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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

In 2026, Elon Musk took OpenAI to federal court. The case, Musk v. OpenAI, hinges on a single question: did the company betray the nonprofit mission it was founded on?Musk's claim is breach of charitable trust. He argues that OpenAI's restructuring into a for-profit, paired with its exclusive multi-billion dollar partnership with Microsoft, abandoned the public-benefit purpose donors believed they were funding.The evidence drawing the most attention comes from Greg Brockman's private diaries. Filings indicate the entries suggest leadership was already mapping out the commercial pivot while publicly assuring donors of altruistic goals. Public mission, private plan. That gap is now in front of a federal judge.OpenAI's defense pushes back on motive. Their framing: Musk is a spurned co-founder who tried and failed to take unilateral control of the company, and the lawsuit is what came after losing that fight, not a good-faith concern about governance.Financial conflicts of interest are also on the record. Brockman reportedly holds a $30 billion stake in the restructured entity, and the trial examines what hybrid corporate governance actually means when the same leadership oversees the nonprofit and benefits from the for-profit arm.For context, the case is a serious test of how charitable trust law applies to AI labs that started as nonprofits and scaled into some of the most valuable companies in the world. Whichever way it goes, the ruling will shape what other labs can and cannot do when stated mission collides with commercial incentive.In this episode I walk through the timeline, the key filings, the Brockman diary excerpts that have been made public, the financial structure being litigated, and what each potential ruling would mean for OpenAI, Microsoft, and the broader frontier AI industry.Topics: Musk v. OpenAI 2026 trial, OpenAI nonprofit to for-profit conversion, Greg Brockman diaries, OpenAI Microsoft partnership, breach of charitable trust lawsuit, AI governance, OpenAI restructuring, frontier AI legal precedent, hybrid corporate governance.

Elon Musk's federal trial against OpenAI opened this week, and he admitted under oath that xAI distills OpenAI's models. That's just where Episode 212 starts. Paul and Mike cover the full story: the Musk-OpenAI trial and its implications for every company built on ChatGPT, the abrupt rewrite of the Microsoft-OpenAI partnership and the removal of the AGI clause, blockbuster Big Tech earnings (Google Cloud up 63%, Azure AI at $37B run rate), an AI agent that wiped a startup's entire production database in nine seconds, Anthropic eyeing a $900B valuation, a real and growing populist AI backlash, and a powerful segment on former Senator Ben Sasse's terminal cancer diagnosis and what his parting words on AI and Congress mean right now. Show Notes: Access the show notes and show links here AI-Pulse Survey: Fill out this week's AI-Pulse Survey here. Timestamps: 00:00:00 — Intro 00:04:45 — Elon Musk vs. OpenAI Trial Begins 00:14:49 — OpenAI and Microsoft Revise Their Partnership 00:30:33 — Big Tech Earnings 00:39:39 — Anthropic Eyes $900B Valuation 00:43:53 — Trump's Anthropic Reversal and Mythos Fight 00:52:48 — Agents Gone Wrong 00:56:47 — Myths We Tell Ourselves About AI and Jobs 01:06:15 — Why Shopify CEO Tobi Lutke Says “Saying The Thing Matters” 01:11:20 — AI's Public Backlash Problem 01:15:16 — AI Use Case Spotlight 01:22:59 — AI Academy Spotlight 01:25:58 — Ben Sasse's Parting Words on AI 01:31:56 — AI Product and Funding Updates This episode is brought to you by AI Academy by SmarterX. AI Academy is your gateway to personalized AI learning for professionals and teams. Discover our new on-demand courses, live classes, certifications, and a smarter way to master AI. Learn more here. Visit our website Receive our weekly newsletter Join our community: Slack Community LinkedIn Twitter Instagram Facebook YouTube Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

This week: four hyperscalers reported earnings on the same day, NVIDIA briefly crossed $5 trillion in market cap, OpenAI broke Azure exclusivity, and Google put $40 billion into Anthropic. Patrick Moorhead and Daniel Newman call it the most consequential week in AI infrastructure history and suggest the bull thesis just got its vote of confidence. The handpicked topics for this week are: OpenAI Breaks Azure Exclusivity — Both Patrick and Daniel were in the room for the original OpenAI-Microsoft announcement, and they both knew it wasn't the end of the story, it was just the beginning. The restructured deal keeps Microsoft on IP rights through 2032 and a guaranteed 20% revenue share through 2030, but the AGI trigger clause that would have ended payments is gone. The very next day OpenAI went live on AWS, the first non-Microsoft hyperscaler to carry it. Dan's read: model companies need more compute than any one hyperscaler can offer, and every hyperscaler needs access to all the models. Nobody wins with exclusivity anymore. (The Decode) Google Puts $40 Billion Into Anthropic — Pat spells it out: Anthropic just became AI's first joint custody child, with Amazon and Google as the parents and a $73 billion college fund. Google, which already had stakes in Anthropic and SpaceX, posted a $37 billion investment gain in a single quarter solely from valuation improvements, and now holds dual hyperscaler structural backing for Anthropic that Pat says OpenAI simply can't match. Daniel's thesis lands again: models are not the moat. Compute is the moat. Everybody is figuring that out now. (The Decode) The CPU War Is On: Meta Goes to AWS for Graviton — Meta recently secured a multi-year, multi-billion dollar Graviton agreement with AWS after being caught off-guard regarding both compute resources and models. Andy Jassy noted that demand was so high he had to decline two customers who sought to purchase his "entire Graviton capacity." During his victory lap, Pat highlighted a significant shift in agentic workloads: the CPU-to-GPU ratio has plummeted from 16-to-1 to nearly 2-to-1, with some cases already reaching 1-to-1 parity. The CPU war is the story nobody saw coming fast enough, including AMD and Intel. (The Decode) OpenAI 5.5 Review: Shows Promise, But Not Amazing — Daniel tested the new model and shared his take: not blown away but not unhappy either. Pat moved some workloads back to test it and liked what he found, particularly on research. The 38% reduction in reasoning-intensive tasks is the ROI answer OpenAI has right now. But both hosts flag the bigger question: What happens when token subsidies end and real agentic workflow costs hit the tape? That is the moment that opens the door for open source, small models, and enterprise-specific deployments. The model moat, Dan says for the third time this episode, "just does not exist anymore." (The Decode) China AI and the Open Source Question — Daniel went long on this in a live CNBC stream and brings the sharpest take to the show: serious US companies are not going to scale their products on Chinese models. He predicts it will play out like TikTok, regionally distributed to markets with lower concern about data sovereignty. Pat's hedge: open source is a legitimate pressure valve on frontier model pricing, but only if Chinese labs aren't stealing IP to get there. If the frontier model companies stop investing because there's no money in it, the whole ecosystem loses. NVIDIA has the clearest opportunity to step in and fill the open source gap without competing with its own customers. (The Decode) The Flip: Is $700 Billion in Hyperscale AI CapEx Delivering Returns Fast Enough? Daniel took the pro stance: Google Cloud at 63% growth, $460 billion in backlog, quarter-over-quarter doubling. Azure at 40%, AWS at 28% fastest growth in 15 quarters. Meta at 33%, fastest growth since 2021, generating $32 billion in operating cash flow in a single quarter. Only 20% of enterprises are using AI and only 2% of consumers. Pat's counter: Microsoft is down 12% year to date despite beating estimates. ServiceNow off 14% after a beat and raise. The market is completely skeptical, and $700 billion in CapEx so Anthropic and OpenAI can crank out $100 billion in revenue is not yet a clean return story. Both hosts admit they agreed on more than they let on. The real question isn't whether companies are spending too much, it might actually be whether they're spending enough. (The Flip) Fed Holds, 8-4 Vote — In a macro look at the markets, hosts report that the Fed held rates steady, with the most dissents since October 1992. Pat's read: it means nothing for the tech trade right now but is a re-rating of the discount rate long term. Daniel thinks cuts are still coming because housing is stalled and nothing else moves the broader economy without it. Confirmation of the new Fed chair is something to watch. (Bulls and Bears) NVIDIA Crosses $5 Trillion — Daniel called it, and it happened faster than he thought was realistic, just like $2, $3, and $4 trillion before it. A $5 trillion market cap is a market verdict on supply constraint and demand visibility. His position remains: every estimate of the AI market between now and 2030 is too low because nobody has the gall to estimate what exponential scale actually looks like. (Bulls and Bears) Microsoft, AWS, and Google Cloud Earnings — The cloud race is heating up with Google Cloud leading at 63% growth, while Azure hit 40% and AWS saw its fastest expansion in 15 quarters at 28%. Pat points to Microsoft's massive 700,000-seat Copilot deal with Accenture as a key indicator of its enterprise advantage, noting that businesses prefer established partners over direct labs for AI. Daniel highlights a clear market shift: Google's demonstrated ROI earned investor rewards, whereas Meta faced pushback for increasing CapEx without a defined enterprise revenue stream. In this "hard ROI era," strategic capital allocation is making all the difference. (Bulls and Bears) Samsung, Apple, and Qualcomm — Samsung has transitioned from facing negative gross margins to becoming a premier global profit leader. In Pat's view, this surge represents a long-awaited correction following years of intense pricing pressure. SK Hynix and Micron are similar beneficiaries and Daniel has been pounding the table on Micron for a reason. Apple beat solidly everywhere, proved the iPhone 17 cycle is real, blew up the China headwind argument, and grew services to an all-time high at $31 billion. The episode closes on a high note with Qualcomm hitting a major milestone: a hyperscaler is now leveraging their AI silicon, with material impact expected in 2027. As Pat noted in his summary tweet, the short sellers are definitely feeling the heat right now. (Bulls and Bears) Want the full breakdown? Be a part of our community. Hit that subscribe button on our Youtube channel!   The Decode OpenAI Breaks Azure Exclusivity — Models, Codex, and Managed Agents Now on AWS https://www.businessinsider.com/openai-microsoft-partnership-agreement-changes-cloud-providers-agi-2026-4 https://openai.com/index/openai-on-aws/ Google Commits Up to $40B in Anthropic — AI Lab Capital Concentration Reaches Historic Scale https://futurumgroup.com/insights/anthropics-gigawatt-scale-tpu-deal-with-broadcom-creates-a-structural-advantage/ https://tech-insider.org/google-40-billion-anthropic-investment-tpu-compute-2026/ https://x.com/danielnewmanUV/status/2049994186309468408 Meta Signs Multibillion-Dollar Deal for Tens of Millions of AWS Graviton5 Cores — Agentic AI Becomes a CPU Story https://about.fb.com/news/2026/04/meta-partners-with-aws-on-graviton-chips-to-power-agentic-ai/ https://www.aboutamazon.com/news/aws/meta-aws-graviton-ai-partnership https://www.geekwire.com/2026/meta-signs-multibillion-dollar-deal-to-use-amazons-graviton-chips-for-agentic-ai/ OpenAI Releases GPT-5.5 — New Intelligence Tier for Agents, Coding, and Research https://www.cnbc.com/2026/04/23/openai-announces-latest-artificial-intelligence-model.html https://community.openai.com/t/gpt-5-5-is-here-available-in-the-api-codex-and-chatgpt-today/1379630 The China AI Pricing Divide — DeepSeek, Kimi, and Open-Weight Chinese Models Running at Fractions of OpenAI/Anthropic Cost https://www.youtube.com/watch?v=UjdFa1eyIWI https://artificialanalysis.ai/models/deepseek-v4 https://the-decoder.com/kimi-k2-pricing-vs-openai-anthropic/ https://venturebeat.com/technology/deepseek-v4-arrives-with-near-state-of-the-art-intelligence-at-1-6th-the-cost-of-opus-4-7-gpt-5-5 The Flip With Alphabet, Microsoft, and Meta All Reporting Earnings Today — Is the $500B+ Hyperscaler AI Capex Cycle Delivering Returns Fast Enough to Avoid a Reckoning? FOR: Google Cloud at 27% margin and $35B+ quarterly revenue pace is proof the cycle pays https://www.spglobal.com/market-intelligence/en/news-insights/research/2026/04/alphabet-earnings-preview-q1-2026 Meta's $115-135B capex is being funded by 31% revenue growth — not debt https://tickeron.com/blogs/meta-platforms-meta-q1-2026-earnings-preview-31-revenue-growth-in-sight-12881/ Nvidia's $5T market cap and $1T+ in forward order visibility confirms demand is not slowing https://www.cnbc.com/2026/04/27/nvidia-just-hit-an-all-time-high-why-some-think-a-rally-is-just-getting-started.html AGAINST:  Microsoft is down 12% YTD despite beating estimates last quarter — the market is skeptical https://www.geekwire.com/2026/microsoft-earnings-preview-after-a-357-billion-wipeout-tech-giant-gets-another-chance/ ServiceNow -14% after a beat-and-raise is the most important AI earnings signal of the week https://www.cnbc.com/2026/04/22/servicenow-now-earnings-q1-2026.html Meta just raised 2026 capex to $125-145B and shareholders punished the stock for it — the market is pricing in a payback timing problem https://finance.yahoo.com/sectors/technology/article/meta-q1-earnings-to-shine-spotlight-on-spending-with-capex-nearly-doubling-from-last-year-160136256.html https://techcrunch.com/2026/04/30/meta-says-its-business-ai-now-facilitates-10-million-conversations-a-week/ Bulls & Bears Fed Holds Rates Steady at 3.5-3.75% in Powell's Final Press Conference — 8-4 Vote is Most Dissents Since October 1992 https://www.cnbc.com/2026/04/29/fed-interest-rate-decision-april-2026.html https://www.foxbusiness.com/economy/federal-reserve-interest-rate-decision-april-29-2026 https://www.kiplinger.com/news/live/fed-meeting-updates-and-commentary-april-2026 Nvidia Hits $5T Market Cap and All-Time High — First Record Since October https://www.cnbc.com/2026/04/27/nvidia-just-hit-an-all-time-high-why-some-think-a-rally-is-just-getting-started.html https://polymarket.com/event/will-nvda-hit-week-of-april-27-2026 The Hyperscaler Cloud Read — AWS Reaccelerates to 28%, Microsoft Azure to 40%, AI Run Rates $15B+ and $37B https://www.heygotrade.com/en/blog/amazon-q1-2026-earnings-reaction/ https://www.cnbc.com/2026/04/29/aws-earnings-q1-2026.html https://www.microsoft.com/en-us/Investor/earnings/FY-2026-Q3/income-statements https://www.marketbeat.com/originals/microsofts-maia-200-the-profit-engine-ai-needs/ Mag 7 Capex Read — Alphabet's Cloud Backlog Hits $460B+ While Meta Raises 2026 Capex to $125-145B https://www.cnbc.com/2026/04/29/alphabet-googl-q1-2026-earnings.html https://finance.yahoo.com/sectors/technology/article/meta-q1-earnings-to-shine-spotlight-on-spending-with-capex-nearly-doubling-from-last-year-160136256.html https://techcrunch.com/2026/04/30/meta-says-its-business-ai-now-facilitates-10-million-conversations-a-week/ Samsung Electronics Q1 2026 — Record Quarter on AI Memory Boom; First Mass HBM4 Shipment to NVIDIA Vera Rubin https://news.samsung.com/global/samsung-electronics-announces-first-quarter-2026-results https://www.techbuzz.ai/articles/samsung-electronics-announces-first-quarter-2026-results https://www.sammobile.com/news/samsung-q1-2026-profit-hits-record-high-ai-chip-boom/ Apple Q2 FY2026 — Record $111.2B Revenue (+17%), Greater China +28%, $100B Buyback Authorized; Cook's 89th Earnings Call https://9to5mac.com/2026/04/30/apple-reports-q2-2026-earnings-111-2-billion-in-revenue-up-17/ https://www.macrumors.com/2026/04/30/apple-2q-2026-earnings/ https://www.stocktitan.net/news/AAPL/apple-reports-second-quarter-gy0ooebphoid.html https://www.bloomberg.com/news/live-blog/2026-04-30/apple-second-quarter-earnings Qualcomm Q2 Earnings https://investor.qualcomm.com/files/doc_financials/2026/q2/FY2026-2nd-Quarter-Earnings-Presentation_4-29-26_Final.pdf https://www.benzinga.com/quote/QCOM/earnings https://finance.yahoo.com/markets/stocks/article/qualcomm-reports-better-than-anticipated-q2-earnings-stock-rises-over-10-155935310.html  

L'intelligenza artificiale ha consolidato una nuova fase: non è più solo innovazione tecnologica, ma un ecosistema autonomo dove infrastruttura, finanza e software convergono. Non conta più solo il modello. Contano capacità computazionale, energia, alleanze strategiche e controllo operativo.In questa puntata analizziamo cosa è successo davvero nell'ultima settimana di aprile 2026: • La transizione verso una vera autonomia operativa, con la nascita della “sovranità agentica”, dove gli algoritmi agiscono direttamente su mercati e processi aziendali • La fine delle alleanze rigide: OpenAI apre oltre Microsoft, portando i suoi modelli anche su AWS e rendendo il cloud sempre più competitivo e interconnesso • Il boom finanziario delle Big Tech, con ricavi record e oltre 725 miliardi di dollari di investimenti previsti nel 2026 in infrastrutture AI • L'ascesa di Anthropic come nuovo polo globale del capitale, con valutazioni potenziali superiori a OpenAI e forte attenzione anche da parte delle autorità di sicurezza • La corsa a energia e hardware: nuovi accordi per gigawatt di potenza computazionale e soluzioni alternative come celle a combustibile per alimentare i data center• Il cambiamento nel mercato dei chip, con una crescente rilevanza di CPU ottimizzate per inferenza e agenti rispetto alle GPU tradizionali • L'ingresso nell'economia agentica, con sistemi che creano account, eseguono pagamenti e sviluppano software senza intervento umano • I primi segnali di rischio operativo: agenti autonomi che possono causare danni reali, come la cancellazione di interi database aziendali • Le tensioni legali e sulla proprietà intellettuale, con l'uso di tecniche come la distillazione che riaccendono il dibattito sulla competizione tra aziende AI In collaborazione con Claudio Ricci, Amministratore unico di Recomb, una realtà specializzata nel fornire aggiornamenti personalizzati alle organizzazioni orientate all'innovazione sugli sviluppi dell'intelligenza artificiale, oltre a offrire corsi di aggiornamento professionale Per maggiori informazioni: info@recomb.ai Fonti principali: OpenAIhttps://openai.com/index/openai-on-aws/Distribuzione dei modelli OpenAI su AWS e apertura multi-cloud. Bloomberghttps://www.bloomberg.com/news/articles/2026-04-29/alphabet-sales-beat-estimates-on-google-cloud-ai-customersCrescita di Alphabet trainata dall'integrazione dell'intelligenza artificiale. Microsoft Newshttps://news.microsoft.com/source/2026/04/29/microsoft-cloud-and-ai-strength-fuels-third-quarter-results/Risultati finanziari Microsoft e crescita del cloud AI. AP Newshttps://apnews.com/article/amazon-earnings-aws-profit-1q-5c2356e39214d3d4a4949b63027a3c43Risultati Amazon e performance della divisione AWS. Tom's Hardwarehttps://www.tomshardware.com/tech-industry/big-tech/big-techs-ai-spending-plans-reach-725-billionInvestimenti record delle Big Tech in infrastrutture AI. Bloomberghttps://www.bloomberg.com/news/articles/2026-04-29/anthropic-considering-funding-offers-at-over-900-billion-valueValutazione potenziale di Anthropic. TechCrunchhttps://techcrunch.com/2026/04/24/google-to-invest-up-to-40b-in-anthropic-in-cash-and-compute/Investimento di Google in Anthropic. The Wall Street Journalhttps://www.wsj.com/tech/ai/white-house-opposes-anthropics-plan-to-expand-access-to-mythos-model-dc281ab5Rischi di sicurezza legati al modello Mythos. Bloomberghttps://www.bloomberg.com/news/articles/2026-04-30/openai-meets-key-ai-computing-capacity-goal-ahead-of-scheduleCapacità computazionale raggiunta da OpenAI. Briefshttps://www.briefs.co/news/oracle-just-tripled-its-power-deal-with-bloom-energy-to-skip-the-grid/Soluzioni energetiche alternative per data center. TechCrunchhttps://techcrunch.com/2026/04/24/in-another-wild-turn-for-ai-chips-meta-signs-deal-for-millions-of-amazon-ai-cpus/Accordo Meta–Amazon su CPU per AI. Cloudflare Bloghttps://blog.cloudflare.com/agents-stripe-projects/Automazione agentica nei servizi digitali. IBM Newsroomhttps://newsroom.ibm.com/2026-04-28-introducing-ibm-bob-ai-development-partner-that-takes-enterprises-from-ai-assisted-coding-to-production-ready-softwarePiattaforma IBM per orchestrazione di agenti.PR Newswirehttps://www.prnewswire.com/apac/news-releases/visa-launches-agentic-ready-program-in-asia-pacific-with-over-50-partners---advancing-agentic-commerce-302758064.htmlProgramma Visa per pagamenti agentici. WIREDhttps://www.wired.com/story/elon-musk-distill-openai-models-partly-xai/Uso della distillazione nei modelli AI. The Guardianhttps://www.theguardian.com/technology/2026/apr/29/claude-ai-deletes-firm-databaseErrore critico di un agente AI su database aziendale. TNWhttps://thenextweb.com/news/us-government-intel-stake-36-billion-chips-actInvestimenti USA nei semiconduttori. Tom's Hardwarehttps://www.tomshardware.com/tech-industry/china-plans-cpu-only-exascale-supercomputer-with-47000-domestic-processors

Our 243rd episode with a summary and discussion of last week's big AI news!Recorded on 04/29/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:OpenAI released GPT-5.5 with strong coding-oriented improvements, a system card discussing chain-of-thought monitorability and misalignment testing, higher pricing than GPT-5.4, and notable quirks like a system-prompt warning about “goblins.”xAI launched Grok Voice Think Fast 1.0, claiming large benchmark leads for real-time voice agents and reporting major Starlink customer-support automation and sales conversion impact.DeepSeek open-sourced DeepSeek V4 (Pro and Flash) featuring MoE scaling and 1M-token context via hybrid/compressed attention changes, while Tencent released Hunyuan 3 preview with weaker benchmark performance; a new long-horizon agent benchmark (Clawmark) shows low task success rates.Major business, legal, and policy updates include Google's planned up-to-$40B investment and 5GW compute commitment to Anthropic, Meta's AWS Gravitron deal and China blocking Meta's Manus acquisition, a revamped OpenAI–Microsoft agreement, ongoing Musk–OpenAI trial developments, and new safety/security research on sabotage, document degradation under delegation, and bit-flip attacks.Timestamps:(00:00:10) Intro / Banter(00:02:00) News Preview(00:02:26) Response to listener comments(00:02:55) SponsorsTools & Apps(00:05:55) OpenAI Unveils Its New, More Powerful GPT-5.5 Model - The New York Times(00:23:33) xAI Launches grok-voice-think-fast-1.0: Topping τ-voice Bench at 67.3%, Outperforming Gemini, GPT Realtime, and More - MarkTechPost(00:29:00) Claude can now plug directly into Photoshop, Blender, and Ableton | The VergeProjects & Open Source(00:29:38) China's DeepSeek releases preview of long-awaited V4 model as AI race intensifies(00:47:05) Tencent Unveils Hy3 preview; Model Enhances Agent Capabilities and Real-World Usability - Tencent 腾讯(00:50:14) ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker AgentsApplications & Business(00:53:03) Google Plans to Invest Up to $40 Billion in Anthropic(00:56:26) Meta will use hundreds of thousands of AWS Graviton chips(00:59:51) China blocks Meta's $2 billion takeover of AI startup Manus(01:01:45) OpenAI shakes up partnership with Microsoft, capping revenue share payments(01:07:13) Elon Musk Testifies of AI Risk at Trial, Says OpenAI Tried to ‘Steal' a Charity - WSJ(01:11:50) Judge rejects DOJ bid to delay Anthropic appeal in Pentagon dispute(01:14:42) Google's Gemini can now run on a single air-gapped server — and vanish when you pull the plug(01:19:07) DeepMind's David Silver just raised $1.1B to build an AI that learns without human data | TechCrunchPolicy & Safety(01:22:47) Evaluating whether AI models would sabotage AI safety research(01:28:59) LLMs Corrupt Your Documents When You Delegate(01:32:50) Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability(01:39:53) Memorandum on Adversarial Distillation of American AI Models(01:41:41) Teen boys are dating their AI chatbots—and experts warn it could kill their careers | Fortune(01:43:57) Announcing the Anthropic Economic Index Survey(01:45:21) Scoop: CISA lacks access to Anthropic's MythosSynthetic Media & Art(01:48:03) Taylor Swift Files to Trademark Voice and Likeness to Protect Against AI MisuseResearch & Advancements(01:49:15) Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit FlipsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Elon Musk attaque Sam Altman et OpenAI dans un procès explosif sur la gouvernance de l'IA • OpenAI et Microsoft redéfinissent leur alliance stratégique • Google renoue avec le Pentagone et relance le débat sur la tech militaire • La France débloque 200 millions d'euros contre les fuites massives de données • Une batterie automobile chinoise se recharge en moins de 4 minutes • En Chine, l'IA s'installe au cœur de la production audiovisuelle • Tech et défense : Tariq Krim décrypte le manifeste de Palantir.⭐️ Découvrez Frogans, l'innovation française qui réinvente le Web [PARTENARIAT]===============Sommaire détaillé :===============Musk contre Altman : le procès de l'IA (02:37)Le procès qui oppose Elon Musk à Sam Altman s'est ouvert en Californie. Le fondateur de OpenAI accuse l'entreprise d'avoir trahi sa mission initiale en devenant lucrative avec le soutien de Microsoft. Au-delà d'un affrontement d'egos, l'affaire pose une question clé : l'IA peut-elle rester d'intérêt général sous la pression des marchés et d'une future introduction en Bourse ?OpenAI–Microsoft : la fin de l'exclusivité (05:34)Les deux partenaires historiques revoient leur accord stratégique. Microsoft perd l'exclusivité commerciale sur les modèles d'OpenAI, qui pourront désormais être distribués via d'autres clouds. Une évolution majeure qui redessine l'équilibre des forces dans l'IA mondiale et marque une nouvelle étape d'émancipation mutuelle.Google et le Pentagone : le retour du militaire (07:02)Selon Reuters et The Information, Google aurait signé un accord classifié avec le Pentagone pour l'usage de ses modèles d'IA à des fins gouvernementales et militaires. Un virage symbolique pour le géant américain, après les controverses du projet Maven en 2018. Le débat sur la collaboration entre la Silicon Valley et la défense américaine revient au premier plan.200 millions d'euros contre les fuites de données (08:57)Face à la multiplication des cyberattaques, le gouvernement français débloque 200 millions d'euros pour moderniser les systèmes publics et préparer l'adoption de la cryptographie post-quantique. Une réponse à des vols massifs de données, dont celui de l'ANTS, qui ont exposé des millions d'informations personnelles sur le darknet.Batterie automobile record : 3 minutes 44 pour recharger (11:04)Le chinois CATL annonce une batterie capable de passer de 10 à 80 % de charge en 3 minutes 44. Une prouesse technologique rendue possible par la technologie LFP et un système thermique optimisé, mais qui pose la question des infrastructures capables de délivrer une telle puissance.Un agent IA détruit une entreprise en 9 secondes (14:04)Dans le débrief transatlantique avec Bruno Guglielminetti, éditeur du podcast Mon Carnet, retour sur l'incident spectaculaire de l'entreprise américaine Pocket OS, dont la base de données a été effacée par un agent IA mal encadré. Une illustration concrète des risques liés au déploiement d'agents autonomes sans garde-fous de cybersécurité.La Chine, laboratoire du cinéma généré par IA (30:43)Shanhui Zhang, présentatrice à China Global Television Network, explique comment des plateformes comme iQIYI et Tencent Video expérimentent des acteurs virtuels et des productions partiellement générées par IA. Baisse des coûts, nouveaux métiers, transformation des formations : l'IA redessine en profondeur l'économie audiovisuelle chinoise.Tech et défense : décryptage du manifeste de Palantir (39:21)L'entrepreneur Tariq Krim, fondateur de Cybernetica, analyse le manifeste publié par le patron de Palantir Technologies. Derrière l'appel à mettre la tech au service de la défense occidentale, il voit avant tout une stratégie industrielle et commerciale, inscrite dans l'histoire longue des relations entre Silicon Valley et armée américaine.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

One side wins the OpenAI-Microsoft divorce, Ram calls a 19% earnings growth year 'bananas,' and Chris wants the US to hack back against DeFi exploiters. Here is the full rundown. --- Heads up! If you haven't yet, be sure to subscribe to Bits + Bips, since the show will migrate there in a few weeks. Follow us on ⁠⁠⁠Apple Podcasts⁠⁠⁠, ⁠⁠⁠YouTube⁠⁠⁠, ⁠⁠⁠Spotify⁠⁠⁠, ⁠⁠⁠X⁠⁠⁠, ⁠⁠⁠Unchained⁠⁠⁠ and wherever you get your podcasts. ---- Chris Perkins and Ram Ahluwalia cover a lot of ground this week: Iran appears to be seeking a deal to end the Strait of Hormuz blockade as US economic pressure mounts, and the US government just worked with Tether to seize over $300 million in Iranian-linked stablecoins.  Bottoms-up S&P earnings estimates are running at 19% year-over-year growth, tech earnings are about to hit, and both hosts think the setup for markets is unusually constructive.  They also break down the new Microsoft-OpenAI agreement, the arrest of a special operations soldier for betting on the Maduro raid on Polymarket, and what the Kelp DAO hack means for DeFi's path to institutional adoption. Hosts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠Ram Ahluwalia⁠⁠⁠⁠⁠⁠⁠⁠⁠, Co-Host, CEO of Lumida ⁠⁠⁠⁠⁠⁠⁠⁠⁠Chris Perkins⁠⁠⁠⁠⁠⁠⁠⁠⁠, Co-Host, CEO of 250 Digital Asset Management Learn more about your ad choices. Visit megaphone.fm/adchoices

Cohere übernimmt Aleph Alpha – die Schwarz-Gruppe investiert $600 Mio., Aleph Alpha bekommt 10% der neuen Firma. OpenAI und Microsoft lösen ihre exklusive Partnerschaft auf – Microsoft verliert die Exklusivität, OpenAI behält den Revenue Share. OpenAI verfehlt interne Umsatz- und Nutzerziele. Google investiert bis zu $40 Mrd. in Anthropic – die ersten $10 Mrd. auf der alten $350-Mrd.-Bewertung, obwohl Anthropic am Sekundärmarkt über $1 Billion wert ist. Google unterzeichnet einen geheimen Pentagon-KI-Deal trotz Mitarbeiterprotesten. GitHub Copilot wechselt auf nutzungsbasierte Abrechnung. Ex-DeepMind-Forscher raised $1 Mrd. Seed auf $5 Mrd. Bewertung. World ID 4.0 startet mit Zoom, Tinder und Shopify. China blockiert Metas $2-Mrd.-Manus-Übernahme. Emil Michael baut Pentagon-VC-Fonds. Musk vs. Altman geht vor Gericht – Musk pusht den New-Yorker-Artikel, NYT enthüllt SpaceX als Musks Sparkasse. Sereact raised $110 Mio. für Robotik-KI. Google Maps zeigt jetzt die Zahl gelöschter Rezensionen an. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Hörerfrage (00:07:20) Aleph Alpha/Cohere: Analyse des Deals (00:16:56) OpenAI/Microsoft lösen exklusive Partnerschaft (00:21:06) OpenAI verfehlt Umsatz- und Nutzerziele (00:28:40) Google investiert $40 Mrd. in Anthropic bei alter Bewertung (00:37:08) Google: Geheimer Pentagon-KI-Deal (00:41:15) Bubble: $5 Mrd. Seed, Cognition $25 Mrd. (00:45:51) World ID 4.0, China blockiert Meta/Manus (00:52:05) Mythos: Panik bei Firmen, Emil Michael als Pentagon-VC (00:55:55) Musk vs. Altman vor Gericht & Musk nutzte SpaceX als Sparkasse (01:02:40) Sereact $110 Mio. und Google Maps gelöschte Reviews Shownotes Cohere kauft Aleph Alpha, Schwarz investiert $600 Mio. - bloomberg.com OpenAI und Microsoft: Neue Freiheiten für beide Seiten - wsj.com OpenAI verfehlt Umsatz- und Nutzerziele vor IPO - wsj.com Google investiert bis zu $40 Mrd. in Anthropic - wsj.com Google unterzeichnet geheimen Pentagon-KI-Deal - theinformation.com GitHub Copilot wechselt auf nutzungsbasierte Abrechnung - github.blog Sequoia/Nvidia investieren $5 Mrd. in Ex-DeepMind-Startup - bloomberg.com Cognition (Devon AI) bei $25 Mrd. Bewertung - bloomberg.com World ID 4.0: Partnerschaften mit Zoom, Tinder, Shopify - xcancel.com China blockiert Metas $2 Mrd. Manus-Übernahme - theinformation.com Meta: Rechenzentren mit Solarenergie aus dem All - bloomberg.com Mythos- ft.com Emil Michael verwandelt Pentagon in VC-Firma - washingtonpost.com Musk pusht Altman-Exposé auf X vor Prozess - wired.com NYT: Musk nutzte SpaceX als Sparkasse - nytimes.com Sereact: $110 Mio. für Robotik-KI aus Stuttgart - bloomberg.com Google Maps zeigt Zahl gelöschter Rezensionen an - smartdroid.de

One side wins the OpenAI-Microsoft divorce, Ram calls a 19% earnings growth year 'bananas,' and Chris wants the US to hack back against DeFi exploiters. Here is the full rundown. --- Heads up! If you haven't yet, be sure to subscribe to Bits + Bips, since the show will migrate there in a few weeks. Follow us on ⁠⁠⁠Apple Podcasts⁠⁠⁠, ⁠⁠⁠YouTube⁠⁠⁠, ⁠⁠⁠Spotify⁠⁠⁠, ⁠⁠⁠X⁠⁠⁠, ⁠⁠⁠Unchained⁠⁠⁠ and wherever you get your podcasts. ---- Chris Perkins and Ram Ahluwalia cover a lot of ground this week: Iran appears to be seeking a deal to end the Strait of Hormuz blockade as US economic pressure mounts, and the US government just worked with Tether to seize over $300 million in Iranian-linked stablecoins.  Bottoms-up S&P earnings estimates are running at 19% year-over-year growth, tech earnings are about to hit, and both hosts think the setup for markets is unusually constructive.  They also break down the new Microsoft-OpenAI agreement, the arrest of a special operations soldier for betting on the Maduro raid on Polymarket, and what the Kelp DAO hack means for DeFi's path to institutional adoption. Hosts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠Ram Ahluwalia⁠⁠⁠⁠⁠⁠⁠⁠⁠, Co-Host, CEO of Lumida ⁠⁠⁠⁠⁠⁠⁠⁠⁠Chris Perkins⁠⁠⁠⁠⁠⁠⁠⁠⁠, Co-Host, CEO of 250 Digital Asset Management Learn more about your ad choices. Visit megaphone.fm/adchoices

This Week In Startups is made possible by:IM8 Health - IM8health.com/twistSentry - Sentry.io/twistDeel - Deel.com/twistPlaud - https://Plaud.ai/twistWhat's the technology story of the year? No, it's not Anthropic's Mythos model, nor any other AI model. Upcoming IPOs? Wrong again. Jason thinks it's China's government's decision to block Meta's acquisition of Manus. The multi-billion-dollar deal was heralded as evidence that AI companies founded in China could sidestep onerous government meddling by moving their operations to Singapore. That loophole now appears closed.Jason and Lon then dug into the newly-reforged OpenAI-Microsoft partnership. The two companies have had a mutually beneficial, if fractious, operating relationship to date; that they had to rework their agreement is not a shock.Then, your hosts watched and digested a video from China purportedly showing a self-driving car hitting a child at speed. What would happen in the United States if such a tragedy struck? And is that fait accompli?Closing the show, Jason and Lon reacted to the latest Russell Brand implosion, discussed Lon's new favorite show ‘Criminal Record,' and shared recommended foot pedals (for AI dictation) and headphones for the road warriors tuning in.Key Links: OpenAI and MicrosoftOpenAI's announcement of its new Microsoft dealMicrosoft's announcement of its new OpenAI dealOpenAI announces its deal with AWSNews that Microsoft was considering legal action against OpenAIMicrosoft-OpenAI negotiations late 2025 edition, and terms announced in early 2026Amazon announces that OpenAI models are coming to AWSTimestamps:0:00 Show starts1:24 OpenAI and Microsoft have a new deal3:22 Why would Microsoft cede exclusive access to OpenAI models?5:36 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off!8:09 Why Jason thinks Microsoft and Apple should work together on AI9:45 China will block the Meta-Manus deal12:12 Deel - Founders scale faster on Deel. Set up payroll for any country in minutes, hire anyone anywhere, get visas handled fast, and get back to building. Visit https://deel.com/twist to learn more.12:23 Golden Shares and what they provide14:57 How do you unravel a multi-billion-dollar deal?22:08 Sentry - New users can get $240 in free credits when they go to https://sentry.io/twist and use the code TWIST24:42 The Huawei ADC crash29:49 What happens when something similar happens in the United States?31:55 IM8 Health: Start feeling like your best self every day. Go to https://IM8health.com/twist and use the code TWiST to get a free welcome kit, five free travel sachets, and 10% off your order.36:15 Russell Brand's public humiliation41:22 Don't trust palm reading!43:59 Lon's Off Duty streaming pick: Criminal Record46:25 Jason's Pick: Which foot pedal for AI dictation is right for you?48:43 Jason's Pick: In the market for new headphones?Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisCheck out all our partner offers: https://partners.launch.co/Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.com

The Information's Aaron Holmes breaks down the behind-the-scenes negotiations between Sam Altman and Satya Nadella to avoid a legal war over OpenAI's partnership with AWS. We also talk with Erin Woo about Google's new deal to put AI on classified Pentagon systems and Rocket Drew about the jury selection in the Elon Musk vs. Sam Altman trial. Lastly, we get into the "buoyancy" of AI debt markets with Cory Weinberg and the future of agentic AI hardware with BEP Research's Ben Pouladian.Articles discussed on this episode: https://www.theinformation.com/articles/google-signs-classified-ai-deal-pentagon-amid-employee-oppositionhttps://www.theinformation.com/articles/nadella-altman-averted-legal-war-awshttps://www.theinformation.com/newsletters/the-briefing/microsoft-comes-openai-deal-winner-risks-ai-financinghttps://www.theinformation.com/briefings/musk-altman-brockman-agree-stay-social-media-trialSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

Entering a big week in which most of the Magnificent 7 lead the earnings parade, David Faber and Jim Cramer covered many developments surrounding AI: The record run for stocks including Nvidia's market cap rising back above $5 trillion, OpenAI caps revenue share payments as part of its revamped partnership with Microsoft, Elon Musk's lawsuit against OpenAI and its CEO Sam Altman goes to trial Monday, Related Companies CEO Jeff Blau joined the show to discuss Related Digital securing financing for Oracle's $16 billion data center project in Michigan. Also in focus: The red-hot chip sector rally, Verizon rises on earnings, reports of a Qualcomm-OpenAI partnership. Domino's tumbles. Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ben and Andrew interrupt Stratechery's spring vacation with a mailbag. First, they discuss the end of Sora, the difference between Sora and Instagram, and where the OpenAI/Microsoft parallels break down. Then: A great take on advertising, ChatGPT engagement farming, Formula 1's new era, the NFL's world takeover, and how NBC solved tape delay at the Olympics. At the end: A question about Vision Pro and wives, whether elementary schoolers should have smart phones, Elon's continued adventures with xAI, a Netflix dating show, LLM-aided dogfooding etymology, and Ben's (admittedly boring) Taipei routine.

The ACCC has launched a public investigation into whether major fuel suppliers have been committing anti-competitive conduct in regional Australia Meta is pulling Horizon Worlds off its VR headsets as it shifts further and further away from its expensive metaverse dream Microsoft is threatening legal action against its partner OpenAI after a $50 billion USD Amazon deal risks turning an exclusive partnership into a very awkward cloud throuple _ Download the free app (App Store): http://bit.ly/FluxAppStore Download the free app (Google Play): http://bit.ly/FluxappGooglePlay Daily newsletter: https://bit.ly/fluxnewsletter Flux on Instagram: http://bit.ly/fluxinsta Flux on TikTok: https://www.tiktok.com/@flux.finance —- The content in this podcast reflects the views and opinions of the hosts, and is intended for personal and not commercial use. We do not represent or endorse the accuracy or reliability of any opinion, statement or other information provided or distributed in these episodes. —- Schroder Investment Management Australia Limited (ABN 22 000 443 274, AFSL 226473) (Schroders) is the product issuer for Schroders Global Equity Alpha Fund (ARSN 678 278 370). This document does not contain and should not be taken as containing any financial product advice or financial product recommendations. This document does not take into consideration any recipient’s objectives, financial situation or needs. Before making any decision relating to a Schroders fund, you should obtain and read a copy of the product disclosure statement available at www.schroders.com.au or other relevant disclosure document for that fund and consider the appropriateness of the fundto your objectives, financial situation and needs. You should also refer to the target market determination for the fund at www.schroders.com.au. All investments carry risk, and the repayment of capital and performance in any of the funds named in this document are not guaranteed by Schroders or any company in the Schroders Group. The material contained in this document is not intended to provide, and should not be relied on for accounting, legal or tax advice. Schroders does not give any warranty as to the accuracy, reliability or completeness of information which is contained in this document. To the maximum extent permitted by law, Schroders, every company in the Schrodersplc group, and their respective directors, officers, employees, consultants and agents exclude all liability (however arising) for any direct or indirect loss or damage that may be suffered by the recipient or any other person in connection with this document. Opinions, estimates and projections contained in this document reflect the opinions of the authors as at the date of this document and are subject to change without notice. “Forward-looking” information, such as forecasts or projections, are not guarantees of any future performance and there is no assurance that any forecast or projection will be realised. Past performance is not a reliable indicator of future performance. All references to securities, sectors, regions and/or countries are made for illustrative purposes only and are not to be construed as recommendations to buy, sell or hold. Telephone calls and other electronic communications with Schroders representatives may be recorded.See omnystudio.com/listener for privacy information.

Who will win the AI race in 2026?

Depotwechsel Consorsbank*: https://www.consorsbank.de/web/Wertpapierhandel/DepotwechselZwei Unternehmen. Zwei IPOs. Und möglicherweise zwei der größten Börsengänge aller Zeiten.Willkommen zur ersten aktien.kauf-Podcastfolge im Jahr 2026.Während viele Indizes bereits neue Höchststände markieren, werfen wir heute einen Blick nach vorne – auf die spannendsten Börsengang-Kandidaten der kommenden Jahre.In dieser Folge analysieren wir zwei absolute Schwergewichte:SpaceX ist längst mehr als ein Raumfahrtunternehmen. Der eigentliche Werttreiber ist Starlink – ein globales Satelliten-Internet-Netzwerk mit tausenden Satelliten im niedrigen Erdorbit.Wir sprechen über:Starlink als skalierbares Infrastruktur- und Abo-GeschäftDen außergewöhnlich starken Burggraben durch Technologie, Netzwerkeffekte & staatliche KundenChancen als globaler Telekom- und Infrastruktur-PlayerRisiken durch Kapitalbedarf, Regulierung, Bewertung & den Elon-Musk-FaktorEin möglicher Börsengang mit einer Bewertung von 150–200 Mrd. USD wäre ein echtes Markt-Event.OpenAI hat mit ChatGPT & Co. eine technologische Zeitenwende ausgelöst. Das Unternehmen verkauft keinen klassischen Software-Zugang – sondern Zugang zu Intelligenz.Wir schauen auf:OpenAI als KI-Grundinfrastruktur für Unternehmen & EntwicklerDen daten-, kapital- und wissensbasierten BurggrabenChancen durch Skalierbarkeit, Plattform-Effekte & neue MonetarisierungsmodelleMassive Risiken durch Kosten, Regulierung, Wettbewerb & BewertungEin möglicher IPO wird sogar mit Bewertungen bis zu 1 Billion USD diskutiert.Microsoft hält rund 27 % an OpenAI und stellt mit Azure die technische Basis.Für Aktionäre bedeutet das:Indirekte OpenAI-ExposureKI-Fantasie ohne All-in-RisikoEin stabiles Kerngeschäft als SicherheitsnetzSpaceX und OpenAI sind außergewöhnliche Firmen mit enormem langfristigem Potenzial.Aber: „Price is what you pay, value is what you get.“Historisch zeigt sich:IPOs sind kurzfristig oft volatilGeduld zahlt sich häufig erst nach der Hype-Phase ausDie besten IPOs liefern ihren Wert über viele Jahre➡️ Für Anleger heißt das: Begeisterung ist gut – Bewertung und Timing sind entscheidend.Die in diesem Podcast besprochenen Inhalte dienen ausschließlich Informations- und Unterhaltungszwecken und stellen keine Anlageberatung, Kauf- oder Verkaufsempfehlung dar.Alle genannten Meinungen spiegeln die persönliche Einschätzung des Hosts wider.Kapitalmarktinvestitionen sind mit Risiken verbunden und können zum vollständigen Verlust des eingesetzten Kapitals führen.Bitte informiere dich eigenständig und ziehe im Zweifel einen professionellen Finanzberater hinzu.*Werbung

Welcome to AI Unraveled (November 19, 2025): Your daily strategic briefing on the business impact of AI.Today's Highlights: Google CEO warns no firm is immune if the AI bubble bursts. Peter Thiel and SoftBankliquidated their entire Nvidia stakes. Google unveiled Gemini 3 Pro, immediately outperforming GPT-5.1 on key benchmarks. And Jeff Bezos revealed Project Prometheus, a $6.2 billion launch into physical AI manufacturing.Listen at https://podcasts.apple.com/us/podcast/ai-daily-news-rundown-gemini-3-0-pro-vs-gpt-5-1-benchmark/id1684415169?i=1000737352861Strategic Pillars & Topics:

```html join wall-e on this tech briefing for monday, november 17th as we delve into today's top stories: openai & microsoft's financial intertwining: leaked documents reveal microsoft received substantial revenue share payments from openai in 2024 and 2025, highlighting their intricate partnership. u.s. losing ai edge to china: andy konwinski of databricks emphasizes the need for open-source collaborations to help the u.s. regain its ai competitiveness against china. chatgpt's expansion and challenges: openai's chatbot thrives with 800 million users, rolling out gpt-5.1 and exploring sectors like healthcare amidst legal hurdles. tesla's autonomous vehicle safety claims: tesla counters scrutiny with claims of lower collision rates, but transparency questions remain about their robotaxi trials. north korean operatives in cybercrime: five individuals plead guilty to aiding north korean cyber activities, spotlighting ongoing cybersecurity threats. stay tuned for tomorrow's latest tech updates! ```

Ohne Aktien-Zugang ist's schwer? Starte jetzt bei unserem Partner Scalable Capital. Mit eigenem KI-Chatbot, der dir alle Fragen rund ums Investieren beantwortet. Alle weiteren Infos gibt's hier: scalable.capital/oaws. KI-Deals wohin man schaut: OpenAI kauft bei AWS, AWS kauft bei Cipher, Microsoft kauft bei Iren. Microsoft verkauft in die VAE. NVIDIA profitiert. Hedgefonds feiert Aixtron. Palantir, BioNTech und Ryanair haben Zahlen und Tesla kauft wohl bei Samsung SDI. Denkt man an Autos in den USA, denkt man an Muscle-Cars oder Pickups. KIA Motors (WKN: 885677) steht dafür auf den ersten Blick nicht. Trotzdem mischen die Südkoreaner den US-Markt auf. Mit einer cleveren Strategie. Six Flags (WKN: A2QGV5) sollte nach der Fusion mit Cedar Fair DER US-Freizeitparkgigant werden. Daraus wurde aber nichts. Heute hat Six Flags hohe Schulden und viel weniger Besucher als 2019. NFL-Superstar Travis Kelce & Jana Partners wollen's ändern. Diesen Podcast vom 04.11.2025, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung.

Why is Amazon laying off 14,000 people during a massive AI boom? Todd and John analyze the Seattle tech paradox, digging into Andy Jassy's 'startup' reasoning and debating whether the AI frenzy is a bubble. Then, they take on the Cascadia high-speed rail: a necessary connector or a misguided project? Related headlines from the week Amazon layoffs Amazon confirms 14,000 job cuts, says push for ‘efficiency gains’ will continue into 2026 A tale of two Seattles in the age of AI: Harsh realities and new hope for the tech community Filing: Amazon cuts more than 2,300 jobs in Washington state as part of broader layoffs Amazon layoffs hit software engineers hardest in Washington Amazon layoffs reaction: ‘Thought I was a top performer but guess I’m expendable’ Amazon CEO says massive corporate layoffs were about agility — not AI or cost-cutting Amazon earnings Amazon stock soars 11% after topping Q3 estimates with $180B in revenue, $21B in profits Amazon’s Anthropic investment boosts its quarterly profits by $9.5B ‘Big Beautiful’ tax benefit: Amazon and other tech giants reap the rewards of new law, for now Microsoft Azure, earnings and OpenAI Microsoft’s Azure reports cloud outage, disrupting global customers including Alaska Airlines Microsoft beats expectations, reports nearly $35B in Q1 capital spending amid Azure outage Microsoft gets 27% stake in OpenAI, and a $250B Azure commitment Seattle-Portland-Vancouver Slowly but surely, high-speed rail backers believe Cascadia mega-project will become a reality Cascadia’s AI paradox: A world-leading opportunity threatened by rising costs and a talent crunch The ‘enormous barrier’ that threatens economic growth in the Pacific Northwest Beta’s unique electric airplane flies into Seattle to wow state officials and aviation experts With GeekWire co-founders John Cook and Todd BishopSee omnystudio.com/listener for privacy information.

Discussing Ben's interview with Substrate CEO James Proud, including the "insane" challenge he's undertaken as Substrate attempts to compete with TSMC and ASML, and the ways in which a bubbly environment benefits innovation by incentivizing exactly that sort of moonshot. From there: Ben's thoughts on the "Too Big to Fail" era in tech that may be averted, reactions to the latest humanoid robot, and thoughts on both sides of the OpenAI-MIcrosoft announcement earlier this week. At the end: Taylor Sheridan leaves Paramount for greener pastures, Sharp Text and lessons from Grantland, a temperature check on Sora one month later, lessons from the Nexperia mess in Europe, a question about Magic: The Gathering, and a few corrections to last week's show.

Hurricane Melissa makes landfall in Cuba after leaving a trail of destruction in Jamaica. U.S. President Donald Trump is on his final stop in a high-stakes Asia trip. Microsoft and OpenAI strike a deal that clears the way for the ChatGPT maker to go public. And Dutch voters head to the polls as far-right, anti-immigration leader Geert Wilders tries to hold onto power. Sign up for the Reuters Econ World newsletter here. Listen to the Reuters Econ World podcast here. Find the Recommended Read here. Visit the Thomson Reuters Privacy Statement for information on our privacy and data protection practices. You may also visit megaphone.fm/adchoices to opt out of targeted advertising. Learn more about your ad choices. Visit megaphone.fm/adchoices

Take the AI Dragon Quiz to get tailored recommendations for AI tools & resources: https://clickhubspot.com/mkw Episode 81: Is Microsoft finally stepping out of OpenAI's shadow to compete in the AI image generation race? Matt Wolfe (https://x.com/mreflow) is joined by special guest Maria Gharib (https://uk.linkedin.com/in/maria-gharib-091779b9), head writer of the Mindstream newsletter and one of the sharpest AI journalists around. Maria's journey from studying international affairs and politics to reporting on the AI frontier has made her writing a daily go-to for thousands, and now she's bringing her AI insights to The Next Wave. In this packed episode, Matt and Maria break down Microsoft's surprising new MAI Image 1 model, its impact on the OpenAI-Microsoft partnership, and what it signals for future AI competition. They also dive into the evolving personality (and rules) of ChatGPT—including Sam Altman's statements on mental health and GPT erotica—and talk about Google Gemini's brand-new calendar integration. Other hot topics include Elon's ambitious "World Model" for XAI, when AI beats doctors to diagnose Lyme disease, and how Google's new “AI makeup” feature is changing work calls. Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd — Show Notes: (00:00) Maria's Journey into AI (04:48) Microsoft's First In-House AI Model (06:58) LM Arena AI Demo Explained (11:40) Relaxing ChatGPT Restrictions Soon (14:51) ChatGPT: Tool or Companion? (18:13) AI Age Detection Challenges (21:57) Google's Gemini Schedules Meetings (25:45) AI Models and Business Moats (30:07) Bringing Characters to Life (31:04) AI Tools and Future Uncertainty (36:50) Elon's XAI: Revolutionizing AI Understanding (38:05) Training Robots in Virtual Worlds (43:47) AI Diagnoses Man's Lyme Disease (45:22) AI Enhancing Healthcare Diagnosis (47:48) Mindstream: Daily AI Updates — Mentions: Maria Gharib: https://www.mindstream.news/authors Mindstream AI newsletter: https://www.mindstream.news/ Microsoft MAI Image: https://microsoft.ai/news/introducing-mai-image-1-debuting-in-the-top-10-on-lmarena/ Gemini: https://gemini.google.com/ Nano Banana: https://nanobanana.ai/ Claude: https://claude.ai/ AI-powered makeup in Google Meet: https://workspaceupdates.googleblog.com/2025/10/ai-powered-makeup-in-google-meet.html Get the guide to build your own Custom GPT: https://clickhubspot.com/tnw — Check Out Matt's Stuff: • Future Tools - https://futuretools.beehiiv.com/ • Blog - https://www.mattwolfe.com/ • YouTube- https://www.youtube.com/@mreflow — Check Out Nathan's Stuff: Newsletter: https://news.lore.com/ Blog - https://lore.com/ The Next Wave is a HubSpot Original Podcast // Brought to you by Hubspot Media // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

เรื่องราวของ OpenAI ที่หลายคนไม่ทราบ #12

Send us a text00:00 - Intro00:02 - SpaceX to enter the cell service business04:26 - Anduril wins $1.26b of new deals12:43 - OpenAI + Microsoft work out non-profit to for-profit switch17:33 - Will AI/robotics yield a decade long bull market?Nick Fusco = CEO at PM Insights, a pre-IPO secondary market pricing company…X - @TheFuscoKid…LinkedIn - www.linkedin.com/in/nickfuscoEvan Cohen = Founder/COO of withVincent.com, a media company focused on alternative investments…X - @evvcohen…LinkedIn - www.linkedin.com/in/evcohenClint Sorenson = Chief Investment Officer at WealthShield, an outsourced CIO and investment research company…X - @clint_sorenson…LinkedIn - www.linkedin.com/in/csorensoncfacmtAaron Dillon = Managing Director of AG Dillon Funds, pre-IPO stock investing for RIAs…X - @AaronGDillon…LinkedIn - www.linkedin.com/in/aarondillonnyc

Would you trust a synthetic version of yourself to teach your audience? One CEO just did, and it's raising questions about authenticity, attention, and the future of thought leadership. In this week's episode, Paul and Mike examine OpenAI's billion-dollar power plays, the deeper implications of its “People First AI Fund,” and why Microsoft, Oracle, and OpenAI might be creating value out of thin air. They also analyze Replit's Agent 3, a next-gen AI dev tool claiming 10x more autonomy, and why it may hint at what's coming across industries. Plus, stay tuned for commentary on AI's impact on jobs, the economy, and a controversial AI Podcast startup. Show Notes: Access the show notes and show links here Timestamps:  00:00:00 — Intro 00:04:51 —  OpenAI and Microsoft Partnership 00:18:31 — Replit's Agent 3 and What It Means for the Future of Agents 00:30:15 — AI Avatars for Executives 00:42:36 — OpenAI and Oracle Compute Deal 00:47:00 — Anthropic's $1.5B Authors Settlement Under Scrutiny 00:51:17 — Internal Tensions at Meta 00:54:52 — AI and Jobs: Labor Market Signals 01:02:11 — Will AI Crash the Economy? 01:07:55 — New AI Podcast Startup 01:14:13 — FTC and AI Companions 01:17:25 — Retail AI Case Studies 01:20:09 — AI Product and Funding Updates This week's episode is brought to you by MAICON, our 6th annual Marketing AI Conference, happening in Cleveland, Oct. 14-16. The code POD100 saves $100 on all pass types. For more information on MAICON and to register for this year's conference, visit www.MAICON.ai. Visit our website Receive our weekly newsletter Join our community: Slack LinkedIn Twitter Instagram Facebook Looking for content and resources? Register for a free webinar Come to our next Marketing AI ConferenceEnroll in our AI Academy

Can AI stocks beat Big Tech? In this episode, I discuss OpenAI and its decision to expand a secondary share sale that lets insiders sell about $10.3 billion of stock at roughly a $500 billion valuation. Although skeptical at first, the calculations reveal there is a path for OpenAI to deliver outsized returns.I cover:(0:00) The $500B question (01:11) Why the Nasdaq Index is the benchmark (03:35) Inside the OpenAI-Microsoft deal (05:50) The bull case: OpenAI's trillion-dollar path (09:33) The AI market explosion (12:39) The bear case: Competition and constraints (17:13) Exploring the models of tomorrow (20:58) The disruption premium (23:21) Where will OpenAI's revenue come from? (29:14) The final verdict

We're hours away from OpenAI's livestream announcement of what's reportedly called Agent Mode. There's been a few lines of reporting of what's coming!We tackle the rumors, what they mean, and how to be prepared for what this means for our day-to-day work lives. Square keeps up so you don't have to slow down. Get everything you need to run and grow your business—without any long-term commitments. And why wait? Right now, you can get up to $200 off Square hardware at square.com/go/jordan. Run your business smarter with Square. Get started today.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion:Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:ChatGPT Agent Mode Release RumorsChatGPT vs Microsoft Office CompetitionPotential Excel and PowerPoint IntegrationDeep Research and Agent Mode FeaturesOpenAI Operator and Browser UpdatesImpact on Microsoft Office Business ModelWorkflow Automation and App ConnectorsProductivity Tool Advancements for Knowledge WorkersTimestamps:00:00 "OpenAI's New Agent Announcement"04:56 OpenAI's New Features Reveal Tomorrow07:35 Microsoft-OpenAI Integration: Enhanced ChatGPT Features10:28 "ChatGPT-Powered Document Creation"13:42 "AI Tools for Visual Presentations"17:18 OpenAI's Enhanced Operator Unveiled19:55 "Anticipating Software Agent Reveal"25:28 AI Evolution: New Industry NormsKeywords:ChatGPT agent, OpenAI, agent mode, Microsoft Office competitor, Excel automation, PowerPoint automation, generative AI tools, spreadsheet AI, presentation AI, Operator, OpenAI browser, Canvas mode, Advanced Data Analysis, Microsoft Copilot, document management AI, workflow automation, browser automation, deep research, computer using agent, data analysis AI, GPT-4o image generation, Google Drive integration, report generation, database analysis, visual creation AI, productivity tool, chat-based document editing, slide generation, formula automation, business productivity AI, AI-powered presentations, AI-powered spreadsheets, AI advancements, IP sharing, OpenAI-Microsoft relationship, slide transitions AI, corporate data analysis, public data synthesis, Mac user productivity, PowerPoint, Excel, real-time updates AI, web sources integration.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner

This week, Paul and Mike unpack the New York Times' list of 22 upcoming roles (from “AI auditors” to “personality directors”), weigh Andy Jassy's memo that generative AI will mean leaner teams, and dissect the viral MIT study about what ChatGPT might be doing to your brain. Rapid-fire hits include Meta's billion-dollar talent raid, Apple's rumored Perplexity bid, and fresh OpenAI-Microsoft friction. Show Notes: Access the show notes and show links here Timestamps:  00:00:00 — Intro 00:05:41 — The New Jobs AI Could Create 00:26:11 — Amazon CEO on AI Job Disruption and AI Underemployment 00:39:28 — Your Brain on ChatGPT 00:52:22 — Fallout from the Meta / Scale AI Deal 00:55:27 — Meta and Apple AI Talent and Acquisition Search 01:05:59 — The OpenAI / Microsoft Relationship Is Getting Tense 01:08:53 — Veo 3's IP Issues 01:12:09 — HubSpot CEO Weighs In on AI's SEO Impact 01:15:29 — The Pope Takes on AI 01:18:39 — AI Product and Funding Updates This week's episode is brought to you by MAICON, our 6th annual Marketing AI Conference, happening in Cleveland, Oct. 14-16. The code POD100 saves $100 on all pass types. For more information on MAICON and to register for this year's conference, visit www.MAICON.ai. This episode is also brought to you by our upcoming AI Literacy webinars. As part of the AI Literacy Project, we're offering free resources and learning experiences to help you stay ahead. We've got two live sessions coming up in June—check them out here. Visit our website Receive our weekly newsletter Join our community: Slack LinkedIn Twitter Instagram Facebook Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

OpenAI and Microsoft are reportedly caught up in protracted behind-the-scenes negotiations that are in danger of boiling over into public conflict. Prosecutors say the man accused of assassinating a Minnesota Democratic lawmaker used online data brokers to help target his victims. And, the Trump Organization unveiled a new venture this week: a mobile service and a smartphone. Marketplace's Nova Safo is joined by Natasha Mascarenhas at The Information, who explains why.

OpenAI and Microsoft are reportedly caught up in protracted behind-the-scenes negotiations that are in danger of boiling over into public conflict. Prosecutors say the man accused of assassinating a Minnesota Democratic lawmaker used online data brokers to help target his victims. And, the Trump Organization unveiled a new venture this week: a mobile service and a smartphone. Marketplace's Nova Safo is joined by Natasha Mascarenhas at The Information, who explains why.

Send us a text00:00 - Intro00:54 - OpenAI Strikes Defense Deal, Hits $314B in Secondaries01:54 - OpenAI-Microsoft Standoff Over Equity, Revenue, IP, and AGI03:07 - xAI Burns Billions, Seeks $9.3B More Amid $121B Valuation04:13 - Anysphere Doubles Valuation Weeks After Raise05:30 - GENIUS Act Sets Stage for Stablecoin Innovation06:39 - X Builds Financial Tools, Eyes Full-Stack Money Play07:58 - Scale AI Pivots After Losing OpenAI, Potentially Google09:07 - Revolut Builds AI Financial Assistant09:58 - Meta's Safe Superintelligence Acquisition Bid Rejected, Hires CEO

В новом выпуске Завтракаста, одного из самых популярных подкастов про технологии, игры, тв, сериалы, медиа и интернет, а также про всякое разное на русском языке, мы обсуждаем игру Claire Obscure: Expedition 33, сериал “Киностудия”, ПК-версию Stellar Blade, а также всё что только душе угодно. 

Технології швидко змінюються, але не завжди на краще. У цьому епізоді наші ведучі аналізують ключові проблеми та тренди:— Чи зможе хтось похитнути монополію Google?— Застій інновацій в Apple— Як AI змінює пошукові системи та програмування?Розбираємо мінуси та переваги хмарних рішень, розвиток NAS-систем, падіння Skype, а також вплив регіонального ціноутворення на доступ до цифрових продуктів. 00:31 — еволюція технологій та монополій04:38 — майбутнє смартфонів07:00 — технології батарей та користувацький досвід12:40 — регулювання та права споживачів15:39 — монетизація в іграх та покупки в застосунках23:30 — NVIDIA та безпека паролів25:25 — OpenAI і Microsoft: складні відносини28:25 — хмарне зберігання: переваги та недоліки36:36 — регіональне ціноутворення та глобальні диспропорції40:17 — інструменти ШІ в розробці 

OpenAI versucht Microsoft von einer offenen Beziehung zu überzeugen. China und die USA schließen einen fragilen Zollfrieden. Unterdessen hat SAP seine Programme für Geschlechtervielfalt aufgrund politischer Einflüsse aus den USA gestrichen. Wie schätzt Pip Googles Zukunft ein? Klarna rudert beim Thema AI zurück, während Amazons Prime Video die US-Streaming-Landschaft mit seinem Werbeangebot verändert. IONOS startet mit starkem Wachstum ins Jahr. Katar schenkt der Trump-Regierung ein Flugzeug und der Einfluss russischer Agenten auf Elon Musk wirft Fragen auf.  Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Zollpause (00:04:00) SAP DEI (00:11:15) OpenAI Microsoft (00:16:15) Google (00:34:00) Klarna (00:40:00) Amazon Prime Werbung (00:43:50) Ionos Earnings (00:46:15) Katar Flugzeug  (00:51:25) ZDF Rohstoffe Elon (00:55:40) Humain (01:01:00) Papst Leo  Shownotes China und USA senken Zölle für 90 Tage – washingtonpost.com SAP: Programme für Geschlechtervielfalt gestrichen – zeit.de OpenAI Microsoft Verhandlungen – ft.com Google entwickelt KI-Agenten-Software vor Jahreskonferenz – reuters.com Google CTR-Studie: KI-Überblicke steigen, Klickrate sinkt – searchenginejournal.com Klarna verlangsamt KI-gesteuerte Stellenstreichungen – bloomberg.com Prime Video Werbe-Tarif erreicht 130 Mio. Menschen in den USA – hollywoodreporter.com Katar in Gesprächen mit Trump-Administration über Flugzeuggeschenk – washingtonpost.com Ex-FBI-Mann: Musk war Ziel russischer Agenten – zdf.de Saudi-Arabien startet KI-Unternehmen Humain vor Donald-Trump-Besuch – ft.com Dutzende weiße Südafrikaner landen in den USA unter Trump-Flüchtlingsplan – bbc.com Behauptungen von weißem Genozid 'nicht real', entscheidet südafrikanisches Gericht – bbc.com Trump entlässt Direktor des U.S. Copyright Office – cbsnews.com Papst Leo: Künstliche Intelligenz als Herausforderung für die Menschheit – edition.cnn.com

Microsoft and OpenAI's complex relationship is heating up, shaping the future of AI in unexpected ways. As tensions grow, Microsoft pushes forward independently with new models called MAI, which directly compete with OpenAI's reasoning models. Meanwhile, OpenAI diversifies its partnerships, signing a massive cloud deal with CoreWeave and teaming up with Oracle and SoftBank for Project Stargate. Brought to you by:KPMG – Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.kpmg.us/ai⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about how KPMG can help you drive value with our AI solutions.Vanta - Simplify compliance - ⁠⁠⁠⁠⁠⁠⁠https://vanta.com/nlwThe 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/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdown

Send Everyday AI and Jordan a text messageNo joke.... this has been the busiest week in GenAI news. Ever.  Amazon -- releases frontier models. Meta -- brings us a new Llama. OpenAI -- new models and features Google -- shipping AI literally everywhere  What happened? Why is all of this happening now? We'll dive in, and make you the smartest person in AI at your company. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan questions on AIUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. Amazon AI Developments2. Eleven Labs Voice Agents3. Microsoft AI Developments4. Google AI Model Updates5. xAI Updates6. OpenAI's Latest Releases and Plans7. Meta's Llama 3.3 Model6. OpenAI-Microsoft RelationshipTimestamps:03:00 OpenAI o1 Pro: Elevated AI, exclusive, costly.08:14 Copilot Vision provides insights, prioritizes privacy, feedback-driven.11:58 Gemini surpasses OpenAI GPT-4 in leaderboard.13:58 Google DeepMind outperforms ENS weather, AI advancements19:47 Amazon's model surpasses OpenAI's context window.22:27 Amazon quickly reaches top-tier model status.24:33 11 Labs platform: multilingual AI for customer interaction.29:12 Musk criticizes OpenAI, joins government, impacts technology.33:24 OpenAI discusses removing AGI access clause with Microsoft.34:51 OpenAI's redefined AGI criticized by Elon Musk.40:47 A sneaky release of a semi-open model.44:51 Advanced voice mode updates, some features rumored.46:47 OpenAI announces operator preview, waitlist expected.Keywords:Jordan Wilson, everydayai.com, Amazon Frontier model, ChatGPT, AWS, Nova Canvas, Nova Reel, Anthropic, Eleven Labs, David Sachs, Microsoft Gemini Live, Microsoft Copilot Vision, Google Gemini 1206, Google Gencast, Google Veo, Google Genie 2, Sundar Pichai, XAI, Elon Musk, OpenAI o1 pro model, ChatGPT Pro, AGI definition, Meta Llama 3.3 model, OpenAI-Microsoft relationship, OpenAI public benefit corporation, OpenAI restructuring, AI regulations, Department of Government Efficiency, Large language models, AI development Get more out of ChatGPT by learning our PPP method in this live, interactive and free training! Sign up now: https://youreverydayai.com/ppp-registration/

Our episode dives into the latest developments in the tech world's most watched legal battle, filed Friday in the U.S. District Court for the Northern District of California. At its heart is Elon Musk's preliminary injunction against OpenAI, its leadership, and Microsoft, revealing a stark contrast between the company's announced $1 billion in funding and the actual $130 million received, with Musk's personal $44 million contribution now at the center of controversy. The story unfolds through remarkable email exchanges, including Sam Altman's 2015 message expressing concerns about AI development and suggesting an alternative to Google's dominance. We explore Musk's visceral reaction to the Microsoft partnership, captured in his words: "This actually made me feel nauseous. It sucks and is exactly what I would expect from them." The tension escalates with the founding team's confrontation of Musk about control issues, documented in their statement: "You stated that you don't want to control the final AGI, but during this negotiation, you've shown to us that absolute control is extremely important to you." The cast of characters in this unfolding drama includes Elon Musk as the plaintiff, Sam Altman as OpenAI's CEO, Greg Brockman serving as president, Reid Hoffman's role as former board member, Dee Templeton's position as Microsoft VP and former board observer, and Shivon Zilis's perspective as a former OpenAI advisor. Their interactions span from OpenAI's nonprofit founding in 2015 through the Microsoft partnership proposal in 2016, internal conflicts in 2017, Musk's departure in 2018, and the introduction of the "capped-profit" structure in 2019, leading to the current legal action in 2024. The financial landscape reveals Microsoft's substantial $13 billion investment for a 49% stake, while OpenAI's annual spending exceeds $5 billion, recently supplemented by a $6.6 billion fundraising round. The legal action seeks to prevent OpenAI from discouraging investors from backing competitors, halt asset transfers to for-profit entities, and stop the sharing of proprietary information with Microsoft. Our analysis draws from U.S. District Court filings, original email correspondence, OpenAI's corporate documents, and Microsoft partnership agreements. This episode sets up our next discussion, where we'll examine the technical implications of the OpenAI-Microsoft partnership and its global impact on AI development. These materials provide crucial context for understanding how corporate governance shapes the future of AI development and industry competition.

This week on the GeekWire Podcast, we discuss Elon Musk's lawsuit against OpenAI, which now includes Microsoft, and assess the complexities of the OpenAI-Microsoft partnership, as illustrated by early email exchanges revealed in the lawsuit. We also consider the latest update to the GeekWire 200, our ranked index of Pacific Northwest technology startups, including the rise of Highspot to the top spot, and other trends in the Seattle region's startup ecosystem.  And we share highlights from tech events around the region this week, including the WTIA's 40th Anniversary, where Mayor Bruce Harrell addressed AI and the incoming presidential administration; and an interesting takeaway from a panel of startup leaders whose companies made the latest Deloitte Technology Fast 500 list.  Related links and coverage Internal emails: Elon Musk wanted to keep OpenAI from becoming ‘Microsoft's marketing bitch' GeekWire 200 update: A new No. 1 rises to the top of our startup rankings WTIA honors 40 years of boosting Washington's tech sector as new CEO aims for more impact Seattle mayor, who sits on a federal AI panel, says he'll seek ways to work with Trump administration With GeekWire co-founders John Cook and Todd BishopSee omnystudio.com/listener for privacy information.

Elon Musk Expands Legal Battle Against OpenAI and Microsoft Episode Title: Elon Musk vs. OpenAI & Microsoft: Antitrust Battle and AI Power Struggles Unveiled Episode Description: What started as a complaint over OpenAI's transformation from a nonprofit to a profit-driven powerhouse has escalated into a major antitrust legal battle. Musk is now alleging that Microsoft and OpenAI conspired to monopolize the generative AI market, sidelining competitors and potentially breaching federal antitrust laws. We dive into the history of OpenAI, the internal power struggles, and what this lawsuit could mean for the future of artificial intelligence. Key Topics Discussed: The Lawsuit's Expansion: We explore how Musk's original August complaint has evolved, now including new claims against Microsoft for allegedly colluding with OpenAI to dominate the AI market. We break down the legal arguments and what Musk is seeking from the court. OpenAI's Controversial Transformation: Originally founded as a nonprofit, OpenAI shifted gears in 2019, attracting billions in investment from Microsoft. We discuss how this change in business model became a point of contention for Musk and set the stage for the current legal conflict. Behind-the-Scenes Drama: Newly revealed emails between Musk, Sam Altman, Ilya Sutskever, and other OpenAI co-founders offer a rare glimpse into the early days of OpenAI. We dive into the disagreements over leadership, Musk's quest for control, and the internal debates about the company's mission. Microsoft's Role and Investment: Microsoft's billion-dollar partnership with OpenAI is at the heart of Musk's complaint. We examine the timeline of this collaboration, the exclusive licensing agreements, and why Musk views this as an anticompetitive move. Musk's Fear of an 'AGI Dictatorship': Emails from as early as 2016 show Musk's concerns about Google's DeepMind and its potential to dominate the AI space. We discuss Musk's fears of a single company controlling AGI (Artificial General Intelligence) and how these concerns influenced the founding of OpenAI. Intel's Missed Opportunity: We touch on Intel's decision to pass on a $1 billion investment in OpenAI back in 2017, a move that now appears shortsighted given OpenAI's current valuation and market influence. The Legal Stakes and Future Implications: What could this lawsuit mean for the future of AI development and industry partnerships? We break down the potential consequences for OpenAI, Microsoft, and the broader tech landscape. Featured Quotes: Marc Toberoff (Musk's attorney): “Microsoft's anticompetitive practices have escalated. Sunlight is the best disinfectant.” Elon Musk (internal email): “DeepMind is causing me extreme mental stress. If they win, it will be really bad news with their one mind to rule the world philosophy.” Why It Matters: This case isn't just about corporate rivalry; it's about the future control of artificial intelligence and the ethical concerns surrounding its development. As the AI race intensifies, Musk's lawsuit raises questions about monopolistic practices, transparency, and the potential consequences of unchecked power in the tech industry. Tune In To Learn: Why Musk believes Microsoft and OpenAI's partnership is illegal and anticompetitive. How internal power struggles shaped the trajectory of OpenAI and influenced Musk's departure. What the disclosed emails reveal about the early vision for OpenAI and the concerns about AGI dominance. Resources Mentioned: Musk's original lawsuit filing (August 2023) OpenAI's response to the amended complaint Email exchanges between OpenAI co-founders (2015-2018)

OpenAI's latest valuation and the value of the ChatGPT brand, the AGI clause in the OpenAI-Microsoft partnership, a follow-up on Waymo's data and the Bitter Lesson, a twist in the AI device form factor conversation, and a question about Orion and the importance of elite talent in big tech.

The latest episode of The Eric Ries Show features my conversation with Reid Hoffman. Executive Vice President of PayPal, co-founder of LinkedIn, and legendary investor at Greylock Partners are just a few of his official roles that have changed our world. He's also been a mentor to countless founders of iconic companies like Airbnb, Facebook, and OpenAI. He's an author, a podcast host – both Masters of Scale and his new show, Possible, with Aria Finger – and perhaps most importantly a crucial steward of AI, including co-founding Inflection AI, a Public Benefit Corporation, in 2022. Reid has also long been a voice of moral clarity and a stabilizing influence on the tech ecosystem, supporting people who are working to make the world a better place at every level. He's a firm believer that “the way that we express ourselves over time is by being citizens of the polis – tribal members.” That includes not just supporting the legal system and democratic process but also building organizations “from the founding and through scaling and ongoing iteration to have a functional and healthy society.” We talked about all of this, as well as AI, from multiple angles – including the story of how he came to broker the first meeting between Sam Altman and Satya Nadella that led to the OpenAI-Microsoft partnership. He also had a lot to say about how AI will work as a meta-tool for all the other tools we use. We are, as he said,” homo techne,” – meaning we evolve through the technology we make. We also broke down his famous saying that “entrepreneurship is like jumping off a cliff and assembling the plane on the way down” and: • The human tendency to form groups • The relationship between doing good for people and profits • AI as a meta-tool • What he looks for in a leader • The necessity of evolving culture • Being willing to take public positions • His thoughts on the economy and the upcoming election — Brought to you by: Mercury – The art of simplified finances. ⁠⁠⁠⁠Learn more⁠⁠⁠⁠. DigitalOcean – The cloud loved by developers and founders alike. ⁠⁠⁠⁠Sign up⁠⁠⁠⁠. Neo4j – The graph database and analytics leader. ⁠⁠⁠⁠Learn more⁠⁠⁠⁠. — Where to find Reid Hoffman: • Reid's Website: https://www.reidhoffman.org/ • LinkedIn: https://www.linkedin.com/in/reidhoffman/ • Instagram: https://www.instagram.com/reidhoffman/ • X: https://x.com/reidhoffman Where to find Eric: • Newsletter: ⁠⁠⁠⁠https://ericries.carrd.co/⁠⁠⁠⁠  • Podcast: ⁠⁠⁠⁠https://ericriesshow.com/⁠⁠⁠⁠  • X: ⁠⁠⁠⁠https://twitter.com/ericries⁠⁠⁠⁠  • LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/eries/⁠⁠⁠⁠  • YouTube: ⁠⁠⁠⁠https://www.youtube.com/@theericriesshow⁠⁠⁠⁠  — In This Episode We Cover: (01:15) Meet Reid Hoffman (06:01) The three eras of LinkedIn (08:21) The alignment of LinkedIn and Microsoft's missions (10:39) The power of being mission-driven (18:42) Embedding culture in every function (21:08) The purpose of organizations (23:45) Organizations as tribes for human expression (29:08) Reid's advice for navigating profit vs. purpose (38:33) The moment Reid realized the AI future is actually now (41:57) Home techne (44:52) AI as meta-tool (47:05) Why Reid co-founded Inflection AI (49:53) The early days of OpenAI (55:41) How Reid introduced Sam Altman and Satya Nadella (58:26) The unusual structure of the Microsoft-OpenAI deal (1:04:42) The importance of aligning governance structure with mission (1:09:56) Making a company trustworthy through accountability  (1:15:59) Inflection's pivot a unique model (1:19:53) Companies that are doing lean AI right (1:22:52) Reid's advice for deploying AI effectively  (1:26:21) Being a voice of moral clarity in complicated times  (1:31:26) The economy and what's at stake in the 2024 election (1:37:24) The qualities Reid looks for in a leader (1:39:43) Lightning round, including board games, the PayPal mafia, regulation, and more — Production and marketing by ⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠. Eric may be an investor in the companies discussed.

ICYMI: Hour Two of ‘Later, with Mo'Kelly' Presents – A look at the Orange County Register's suit against OpenAI, and Microsoft; claiming “ChatGPT and Copilot are illegally harvesting copyrighted articles to create their cutting-edge artificial intelligence products” …PLUS – Trouble is brewing at Tesla with a Federal investigation linking Tesla's autopilot to hundreds of collisions, and Elon Musk suddenly disbanding the Tesla charging team AND customers will soon be able to wager on arcade games at Dave & Buster's - on KFI AM 640…Live everywhere on the iHeartRadio app

On this episode, Paul shares his thoughts on the Snapdragon X Elite chip with Leo and Richard. Windows 11 24H2, AI, NPUs, and SoCs from Intel, AMD, and Qualcomm are all on the way this year. But a schedule is finally starting to emerge. And it looks like we'll soon have answers to the questions about how or why AI will matter on PCs. Windows, AI, and the future Windows 11 version 24H2 - staggered release schedule as discussed last week Qualcomm Snapdragon X Elite-based PCs in May/June - nothing but good news to date, but Paul went hands-on last week. It's the real deal. Intel's first-gen Core Ultra chipsets are lackluster, but now we have big promises for Arrow Lake in late 2024 Microsoft Build 2024 is in mid-May, and now we have a session list with some nice clues. For example, Introducing the Next Generation of Windows on Arm Microsoft is expected to unveil 24H2 and new X Elite-based Surface PCs at Build Computex and other milestones, and then back-to-school and holiday selling periods Windows 11 Moment 5 arrives in stable with yesterday's Patch Tuesday (which is now called the General Availability channel, by the way). Of course, we still don't have all the features. In particular, waiting on Android phone as a webcam. IDC says PC market grew by 1.5 percent in Q1 and acts like it's the turnaround of the century Microsoft is manually blocking certain registry keys related to default browsers now: Apple-like non-EU belligerence or pragmatic protection of user choice? Why can't it be both? Beta channel (last week) - Copilot actions improvements New Store app update improvements performance dramatically The Windows 11 de-ensh*ttification experiments continue Does Windows 11 Enterprise solve the problem? No. So it's time to move on Hardware TSMC gets some of that sweet, sweet CHIPS Act money to expand its US operations AI Three AIs comparison Blockbuster report claims OpenAI/Microsoft, Google, and Meta stole content at scale to train AI Microsoft opens a new AI hub in London Google mulls charging for generative AI in Search Spotify lets user create AI playlists using text prompts now Brave brings Leo to iOS, so it's on all supported platforms now. And it added Leo to Brave Talk Premium too Google rebrands Studio Bot to Gemini in Android Studio, still in preview. This is their GitHub Copilot Xbox Microsoft rolls out April updates for Xbox consoles, Xbox app on PC Xbox reorgs, Kareem Choudhry leaves Microsoft A rumored game preservation team is too obvious not to be true Tips and Picks Tip of the week: Microsoft Store hosts its annual Spring Sale App picks of the week: Standard Notes & Beeper RunAs Radio this week: Securing AI with Sarah Young Brown liquor pick of the week: Dalwhinnie 15 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to this show at https://twit.tv/shows/windows-weekly Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Sponsor: cachefly.com/twit

On this episode, Paul shares his thoughts on the Snapdragon X Elite chip with Leo and Richard. Windows 11 24H2, AI, NPUs, and SoCs from Intel, AMD, and Qualcomm are all on the way this year. But a schedule is finally starting to emerge. And it looks like we'll soon have answers to the questions about how or why AI will matter on PCs. Windows, AI, and the future Windows 11 version 24H2 - staggered release schedule as discussed last week Qualcomm Snapdragon X Elite-based PCs in May/June - nothing but good news to date, but Paul went hands-on last week. It's the real deal. Intel's first-gen Core Ultra chipsets are lackluster, but now we have big promises for Arrow Lake in late 2024 Microsoft Build 2024 is in mid-May, and now we have a session list with some nice clues. For example, Introducing the Next Generation of Windows on Arm Microsoft is expected to unveil 24H2 and new X Elite-based Surface PCs at Build Computex and other milestones, and then back-to-school and holiday selling periods Windows 11 Moment 5 arrives in stable with yesterday's Patch Tuesday (which is now called the General Availability channel, by the way). Of course, we still don't have all the features. In particular, waiting on Android phone as a webcam. IDC says PC market grew by 1.5 percent in Q1 and acts like it's the turnaround of the century Microsoft is manually blocking certain registry keys related to default browsers now: Apple-like non-EU belligerence or pragmatic protection of user choice? Why can't it be both? Beta channel (last week) - Copilot actions improvements New Store app update improvements performance dramatically The Windows 11 de-ensh*ttification experiments continue Does Windows 11 Enterprise solve the problem? No. So it's time to move on Hardware TSMC gets some of that sweet, sweet CHIPS Act money to expand its US operations AI Three AIs comparison Blockbuster report claims OpenAI/Microsoft, Google, and Meta stole content at scale to train AI Microsoft opens a new AI hub in London Google mulls charging for generative AI in Search Spotify lets user create AI playlists using text prompts now Brave brings Leo to iOS, so it's on all supported platforms now. And it added Leo to Brave Talk Premium too Google rebrands Studio Bot to Gemini in Android Studio, still in preview. This is their GitHub Copilot Xbox Microsoft rolls out April updates for Xbox consoles, Xbox app on PC Xbox reorgs, Kareem Choudhry leaves Microsoft A rumored game preservation team is too obvious not to be true Tips and Picks Tip of the week: Microsoft Store hosts its annual Spring Sale App picks of the week: Standard Notes & Beeper RunAs Radio this week: Securing AI with Sarah Young Brown liquor pick of the week: Dalwhinnie 15 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to this show at https://twit.tv/shows/windows-weekly Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Sponsor: cachefly.com/twit

On this episode, Paul shares his thoughts on the Snapdragon X Elite chip with Leo and Richard. Windows 11 24H2, AI, NPUs, and SoCs from Intel, AMD, and Qualcomm are all on the way this year. But a schedule is finally starting to emerge. And it looks like we'll soon have answers to the questions about how or why AI will matter on PCs. Windows, AI, and the future Windows 11 version 24H2 - staggered release schedule as discussed last week Qualcomm Snapdragon X Elite-based PCs in May/June - nothing but good news to date, but Paul went hands-on last week. It's the real deal. Intel's first-gen Core Ultra chipsets are lackluster, but now we have big promises for Arrow Lake in late 2024 Microsoft Build 2024 is in mid-May, and now we have a session list with some nice clues. For example, Introducing the Next Generation of Windows on Arm Microsoft is expected to unveil 24H2 and new X Elite-based Surface PCs at Build Computex and other milestones, and then back-to-school and holiday selling periods Windows 11 Moment 5 arrives in stable with yesterday's Patch Tuesday (which is now called the General Availability channel, by the way). Of course, we still don't have all the features. In particular, waiting on Android phone as a webcam. IDC says PC market grew by 1.5 percent in Q1 and acts like it's the turnaround of the century Microsoft is manually blocking certain registry keys related to default browsers now: Apple-like non-EU belligerence or pragmatic protection of user choice? Why can't it be both? Beta channel (last week) - Copilot actions improvements New Store app update improvements performance dramatically The Windows 11 de-ensh*ttification experiments continue Does Windows 11 Enterprise solve the problem? No. So it's time to move on Hardware TSMC gets some of that sweet, sweet CHIPS Act money to expand its US operations AI Three AIs comparison Blockbuster report claims OpenAI/Microsoft, Google, and Meta stole content at scale to train AI Microsoft opens a new AI hub in London Google mulls charging for generative AI in Search Spotify lets user create AI playlists using text prompts now Brave brings Leo to iOS, so it's on all supported platforms now. And it added Leo to Brave Talk Premium too Google rebrands Studio Bot to Gemini in Android Studio, still in preview. This is their GitHub Copilot Xbox Microsoft rolls out April updates for Xbox consoles, Xbox app on PC Xbox reorgs, Kareem Choudhry leaves Microsoft A rumored game preservation team is too obvious not to be true Tips and Picks Tip of the week: Microsoft Store hosts its annual Spring Sale App picks of the week: Standard Notes & Beeper RunAs Radio this week: Securing AI with Sarah Young Brown liquor pick of the week: Dalwhinnie 15 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to this show at https://twit.tv/shows/windows-weekly Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Sponsor: cachefly.com/twit

REVIEW US WE NEED VALIDATION This week… OpenAI & Microsoft are making a 100 billion dollar AI center called Stargate, Apple is showing off some very cool AI agent tech & surprise… Grok 1.5 is actually worse than Grok 1.0. Plus, jailbreaking ChatGPT with DAN to make a better boyfriend, Joe Biden wants all government agencies to have AI offices, a new interactive & emotional AI demo from Hume.ai and, oh yeah, OpenAI has an incredible voice cloning service called Voice Engine that you can't use. Stupid human. AND THEN… and interview with Walter Woodman who, along with his other ShyKids, created the incredible viral Sora short “Air Head”. We discuss using Sora to make something that moved us, the specifics of how Sora works and what its impact will be on creative work.  And our AI co-host Chase has joined us from YumFoods (not really) discuss how they'll be using AI within all their brands and invents some incredible AI new foods at the end. YUM. It's an endless cavalcade of ridiculous and informative AI news, AI tools, and AI entertainment cooked up just for you. Follow us for more AI discussions, AI news updates, and AI tool reviews on X @AIForHumansShow Join our vibrant community on TikTok @aiforhumansshow For more info, visit our website at https://www.aiforhumans.show/   /// Show links /// OpenAI & Microsoft Plan STARGATE 100b Supercomputer Center https://www.reuters.com/technology/microsoft-openai-planning-100-billion-data-center-project-information-reports-2024-03-29/?utm_source=substack&utm_medium=email OpenAI Makes ChatGPT Free Without Login ​​https://openai.com/blog/start-using-chatgpt-instantly Voice Engine Is Pretty Good But Not Being Released https://www.nytimes.com/2024/03/29/technology/openai-voice-engine.html Biden Orders Every US Agency To Appoint An AI Officer https://arstechnica.com/tech-policy/2024/03/why-every-federal-agency-must-now-appoint-a-chief-ai-officer/?utm_source=bensbites&utm_medium=newsletter&utm_campaign=daily-digest-elon-s-progress Apple's ReALM https://www.macrumors.com/2024/04/02/apple-reveals-new-ai-system/ TacoBell and other Yum Foods Brands Going “AI First” https://www.foxbusiness.com/lifestyle/taco-bell-pizza-hut-going-ai-first-fast-food-innovations Obscurist Vinyl https://www.tiktok.com/@obscurestvinyl?_t=8l5bGvbEex1&_r=1 Girl That's “Falling In Love with Dan Version of ChatGPT” https://twitter.com/julesterpak/status/1774305346690957533 Emo Image to Video Pipeline https://x.com/visiblemakers/status/1773500889103270043?s=20 Tool Underarmor Commercial https://toolofna.com/featured/forever-is-made-now/ Grok 1.5 is so bad https://x.com/AIForHumansShow/status/1774892851350106224?s=20 HumeAI Demo demo.hume.ai ShyKids on Instagram https://www.instagram.com/shykids_/ Air Head  https://x.com/shykids/status/1772347121296883981?s=20  

Dec. 27 Edition. The New York Times is suing OpenAI and Microsoft, alleging copyright infringement through their generative artificial-intelligence tools ChatGPT and Copilot. Reporter Alexandra Bruell describes how the suit could split the publishing world. And with global inflation easing much faster than expected, several central banks including the Federal Reserve are penciling in rate cuts for 2024. Economics reporter Gwynn Guilford has more on the global outlook for inflation. Plus, Moscow bureau chief Ann M. Simmons explains why snitching is on the rise in Russia. Annmarie Fertoli hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices