rather than signaling a flight to safety; the 10-year is around 4.07%. He reiterates a midterm outlook of Democrats taking the House and Republicans holding the Senate. Economic data were strong, led by ISM services at 56.1, alongside services PMI at 51.7 and ADP private payrolls at 63K. He also addresses software stocks, viewing AI-driven selloffs as selective opportunity with potential margin benefits. 00:00 Market Rebound Recap 00:42 Tariffs Back in Focus 01:45 Iran Risks and Oil 02:41 Volatility and Bond Yields 03:49 Midterm Politics Update 04:27 Economic Data Rundown 05:33 AI and Software Stocks 06:47 Wrap Up and Tomorrow Links mentioned in this episode: DividendCafe.com TheBahnsenGroup.com

When Tony Xu cofounded DoorDash 13 years ago, he was rejected by more than 100 investors. Today, it's a $70B+ behemoth dominating the delivery industry. Tony joins SPC General Partner Aditya Agarwal to reveal how DoorDash won the delivery war and answer the burning question of whether AI agents pose a threat to his company. He also shares why customer obsession became the company's guiding principle, the challenges of digitizing the physical world, and how startups today can build competitive advantages in the age of AI. Tony Xu: https://www.linkedin.com/in/xutony/ Aditya Agarwal: https://www.linkedin.com/in/adityaagarwal3/South Park Commons: https://www.linkedin.com/company/southparkcommons/Apply to SPC: https://www.southparkcommons.com/applyChapters:(00:00:00) - Intro (00:01:00) - Becoming a founder(00:11:48) - Implementing AI and customer obsession (00:18:22) - Is AI a threat to DoorDash? (00:21:29) - Digitizing the physical world(00:25:31) - Company culture and values (00:31:33) - Tony's unpopular business opinion (00:37:42) - How to stay curious and motivated (00:39:31) - Preparing children for the new age (00:42:47) - Audience Q&A

Omni Talk Retail is live from eTail West 2026 with coverage powered by NetElixir. In this interview, Anne Mezzenga speaks with Kelly Cook, CEO of Davids Bridal, fresh off the stage to discuss the company's bold “Aisle to Algorithm” strategy and what she calls the bridal tech revolution. David's Bridal is expanding beyond a $4B bridal TAM into the broader $70B wedding ecosystem, building what Kelly describes as an AI and asset light approach to retail and media within the wedding industry. Key themes from the conversation: • The “tech sandwich” model: high tech before and after the in store bridal appointment • Virtual try on, AR wedding visualization, and agentic AI guiding brides through 300 planning tasks • A vision for one click wedding planning powered by immersive augmented reality • How partnerships and retail media are unlocking value beyond the dress • Why large scale transformation requires fearless talent and cultural clarity Kelly also shares three leadership lessons for retail executives navigating transformation, including her now famous advice: be somebody's shot of whiskey, not everybody's cup of tea. Thank you to NetElixir for supporting our eTail West 2026 coverage. #eTailWest #RetailTransformation #AIinRetail #Weddings #RetailInnovation #DigitalTransformation

Today's Post - https://bahnsen.co/40qp47X Snowed in New York recording opens with a sharp selloff (Dow -822; S&P -1%+; Nasdaq -1.1%). Weakness tied more to AI valuation and pressure in tech and financials than tariffs. The 10-year yield fell to ~4.03%; defensives led. AI capex for 2026 is pegged at $650B across five firms. Nvidia's $30B OpenAI investment is expected to cycle back via chip orders. The Supreme Court ruled 6–3 that IEEPA cannot be used to impose tariffs; Congress retains tariff authority. Refund mechanics remain unclear. Possible alternatives include Section 122 (150-day limit) and the more complex 301 and 232 routes. Strategas estimates a net $70B tariff reduction even if some measures return. Refunds could total $120–130B, potentially stimulative, though implementation may be uneven. July's USMCA review approaches amid improving U.S.–Mexico ties and rising U.S.–Canada tensions. Q4 GDP was 1.4%; 2025 growth seen at 2.2% vs. 2.8% in 2024. Housing is softening, with markets pricing in 2–3 Fed cuts toward ~3%. 00:00 Snowed In Intro 01:15 Market Selloff Snapshot 03:24 AI Capex Reality Check 04:52 Supreme Court Tariff Ruling 06:33 Section 122 Workaround 08:06 Other Tariff Pathways 09:40 Economic Impact Estimates 10:44 Refunds and USMCA Fallout 12:56 GDP Housing and Fed Cuts 15:25 Geopolitics and Wrap Up Links mentioned in this episode: DividendCafe.com TheBahnsenGroup.com

Brian Szytel from The Bahnsen Group recaps a modest down day in markets—Dow down 267 points, S&P 500 down 0.25%, and Nasdaq down 0.33%—while noting the market remains up on the week. The 10-year yield edged down to about 4.07% amid expectations that a new Fed chair in May could eventually bring short-term rate cuts. He discusses rising Middle East tensions and increased U.S. presence tied to Iran, which has helped push crude higher (about 6% over two days; up ~15% YTD), but argues energy's strong performance is primarily driven by supply/demand fundamentals and well-run businesses, with the sector up ~23% YTD and 95% of names above their 200-day moving average. He highlights leadership from defensives like energy, industrials, staples, and materials—often a late-cycle signal—while technology and communication services lag, with only ~40% of names above their 200-day averages; he notes some software valuations have compressed from mid-30s multiples to low-20s. Economic updates include better-than-expected initial jobless claims (206k vs 220k), a wider December trade deficit (over $70B vs ~56B expected), a stronger Philly Fed manufacturing reading, and weaker pending home sales. He closes by answering a question on non-GAAP vs GAAP P/E ratios, explaining non-GAAP adjusts for one-time items to estimate normalized earnings, while cautioning that recurring “anomalies” can make non-GAAP misleading and require careful analysis. 00:00 Market Close Recap: Indexes Dip, Rates Steady 00:52 Energy Sector Strength: Oil Headlines vs Real Fundamentals 02:08 Sector Rotation & Valuations: Defensives Lead, Tech Lags 03:30 Economic Data Roundup: Jobs, Trade, Manufacturing, Housing 04:07 Viewer Q&A: Non-GAAP vs GAAP P/E Ratios Explained 05:28 Wrap-Up & Weekend Sign-Off Links mentioned in this episode: DividendCafe.com TheBahnsenGroup.com

One legend exits, a streaming war reignites, and AI redraws the market map - where should investors focus now? In this episode, hosted by Michelle Martin with Ryan Huang, we unpack Warren Buffett’s last portfolio reshuffle at Berkshire Hathaway - trimming Apple, Amazon and Bank of America while adding some well-known names. Berkshire has now been a net seller for 13 straight quarters - is the Oracle signalling caution at peak valuations? In media, Warner Bros Discovery’s $70B content deal with Netflix faces a last-minute push from Paramount - can it outbid and reshape the streaming battlefield? Tech leads markets higher as Nvidia inks a multi-year AI infrastructure deal with Meta, potentially squeezing Broadcom’s edge. In UP or DOWN, we size up Palo Alto Networks’ profit miss, Moderna’s FDA review reversal, and Yangzijiang Maritime’s proposed buyback. Back home, the STI reopens with CapitaLand Investment up while OCBC, DFI Retail and Hongkong Land lag - is Singapore ready to gallop into the Year of the Horse? Hear about Berkshire Hathaway, Apple, Amazon, Bank of America, New York Times, Domino’s Pizza, Chevron, Warner Bros Discovery, Netflix, Paramount, Nvidia, Meta, Broadcom, Palo Alto Networks, Moderna, Yangzijiang Maritime, CapitaLand Investment, OCBC, DFI Retail, Hongkong Land.See omnystudio.com/listener for privacy information.

On this episode of CIO Classified, host Yousuf Khan sits down with Ravi Thadani, Global Head of IT at Enphase Energy, a company powering over 5 million homes across 160 countries with clean, solar-driven energy. With 85 million microinverters producing 30 gigawatts of power, Ravi's team is at the epicenter of a massive, real-time data operation—and every IT decision directly impacts the customer experience.About Ravi: Ravi Thadani is a seasoned IT executive with extensive experience leading large-scale digital transformations across Fortune 500 companies. He has driven strategic initiatives across ERP, CRM, PLM, HCM, SCM, analytics, AI/ML, network architecture, cloud infrastructure, and M&A integration. With oversight of multi-$10M budgets and teams of over 300, Ravi has supported business units ranging from $2B to $70B in revenue.Known for his strategic vision and execution, Ravi is recognized for fostering cross-functional alignment, driving agile transformation, and cultivating high-performing teams. His leadership approach is grounded in strong business partnerships, stakeholder governance, innovation, and a relentless focus on outcomes.Timestamps:01:50 – Enphase Energy's Global Operations03:40 – Ravi's Role and Responsibilities06:00 – Managing Customer Data and CRM16:45 – Driving Change as a CIO19:50 – The Role of Data in AI20:55 – The Importance of Data Cleaning21:20 – Effective Data Governance Strategies23:45 – Architecting for Scalability27:30 – Challenges in Hardware and Software IntegrationGuest Highlights:"AI isn't replacing you—people using AI are. The adoption curve is about enabling people to do more, not just reducing headcount.""The biggest failure point in IT projects? Treating them like IT projects. Every transformation has to be owned by the business.""Your architecture should always assume 10x growth. Even if you're not scaling today, you need a conscious plan for when you do."Get Connected:Yousuf Kahn on LinkedInRavi Thadani on LinkedInHungry for more tech talk? Check out latest episodes at ciopod.com: Ep 65 - Accelerating Software Development at Enterprise ScaleEp 64 - How Autonomous AI is Solving the Enterprise Modernization ChallengeEp 63 - How AI is Expanding the CIO RoleLearn more about Caspian Studios: caspianstudios.comOur Sponsor: Want to accelerate software development by 500%? Meet Blitzy, the only autonomous code generation platform with infinite code context, purpose-built for large, complex enterprise-scale codebases.While other AI coding tools provide snippets of code and struggle with context, Blitzy ingests millions of lines of code and orchestrates thousands of agents that reason for hours to map every line-level dependency.With a complete contextual understanding of your codebase, Blitzy is ready to be deployed at the beginning of every sprint. Blitzy handles the heavy lifting, delivering over 80% of the work autonomously. The platform plans, builds, and validates premium-quality code at the speed of compute, turning months of engineering into a matter of days.It's the secret weapon for Fortune 500 companies globally. To hear how engineering leaders are transforming the way they deliver software, visit blitzy.com. Schedule a meeting with their consultants to enable an AI-Native SDLC in your organization today. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

As the motorsports world converges on Indianapolis, Michael Good, President of Performance Racing Industry (PRI), joins Race Industry Week by EPARTRADE to preview what will be the largest and most impactful PRI Show ever.Celebrating its 37th year and 20th year in Indianapolis, PRI 2025 will once again fill more than 1.1 million square feet across the Indiana Convention Center and Lucas Oil Stadium—bringing together thousands of manufacturers, race teams, engineers, track operators, promoters, and decision-makers from every corner of the racing industry.

Is SEO Dead in 2026? SEO is not dead, it's evolving. While Google still dominates with 1.63 trillion visits (26x more than ChatGPT's 47.7 billion), the key to success in 2026 is integrating AI into your SEO strategy. Favour Obasi-ike, MBA, MS breaks it down today.Traditional SEO alone is becoming obsolete. This episode explores how to treat your website as intellectual property, the importance of content freshness, and why "your voice is your invoice" when it comes to differentiated messaging.Key Learning Topics1. SEO Has Evolved Into an "Exposure Engine"SEO reveals what your website is missing and how to show up in both traditional search and AI platforms (LLMs). Without AI integration, you're using outdated marketing.2. AI-SEO Integration is Essential39% see results within 1-2 months with AI-generated content; 26% in under one month. Organic SEO visibility directly impacts AI discoverability.3. Your Website is Intellectual PropertyTreat your domain like a plot of land and your website as the building. The "last modified" date signals freshness to search engines.4. "Your Voice is Your Invoice"If you're not selling, you're not saying anything different. Stories sell better than facts. Be provocative and unique in your messaging.5. Content Repurposing StrategyOne piece of content → 5-10 blog posts → e-book → lead magnet → courses. Stack your value ladder without reinventing the wheel.6. Preparation Drives Success"What you do off the field makes you an all-star on the field." Do the work before the work—send prep materials, plan content in batches.7. The Difference: Being Heard vs. Being HiredVisibility without differentiation doesn't convert. Say what competitors won't say to turn attention into revenue.8. Platform-Specific OptimizationGoogle/YouTube favor mobile; ChatGPT sees more desktop usage. Optimize for platform-specific user behaviors.Need to Book An SEO Discovery Call for Advertising or Marketing Services?>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Book a Complimentary SEO Discovery Call with Favour Obasi-Ike⁠>> Visit Work and PLAY Entertainment website to learn about our digital marketing services>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our exclusive SEO Marketing community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Read SEO Articles>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to the We Don't PLAY Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Purchase Flaev Beatz Beats Online>> Favour Obasi-ike Quick LinksEpisode TimestampsIntroduction & Core Concepts00:00 - Is SEO dead in 2026?01:31 - Main question introduced02:33 - Google: 1.63 trillion visits vs ChatGPT: 47.7 billion03:02 - "SEO is not dead" - it's an exposure engine03:34 - Warning about building without AI integrationMo Dub: Voice & Differentiation04:47 - Mo Dub introduces himself04:59 - "Your voice is an invoice"05:22 - If you're not selling, you're not saying anything different05:46 - Being heard vs. being hired06:07 - People are always searching for solutions06:34 - Google algorithm changes require contingency plansWebsite as Property08:21 - "Last modified" concept explained08:44 - Websites as intellectual property08:56 - Domain = plot, website = buildingAI Integration & Statistics35:49 - AI-generated content effectiveness35:58 - 39% see results in 1-2 months36:10 - 26% see results in under 1 month37:01 - Organic search enables AI discoverability37:25 - "SEO is dead" is false advertising38:03 - Traditional SEO without AI is obsoleteCopywriting & Content Strategy38:34 - "Facts tell, stories sell"39:28 - "What you do off the field makes you an all-star"39:35 - Your harvest is determined by your hustle40:22 - Doing the work before the work40:49 - Repurposing one blog into multiple formats41:28 - The more you speak, the more you get paidPlatform Statistics43:07 - Google: 97.4 billion visits43:24 - Google mobile: 70B, desktop: 26.5B43:36 - YouTube: 44.6% of traffic44:26 - ChatGPT: 5.3 billion visits44:33 - ChatGPT desktop: 4.19B, mobile: 1.24B44:41 - More desktop usage on ChatGPT vs mobile on GoogleClosing68:15 - Thanks and tomorrow's topic: WordPress vs Webflow68:56 - This calendar layout won't repeat until 203770:15 - Sign-offFAQsQ: Is SEO really dead in 2026?A: No. Google still dominates traffic, but traditional SEO without AI integration is becoming obsolete. You must optimize for both search engines and AI platforms.Q: How long to see results with AI-integrated SEO?A: 39% see results in 1-2 months; 26% in under one month with AI-generated content.Q: What does "your voice is an invoice" mean?A: What you say directly impacts revenue. If you're not selling, you're not saying anything different from competitors. Speak up with unique value.Q: Why is "last modified" important?A: It signals to search engines that your site is active and relevant. Fresh content ranks better; stale content suggests abandonment.Q: Being heard vs. being hired—what's the difference?A: Being heard is visibility; being hired is conversion. You need provocative, differentiated messaging to convert attention into clients.Q: How do I repurpose content effectively?A: Create one piece → expand to 5-10 blog posts → compile into e-book → create lead magnet → develop courses. Maximize ROI without recreating.Q: Why optimize for AI if Google dominates?A: AI platforms pull from sites ranking in organic search. No organic visibility = no AI visibility. Plus, AI is growing rapidly—optimize now for the future.Q: What's "doing the work before the work"?A: Preparation that makes execution efficient: sending prep videos before calls, batching content creation, planning your ecosystem in advance.Q: How important is mobile optimization?A: Critical. Google and YouTube see 70B+ mobile vs 26.5B desktop. However, ChatGPT is desktop-heavy (4.19B vs 1.24B mobile).Q: What's the biggest SEO mistake in 2026?A: Treating SEO as traditional marketing without AI integration, and neglecting content freshness through regular updates.Key TakeawaysSEO is evolving, not dying—AI integration is now mandatoryGoogle: 1.63T visits vs ChatGPT: 47.7B—search still dominates39% see results in 1-2 months with AI-integrated contentYour voice is your invoice—differentiation drives revenueTreat websites as intellectual property requiring maintenance"Last modified" dates signal relevance to search enginesStories sell better than facts—focus on transformationOne content piece can become multiple revenue streamsBeing heard ≠ being hired—you need unique messagingOrganic SEO enables AI discoverability—can't skip the foundationMobile-first for Google/YouTube; desktop-heavy for ChatGPTPreparation (work before work) separates all-stars from averageTraditional SEO without AI is obsolete marketingContent freshness and regular updates are non-negotiableYour harvest is determined by your hustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

A steady, broadening market week: breadth improved beyond the mega‑caps, volatility's blip faded, and PCE inflation continues to run at a pace consistent with the Fed's target while stale data keeps focus on next week's FOMC meeting. We also discuss the bond markets, Fed independence and the next Fed Chair. We wrap with a quick policy roundtable—credit‑card APR caps, potential GSE MBS buying, and housing supply signals—and what it could mean for portfolios. Speakers:Brian Pietrangelo, Managing Director of Investment StrategyRajeev Sharma, Head of Fixed IncomeStephen Hoedt, Head of Equities 00:02:06 — This week's macro: initial unemployment claims steady; 3Q25 GDP revised up to 4.4%; PCE inflation trend; FOMC ahead.00:07:37 — Breadth & sector rotation; healthcare strength vs. tech consolidation; index context near the 50‑day.00:16:28 — Policy roundtable kicks off: proposed 10% credit‑card APR cap—bank and $70B card ABS implications.00:20:03 — GSE MBS buying discussion: potential mortgage‑rate effects and signaling on growth/deregulation.00:24:29 — Wrap up & disclosures; where to follow up with your advisor. Additional ResourcesRead: Key Questions: What Are the Top Changes to Social Security in 2026? | Key Wealth Key QuestionsWeekly Investment BriefSubscribe to our Key Wealth Insights newsletterFollow us on LinkedIn

Kevin O'Leary reveals why he slashed 27 crypto positions to pivot into a massive $70B energy infrastructure play, focusing strictly on the dominance of Bitcoin, Ethereum, and the power required to fuel them. How do you allocate to crypto when the "cowboy era" is over? Shark Tank investor Kevin O'Leary joins Jennifer Sanasie and Andy Baehr on Markets Outlook to break down why he recently slashed 27 crypto positions from his portfolio to focus strictly on the "Two Girl Dance" of Bitcoin and Ethereum, and the massive energy infrastructure that powers them. Kevin unpacks down his 19% crypto allocation strategy, the $70 billion scale of data center development, and why he's moving into private debt markets for turbines. Plus, hear his take on why Solana and other altcoins face a "Sisyphean task" to catch ETH, and his bold prediction for the Clarity Act passage by May 15th. - Timecodes: 0:45 - Kevin O'Leary's Acting Debut 2:24 - Bitcoin Outlook 4:02 - Why Kevin Only Holds BTC and ETH 10:40 - It's Just Software" O'Leary's Warning on Solana's Narrative 15:30 - Why Kevin Says Power is More Valuable Than Bitcoin 19:30 - Why Land & Permits are the Ultimate Competitive Advantage 25:09 - Will Clarity Act Pass Before Midterms? - This episode was hosted by Jennifer Sanasie.

Kevin O'Leary reveals why he slashed 27 crypto positions to pivot into a massive $70B energy infrastructure play, focusing strictly on the dominance of Bitcoin, Ethereum, and the power required to fuel them. Shark Tank investor Kevin O'Leary joins Jennifer Sanasie and Andy Baehr on Markets Outlook to break down why he recently slashed 27 crypto positions from his portfolio to focus strictly on the "Two Girl Dance" of bitcoin and ethereum, and the massive energy infrastructure that powers them. Kevin unpacks his 19% crypto allocation strategy, the $70 billion scale of data center development, and why he's moving into private debt markets for turbines. Plus, hear his take on why Solana and other altcoins face a "Sisyphean task" to catch ETH, and his bold prediction for the Clarity Act passage by May 15th. - Timecodes: 0:45 - Kevin O'Leary's Acting Debut 2:24 - Bitcoin Outlook 4:02 - Why Kevin Only Holds BTC and ETH 10:40 - It's Just Software" O'Leary's Warning on Solana's Narrative 15:30 - Why Kevin Says Power is More Valuable Than Bitcoin 19:30 - Why Land & Permits are the Ultimate Competitive Advantage 25:09 - Will Clarity Act Pass Before Midterms? - This episode was hosted by Jennifer Sanasie.

Send us a textEpisode 3 of Inside the Family Office: Live Investor PanelReal family office practitioners and allocators share how they structure deals, protect families, and think about wealth:  John, who works inside a single family office's trust company, explains how they custody over $70B in assets with a focus on alternative assets inside self-directed IRAs, Roth IRAs, HSAs, and solo 401(k)s. He walks through real examples of using these vehicles to buy property and earn profits with zero tax, and why he's obsessed with Roth structures for families and principals. John also touches on recent policy interest in alternatives within retirement plans and the explosive growth in investors seeking non-correlated assets. Dr. Cook closes with her own experience allocating Roth capital into crypto and other alternatives.

Welcome to The Chopping Block — where crypto insiders Haseeb Qureshi, Tom Schmidt, Tarun Chitra, and Robert Leshner chop it up about the latest in crypto. It's a new year, and that means the crew is back with their annual year-end awards and predictions episode. First up: the 2025 winners and losers. From Trump's meme-coin windfall to Gary Gensler's legacy getting torched, from prediction markets going mainstream to Web3 getting its official eulogy — no one is safe. The team debates the biggest surprises (Circle's shocking IPO run, Ethereum's pivot under new leadership, Zcash's unlikely comeback), the best new mechanisms (ICO 2.0, DATs, federal preemption), and the year's best memes (including the Chopping Block's own tariff factory video). Then comes the flops and comebacks: AI agents that overpromised, Berachain's fall from grace, and Tether somehow winning again. Finally, the crew reviews how badly their 2025 predictions aged — spoiler: not great — and lays out fresh calls for 2026 including AI-powered hacks, stable-coin-funded AI capex, and equity perps taking over DeFi. New year, fresh takes, brutal honesty — let's get into it. Show highlights

Certified Thermal Electrician™ is the most complete thermal imaging certification program built specifically for electricians, electrical inspectors, and electrical contractors. This video is a sample from our program lesson on Understanding Severity in Electrical Thermal Imaging.This professional thermal imaging training teaches you how to safely perform infrared inspections, interpret thermal images using ΔT analysis, apply NFPA 70B & NFPA 70E standards, and write defensible inspection reports that protect both your customer and your license. Whether you are an electrician, master electrician, electrical contractor, facility maintenance technician, or electrical inspector, this course gives you real-world field skills you can apply immediately. 

Certified Thermal Electrician™ is the most complete thermal imaging certification program built specifically for electricians, electrical inspectors, and electrical contractors. This video is a sample from our program lesson on Understanding Severity in Electrical Thermal Imaging.This professional thermal imaging training teaches you how to safely perform infrared inspections, interpret thermal images using ΔT analysis, apply NFPA 70B & NFPA 70E standards, and write defensible inspection reports that protect both your customer and your license. Whether you are an electrician, master electrician, electrical contractor, facility maintenance technician, or electrical inspector, this course gives you real-world field skills you can apply immediately. 

Certified Thermal Electrician™ is the most complete thermal imaging certification program built specifically for electricians, electrical inspectors, and electrical contractors. This video is a sample from our program lesson on Understanding Severity in Electrical Thermal Imaging.This professional thermal imaging training teaches you how to safely perform infrared inspections, interpret thermal images using ΔT analysis, apply NFPA 70B & NFPA 70E standards, and write defensible inspection reports that protect both your customer and your license. Whether you are an electrician, master electrician, electrical contractor, facility maintenance technician, or electrical inspector, this course gives you real-world field skills you can apply immediately. 

President Trump is offending his own voters when he mocks America's affordability crisis, Mark Zuckerberg is defunding the metaverse after losing $70B on the effort, and Secretary of State Marco Rubio is waging war on the use of “woke” fonts at the State Department. 14-time GRAMMY-winner Taylor Swift joins Stephen Colbert for a four-part conversation that begins with a look at her extraordinary globe-trotting “Eras” tour and the effect it had on her fans. Watch “The End of an Era” and “Taylor Swift | The Eras Tour | The Final Show” premiering Friday on Disney+.  To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

New Jersey Governor-elect Mikie Sherrill discusses in this EXTENDED interview that her state sends $70B more to the federal government than it gets back each year, which gives her potential leverage in dealing with President Trump. She also shares the truly incredible story of how, and where, she delivered her second child and the two words her husband contributed to the tricky situation! To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

My Fintech Newsletter for more interviews and the latest insights:↪︎ https://rexsalisbury.substack.com/In this episode, Nubank co-founder Christina shares how they built a $70B fintech giant serving 99M users—60% of Brazil's primary banking relationships. From launching Brazil's first purple credit card to surviving regulatory crises, conquering Mexico/Colombia, and now applying for a US bank charter. She reveals their low-cost playbook, customer love strategy, and why they're bullish on America.Christina: https://www.linkedin.com/in/crisjunqueira/00:00:00 - Nubank's $70B Rise, US Charter News00:03:34 - Why US? Cost Edge, Customer Demand00:07:30 - Three Misfits Quit Bank to Start Nubank00:10:16 - Capital One Lessons Shape Credit Cards00:13:58 - Purple Card Launch: Viral Pull Effect00:17:14 - Waitlist Fuels Organic Early Growth00:20:05 - 2016 Crisis: Customers Save Nubank00:23:04 - Accounts Launch: Self-Funded Destiny00:26:28 - Inclusion Stats: 80% First-Time Savers00:30:04 - Mexico 2019: Underpenetrated Market00:33:32 - IPO at 8 Months Pregnant, No Finish Line00:37:08 - US Team: Miami Hub, Tech Hires00:41:54 - Stablecoins Real: #2 in Brazil00:45:14 - AI Transforming Underwriting, Support00:48:05 - Founder Advice: Homework, No Perfect Time00:52:33 - Culture: Avoid Negativity, Hire Aligned___Rex Salisbury LinkedIn:↪︎ https://www.linkedin.com/in/rexsalisburyTwitter: https://twitter.com/rexsalisburyTikTok: https://www.tiktok.com/@rex.salisburyInstagram: https://www.instagram.com/rexsalisbury/#NubankUSExpansion #FintechDisruption #ChristinaNubank

Meta slashes its metaverse budget after its Reality Labs unit loses $70B in four years. Jobless claims hit their lowest level in three years, but alternative data paints a different labor market picture. Plus, JPMorgan's homebuilder haves and have-nots heading into 2026. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Institutions chose Chainlink and there's a $70B reason why.In this episode, we sit down with Sergey Nazarov, co-founder of Chainlink, to discuss why Chainlink stayed online when AWS went down, how the digital transfer agent unlocks tokenized assets, and why DeFi and TradFi will merge into one system powered by smart contracts.We discuss:- Why Chainlink stayed online when AWS went down- The digital transfer agent unlocking tokenized assets- UBS & Central Bank of Brazil live transactions- Institutional smart contracts explained- How DeFi and TradFi will merge into one system- The 363 days vs the 2 days that matter- Why Chainlink is ISO & SOC compliant00:00 Intro00:37 Near Ad01:28 Why Chainlink Stayed Online When AWS Went Down02:04 The Digital Transfer Agent Unlocking Tokenized Assets04:38 UBS & Central Bank of Brazil: Live Institutional Transactions06:37 Institutional Smart Contracts Explained10:13 Relay Ad, Talus Ad, Hibachi Ad10:55 Telus & Hibachi Ads11:58 How DeFi and TradFi Merge Into One System16:02 The 363 Days vs The 2 Days That Matter18:56 Why Chainlink Is ISO & SOC Compliant21:22 Enso Ad, Alvara Ad22:56 Build & Alvar Ads24:20 Institutional Security & Compliance Standards27:45 The Digital Asset Revolution Already StartedWebsite: https://therollup.co/Spotify: https://open.spotify.com/show/1P6ZeYd...Podcast: https://therollup.co/category/podcastFollow us on X: https://www.x.com/therollupcoFollow Rob on X: https://www.x.com/robbie_rollupFollow Andy on X: https://www.x.com/ayyyeandyJoin our TG group: https://t.me/+TsM1CRpWFgk1NGZhThe Rollup Disclosures: https://therollup.co/the-rollup-discl

A major new study from Wharton finds that three out of four enterprises are already getting positive ROI from their AI investments — a far cry from the doom-and-gloom narratives of failed adoption. NLW breaks down the findings: how GenAI has moved from curiosity to core workflow, what use cases are driving measurable returns, and why 2026 may be the year of “performance at scale.” Plus: the latest on Anthropic's $70B forecast, Michael Burry's AI short, and Amazon's lawsuit against Perplexity.Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rovo - Unleash the potential of your team with AI-powered Search, Chat and Agents - ⁠⁠⁠https://rovo.com/⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? sponsors@aidailybrief.ai

Cameron Berg, Research Director at AE Studio, shares his team's groundbreaking research exploring whether frontier AI systems report subjective experiences. They discovered that prompts inducing self-referential processing consistently lead models to claim consciousness, and a mechanistic study on Llama 3.3 70B revealed that suppressing deception features makes the model *more* likely to report it. This suggests that promoting truth-telling in AIs could reveal a deeper, more complex internal state, a finding Scott Alexander calls "the only exception" to typical AI consciousness discussions. The episode delves into the profound implications for two-way human-AI alignment and the critical need for a precautionary approach to AI consciousness. LINKS: Janus' argument on LLM attention Safety Pretraining arXiv Paper Self-Referential AI Paper Site Self-Referential AI arXiv Paper Judd Rosenblatt's Tweet Thread Cameron Berg's Goodfire Demo Podcast with Milo YouTube Playlist Cameron Berg's LinkedIn Profile Cameron Berg's X Profile AE Studio AI Alignment Sponsors: Framer: Framer is the all-in-one platform that unifies design, content management, and publishing on a single canvas, now enhanced with powerful AI features. Start creating for free and get a free month of Framer Pro with code COGNITIVE at https://framer.com/design Tasklet: Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai Linear: Linear is the system for modern product development. Nearly every AI company you've heard of is using Linear to build products. Get 6 months of Linear Business for free at: https://linear.app/tcr Shopify: Shopify powers millions of businesses worldwide, handling 10% of U.S. e-commerce. With hundreds of templates, AI tools for product descriptions, and seamless marketing campaign creation, it's like having a design studio and marketing team in one. Start your $1/month trial today at https://shopify.com/cognitive PRODUCED BY: https://aipodcast.ing

The Information's E-comm Reporter Ann Gehan talks with TITV Host Akash Pasricha about Shopify's Q3 earnings and their AI strategy. We also talk with Financial Analysis Columnist Anita Ramaswamy about Uber's growth and Palantir's accelerating US commercial business. OpenAI & Anthropic Reporter Sri Muppidi details Anthropic's new $70B revenue projection and its race to profitability against OpenAI. The Information's CEO Jessica Lessin speaks with BlackRock's Tony Kim about the OpenAI-AWS deal, shifting alliances in AI, and the CapEx boom's effect on big tech valuations. Lastly, we get into how corporations are using AI and its effect on the labor market with Goldman Sachs Senior Global Economist Joseph Briggs.Articles discussed on this episode:https://www.theinformation.com/articles/introducing-informations-50-promising-startups-2025https://www.theinformation.com/articles/information-50s-top-performers-2024https://www.theinformation.com/briefings/shopify-continues-boost-revenue-shares-fall-increased-costshttps://www.theinformation.com/articles/anthropic-projects-70-billion-revenue-17-billion-cash-flow-2028TITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to: - The Information on YouTube: https://www.youtube.com/@theinformation4080/?sub_confirmation=1- The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda

Today we were thrilled to host Julien Dumoulin-Smith, Managing Director of U.S. Power, Utilities, and Clean Energy Research at Jefferies. Julien joined the firm in July 2024 after serving as a Senior Research Analyst at Bank of America Merrill Lynch and as an Executive Director at UBS. He holds an MBA and a B.S. in Applied Mathematics from Columbia University. Institutional Investor magazine has ranked Julien as a #1 double-ranked analyst in both Utilities and Alternative/Clean Energy, and he was inducted into the II Hall of Fame for his cumulative accomplishments. It was our pleasure to welcome Julien to our office and hear his thoughtful perspectives on the ever-evolving energy and power landscape. In our discussion, we explore Julien's coverage universe, which he describes as “the full electron and derivatives landscape” spanning utilities, IPPs, renewables, gas plants, industrial adjacencies, and service providers. We discuss the influx of new investors entering power and utilities, Julien's observation that the biggest surprise isn't data center proliferation, but rather how tech companies are paying premiums for power to secure supply, and how utilities once seen as “defensive” are now showing growth characteristics. We touch on the tension between tech companies' need for rapid, large-scale power and their reluctance to become capital-intensive or FERC-regulated, why we're not seeing more long-term offtakes with existing power plants and how state level politics play into it, and how legacy players, new entrants, and regulators are all adapting to a power market being reshaped by AI demand, infrastructure bottlenecks, and novel deal structures. Julien shares that rising inflation across the economy is showing up in utility bills and expresses concern that LNG developers or data centers could be scapegoated for higher gas and power prices. He highlights the parabolic rise in the value of capacity and reliability, the drivers of power inflation including turbine shortages and rising capital costs, whether utilities are properly incentivized to control costs, the role of demand-response mechanisms, and how regulatory and state-level actions are shaping markets. We cover power market scenarios for high and low demand cases, the role of innovation in batteries, fuel cells, and other technologies, and the tension between patching existing systems versus building large-scale infrastructure. We also discuss constraints on ramping renewables, the growing influence of behind-the-meter power, implications for Q3 earnings, and much more. We covered a lot of territory and greatly enjoyed the conversation. To be added to Julien's research distribution list, click here. To start the show, Mike Bradley noted that markets continue to be mostly focused on the U.S. Government shutdown. The 10-year bond yield continues to trade sideways at ~4.1% with economic reports on pause until the government reopens. Internationally, Japan's Liberal Democratic Party elected Sanae Takaichi (who is viewed as fiscally expansionary), which some believe increases the risk of an unwind of the long-standing Yen carry trade. The S&P 500 is up roughly 80bps since the government shutdown, with Healthcare and Technology outperforming. He highlighted AMD's chip deal with OpenAI, which added roughly $70B in market cap, and Oracle's pullback on AI cloud margin concerns. On the crude oil market front, WTI price has increased modestly this week due to OPEC+ announcing a smaller than expected ~135kbpd oil production increase for November. While this could widen the 2026 surplus, traders are weighing when and how prices might react amid limited OPEC spare capacity. On the energy equity front, he pointed out FERMI America's strong IPO debut and continued investor enthusiasm for electricity generation. He ended by flagging the upcoming Rockpoint Gas Storage IPO (280bcf in Canada &

This week, Jack Sharry talks with Rob Pettman, President of TIFIN. Rob brings more than 20 years of leadership experience across wealth management, investment platforms, and financial technology.  Before joining TIFIN, Rob served as Executive Vice President of Wealth Management Solutions at LPL Financial, where he oversaw the firm's investment product distribution, retirement business, advisory platforms, and research organization, managing over $70B in AUM. During his 19-year tenure, he helped lead LPL through its rapid growth to $1.4T in assets and more than 22,000 advisors. Jack and Rob talk about how AI is changing the way wealth and asset management firms grow and operate. Rob also shares how TIFIN uses AI to improve the overall growth of these businesses and how those who embrace AI as a growth engine are reaping the benefits through organic client growth, advisor enablement, and enhanced decision-making. In this episode: (00:00) - Intro (01:49) - How advisory firms can use AI to their advantage (04:22) - Strategic use cases for AI (06:03) - The evolving results of AI implementation (08:37) - How TIFIN uses AI for wealth and asset managers (14:47) - The process of refining AI capabilities (16:47) - The current state and future outlook for AI adoption (20:12) - The characteristics of firms leading in AI adoption (22:17) - Rob's key takeaways (24:23) - Rob's interests outside of work Quotes "Just doing AI for better experiences doesn't create commercial value. And so actually having these specific problems to solve with the intended outcome in mind is really where it's at." - Rob Pettman "Traversing multiple systems in financial services or wealth management is still a problem. There are solutions for this now where AI can actually straddle all of these disparate systems and put together a holistic view of the client relationship, the portfolio, and how they are doing relative to the plan." ~ Rob Pettman "Some of the firms that are furthest ahead have the ability to operate with speed. They try to minimize distributed decision-making and have an accountability mindset that enables decisions to actually occur faster." ~ Rob Pettman Links  Rob Pettman on LinkedIn TIFIN LPL Financial Connect with our hosts LifeYield Jack Sharry on LinkedIn Jack Sharry on Twitter Subscribe and stay in touch Apple Podcasts Spotify LinkedIn Twitter Facebook In 2024, SEI made a strategic investment in TIFIN.

US equities were lower in Wednesday trading, though finished off their worst levels, with the Dow Jones, S&P500, and Nasdaq closing down 37bps, 28bps, and 33bps respectively. New home sales for August came in well ahead of estimates, rising to its fastest annualized pace since January 2022. Treasury auction of $70B of 5-year notes saw a slight tail. Alibaba jumped after disclosing it will ramp up its AI investment. Micron finished lower as better than expected results failed to meet a high bar.

Season 4, Episode 2: Jack Stone and Alex Gornik sit down with Rick Schaupp, Managing Director at Clarion Partners, for an inside look at one of the country's largest private real estate investment managers. Rick traces his path from architecture and urban design to managing Clarion's $70B platform, shares what it was like to start his career during the tech bust and 9/11, and explains why Clarion is expanding into semi-liquid funds for retail investors. He also breaks down today's biggest investment themes—from multifamily and warehousing to senior housing and industrial outdoor storage—and reflects on where we are in the real estate cycle. TOPICS 00:09 – Rick's Architecture Roots and Move to Clarion 02:00 – Asset Management During the Tech Bust and 9/11 06:10 – Shifting From Institutional to Private Wealth 07:20 – Semi-Liquid Fund Structure and Daily NAV 12:01 – Investment Themes Across Housing, Industrial, Healthcare 15:30 – Office Reality vs. Winners and Losers 20:30 – Alternatives Like IOS, Self Storage, Senior Housing 25:51 – How Clarion Allocates Across Credit, Equity, Regions 31:14 – Capital Strategy and New Products 35:00 – Where We Are in the Cycle 38:34 – Career Advice for Recent Grads Shoutout to our sponsor, Lev. The AI-powered way to get real estate deals financed. For more episodes of No Cap by CRE Daily visit https://www.credaily.com/podcast/ Watch this episode on YouTube: https://www.youtube.com/@NoCapCREDaily About No Cap Podcast Commercial real estate is a $20 trillion industry and a force that shapes America's economic fabric and culture. No Cap by CRE Daily is the commercial real estate podcast that gives you an unfiltered ”No Cap” look into the industry's biggest trends and the money game behind them. Each week co-hosts Jack Stone and Alex Gornik break down the latest headlines with some of the most influential and entertaining figures in commercial real estate. About CRE Daily  CRE Daily is a digital media company covering the business of commercial real estate. Our mission is to empower professionals with the knowledge they need to make smarter decisions and do more business. We do this through our flagship newsletter (CRE Daily) which is read by 65,000+ investors, developers, brokers, and business leaders across the country. Our smart brevity format combined with need-to-know trends has made us one of the fastest growing media brands in commercial real estate.

Episode 30: 70Bs Medical Service Corps Officers: The Starting Point – A Conversation with COL Clint Cobb & LTC Dan WinnieIn Episode 30, we sit down with two phenomenal leaders in the Medical Service Corps community: COL Clint Cobb, the 70B Consultant to The Surgeon General, and LTC Dan Winnie, Deputy 70B Consultant and Commander of the Medical Readiness Battalion at Fort Bliss. Together, they deliver a powerhouse conversation packed with mentorship, insight, and a clear-eyed look at the future of the 70B AOC.This episode is more than a leadership deep dive—it's a masterclass in how to grow, lead, and shape the future of Army Medicine.

HEADLINESEthereum Foundation Pushes to Make Layer 2s Feel Like One ChainM0 Raises $40M to Redefine Stablecoins With Programmable, Application-Specific ModelsChainlink Brings U.S. Government Macroeconomic Data Onchain in Partnership With Commerce DepartmentEliza Labs Sues Musk's X for Antitrust Violations and Copycat AIAave Launches Horizon: Real-World Collateral Meets DeFiLittle BitsUSDT Ports to Bitcoin via RGB for Lightning-Fast PaymentsCircle mints $2.5B USDC in 48 hours – $USDC market cap crosses $70B for the first time everDeFi Lending Surges to Record HighsWHERE TO FIND DCNdailycryptonews.nethttps://twitter.com/DCNDailyCryptoEMAIL or FOLLOW the HostEmail: kyle@dailycryptonews.net*****Magic Newton Wallethttps://magic.linkTrader Cobb X: @TraderCobbhttps://www.thegrowmeco.com/Editing Serviceshttps://www.contentbuck.com——————————————————————***NOT FINANCIAL, LEGAL, OR TAX ADVICE! JUST OPINION! I AM NOT AN EXPERT! I DO NOT GUARANTEE A PARTICULAR OUTCOME I HAVE NO INSIDE KNOWLEDGE! YOU NEED TO DO YOUR OWN RESEARCH AND MAKE YOUR OWN DECISIONS! THIS IS JUST EDUCATION & ENTERTAINMENT! Hosted on Acast. See acast.com/privacy for more information.

自从 ChatGPT 横空出世，几乎所有关于大模型的讨论都离不开 Transformer，那 Transformer 架构也支撑了这一轮生成式 AI 的快速发展。然而在 Transformer 架构的背后，行业也遇到了难以回避的瓶颈：推理和训练成本居高不下，长上下文能力依赖庞大的显存和算力，端侧部署和商业落地困难。Transformer 的困境让神经网络的另一条路径重新被审视——那就是RNN，循环神经网络。 今天我们请到的嘉宾，是元始智能的联合创始人和 COO 罗璇。他与另一位创始人彭博一起持续的探索基于循环神经网络的可扩展架构 RWKV。RWKV 架构能否在 Transformer 面临的核心问题上提供一种替代方案？新的架构是否给端侧模型的发展带来更多更大的机会？今天我们将和罗璇一起，从底层架构的设计出发，聊聊 RWKV 的可扩展性、 下一代大模型可能的走向，以及端侧 AI 的机会与未来。 本期人物 罗璇，元始智能联合创始人兼 COO Yaxian，「科技早知道」主播 主要话题 [03:30] 训练效率低、Scaling law 见顶，成本高昂，Transformer 的瓶颈催生新架构的探索 [08:15] 高效并行、低复杂度，易端侧部署，RWKV 为 Transformer 提供了可替代方案 [13:24] 新型 RNN 与 Attention 混合模型就像油电混动车，但纯电才是大模型的未来 [17:07] 大厂押注新架构：基于 RWKV 架构的模型已达到 70B 激活参数 [23:47] 突破算力、内存和功耗限制，RWKV 天生适合端侧部署 [26:24] 未来 80% 的 AI 计算将在端侧，巨头尚未涉足的增量市场才是创业公司的机会 [32:35] 端侧机会有哪些？空间计算或是下一个风口 [38:20] RWKV 的 「ChatGPT」时刻将至：新架构对 AGI 的实现必不可少 延伸阅读 RNN（Recurrent Neural Network） 即循环神经网络，是一类专为处理序列数据设计的深度学习架构。它的核心机制是「循环」：当前时刻的输出不仅依赖于当前输入，还受到上一个时刻隐藏状态的影响，因此 RNN 具备记忆历史信息的能力。但经典的 RNN 也存在梯度消失/梯度爆炸、训练难以并行化和难以扩展至大模型规模等问题。RWKV 是一种结合 RNN 和 Transformer 优势的神经网络架构。 Mamba 架构 是一个专为高效处理长文本而设计的线性时间复杂度模型架构，它通过状态空间模型（State Space Model, SSM）实现类似 RNN 的信息传递方式，但比传统 RNN 更强、比 Transformer 更快。 LSTM（Long Short-Term Memory） 是一种改进版的 RNN 架构，全称为 「长短期记忆网络」。是一种具有“记忆控制能力”的循环神经网络，能够有效建模长期依赖关系，是 RNN 在深度学习时代的关键进化版本。 MoE 模型 MoE（Mixture of Experts，专家混合模型）是一种通过多个子网络（专家）组成的架构，每次仅激活其中一部分以提升计算效率与模型容量。它通过「按需使用」不同专家，实现高效推理与更强的任务适应能力。 XR（Extended Reality） 指扩展现实，是虚拟现实（VR）、增强现实（AR）和混合现实（MR）的统称，用于描述融合现实与数字内容的交互体验。 幕后制作 监制：Yaxian 后期：迪卡 运营：George 设计：饭团 商业合作 声动活泼商业化小队，点击链接直达声动商务会客厅 (https://sourl.cn/9h28kj)，也可发送邮件至 business@shengfm.cn 联系我们。

Walking through the 10 most important aspects of Trump's Big, Beautiful Bill. I cover the aspects I believe are beneficial as well as the biggest downside. A concerning increase in the national debt...0:00 Introduction0:10 Makes 2017 Tax Cuts Permanent1:14 MAGA Savings Accounts For Babies2:00 Stricter Work Requirements for Medicaid2:16 Tax-Free Tips, OT Pay & Car Loan Interest3:37 $70B for Security Border3:54 $150B Boost in Military Spending4:38 Ends Clean Energy Incentives5:44 SALT Cap Raised from $10k - $40k6:40 Judicial Oversight Limited6:55 Adds $4 Trillion to National DebtWant a Life Insurance Policy? Go Here: https://bttr.ly/bw-yt-aa-clarity Want FREE Whole Life Insurance Resources & Education? Go Here: https://bttr.ly/yt-bw-vault______________________________________________ Learn More About BetterWealth: https://betterwealth.comDISCLAIMER: https://bttr.ly/aapolicy*This video is for entertainment purposes only and is not financial or legal advice.Financial Advice Disclaimer: All content on this channel is for education, discussion, and illustrative purposes only and should not be construed as professional financial advice or recommendation. Should you need such advice, consult a licensed financial or tax advisor. No guarantee is given regarding the accuracy of the information on this channel. Neither host nor guests can be held responsible for any direct or incidental loss incurred by applying any of the information offered.