Podcasts about aie

(The full Dish returns on September 4. In the meantime, we are re-airing a handful of the most downloaded episodes from the early years of the Dishcast.)Charles is a writer, social scientist, and longtime friend. He currently holds the F.A. Hayek Chair Emeritus in Cultural Studies at AIE, and his many books include Losing Ground, The Bell Curve (co-authored with Richard Herrnstein), Coming Apart, Facing Reality, and Taking Religion Seriously (which we covered last year). In this May 2021 pod, he and I discussed Human Diversity — a book that received little attention compared to the others. If you think you know who Charles is from the way the MSM has portrayed him for years, this conversation may surprise you.Like Cornel West, Charles is a one-off: absurdly brave (or dumb) in wading into truly touchy subjects, and always with reason. In many ways, I think if we'd been able to have a serious debate about The Bell Curve all those years ago — and I tried to encourage one — we might have avoided some awful subsequent mistakes. But we resorted to abuse and hysteria and went on to enact Woke 1.0 and, now, Woke 2.0.Go here for a polished transcript of Charles. We also have three clips on YouTube: on the different career choices of high IQ women; the “unearned gift” of a high IQ; and IQ's irrelevance to the human worth, dignity, and essential equality of all people. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit andrewsullivan.substack.com/subscribe

Jaume Segalés y su equipo analizan los temas de actualidad y comentan la agenda cultural. Hoy en Km0, tras repasar la actualidad informativa y deportiva, profundizamos en los siguientes asuntos:AI En Ruta Artistas Promover la música en directo y favorecer la circulación de nuevos talentos por todo el territorio nacional. Estos son los objetivos de la iniciativa AI En Ruta Artistas. Un programa que, impulsado por la Sociedad de Artistas Intérpretes o Ejecutantes (AIE) desde 1995, se ha consolidado como uno de los principales circuitos de música en vivo de España. Ofrece a músicos, emergentes o independientes, la oportunidad de presentar su trabajo ante nuevos públicos y de establecer vínculos con programadores, salas y festivales de diferentes comunidades autónomas aportando difusión y soporte económico para las giras. Desde su inicio, AIE ha programado más de 5.000 conciertos de 1.200 músicos y agrupaciones de distintas procedencias y estilos, con una asistencia de unos 300.000 espectadores en las más de 150 salas que han acogido este programa en 60 ciudades españolas. Entre los artistas que han participado en el circuito en estos 32 años figuran: Albert Plá, Miguel Poveda, Diego el Cigala, Estopa, Love of Lesbian, El Columpio Asesino, Sex Museum, Los Coronas, Depedro, Vetusta Morla, Coque Malla, Annie B Sweet, Russian Red, Toundra, Bala y Carlangas, entre muchísimos otros. Últimos días para inscribirse en el Primer Ciclo 2027 de la 32º edición del Circuito AI En Ruta Artistas. Plazo abierto hasta el 28 de agosto. Entrevistamos a responsable de comunicación de estos ciclos, Carlos Igual.Cómo afectará el eclipse a las mascotas El eclipse solar que va a tener lugar este miércoles 12 de agosto también puede cambiar el comportamiento de perros y gatos. La oscuridad repentina y la bajada de la temperatura pueden hacerles creer que ha llegado la noche. Algunos buscarán descansar. Los cambios de luz y temperatura pueden causarles una ligera desorientación o desconcierto momentáneo, sin representar un peligro biológico directo Los veterinarios recomiendan mantener la rutina habitual. También aconsejan evitar aglomeraciones y situaciones que puedan generar estrés. El eclipse no supone un peligro para las mascotas. Sin embargo, los cambios en el entorno sí pueden provocar inquietud en los animales más sensibles. Entrevistamos a la veterinaria de Anicura, Elena Diéguez Ordóñez.

Watch the full episode on YouTube:We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3:And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF:Three years ago, inference engineering barely existed as a category.Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem.In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out.Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles.In this episode, Baseten's Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API.We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model.The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them.We discuss:* What happens when a 200,000-token request enters an inference system* Cache-aware routing and reusing previously computed KV cache* Why prefill and decode are increasingly handled by different GPUs* When dedicated deployments become cheaper and more reliable than shared APIs* How speculative decoding uses a smaller model to accelerate a larger one* Tool calling, structured outputs, and what LLMs actually do* What it takes to support a new open model on day zero* Grafting Kimi's vision encoder onto GLM-5.2* Retrofitting inefficient model layers with components from other architectures* Why models sometimes collapse into repeating the same token* How hardware, kernels, and race conditions create nondeterministic failures* Preserving model fidelity while making inference faster* How quantization errors can cancel each other out* Why inference optimizations still deliver gains of 20%, 100%, and 200%* How optimized serving can make a model up to 10× faster* NVIDIA Dynamo, KV-aware routing, and distributed model serving* Speculative decoding the speculative decoder* Why local AI is about making models less dumb while data-center AI is about making them less slow* Tensor, expert, and pipeline parallelism across GPUs* Hardware-aware model design, auto-tuning, and the case against mega kernels* Rubin and why inference is becoming a systems problem* Whether modern GPUs are evolving into programmable AI ASICs* Why enormous models like Kimi K3 require GB300-class hardware* Why open-source video generation still trails Veo, Kling, and other closed models* The quadratic attention bottleneck behind long-form AI video* Autoregressive video, real-time generation, and compounding quality drift* Why future video systems may combine autoregressive and diffusion architectures* Training for inference and inference for training* Continuous post-training, deployment, evaluation, and improvement loops* How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself* Why faster networking could unlock dramatically faster decoding* Continual learning, KV-cache compaction, and persistent model memoryShow Notes* How to build a day-0 API for Kimi K3* 22580: From GPT2 to Kimi3, ExplainedPhilip Kiely* LinkedIn: https://www.linkedin.com/in/philipkiely* X: https://x.com/philipkiely* Inference Engineering: https://www.baseten.co/inference-engineering/Ali Taha* LinkedIn: https://www.linkedin.com/in/aliestaha/* X: https://x.com/waterloointernTimestamps00:00:00 Introduction and the 200K-Token Prompt00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling00:11:26 Launching Production-Ready Open Models00:19:06 Model Retrofits, Failure Modes, and Nondeterminism00:28:22 Quantization and Canceling Errors00:32:15 The Race to 10× Faster Inference00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips01:10:03 Giant Models and the Limits of GPU Memory01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation01:21:47 Audio, Images, and Diffusion Models01:27:32 Training, Self-Optimizing Models, and Continual Learning01:40:06 Closing ThoughtsTranscriptIntroduction: Baseten, Waterloo Intern, and Inference EngineeringSwyx [00:00:00]: Okay, we're here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you've done, you and I have done before, as well as Ali. Welcome.Ali [00:00:15]: Pleasure to meet you.Swyx [00:00:15]: Waterloo intern.Ali [00:00:16]: Waterloo intern, always.Swyx [00:00:17]: When did you get “Waterloo intern” as a handle?Ali [00:00:19]: As a handle? Oh.Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.”Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer.Philip [00:00:30]: So we have to figure out who's gonna get the handle.Ali [00:00:33]: Well, I'll pass the torch over to the next intern.Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad.Ali [00:00:37]: To another Waterloo intern. No, bruh.Philip [00:00:39]: Yeah.Ali [00:00:39]: Intern.Swyx [00:00:40]: Intern, yeah.Ali [00:00:40]: And no.Philip [00:00:41]: You gotta get an intern from Waterloo.Ali [00:00:42]: Yeah, I've gotta get an intern from Waterloo.Swyx [00:00:44]: Right.Ali [00:00:44]: But they have to follow the path.Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it's like whoever Baseten gets from Waterloo.Ali [00:00:48]: Right.Swyx [00:00:49]: Has the title of Waterloo.Ali [00:00:50]: It stays in the ecosystem.Philip [00:00:51]: Exactly.Ali [00:00:52]: Halfway through the internship, you either get it or you're out.Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle.Ali [00:00:59]: Just say it.Philip [00:00:59]: For everybody.Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you're an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten's inference? What's the process of query through GPU model routing, balancing, all that? What is all the stuff that we don't think about?Long Context Requests, KV Cache, and Cache-Aware RoutingPhilip [00:01:26]: With a long query specifically, the first thing that I'm gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it's gonna be a lot easier for me and a lot cheaper for you. So the first thing that we're gonna look at is some cache-aware routing, where we're going to see, we probably have a number of instances, a number of replicas up serving whatever model you're hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you're doing two hundred thousand tokens, it's probably coding or a multi-turn agent or something where you would expect to have that cached. If you don't, we're gonna have to send it to a prefill worker. We've at least on certain models disaggregated prefill and decode, so you're going to have one set of GPUs that's solely going to process the input, create the KV cache, and get you your first token, and then that's going to be passed over to a separate set of GPUs, which is going to run decode. We're going to iteratively make those tokens. We're probably going to have some speculator model in front of that. I'm going to assume that you're doing coding, and because of that, our speculator model, which assumes you're doing coding, is gonna have a high draft token acceptance rate. If I'm wrong and you're asking me to summarize every Harry Potter book, it's gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?”Swyx [00:03:04]: Except Baseten doesn't charge by pennies.Philip [00:03:07]: Well, yeah, we charge. I'm assuming that we're talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it's not pennies.Public APIs vs. Dedicated DeploymentsSwyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it's up to you to figure out how to saturate the box.Ali [00:03:31]: And more often than not, it's, like, way cheaper if you're pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token.Philip [00:03:37]: Yeah, they do. I think that we've increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that's really sticky, then they move over to dedicated.Swyx [00:03:51]: Is there a best practice on when it's time to swap over?Philip [00:03:54]: Couple reasons. Yeah, reliability, that's a big one, right?Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic.Swyx [00:04:04]: Spec dec is speculative decoding.Speculative Decoding and Custom SpeculatorsAli [00:04:05]: Speculative decoding, yeah.Swyx [00:04:07]: You have to explain.Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you're summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I'm gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn't be able to provide this to you if you're a shared endpointSwyx [00:04:53]: YeahAli [00:04:53]: ‘cause I have no idea if you're doing Harry Potter, if you're doing coding, if you're doing English. We don't know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that?Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you're trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn't pass your benchmarks and you wanna run a model at higher precision, you could do that. There's just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don't have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users.Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it's people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you're generating JSON or is there more complication beyond that?Tool Calling, JSON, and Structured OutputsAli [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that's not just, like parse a file or go find the weather. It's something that's very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn't require its own like sandbox. It's not like it's going to use that tool calling to like escape a sandbox or like it doesn't have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you're dealing with all of the JSON outputs, if it doesn't like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn't see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model.Philip [00:06:56]: Yeah, that's a challenge on the training side and then on the inference side, there's work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember backSwyx [00:07:27]: Yeah, the specific grammar is,Philip [00:07:29]: Yeah, exactlySwyx [00:07:30]: GML had this thing.Philip [00:07:31]: Yeah. So it's like the old-school “make sure this is only JSON”, return only JSON orSwyx [00:07:38]: YeahPhilip [00:07:38]: Grandma's gonna die type of prompts.Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR.Philip [00:07:47]: In our inference system, it's just a specified output format. And you get the guarantee that your output's gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn't solve the certainty problem but it at least solves the output structuring problemSwyx [00:08:10]: YeahPhilip [00:08:10]: Within tool calls.Swyx [00:08:12]: And MCP is just another form of tool, right.Philip [00:08:14]: Yeah, exactly.Swyx [00:08:15]: As far as there's no special thing there.Philip [00:08:16]: The thing I'm always like explaining to people is the LLM is not capable of doing anything. It's only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs.Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you're right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don't know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don't have the same exact quality outputAli [00:08:56]: Right.Swyx [00:08:57]: When you just swap from a big model, right?Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper.Ali [00:09:04]: But, I had expected that something would replace JSON because it's hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it's hard to parse something or validate something while it's being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it's something like TOML, something like YAML. But JSON seems to be dominant still.Philip [00:09:30]: The JSON outputs aren't that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it's a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn't be as valuable, but maybe I'm wrong about that.Ali [00:10:02]: I think you're also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you'- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn't be that much of a difference. Also more profitable if it outputs more tokens probably.Swyx [00:10:25]: Depends on your business model.Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there's paragraphs in every field because I'm trying to structure it, right?Philip [00:10:44]: Right.Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let's, let's recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there's a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let's call it GLM-5.2, Kimi K3. I had previously assumed, especially if it's like, well, GLM 5 to 5.1 to GLM-5.2, like that you've supported them before. Is it that much work?What It Takes to Support a New Open ModelAli [00:11:26]: It's a lot of work.Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I'm like, “Yeah, of course we support it.” But what goes into that? What goes intoPhilip [00:11:40]: I think it's more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we're at 150. The nextSwyx [00:11:55]: I kinda kicked that off with the GLM-5.2.Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views,Ali [00:12:02]: Based on being numberSwyx [00:12:03]: YeahAli [00:12:04]: Or it's for something else.Swyx [00:12:05]: Yeah. Which,Ali [00:12:06]: Oh my GodSwyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and,Philip [00:12:14]: There's a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model.Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there's going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar.Quantization, Speculators, and Production ReadinessAli [00:13:16]: Yeah. It was pure continued post-trainingPhilip [00:13:18]: YeahAli [00:13:18]: If I remember correctly.Philip [00:13:19]: Even in those cases, there's still stuff you have to do. You have to redo the quantization work. You're taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we're not causing any regression in the model's intelligence. And then we also have to train the speculator, as we've talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don't know exactly the traffic that people are sending us, but we know what's popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you're getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there's that process which you need the real model weights for. And then there's of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there's a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 hadAli [00:14:53]: Sparse attention.Philip [00:14:54]: Yeah,Ali [00:14:54]: YeahPhilip [00:14:54]: the DSA.Ali [00:14:55]: Right. Which is brought from DeepSeek.Philip [00:14:57]: Yeah. AndAli [00:14:59]: So you can copy-paste then?Philip [00:15:01]: It kindAli [00:15:01]: I don't know how this works.Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you're right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn't have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2.Retrofitting Vision into GLM-5.2Ali [00:15:27]: We'll be training the projector.Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there's the encoder, which is the part that looks at the image and turns it into latent information, and then there's the projector which likeAli [00:15:38]: You can say latent space. It's okay.Philip [00:15:41]: And then there's the projector that maps it onto, the model itself, and then there's the model weights. You don't wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters.Ali [00:16:02]: That would be, yeah.Philip [00:16:02]: Yeah.Ali [00:16:03]: Can you show the training one?Ali [00:16:04]: Like the way it groksPhilip [00:16:05]: YeahAli [00:16:06]: Very interesting.Philip [00:16:06]: And maybeAli [00:16:07]: That right therePhilip [00:16:07]: Maybe Ali, you should take it from here. You've got a betterAli [00:16:10]: Ooh, double the sandPhilip [00:16:11]: Understanding of this than I do.Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here's a picture of a mountain. Can you describe what's in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we're trying to teach it is to translate the encoded. Like it's already taken the encoder from Kimi K. It's taken the image. It'Philip [00:16:31]: Yeah. FrozenAli [00:16:31]: FrozenPhilip [00:16:32]: With adapter.Ali [00:16:32]: Exactly.Philip [00:16:33]: Yeah.Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It's just we're tryingPhilip [00:16:37]: AlignAli [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he's like, “Oh, can you describe what's in this image?” And he's like, “Oh, it's a mountain,” or it's a person or it's a human, whatever the case is. But that didn't cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn't perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn't get it, but it will say something like, “This is Albert Einstein.” Like it still understandsPhilip [00:17:25]: Close enoughAli [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that's like really cool.Philip [00:17:32]: Yeah. So, we've covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that's very foundational work for anyone who hasn't done vision work before.Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questionsPhilip [00:17:47]: RightAli [00:17:47]: Off the image and how much better you can get performance.Philip [00:17:50]: Right. Right. Right. Yeah. But what's, what's so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It's not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you're running this model, you haven't suffered any loss on your GLM-5.2 quality. If you don't have an image, it'll just behave exactly the way it used to. And ultimatelyAli [00:18:14]: Which in the inference code you literally do not include the other part, right?Philip [00:18:18]: Yeah. You would just skip the encoder if you don't have an image input.Ali [00:18:22]: Okay.Philip [00:18:22]: Just confirming.Philip [00:18:23]: YeahAli [00:18:23]: Does it affect a lot on the overall inference side? Like you're not adding much, you're adding a very small vision encoder. These are typically likePhilip [00:18:30]: They're super fineAli [00:18:31]: Less than a billion parameters, right?Philip [00:18:32]: Yeah. It's, - There's a little bit less standardization among vision encodersSwyx [00:18:37]: YeahPhilip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it's a pretty, it's a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model.Open Source Model Grafting and Franken-MergesPhilip [00:18:56]: And that's, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that's better than anyoneSwyx [00:19:05]: YeahPhilip [00:19:05]: Can be individually.Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take likePhilip [00:19:10]: YeahSwyx [00:19:10]: Layers from each model.Swyx [00:19:11]: Does anyone do that anymore?Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you're doing auto-regressive token generation for three tokens, and you're doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it's not sparse, it's not top K. So we find it better to like, okay, we're gonna replace this, we're gonna replace this layer with a layer from another model that's using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That's like, I feel like more and more becoming true.Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready?Loop Detection, Race Conditions, and Non-DeterminismPhilip [00:20:26]: Yeah. I think that there's also a question of just, we can test a model to a pretty extensive degree, but we're trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there's going to be, so many more varieties of things given to it that you're able to, discover and patch things. So it's not just a, day zero process, it's then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance?Ali [00:21:21]: What do you mean you don't want your model outputting S?Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising.Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it's four times the same token, it's probably collapsed.Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that?Ali [00:21:48]: You want that?Ali [00:21:50]: I think there's a way that we have to handle it. I'm not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there's a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters.Swyx [00:22:07]: Yeah.Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2Swyx [00:22:11]: OhAli [00:22:11]: And I think it was DSV 4 as well. Like you'd just have like looping issues where like you literallySwyx [00:22:17]: ItAli [00:22:17]: Just have like S.Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomlyAli [00:22:21]: It just seems to be the one token involved.Swyx [00:22:23]: Yeah. And it'Philip [00:22:24]: Is thereSwyx [00:22:24]: And it's only temperature 0Ali [00:22:27]: NoSwyx [00:22:27]: Even at other temperaturesAli [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse.Swyx [00:22:30]: That's weird, right?Ali [00:22:30]: It's, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we'll find that it fixes it. Or oftentimes this will only happen in an inference engine that you're using like SGLang. But if you were to switch to vLLM, that isn't the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It's not like a weights problem. Like I'- we'll say like, “Oh, it's a problem with the quant. We did PTQ wrong,” right? But that isn't, that doesn't make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it's, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem.Swyx [00:23:19]: Oh my God.Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn't. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We're gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware?Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right?Ali [00:23:46]: Right.Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won't always get the same output.Swyx [00:23:52]: Even-- But I'm surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order.Ali [00:24:02]: Well, yeah, true. Like I'm not, I'm not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that's like ‘cause you want to do that because there'sSwyx [00:24:12]: It's like pipeliningAli [00:24:12]: Expense. Exactly.Swyx [00:24:13]: Yeah.Ali [00:24:13]: But it'- But you don't do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you're designing a kernel and you want it to make it to be very fast, if you don't test it extensively, you'll, you'll have certain threads access data points from registers before they've been written to by other threadsSwyx [00:24:36]: YeahAli [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, andSwyx [00:24:42]: And there's no like borrow checkerAli [00:24:45]: What does that mean?Swyx [00:24:46]: Like Rust. Like the. If you're trying to have like memory safety It sounds like a comparable problem.Ali [00:24:52]: Well, yes, but you're working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that's what modular is supposed to do. I don't know.Quantization Quality and Vendor FidelityVibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoderAli [00:25:07]: RightVibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarksAli [00:25:22]: YeahVibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes intoPhilip [00:25:27]: There's a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you're preserving all the outliers. There's other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what's gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn't need the full million token context, for example, you can get them better performance. I don't know if that's exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model.Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it's getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking hereAli [00:27:41]: YesPhilip [00:27:41]: Where they haveAli [00:27:42]: They released an actual vendor benchmark.Philip [00:27:43]: Exactly, yeah.Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi's benchmark.Philip [00:27:50]: Yeah.Philip [00:27:51]: So, with Reflect we probablyVibhu [00:27:52]: This was a long time ago, right?Philip [00:27:54]: No.Ali [00:27:54]: Yeah, like threeVibhu [00:27:55]: They alsoAli [00:27:55]: Four, five months agoVibhu [00:27:57]: This also happened with, I don't remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have beenPhilip [00:28:03]: Kimi Vendor Verifier.Ali [00:28:04]: Yeah.Philip [00:28:05]: Yeah.Ali [00:28:05]: Yeah, ‘cause you, ‘cause you'd be pissed, right? Like if you'Philip [00:28:07]: Yeah.Ali [00:28:07]: If like if I'm a consumer and I'm using like Amazon's endpoint for instance, and I've used Kimi and I'm like, “Oh my God, like this is bad,” I'm not gonna say, “Oh, Amazon quantized the model in a bad way.” I'm gonna say, “Oh, Kimi sucks.” Right?Philip [00:28:17]: Yeah.Ali [00:28:17]: So it seems like that makes sense.Philip [00:28:19]: Yeah, they care. They care.Vibhu [00:28:21]: Justifiably.Ali [00:28:21]: Yeah, justifiably.Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization?Philip [00:28:28]: Yeah.Vibhu [00:28:28]: Like, is quantization always strictly worse?Ali [00:28:30]: Well technicallyVibhu [00:28:32]: NoAli [00:28:32]: It's a lossy. QuantizationPhilip [00:28:33]: YeahAli [00:28:33]: Is a lossy, it's a lossy implementation.Philip [00:28:36]: Speed improvesVibhu [00:28:36]: Speed improves.Ali [00:28:37]: It the number, likeVibhu [00:28:38]: No, I' always look for inverse scaling laws.Philip [00:28:40]: Yeah.Ali [00:28:40]: Yeah.Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do.Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your,Ali [00:28:52]: YeahPhilip [00:28:52]: NVFP4 quant is like, two basis points higher than yourAli [00:28:56]: No, it's noise. It's noise.Philip [00:28:57]: Yeah, exactly. I'm like, yeah, it's, it's within. That's why I always say within margin of error.Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we're barely inside of that to the worst, so we're saying. But yeah, sometimes it's just like, gives you a higher output score. But like Ali said, that's noise. To my knowledge, you're not necessarily making the results better. You're just trying to, again, like keep your fidelity as close to 100% to the original model.Layer Selection, KL Divergence, and Better QuantizationAli [00:29:27]: There is, to your point, research that we did on MP. I don't know if you are able to pullPhilip [00:29:31]: YeahAli [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it's a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It's. You're compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you're losing some information, and you're trying to minimize that. And so when I say that I'm gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don't quantize modulation layers, and I don't quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn't have the. Yeah. It's a long paper. I don't know if I can findVibhu [00:30:25]: If there's a part to search or it's probably in the thread.Ali [00:30:28]: It's probably in the thread.Vibhu [00:30:29]: Yeah.Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that's 20% more quantized than another provider, so you get 20% more throughput of it because there's more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you're probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it's gonna be, ‘cause the more loss you introduce. That's not exactly, not necessarily true. So yeah, doesn't improve it, but can cancel out.Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers.Philip [00:32:03]: But very interesting. Didn't know this was a whole paper you guys put out.Ali [00:32:06]: It's. Fun fact, it was originally 72 pages, this paper, and then we decidedPhilip [00:32:11]: WowAli [00:32:11]: We can't tell. We couldn't release it. So it's now 45.Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what's possible in terms of speedup? Like it's like probably like the numberInference Speedups and BenchmarkingSwyx [00:32:25]: Thing that people do wanna care about, and it's something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing?Philip [00:32:36]: So what's cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you're in finance, you measure how much better you got in basis points. It's like, “Oh, I got five basis points better, like twentieth of 1% better,” that's huge news because everything is so optimized. When we publish optimizations, it's 20%, it's 100% it's 200%. So there's still probably like a lot further to go, honestly. Like you'll, you'll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something.Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would findAli [00:33:27]: And like 20%, tens of percent.Swyx [00:33:29]: That's. Yes.Philip [00:33:29]: Yeah.Swyx [00:33:30]: And now it'Philip [00:33:31]: Tiny fractionsSwyx [00:33:32]: For those people interested, look up Andrew Lo's paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool.Philip [00:33:48]: Exactly, and we're at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there's so many variables that go into it. What hardware are you using? How much load do you have on the system? What's the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you're looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there's two tokens per second. There's tokens per second, the throughput number, and the latency number.Ali [00:34:31]: TTMT, yeah.Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don't.Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that's an 8X gain. That's the order of magnitude that we're working with in this space. We're trying to make things substantially faster, not just go from like 70 to 90.Swyx [00:35:38]: Are you saying you've. You have done that?Philip [00:35:40]: So let's say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you're just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you're, you're probably, yeah, looking at that like 30 to 40. You think that's like a reasonable baseline?Swyx [00:36:12]: Right. Right.Philip [00:36:12]: To get to something like 10X, there's a lot of trade-offs that you're making. If we're running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It's oftentimes maybe more of a four to six times improvement. But that's the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens.Stacking Optimizations: NVFP4, Speculation, and DisaggregationAli [00:37:19]: It's also, like, hardware dependent. Like, ifPhilip [00:37:20]: YeahAli [00:37:20]: If you have a thing where you're serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs.Philip [00:37:35]: Yeah. Then you're looking at, like, a two to 4X improvementAli [00:37:38]: Right. RightPhilip [00:37:38]: Depending on the inference optimizations. So yeah, it's. Some of it's, what's the call, and some of it's who's the driver.Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2Ali [00:37:51]: YeahVibhu [00:37:51]: On B200sAli [00:37:53]: YeahVibhu [00:37:53]: Single node, right? What's, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right?Ali [00:38:01]: Spectre quantization. Yeah.Vibhu [00:38:03]: Spectre quantization.Ali [00:38:04]: That's, that's, that's like 95%. LikeVibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it?Philip [00:38:23]: If you're doing it up front, it's quite a lot of work. If you're doing it today, there's going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we're thinking about, like, what are the 2Xs we're stacking, going from, BF16 to NVFP4 is, it's not quite a 2X, right? It's like. I think it's about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn't quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you're able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that's how it stacks up.Ali [00:39:21]: YeahPhilip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they're doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you haveAli [00:39:39]: Once set up. Once set up. YeahPhilip [00:39:40]: Yeah, getting disagg working for the first time, I'm saying, of course, is very difficult.Philip [00:39:44]: The marginal implementationAli [00:39:48]: Like, if you're just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you're wondering, “How can I just host it myself?” You don't need to quantize the model yourself. There's always gonna be, like, an open source quantized checkpoint. NVIDIA's gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they've trained as well. You don't need to train your own spec dec. You can just use that as well.Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP.Ali [00:40:13]: Right. Right.Vibhu [00:40:14]: What's multi token prediction?Philip [00:40:15]: Yes.Ali [00:40:16]: I'm justVibhu [00:40:16]: Can you explain that?Ali [00:40:16]: I'm just an expert.Ali [00:40:18]: I can do it for you in case I get it wrong?Vibhu [00:40:20]: No.Vibhu [00:40:21]: Yeah, you should correct if we're wrong, but their multi-token prediction can be used for self-speculative decoding.Ali [00:40:27]: I'm not sure. I'm not gonna correct that.Vibhu [00:40:28]: Okay. I'm semi-confident in thatAli [00:40:30]: Okay. YeahVibhu [00:40:30]: But someone can check. But it's useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM.Ali [00:40:48]: Right.Vibhu [00:40:49]: I was waiting for a mention of Dynamo.Vibhu [00:40:51]: I feel like, that's supposed to be the baseline that you measure against.Dynamo, KV Routing, and Disaggregation ToolkitsPhilip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA.Ali [00:41:17]: We've done a pod with KylePhilip [00:41:18]: OkayAli [00:41:19]: Kyle Cranin.Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting.Ali [00:41:28]: But it's just a router, it's not like an optimizer layer.Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around.Philip [00:41:49]: That doesn't mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It's more of a developer toolkit.Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out.Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we've got to, we've got to benchmark against, like, what we're seeing in the wild.Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-SpecVibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book.Philip [00:42:31]: Yeah.Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLEPhilip [00:42:35]: YeahVibhu [00:42:36]: 524 on gram.Philip [00:42:37]: It's 55, would be disaggregationAli [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bitPhilip [00:42:44]: YeahAli [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques.Philip [00:42:51]: Yeah.Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe.Vibhu [00:42:55]: Medusa is quite old.Philip [00:42:56]: Yeah, Medusa's old.Ali [00:42:58]: It was old.Vibhu [00:42:58]: But is it in the book as a good, here'sPhilip [00:43:01]: BaselineVibhu [00:43:01]: Baseline vanilla understand it?Philip [00:43:02]: Like you should know this.Vibhu [00:43:03]: Like I read the paper, I'm like, “ it makes so much sense.”Philip [00:43:05]: Yeah.Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there's DFlash, dSpark. There's, there's newer techniques even than EAGLE, although EAGLE is still very commonly used.Ali [00:43:51]: SpecSpecta.Philip [00:43:52]: Yes. Speculative decoding.Vibhu [00:43:54]: What canAli [00:43:56]: Oh, it's a paper by Tri Dao and it's like, it's doing speculative decodingVibhu [00:44:00]: HuhAli [00:44:01]: For the speculative decoder.Philip [00:44:02]: Oh, in spec- oh my God.Ali [00:44:02]: It's literally just an another. It's like, yeah, that's the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it's almost like in our mind at least, it's almost as complex as training GANs. Like it's like a very delicate balance and oftentimes you, it's just but yeah, it's literally speculative decoding on speculative decoding.Vibhu [00:44:21]: Speculative.Ali [00:44:22]: Yeah. We saw this paper.Vibhu [00:44:24]: It's interesting, right?Ali [00:44:24]: Yeah.Vibhu [00:44:24]: I wouldn't even expect it to be very particular to train, I wouldAli [00:44:29]: Right.Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder.Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It's like, it's like almost like the iPhone auto predict version but for a normal model, right? Like you're just, you're just, generating three tokens and you're like, okay, I'll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model?Ali [00:44:53]: The other question there is what are the size of speculators? So say forPhilip [00:44:58]: Right. It's like a billion parameters.Ali [00:45:01]: Like for MiniMax, it's. Yeah. It's like one layer. It's like one 60th of the original model usually.Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office.Philip [00:45:10]: SpeculativeAli [00:45:11]: SpeculativePhilip [00:45:11]: Decoding.Ali [00:45:13]: No, it's, it does seem like how, when do you stop? But then it also seems like if you're able to train spec-spec decode for instance, right? Like if you're able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model's gonna predict, then why not just use that smallest model directly, right?Vibhu [00:45:34]: Yeah. This isAli [00:45:35]: Like it seems likeVibhu [00:45:35]: Adjacent to the routing problem.Ali [00:45:36]: Right.Vibhu [00:45:36]: Yeah.Ali [00:45:36]: Right.Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you're running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process.Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it's the same thing, it's just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same?Local AI vs. Data Center InferencePhilip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it's how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it's how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don't touch, in the pruning, in the distillation, in the, layer removal. There'Ali [00:47:42]: Layer removal matters less.Philip [00:47:43]: Yeah. There'Ali [00:47:44]: No one loves pruning really.Philip [00:47:45]: Yeah. Well, but the, but they doVibhu [00:47:46]: Which is surprising, right? But that's, that's a whole different thingPhilip [00:47:48]: Just to fit something on the laptop.Ali [00:47:50]: Right.Philip [00:47:50]: So yeah, it's a, it's an interesting, it's an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire.Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we've seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don't need decrease the storage that much. You don't need to do, FP4 KV cache. You don't need to use a requant. There's, there's, there's better optimizations to be made. But on Edge devices, it's extremely important, it's extremely useful. So, seems to be, like, different optimizations there, but then they're all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with bothPhilip [00:49:18]: Principles.Ali [00:49:19]: Yeah, exactly. Exactly. Exactly.Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks.Ali [00:49:35]: Yeah, this is the Exo Labs guys.Philip [00:49:36]: Yeah. You have, a nu

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren't traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:We've been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex's most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex's user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team.However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company's broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.Side note: also don't miss Abhihek's sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.We discuss:* Why Codex unexpectedly took off among non-developers inside OpenAI* Why employees felt like using Codex gave them a new superpower* The product insight that led OpenAI to build ChatGPT Work* Why Codex and ChatGPT Work share the same underlying agent harness* How their UX, Git visibility, artifacts, and sandboxing defaults differ* Why OpenAI merged its agent experiences instead of building separate products* How AI is blurring the boundaries between engineering, design, strategy, and operations* Why OpenAI wants the default model configuration to work for most users* When power users should use deeper reasoning, Ultra, or multi-agent modes* Artifacts, agentic spreadsheets, and creating high-fidelity work products* Why interactive Sites may replace decks and spreadsheets* The challenge of designing a simple interface for an agent that can build almost anything* Why users should retry tasks that models could not handle three or six months ago* How AI can gather context for performance reviews without replacing human judgment* The OpenAI automation that turns internal Slack and document activity into memes* What reaching ten million ChatGPT Work and Codex users means for the product* How OpenClaw inspired persistent environments, scheduled tasks, and personal agents* Using ChatGPT for financial planning, budgeting, workouts, meals, and household management* The design tradeoffs behind sub-agents and how much of their work users should see* ChatGPT memory, Chronicle, and long-term context* Why AI may make more people generalists with deep specialties* Why ideas and taste become more important when almost anyone can build* Why LLMs still struggle with the instruction “bring me new ideas”* Measuring productivity through quality at-bats instead of commits, tokens, or pull requests* The critical difference between AI-generated motion and meaningful progressAkshay Nathan* LinkedIn: https://www.linkedin.com/in/akshaynathan/* X: https://x.com/akshaynathan_Timestamps00:00:00 Introduction and Bringing the Power of Code to Everyone00:01:33 Joining OpenAI and Preserving a Startup Culture00:02:40 What OpenAI Learned from Enterprise AI Adoption00:05:28 Why OpenAI Built ChatGPT Work00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness00:12:07 Why OpenAI Merged Its Agent Experiences00:16:24 Models, Reasoning Levels, and Choosing the Right Default00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration00:24:22 Why Sites Could Replace Decks and Spreadsheets00:30:08 Designing an Agent That Can Build Almost Anything00:34:28 From Developer Agents to Knowledge Work—and Everyone00:36:07 Power-User Advice and AI-Assisted Performance Reviews00:40:41 OpenAI's Internal AI Memes and the Ten-Million-User Launch00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need00:54:39 ChatGPT Memory, Personalization, and Chronicle01:00:19 How AI Is Reshaping Product Development and Tech Roles01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. ProgressTranscriptIntroduction: Akshay Nathan, ChatGPT Work, and the No-Code ArcSwyx [00:00:00]: We're here in the studio with Akshay from OpenAI. Welcome.Akshay Nathan [00:00:07]: Thank you.Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It's been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.Akshay Nathan [00:00:32]: Yeah. It's funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It's funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that'd be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what's going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we've been up to is, like, the manifestation of that.From Walrus and Airtable to OpenAIVibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we've got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?Joining OpenAI and What Hasn't ChangedAkshay Nathan [00:01:44]: I think the more interesting thing is how things haven't changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn't changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we're probably gonna, like, try different products and have different things that succeed and don't. But the vision has stayed the same, and the mission has stayed the same, and we're starting to see the pieces, fall together, and that's really cool.Enterprise Lessons: No One-Size-Fits-All AISwyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you're bringing into your work now?Akshay Nathan [00:02:52]: I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you'd get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it's interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it'on the flip side, it also means that, like, you don't know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who's, like, looking at their computer or looking at their phone, like, it's our job in the product to, like, be enabling them and showing them where to go. So we're really excited about that.Vibhu [00:04:24]: Do you think there's been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or have things changed?Adoption, Agents, and the Next 10x MarketAkshay Nathan [00:04:39]: We're seeing now that, like, there's this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there's, like, this, like, 10x or 100x bigger market where, like, they don't yet get that, or they don't yet see that. And so I think that's the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That's where we wanna play, especially with ChatGPT Work.ChatGPT Work, Codex, and the Super App MergeSwyx [00:05:27]: Yeah. well, let's, let's skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we're officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it's only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, their use cases. That part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, likeSwyx [00:06:34]: It's like, “I'm not supposed to be using it, but I am.”Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky thing, right? There's many ways you can go about it. And so that's what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?Akshay Nathan [00:07:36]: I think we want to get it to position it for if you're doing work-related things, for lack of a better word, right?Who ChatGPT Work Is ForAkshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that's the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there's also personal productivity, right? And, like, I think ChatGPT Work is I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn't receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, work-related or productivity-related things, I think that's what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there's, like, some opinions that go behind that, but we do want We don't want the user to need to choose which experience they're in.Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don't want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?Shared Harness, Different UX: Codex vs. WorkAkshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.Swyx [00:10:16]: I'm just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you'll see, like, the diffs of, like, the sheet that it's creating and stuff like that, and the file edits. But in Work you won't be able to see that.Swyx [00:10:42]: I think that's, that's super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn't productivity everything?Productivity Teams and Core ChatAkshay Nathan [00:10:55]: SoSwyx [00:10:55]: Science?Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there'People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there's so much more inside to create images. And there's so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there's teams focused on enterprise and infrastructure and API and stuff like that, so.Swyx [00:11:33]: I will bring it up.Retirement Calculator Demo and Git-First UXSwyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There's a Codex version here. I picked “Five Little Ducks” song, so this will take a while.Akshay Nathan [00:11:43]: Huh.Swyx [00:11:43]: I think we'll just keep it in the background and, as they finish, we'll look into some of the differences.Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you'll see that,Swyx [00:11:53]: That it assumesAkshay Nathan [00:11:54]: Like theSwyx [00:11:54]: It assumes Git. Yeah. Yeah.Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you're in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let's not do it?” Like, what's the thinking behind that?Why Merge the ExperiencesAkshay Nathan [00:12:14]: In, ChatGPT Work?Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, whySwyx [00:12:22]: Different apps.Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I'm, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, this technology is giving people leverage. Like, the things, maybe it's the more mundane parts of your job or parts that, like, if you were able to automate, you'd be able to share more ideas faster or whatever, like, you're able to do now. And because of that, like, that might blur the lines between someone who's, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn't box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It's this thesis that, like, eventually things are gonna come together and we don't wanna be Like, we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.Swyx [00:13:45]: I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can't imagine what that was, but maybe they're more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you've seen it.Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,Harness Engineering: ChatGPT vs. CodexSwyx [00:14:08]: The classic, right?Akshay Nathan [00:14:09]: TheVibhu [00:14:09]: You just start a new chat, and you don't go under Work, right?Akshay Nathan [00:14:13]: Yeah. If you startVibhu [00:14:13]: SoAkshay Nathan [00:14:14]: A new chat and go to chat, then you're, you're talking to ChatGPT with the instant model.Vibhu [00:14:16]: Oh, we can technically do another. But on instant.Swyx [00:14:21]: Yeah. So this one's not gonna code or it's gonna be in line. It's on a in line in a sandbox.Akshay Nathan [00:14:26]: It'llVibhu [00:14:27]: Oh, that's coolAkshay Nathan [00:14:27]: We try to push you to go to Work if you're creating a spreadsheet. Yeah, but this isSwyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision?Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you're able to. or you're trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?Swyx [00:14:48]: It's more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let's call it the ‘01 era, until now, and now it's being replaced by the Codex harness effectively. And they're, they're overlapping somewhat, but I'm curious what changed if there is.Akshay Nathan [00:15:10]: My perspective on this is, like, there's, there's, there's there's like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I'm sure there'll be work down the road in order to get things to be, equivalently capable in all scenarios. But it's just a question of, like, what we've been focusing on the product on historically and what we're focusing on now.Models, Defaults, and the Reasoning SliderVibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all theseAkshay Nathan [00:16:44]: There's 32 options.Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don't have the breakdown of what all this is what's, what's the advice, right?Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've we've chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we're, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you're not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.Swyx [00:18:09]: I have, I'm just gonna run something by you since you have way more experience than me. I've recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?Akshay Nathan [00:18:29]: Yeah. It's hard to say becauseSwyx [00:18:31]: Yeah. It's like an interaction effect.Akshay Nathan [00:18:33]: exactly. It's like there's a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it's doing the right thing?Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they don't fall into either of those buckets. And so like at least when they're starting, and so that's why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.Vibhu [00:19:36]: It's nice on mobile at least. There's a nice slider there.Swyx [00:19:38]: It's nicer.Vibhu [00:19:39]: I haven't tried it.Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah.Vibhu [00:19:44]: Ooh, it's just a nice slider. Yeah.Swyx [00:19:46]: Very pretty, very colorful.Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.Artifacts, Spreadsheets, and the Work LaunchSwyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lowerVibhu [00:20:07]: NoSwyx [00:20:07]: Grounds I would've usedVibhu [00:20:08]: I think the slider, if I'm not mistaken, isSwyx [00:20:09]: Terra.Vibhu [00:20:10]: Oh, it is.Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.Vibhu [00:20:22]: I'm the reason. Here's ten minutes of ourSwyx [00:20:24]: There you goVibhu [00:20:25]: Retirement calculator.Swyx [00:20:26]: Oh, that's the Excel thing working for you.Vibhu [00:20:28]: This is,Swyx [00:20:28]: Oh my God. Look at thatVibhu [00:20:28]: This is work, and then Codex is still cooking, so we'll get back into it. I think it'll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.Swyx [00:20:41]: Yeah. And by the way, so I've, do Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it's agentic Excel.Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right?Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you'll see that there's been pretty dramatic improvements in the quality of these artifacts and then also on the product side.Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like itSwyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I'll need to take the visuals here, but we-we'll, we'll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that's like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you're like making a retirement calculator.Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah.Akshay Nathan [00:22:24]: You wanna iterate and like when you're seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?Multiplayer Artifacts and CollaborationSwyx [00:22:46]: You can already share it, right?Akshay Nathan [00:22:48]: Yeah. It's inter It's something that, we're actively thinking about. one thing that, we've noticed internally without talking too much about the roadmap is that like there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.Akshay Nathan [00:23:04]: And then I'll be thinking likeVibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted.Akshay Nathan [00:23:07]: Exactly. And I'll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that could be interesting.Vibhu [00:23:28]: Yeah, it'sSwyx [00:23:28]: So like the answer was preemptively respond to every inbound request?Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job.Swyx [00:23:36]: I know you copy-paste and then you're just a message forwarding serviceAkshay Nathan [00:23:39]: Yeah. Yeah, exactlySwyx [00:23:39]: From AI to AI.Vibhu [00:23:40]: But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don't realize until they try or someone shows you, and then you're like, “Oh, okay. Okay, I see.”Swyx [00:23:52]: I think it's als there's also like a, light security issue, where like you're the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it. And that there's no way I would know because I'm not supposed to know what I don't know.Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we're, we're asking you to connect your plug-ins and, it's pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that's something I think we need to preserve, so that'll be definitely a challenge.Swyx [00:24:22]: There's Excel, there's PowerPoint, there's Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like what does that look like if you ever ended up doing it?Akshay Nathan [00:24:41]: It's a really good question. I think,Formats of Work: Sites as Knowledge ArtifactsAkshay Nathan [00:24:43]: one that you didn't bring up was Sites, and I think that wasSwyx [00:24:46]: SitesAkshay Nathan [00:24:46]: A core part of this launch. There's one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who's on like our corporate finance team, and like we were mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it's like, it's like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it's been really valuable.Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, it's like a little board game that youSites, Auto Research, and Research DashboardsSwyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thingAkshay Nathan [00:26:32]: WowSwyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that's, thatAkshay Nathan [00:26:45]: That's your auto researchSwyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they're, they're gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn't gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it's created this lab, panel. Where is there a, is there a shortcut for a site that is created?Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.Swyx [00:27:33]: This one? Oh, left?Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top.Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah.Akshay Nathan [00:27:39]: Ooh.Swyx [00:27:40]: So it create, it creates the sites. I don't, I don't think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it's an interesting transition from Markdown effectively that you're putting out to, you're putting out a whole functional site.Akshay Nathan [00:28:41]: I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they're quite verbose. I don't need a lot of this information.Swyx [00:28:57]: It's very verbose.Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don't, right?Swyx [00:29:05]: Yeah. I don't know if, any that triggers any stories for you of how it's run internally. Am I doing this right?Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we're seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it's just HTML, you can like. It's infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There's more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they're very, they're long and verbose, could be broken up. I'm sure that there's still something to do there.Swyx [00:29:53]: They're super long. Yeah.Akshay Nathan [00:29:54]: Yeah. But I think we're starting to see that like there is this aspect of this is a really interesting, format, for people to use, that's like much more flexible than what they ever had before.Swyx [00:30:07]: I think your job also comes becomes meta. You're not designing the products. You're designing a product to make products, and I'm curious how you manage that.Designing a Product That Makes ProductsAkshay Nathan [00:30:18]: I think one thing that we've been Like when we look at the UX, like that we've been thinking a lot about is how can we balance like simplicity with capability? Like if we're designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can't put that all in front of you because you'll get overwhelmed.Vibhu [00:30:41]: Yes.Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there's so much that can be done, I think the balance that we're constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.Games, Private Evals, and Show-Don'TellVibhu [00:31:19]: Yeah. It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I've got a similar version. not with all the auto research and whatnot, butAkshay Nathan [00:31:39]: You gotta do all the latest trends.Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it's interesting, right?Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. LikeVibhu [00:31:51]: AndAkshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets.Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.Akshay Nathan [00:32:15]: Oh, yeah.Vibhu [00:32:15]: GPT-5.6Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?Akshay Nathan [00:32:27]: Yeah. It's some private eval. That is not this private.Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this.Akshay Nathan [00:32:36]: It's a hard game. He's very good.Vibhu [00:32:39]: It's good to when no one is competing with you. But yes, it's a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we've we're not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.Akshay Nathan [00:33:18]: So meeting them in the moment.Vibhu [00:33:19]: It's a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you're like, “What do you mean? You don't, you don't need.” your job is to tell. And then. But the product people are like, “Well, we don't need you if our product is intuitive enough.” SoAkshay Nathan [00:33:37]: Yeah. that's the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they've done in the past, exactly where they are on the adoption journey. So I think that's like gonna be a super big opportunity.Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?From Developers to Knowledge Work to EveryoneAkshay Nathan [00:34:12]: Productivity.Vibhu [00:34:12]: Productivity. So howAkshay Nathan [00:34:12]: Which is now work.Vibhu [00:34:14]: Is it work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn't targeting?Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that's where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there's inherent challenges with like, this show not tell thing that we're talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. And we're already seeing that a little bit. Like this game example that you have is, something that's like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I'm doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.Vibhu [00:35:51]: ChatGPT life.Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there's a lot of, there's a lot of opportunity there, but I see it as like, we're, we're built we built a foundation in software engineering, and we're gonna take the same learnings that we take from software engineering to knowledge work to everyone.Vibhu [00:36:07]: Do you have any power user advice? I feel like, there's a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there's a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you've found that help bridge that gap?Power User Advice: Push the Frontier of ImaginationAkshay Nathan [00:36:30]: I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it's like, wow, it's like it can. Like,Swyx [00:36:52]: Give an exampleAkshay Nathan [00:36:52]: We're going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there's a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousnessSwyx [00:37:09]: And it can evaluate it as well.Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I've found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they've like the things that they've done to make a difference, highlighting like, wins that they've had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they've caught, reviews, Slack, everything. And so it's, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn't even I tried using it, but it was not at all helpful. And this time it's been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what's possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.Reviews, Agentic Search, and Context GatheringSwyx [00:38:27]: One thing I just wanna talk about the review stuff because I'm still that's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don't care.Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what's the etiquette?Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That's the place where it's incredibly helpful.Swyx [00:39:08]: So it's just search.Akshay Nathan [00:39:09]: Yeah, exactly.Swyx [00:39:09]: It's agentic search. Yeah.Akshay Nathan [00:39:10]: It's like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it's all there's a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?Remembering What Humans MissAkshay Nathan [00:39:50]: Probably, but I think that I also miss things.Swyx [00:39:52]: Like, it doesn't matter, right?Vibhu [00:39:53]: I think sometimes it'sSwyx [00:39:53]: Like it's, as it needs to be human-levelAkshay Nathan [00:39:54]: It's all relative, right? Yeah.Vibhu [00:39:56]: Sometimes it's nice when it finds things you wouldn't, right? Like right now, my Codex system prompts, they're set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it'll be like: Oh, there's this project you did like four months ago. Here's a note that we had, and it randomly pulls it back into context that I would never do, I haven't thought about.Vibhu [00:40:20]: And I'm like, okay, this is quite superhuman, right? Like, stuff that would. And, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that's going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.Swyx [00:41:13]: Funny. Nice.Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that's like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that's emerging, which is just like to find information that you otherwise would not know of.Launch Momentum and the 10 Million User MilestoneSwyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that's like huge and super exciting. The flip side of that is like, there's so much more to do too. Like, that's also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that's the next step from here. But yeah, I think extremely pumped about how it's going so far and the opportunities.Swyx [00:42:46]: Awesome. I did want to also Because I've, I've, I've been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they're same harness. The whole point is that you don't, you can't, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn't that the default on ChatGPT or no?Codex, ChatGPT Work, and the Developer BrandAkshay Nathan [00:43:11]: We don't default you into ChatGPT Work if you're on ChatGPTSwyx [00:43:14]: If you're free. YeahAkshay Nathan [00:43:15]: It's also only available to paid users right now. And I think there's like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it'll be a journey.Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we'll just toggle between them as needed for UI stuff.Akshay Nathan [00:43:44]: Yeah, I think it's even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there's, there's so much more that we can do to make Codex great specifically for, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.Akshay Nathan [00:44:20]: It's funny, like we call it artifacts internally ‘cause that's what the teams call it.Swyx [00:44:23]: It's nice. Yeah.Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they're used to, right? So if, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's what we want.OpenClaw, Personal OS, and Persistent ComputersSwyx [00:44:38]: One big Another, it's July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.Akshay Nathan [00:45:06]: I think there's a lot of inspiration. I did go through my own OpenClaw moment. I,Swyx [00:45:10]: Yeah, tell the storyAkshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it's like a work-related thing, right? It's like not work necessarily, but it's like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they're independent, so.Akshay Nathan [00:46:20]: Yeah, I'm, I'm not close to it, so I can't speak to the OpenClaw roadmap, but I don't think so. I think that there's gonna be, there's always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that's the goal. It's like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you're doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There'll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?Finance, Data Access, and Centralized ContextAkshay Nathan [00:47:50]: I tried it. like ChatGPT doesn't yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that's all possible with ChatGPT today. So, I feel

On this episode of The AIE Podcast… We aren't podfading, we promise We have your World of Warcraft Summer of Love schedule Try something casual this summer with SWTOR Ill Omens are on the horizon for LOTR and, we are talking amongst ourselves All that and more coming up right now… Podcast Audio Raw Video http://youtu.be/iW0bmgxf-Zg Open Welcome to episode #444 of the podcast celebrating you, the Alea Iacta Est gaming community, the die has been podcast. This is Tetsemi: To my left is Mkallah: – (hey guys, there is a lemon meringue pie in the guild kitchen, and chocolate chip cake, and pineapple upside down cake- I've been busy). And to my right is Mewkow: (catch phrase here). And, contrary what it may seem like, we aren't podfading, it has just been a crazy summer so far. But, it's time to get back on track cast those die! Ok, we'll be digging into what all we have been up to, but first, let's cover this week's news… AIE News Community Mandatory Fun Nights Where the fun is mandatory but the attendance is not. Schedules subject to change due to summer holidays, of course Sunday – Destiny 2 8:30 pm Eastern Monday – GW2 9:30 pm Eastern Monday – STO 10:00 pm Eastern Tuesday – SWTOR 9:00 pm Eastern Wednesday – HFO Mythic+ Mayhem (WoW) 8:00 pm Eastern Friday – ESO 9:00 pm Eastern Saturday – LotRO 8:30 pm Eastern Saturday – FFXIV (Maps) 9:30 pm Eastern Saturday – Noob Raid (WoW) 11:00 pm Eastern Streaming and Guild Podcast News We have a ton of AIE member podcasts! Want to know where to find them? Look no further than here- New Overlords Podcast (Max and Sema) https://www.newoverlords.com Boards and Swords (Chris and Philip) https://boardsandswords.com/blog?category=Boards%20%26%20Swords Dr. Gameology ( Dr. Daniel Kaufmann ) https://drgameology.com/ STO – Fleet Action Report (Grebog and Nikodas) https://www.youtube.com/@fleetactionreport A Podcast Reborn: A FFXIV Community Podcast (Brandon aka Old Man Franks, Meagan, and Rho) – NSFL https://www.bonusroll.gg/directory/a-podcast-reborn/ WoW Our Summer of Love event will begin on Monday, July 27 and run through Sunday, Aug 2. I will be creating a channel shortly, but here’s the schedule so far. Mon, July 27 8pm Eastern Ugly Mount Parade & Contest in Silvermoon. Show off your best and ugliest mounts. They need love too! Tues, July 28, 8pm Eastern Another Great Race: Alliance and Horde players create a new Haranir and race to the opposite faction’s capital. Who will get there first? Wed, July 29, 7pm Eastern Decor Home Tours: Come be inspired by the creativity and whimsy our guildies. While not required, the Guestbook addon allows you to leave notes for the home owner. (Such as, “Gusty, why is there a filthy Peon’s Work Bucket under your stairs?”) Thurs, July 30, 7pm Eastern Guild Hide & Seek: Hop in Discord and we will all queue at once for this new game in Silvermoon. We probably won’t be paired with guildies, but merriment will ensue. Fri, July 31, 6pm Eastern Decor gathering (and maybe a battle pet or two). Quests, dungeons, old raids. A couple of Warlocks and Mages would be welcome as some quests are faction specific. Sat, Aug 1 , 4pm Eastern Decor Craft Fair: BYO Lumber and let Gusty know in advance 1-2 items you are looking for, or any you can make . I’ll provide mats within reason, but donations are welcome. We’ll make more decor if possible. (PS Gusty has lots of Elek Plushies thanks to Shojo, and various other decor to give away). Sun Aug 2, 2pm Eastern Trial of Style Strut your stuff in this transmog fashion show competition. Mismatch Mayhem IS a category and It’s extra fun with Guildies! Sun, AUG 2 8pm Eastern – 7pm Central Remembrance Day. This is the signature AIE annual event, which takes place across all of our game communities. Meet at the Shrine of the Fallen Warrior near Crossroads to honor friends and family we have lost, followed by a procession to the back gates of Orgrimmar for a picnic and fireworks. And, there is a Discord channel for Summer of Love now, check there for schedules and updates WoW Classic All is quiet on the western front SWTOR In Swtor, the guild has largely finished up with Galactic Season 10 with a few people getting those last few objectives in for the meta achievement. After all, who doesn’t like a good meta achievement? The PvP Season is ongoing and we do have some hardy souls participating in that. News of 8.0 is on the horizon, which will include new levels, new zones, new flashpoints, and even a new operation. We should hear more about it soon. Now is a pretty good time to get in and level up a character. For some casual group fun, we continue to have our regular Tuesday night MFN. Here’s where you can learn more about the game, chat about all things nerdy, generally just hang out, and get a few objectives done. Everyone is welcome. We will be celebrating Summer of Love with a special MFN on July 2th and we will see the return of Star Wars Trivia! ESO The new Season One just started and includes a new quest series for the Thieves Guild. The Microsoft layoff’s hit ZeniMax Online pretty hard. It seems like they lost about half of the active developers that work on content, events and dungeons. The previous roadmap for updates will probably shift considerably, but there are no specifics yet. GW2 https://www.guildwars3.com/en/ Guild Wars 3 has been announced and beta being in Fall 2027 and sign ups are available at the link above. FFXIV The Berlin fanfest is happening on 25th. New job reveal and more expected https://na.finalfantasyxiv.com/lodestone/topics/detail/fa499f335a87707b8dfc578e8662aa2a724cdca4 LOTRO Lots of stuff coming in the near future for lotro that we can group up for. Starting on July 23rd and running through August 11th will be the ill omens skirmish event. We can count on Maellung to be spending as much time doing skirmishes during this event. You are welcome to join him. The delving armour will be updated soon. Good opportunity to upgrade our armour at level cap. If you play on the legendary servers, later this summer Angmar and Mordor reach a major milestone. As players venture into the Mordor and North Mirkwood level bands, all Nine Nazgûl enter the fray, opening the door to some of the most challenging encounters yet. In PvMP news, monster players will be getting a new class for the first time in 18 years: the Angmarim Sorceress. And on July 31st we are expecting the new expansion name, setting and more to be revealed to us. Too much coming for us to remain patient for. And with that, let's find out what we've all been doing in and out of game GAME NEWS CLOSE And that's our show for tonight. While the chatroom begins suggesting show titles, we want to thank us for joining us. If you have a question or comment about our show, you can email us at podcast@aie-guild.org You can find us on the AIE Discord and BlueSky https://bsky.app/profile/aiepodcast.bsky.social. We record live with video once a month on Sunday at 8pm Eastern/5pm Pacific. You can join the chatroom and play along with us on our website at https://aie-guild.org/podcast-live-stream/ and look for the link to our discord server at https://aie-guild.org. And for past episodes, you can see them on our Youtube channel, https://www.youtube.com/c/TheAIEPodcast ! Our theme was composed by the amazing Andrew Allen, follow him at @keyswithsoul! And now it's time to play all of the great AIE member segments we received this week, including… We will see you all in August! So until then, AIE… – This is Tetsemi – This is Mkallah – This is Mewkow And this has been… The AIE Podcast.

Con un pie en el pop más honesto y otro en el indie, Carmesí compone canciones que se sienten como un: «Ey, estoy aquí contigo». Historias honestas, vulnerables y sin pretensiones, pensadas para abrazar y sostener. Su último álbum, 'Tiro al palo’ (2025), consolida esa identidad: guitarras eléctricas, sensibilidad melódica y una energía nostálgica que remueve el pecho mientras invita a bailar. Murciana, músico de conservatorio, multiinstrumentista y coproductora del disco, ha llevado su propuesta por toda España, pasando por diversos festivales y formando parte de las giras GPS y AIE.Escuchar audio

Juan Zelada se crio en un entorno de músicos y en 2006 completó sus estudios musicales becado por AIE en el Liverpool Institute for Performing Artists (LIPA), donde recibió de manos de Paul McCartney el reconocimiento a la mejor composición de su promoción. Tras su periplo por el Reino Unido de casi 10 años sonando en la prestigiosa BBC, girando por Reino Unido y teloneando a artistas como Amy Winehouse, lleva instalado en España más de una década. En este tiempo ha creado 5 discos más, ha ofrecido conciertos potentes, canciones fronterizas, colaboraciones con artistas establecidos a nivel nacional, y un sin fin de canciones nuevas que demuestran su dedicación, compromiso y amor por la música.Escuchar audio

Irán acaba de responder con lanzamiento de misiles a una base aérea de EEUU al ataque esta noche a una instalación militar del sur del país y al derribo de cuatro drones de ataque lanzados sobre su flota. La electricidad captará el 60% de la inversión en energía en 2026, sube el gas y el petróleo pierde peso, según el último informe de la AIE. La compra de viviendas cae un 4,7% y cierra un trimestre a la baja. El precio sube un 7%.

Semarnat frena proyecto turístico en MahahualConagua impulsa obras hidráulicas en AcapulcoMás información en nuestro Podcast#grc

El director general de la AIE, Fatih Birol, advierte de que el mercado del petróleo puede entrar en situación de peligro en verano si no se resuelve el cierre del estrecho de Ormuz. Precisamente hoy el Eurogrupo analiza la situación económica tras las últimas proyecciones de la Comisión y tratarán la respuesta fiscal a la guerra en Oriente Medio. Los sindicatos convocan paros este viernes en las fábricas de Renault en Palencia, Valladolid y Sevilla y el centro de Madrid.

En un contexto de tensión en los mercados energéticos por la guerra en Oriente Medio y la caída a un ritmo acelerado de las reservas, Venezuela reaparece como un proveedor útil para Estados Unidos. Aunque su peso global sigue siendo limitado, su papel gana relevancia por su valor estratégico y geopolítico, explica el analista José Enrique Arrioja. En medio de la subida de los precios del crudo a causa de la guerra en Oriente Medio y la reducción récord de las reservas de petróleo, el aporte de Venezuela como país productor, aunque menor, resulta particularmente beneficioso para Estados Unidos, afirma José Enrique Arrioja, analista económico del Centro de Expertos del Consejo de las Américas. Cuando se cumplen ya más de diez semanas de guerra en Oriente Medio, las reservas mundiales de petróleo se están agotando a un ritmo récord debido a las interrupciones del flujo a través del estrecho de Ormuz, que siguen restringiendo la oferta, informó este miércoles la Agencia Internacional de la Energía (AIE). Según la AIE, el mercado del petróleo seguirá previsiblemente en déficit hasta el último trimestre del año, mientras las interrupciones en el estrecho de Ormuz limitan la oferta y aumentan el riesgo de una nueva volatilidad de los precios. En este contexto de crisis en torno a la producción de crudo, Venezuela es un jugador menor, pero desempeña un papel significativo. Desde la detención de Nicolás Maduro en enero por Washington, la producción de barriles ha aumentado en ese país en un 22,9 %, cifra inédita desde 2019. Para José Enrique Arrioja, analista económico del Centro de Expertos del Consejo de las Américas, Venezuela se ha vuelto bastante útil para Estados Unidos. Venezuela, segundo proveedor de crudo a Estados Unidos  "En estos instantes, Venezuela se ha convertido en el segundo proveedor de crudo a Estados Unidos después de Canadá. En el equilibrio energético global, la cifra todavía tiene poco peso, sobre todo si se contrasta con las producciones que tienen otros países de la OPEP. Pero hay detrás de la interpretación de estos números fríos y escuetos un simbolismo y un valor estratégico y geopolítico, que es lo que históricamente le ha dado realce e importancia a la producción petrolera venezolana", explica. Debido a las sanciones impuestas por Estados Unidos en 2018 y 2019, Venezuela vendía la gran mayoría de su crudo a Asia, sobre todo a China. Pero parte del plan de Washington también es influir en este mercado. "En este mes de mayo, prácticamente 600.000 barriles de petróleo se están vendiendo a Estados Unidos. El resto de las exportaciones siguen yendo a destinos en el mercado asiático, pero definitivamente la presión de Estados Unidos sobre el gobierno liderado por Delcy Rodríguez para cortar relaciones con China está generando una recomposición en las ventas de Petróleos de Venezuela, haciendo que estas tiendan cada vez más a irse hacia Estados Unidos en lugar de dirigirse a los mercados asiáticos". Venezuela, importancia estratégica Estados Unidos tiene especial interés en el crudo de Venezuela, ya que sus refinerías pueden adaptarse al petróleo pesado que produce el país caribeño. Es por ello que se espera que la producción continúe aumentando y generando crecimiento. Se espera una revitalización de la contratación de mano de obra. Compañías al estilo de BP, Repsol, ENI están ya firmando acuerdos importantes para reactivar o retomar presencia. Se tuvo en Venezuela, en el caso de Repsol, la intención de triplicar la producción que tiene hoy en día la compañía en ese país, pasando de unos 45.000 barriles por día a 150.000 barriles por día en los próximos tres años. Arrioja concluye afirmando que el conflicto en Oriente Medio no convierte a Venezuela en una amenaza económica para el resto de los países de la OPEP, pero pone de manifiesto la importancia geoestratégica de este país en el panorama internacional.

En un contexto de tensión en los mercados energéticos por la guerra en Oriente Medio y la caída a un ritmo acelerado de las reservas, Venezuela reaparece como un proveedor útil para Estados Unidos. Aunque su peso global sigue siendo limitado, su papel gana relevancia por su valor estratégico y geopolítico, explica el analista José Enrique Arrioja. En medio de la subida de los precios del crudo a causa de la guerra en Oriente Medio y la reducción récord de las reservas de petróleo, el aporte de Venezuela como país productor, aunque menor, resulta particularmente beneficioso para Estados Unidos, afirma José Enrique Arrioja, analista económico del Centro de Expertos del Consejo de las Américas. Cuando se cumplen ya más de diez semanas de guerra en Oriente Medio, las reservas mundiales de petróleo se están agotando a un ritmo récord debido a las interrupciones del flujo a través del estrecho de Ormuz, que siguen restringiendo la oferta, informó este miércoles la Agencia Internacional de la Energía (AIE). Según la AIE, el mercado del petróleo seguirá previsiblemente en déficit hasta el último trimestre del año, mientras las interrupciones en el estrecho de Ormuz limitan la oferta y aumentan el riesgo de una nueva volatilidad de los precios. En este contexto de crisis en torno a la producción de crudo, Venezuela es un jugador menor, pero desempeña un papel significativo. Desde la detención de Nicolás Maduro en enero por Washington, la producción de barriles ha aumentado en ese país en un 22,9 %, cifra inédita desde 2019. Para José Enrique Arrioja, analista económico del Centro de Expertos del Consejo de las Américas, Venezuela se ha vuelto bastante útil para Estados Unidos. Venezuela, segundo proveedor de crudo a Estados Unidos  "En estos instantes, Venezuela se ha convertido en el segundo proveedor de crudo a Estados Unidos después de Canadá. En el equilibrio energético global, la cifra todavía tiene poco peso, sobre todo si se contrasta con las producciones que tienen otros países de la OPEP. Pero hay detrás de la interpretación de estos números fríos y escuetos un simbolismo y un valor estratégico y geopolítico, que es lo que históricamente le ha dado realce e importancia a la producción petrolera venezolana", explica. Debido a las sanciones impuestas por Estados Unidos en 2018 y 2019, Venezuela vendía la gran mayoría de su crudo a Asia, sobre todo a China. Pero parte del plan de Washington también es influir en este mercado. "En este mes de mayo, prácticamente 600.000 barriles de petróleo se están vendiendo a Estados Unidos. El resto de las exportaciones siguen yendo a destinos en el mercado asiático, pero definitivamente la presión de Estados Unidos sobre el gobierno liderado por Delcy Rodríguez para cortar relaciones con China está generando una recomposición en las ventas de Petróleos de Venezuela, haciendo que estas tiendan cada vez más a irse hacia Estados Unidos en lugar de dirigirse a los mercados asiáticos". Venezuela, importancia estratégica Estados Unidos tiene especial interés en el crudo de Venezuela, ya que sus refinerías pueden adaptarse al petróleo pesado que produce el país caribeño. Es por ello que se espera que la producción continúe aumentando y generando crecimiento. Se espera una revitalización de la contratación de mano de obra. Compañías al estilo de BP, Repsol, ENI están ya firmando acuerdos importantes para reactivar o retomar presencia. Se tuvo en Venezuela, en el caso de Repsol, la intención de triplicar la producción que tiene hoy en día la compañía en ese país, pasando de unos 45.000 barriles por día a 150.000 barriles por día en los próximos tres años. Arrioja concluye afirmando que el conflicto en Oriente Medio no convierte a Venezuela en una amenaza económica para el resto de los países de la OPEP, pero pone de manifiesto la importancia geoestratégica de este país en el panorama internacional.

Donald Trump è arrivato a Pechino per il primo viaggio di un presidente americano in Cina dal 2017. Al centro del vertice con Xi Jinping ci saranno i dazi, le restrizioni sui semiconduttori, Taiwan e la tenuta dei rapporti commerciali tra le due superpotenze. Xi punta a prolungare la tregua commerciale e ad allentare le limitazioni tecnologiche americane, mentre Trump è accompagnato dai vertici di grandi aziende Usa come Tesla, Apple, BlackRock e Boeing. Secondo diversi analisti, il summit potrebbe portare a una tregua sui dazi non per fiducia reciproca, ma per necessità economica, in un contesto in cui la Cina domina filiere strategiche globali come terre rare, batterie e componentistica. Il commento è affidato a Giuliano Noci Professore ordinario in Ingegneria Economico-Gestionale, presso il Politecnico di Milano. Dal 2011 è Prorettore del Polo territoriale cinese del Politecnico di MilanoTensione fra i soci in vista dell'assemblea Ferretti, Kkg scrive al GovernoAlla vigilia dell’assemblea chiamata a rinnovare il board di Ferretti, si intensifica lo scontro tra il socio di maggioranza cinese Weichai e la holding ceca Kkcg Maritime. Quest’ultima ha scritto al Governo italiano segnalando possibili violazioni delle norme sul Golden Power dopo l’aumento delle quote detenute da soggetti riconducibili all’orbita cinese, tra cui Bank of China e AdTech Advanced Technologies. Sullo sfondo resta il tema della sicurezza nazionale e dell’influenza cinese in un gruppo considerato strategico. Domani gli azionisti dovranno scegliere tra la lista sostenuta da Weichai, azionista al 39%, e quella promossa da Kkcg, che dopo l’Opa è salita al 23,2% del capitale. Ne parliamo con Raoul de Forcade, Il Sole 24 OrePetrolio: Aie, shock offerta senza precedenti con blocco Hormuz, record calo scorteL’Agenzia Internazionale dell’Energia lancia l’allarme sull’impatto della guerra con l’Iran sul mercato energetico globale. Secondo l’Aie, nel 2026 la domanda mondiale di petrolio rallenterà, ma l’offerta non riuscirà comunque a soddisfare i consumi a causa delle pesanti perdite di approvvigionamento legate al blocco dello Stretto di Hormuz. Oltre 14 milioni di barili al giorno risultano attualmente bloccati e le scorte globali stanno diminuendo a ritmi record. L’Aie parla di uno “shock di offerta senza precedenti”. Intanto l’Ue, attraverso il commissario all’Energia Dan Jorgensen, rilancia sulla necessità di accelerare la transizione energetica per ridurre la dipendenza dai combustibili fossili. Interviene Sissi Bellomo, Il Sole 24 Ore.

Paolo VerriPresidente Fondazione Mondadoriwww.fondazionemondadori.itLa Fondazione Mondadori al Salone Internazionale del Libro di Torino  Carlo Fruttero torna al centro della scena: a cent'anni dalla nascita, la Fondazione Mondadori ne rilancia il lavoro editoriale, di intellettuale e scrittore e la figura pubblica con una mostra al Circolo dei lettori e delle lettrici e un programma di appuntamenti al Salone Internazionale del Libro di Torino che ne attraversa eredità e attualità. Dal 14 al 18 maggio 2026, nello Stand T93 del Padiglione Oval, la Fondazione Mondadori presenta un calendario che tiene insieme ricerca d'archivio, formazione e confronto sul presente dell'editoria, a partire dal centenario di Fruttero.Fruttero100. Cretini, nottambuli e altre storie è un incontro sulla verve di Carlo Fruttero, maestro di scrittura, editoria, comicità, cultura, raccontata con le sue parole e dal punto di vista dei suoi compagni di letteratura e di vita, dagli esordi all'Avanti fino alla Prevalenza del Cretino e alle Donne informate dei fatti, passando per la lettura dei Nottambuli. Intervengono Carlotta Fruttero, Angela Finocchiaro, Alessandro Piperno, Michele Serra; modera Giacomo Papi (giovedì 14, ore 17-18, Lingotto, Padiglione 1, Sala Rossa).La Finale della Finale è la premiazione del concorso per le scuole di lettura per il centenario: oltre 400 giovani, lombardi e piemontesi, hanno riletto ma anche riscritto i libri di Carlo Fruttero, quelli di cui è unico autore e quelli a quattro mani con Franco Lucentini. I risultati, divertenti e innovativi, vengono portati in discussione dai giovani studenti delle scuole superiori e premiati al Salone (lunedì 18, ore 13.45-14.45, Lingotto, Piazzale Oval, Sala Bianca).Con Maremma Fruttero, venerdì 15 alle 11, Carlotta Fruttero e Paolo Verri presentano le attività del Club Fruttero in Toscana: un progetto che sposta l'attenzione dal ricordo alla pratica, attivando nuovi contesti e pubblici.Infine, la semifinale della competizione per scrittori emergenti Incipit Offresi si intreccia con il centenario, mettendo in relazione nuove voci e un modello di scrittura capace di parlare ancora al presente (sabato 16, ore 18). Accanto al focus su Fruttero, la Fondazione Mondadori porta al Salone del Libro altri momenti per raccontare il lavoro sugli archivi come strumenti attivi di lettura del presente, mettendo in relazione ricerca, produzione culturale e formazione.Giovedì 14, ore 15-16, è in programma la premiazione di Futura che conclude un percorso di selezione e accompagnamento di autori emergenti con un'attenzione concreta alla costruzione di nuove scritture. Per le Olimpiadi Invernali di Milano Cortina 2026, oltre 400 racconti hanno provato a definire i nuovi volti e le nuove storie della Lombardia, una regione in continuo cambiamento. I 26 vincitori sono al Salone per raccontare il loro punto di vista raccolto in volume e distribuito nell'occasione. Il concorso è stato indetto in collaborazione con la Regione Lombardia. Intervengono Francesca Caruso, Fabio Del Giudice, Pierluigi Panza; modera Paolo Verri (Lingotto, Padiglione 2, Sala Rosa).Il futuro del libro e altri futuri presenta la 25esima edizione del Master in editoria dell'Università Statale di Milano di Fondazione Mondadori e AIE. Con Fabio Del Giudice, Mauro Novelli, Paolo Verri (lunedì 18, ore 11.30-12.30, Lingotto, Piazzale Oval, Sala Bianca).Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.IL POSTO DELLE PAROLEascoltare fa pensarehttps://ilpostodelleparole.it/

Julien Villeret dirige l'innovation du groupe EDF, on s'est retrouvés un jour de pluie, ce qui tombait plutôt bien pour parler d'énergie. Julien est l'un de ces rares interlocuteurs capables de parler du mix énergétique français sans perdre la nuance ni tomber dans le discours institutionnel. Il connaît le sujet de l'intérieur, et il n'a pas peur d'aller là où ça grince.Dans cet épisode, nous parlons de nucléaire, bien sûr, mais aussi de ce qu'on ne comprend pas sur l'électricité en général. J'ai questionné Julien sur les déchets nucléaires (leur volume réel vous va surprendre), sur les compétences qu'on a perdues en arrêtant de construire des centrales, sur pourquoi une voiture électrique en Allemagne, c'est techniquement une voiture au charbon, et sur l'hydrogène, qu'on nous vend comme la grande révolution alors que la réalité est beaucoup plus complexe.On parle aussi de fusion nucléaire, de SMR, de la panne en Espagne, du compteur Linky, de l'IA et de sa consommation d'énergie, et des SAF, ces carburants d'aviation qui permettent de voler à neutralité carbone dès aujourd'hui.Ce qui m'a frappé dans cet échange, c'est la posture. Julien ne survend pas, il ne minimise pas. Il essaie juste de remettre des faits là où il y a trop souvent des fantasmes.CITATIONS MARQUANTES1. "Si on n'a plus d'énergie, on n'a plus de plastique. Et si on n'a plus de plastique, on n'a plus d'hôpitaux." (Julien Villeret, ~0:03:44)2. "Tous les déchets nucléaires produits par le parc français depuis les années 60, c'est en gros deux piscines olympiques en volume." (Julien Villeret, ~0:17:25)3. "Une centrale nucléaire, ça ne peut pas exploser. C'est un fantasme." (Julien Villeret, ~0:36:26)4. "Les plus grandes batteries du monde aujourd'hui, ce sont des barrages." (Julien Villeret, ~0:11:15)5. "On a arrêté de construire des centrales pour des raisons idéologiques. Les gens qui savaient faire sont partis à la retraite." (Julien Villeret, ~0:26:25)IDÉES MARQUANTES1. L'énergie est consubstantielle à la civilisation, pas optionnelle Timestamp : ~0:02:51 L'énergie n'est pas un confort ou un luxe, c'est le socle de tout : la santé, la nourriture, la fabrication industrielle, la vie moderne dans son entier. Le rejet d'une écologie radicale par les populations vient en partie de là : on leur demande de renoncer à quelque chose qui est aussi fondamental que l'air qu'ils respirent. Pourquoi c'est important : tant qu'on ne pose pas ce cadre, on ne peut pas avoir un débat énergétique honnête.2. L'électricité propre ou sale dépend de comment elle est produite, pas de comment elle est consommée Timestamp : ~0:07:00 Une voiture électrique en France est l'une des plus propres au monde. La même voiture en Allemagne fonctionne au charbon. Ce n'est pas l'usage qui définit l'empreinte carbone, c'est la chaîne de production entière. Pourquoi c'est important : ça remet en question beaucoup de discours simplistes sur la mobilité électrique et force à penser en systèmes.3. Les barrages hydrauliques sont les plus grandes batteries du monde Timestamp : ~0:10:18 L'eau stockée dans un barrage, c'est de l'électricité en réserve. On ouvre ou on ferme selon le besoin. C'est une batterie géante, naturelle, disponible immédiatement. La France l'utilise pour réguler son réseau depuis des décennies. Pourquoi c'est important : cette réalité physique remet en question l'idée que le stockage d'électricité est un problème sans solution.4. Les compétences nucléaires se perdent quand on arrête de construire Timestamp : ~0:26:08 La France a arrêté de construire des centrales pour des raisons politiques. Résultat : les ingénieurs et soudeurs spécialisés ont vieilli et pris leur retraite, et les jeunes ne se sont pas formés sur des métiers qu'on disait sans avenir. Aujourd'hui, EDF recrute 10 000 personnes par an pour rattraper le retard. Pourquoi c'est important : les décisions politiques sur l'énergie ont des conséquences industrielles qui prennent des décennies à corriger.5. Penser l'énergie en statique est une erreur de raisonnement Timestamp : ~0:47:53 Il y a 15 ans, on prédisait que les data centers représenteraient 10% de la consommation mondiale d'électricité. Aujourd'hui on est à 2,2%. Pourquoi ? Parce que les technologies deviennent plus efficaces au fur et à mesure. Tirer la droite et extrapoler lineairement est une erreur systématique dans tous les grands débats énergétiques. Pourquoi c'est important : c'est le même réflexe qu'on applique aujourd'hui à l'IA, et probablement avec les mêmes erreurs de projection.6. La fusion nucléaire : entre le Graal et la promesse impossible Timestamp : ~1:01:58 La fusion produirait une énergie presque illimitée, décarbonée, peu coûteuse et quasi sans déchets. C'est la centrale nucléaire idéale sur le papier. Sauf qu'on ne sait pas encore si on arrivera à la construire, et que les horizons varient de 2035 (optimistes) à 2070 (scientifiques). Les premières centrales en production : probablement 2080-2100. Pourquoi c'est important : ça relativise les discours apocalyptiques sur l'énergie et rappelle qu'on a des décennies pour construire, pas juste quelques années.7. L'hydrogène vert : trop cher, trop dangereux pour la mobilité légère Timestamp : ~1:07:41 EDF ne croit pas à l'hydrogène pour les voitures particulières. Trop cher à produire, trop dangereux à stocker sous pression, infrastructure à construire from scratch. En revanche, pour les bus et les camions approvisionnés depuis une station centralisée, ça peut faire du sens. Les avions, eux, se tournent vers les SAF (Sustainable Aviation Fuels), qui sont opérationnels dès aujourd'hui. Pourquoi c'est important : l'hydrogène est massivement sur-promu dans le débat public, et la réalité industrielle est beaucoup plus about de niche use cases que de révolution générale. QUESTIONS POSÉES DANS L'INTERVIEWQu'est-ce que les gens ne comprennent pas sur l'énergie, et ce serait bien qu'ils comprennent ?Est-ce que le rejet de l'écologie radicale vient du fait qu'on demande aux gens d'arrêter quelque chose de consubstantiel à leur vie ?Comment chez EDF observez-vous l'évolution de la consommation d'énergie, notamment la tension entre développement des usages et efficacité énergétique ?Quelle est l'intermittence réelle des éoliennes et des panneaux solaires, en chiffres concrets ?Qu'est-ce que le compteur Linky exactement, et pourquoi a-t-il généré autant de fantasmes ?Où en est-on de l'innovation sur les déchets nucléaires, et peut-on les recycler ?La France a-t-elle perdu des compétences nucléaires en arrêtant de construire ? Lesquelles ?Est-ce que les SMR (Small Modular Reactors) peuvent accélérer le déploiement du nucléaire ?Est-ce que l'IA et la blockchain vont créer une pénurie d'électricité, ou est-ce une projection trop statique ?Pourquoi l'hydrogène ne fonctionnera probablement pas pour la mobilité légère, et où peut-il avoir du sens ?RÉFÉRENCES CITÉESSites / DonnéesOur World in Data (mentionné comme "The World in Data") : site recommandé par Julien pour visualiser l'évolution du bien-être mondial sur 100-300 ans. (~1:16:20)Agence mondiale de l'énergie (AIE) : citée sur les prévisions de consommation électrique liée à l'IA. (~0:49:30)Institutions / OrganismesANDRA (Agence nationale pour la gestion des déchets radioactifs) : gestion des déchets nucléaires en France. (~0:17:25)Autorité de Sûreté Nucléaire (ASN) : régulation et surveillance du parc nucléaire français. (~0:17:25)ITER : projet international de fusion nucléaire basé en France. (~1:03:30)Enedis : opérateur du réseau de distribution électrique, gestionnaire du compteur Linky (distinct d'EDF). (~0:14:44)RTE : réseau de transport d'électricité française. (~0:44:12)ANSI / ANSSI : agence nationale de sécurité des systèmes d'information, mentionnée pour la cybersécurité des infrastructures. (~0:46:45)Projets / TechnologiesCIGEO : projet d'enfouissement des déchets nucléaires dans des couches géologiques profondes, mené par l'ANDRA. (~0:18:30)Flamanville 3 : prochain réacteur nucléaire français, sur le point d'être raccordé au réseau. (~0:21:03)Hinkley Point C : réacteur en construction au Royaume-Uni par EDF. (~0:28:18)Sizewell : projet de réacteur au Royaume-Uni. (~0:28:18)New World (projet EDF) : SMR développé par EDF. (~0:42:17)SAF / e-fuel (Sustainable Aviation Fuels) : carburant d'aviation bas carbone, obligation réglementaire croissante en Europe. (~1:12:32)ÉvénementsAccident de Fukushima : analysé en détail comme tsunami avant d'être un accident nucléaire, utilisé comme base d'apprentissage mondial. (~0:19:00)Panne électrique en Espagne et Portugal : analysée comme "orage parfait" lié à la nature analogique de l'électricité. (~0:51:33)Record d'exportation d'électricité EDF : 90 TWh exportés, record historique. (~0:48:11)Découverte scientifiqueHydrogène blanc : gisement potentiellement record découvert en France, hydrogène naturel présent dans le sol. (~1:06:40)TIMESTAMPS CLÉS (YouTube)00:00 Introduction : et si on se réjouissait à nouveau du futur ? 01:55 Présentation de Julien Villeret, directeur de l'innovation EDF02:05 L'énergie, c'est quoi au fond ? Ce que les gens ne comprennent pas L'énergie est consubstantielle à la civilisation depuis toujours. Sans électricité aujourd'hui, on perd tout : la santé, la nourriture, la fabrication industrielle. C'est le cadre que pose Julien avant d'aborder quoi que ce soit.04:18 Pourquoi l'écologie radicale ne passe pas dans l'opinion publique Le rejet du discours radical vient d'une réalité simple : on ne peut pas demander aux gens d'arrêter quelque chose d'aussi fondamental que l'énergie. La vraie question n'est pas d'arrêter, c'est comment produire et consommer différemment.06:29 Le pic du charbon et la réalité du mix énergétique mondial On continue de brûler beaucoup de charbon pour produire de l'électricité, notamment en Allemagne et en Pologne. Ce qui explique directement le sujet suivant.06:51 Voiture électrique en Allemagne = voiture au charbon ? Si l'électricité est produite au charbon, une voiture électrique n'est pas vertueuse. La chaîne complète de production compte, pas seulement le mode de transport. La France à 98% sans CO2 est une exception mondiale.08:37 Peut-on imaginer 100% d'énergie renouvelable ? Techniquement oui, économiquement non. Le problème de l'intermittence (les renouvelables produisent environ 25-30% du temps) et du coût du stockage rendrait la facture 10 à 20 fois plus élevée qu'aujourd'hui.10:18 Les barrages : les plus grandes batteries du monde L'eau stockée dans un barrage, c'est de l'électricité en réserve. Un lac, c'est une batterie géante naturelle. Les barrages hydroélectriques sont aussi des outils de régulation du réseau, activés ou coupés selon les besoins du moment.13:30 L'intermittence des renouvelables en chiffres concrets Éoliennes et panneaux solaires produisent à pleine puissance environ 25 à 30% du temps. Le pic de production solaire est autour de midi, soit rarement au moment des pics de consommation (matin, soir).14:34 Le compteur Linky : derrière les fantasmes, la réalité Linky ne surveille personne. Il envoie l'index de consommation une fois par jour, pendant 10 secondes, via les fils électriques, sans aucune émission d'ondes. Le détail au quart d'heure est opt-in. Ce sont surtout des fraudeurs que Linky a gênés.17:05 Les déchets nucléaires : vraiment deux piscines olympiques depuis les années 60 Tout le parc nucléaire français depuis le début des années 60 a produit environ 4 000 m3 de déchets à longue vie, soit deux piscines olympiques. Ils sont stockés à La Hague dans de l'eau (meilleur protecteur contre les radiations), avec un projet d'enfouissement géologique profond (CIGEO).21:47 Peut-on recycler les déchets nucléaires ? Oui, une partie du combustible usé est retraitée et réinjectée dans les centrales. Des recherches sont en cours pour fermer complètement le cycle : des réacteurs qui réutilisent en permanence le même combustible sans presque générer de déchets. Horizon : 2050-2070.22:53 Dépendances géopolitiques : uranium, gaz, pétrole, panneaux solaires Le pétrole et le gaz viennent du Moyen-Orient, de Russie et des États-Unis. Les panneaux solaires viennent quasi-exclusivement de Chine. L'uranium, lui, est présent dans de nombreux pays, n'est pas cher, et est stocké sur plusieurs années par sécurité.26:08 Les compétences nucléaires perdues et les 10 000 recrutements par an En arrêtant de construire des centrales pour des raisons politiques, la France a perdu des savoir-faire spécifiques : béton nucléaire, générateurs de vapeur, soudure qualifiée. EDF recrute maintenant 10 000 personnes par an pour reconstruire ces compétences. Un soudeur nucléaire gagne entre 3 000 et 4 000 euros par mois.32:04 Où seront construits les 6 nouveaux réacteurs français ? Sur les terrains déjà acquis à côté des centrales existantes (ex : Penly). Les riverains d'une centrale sont généralement très favorables : emplois, taxes locales, vie locale développée. Une centrale qui ne tourne pas, c'est un million d'euros de pertes par jour.36:21 Une centrale peut-elle exploser ? Les accidents nucléaires démystifiés Non, les centrales françaises ne peuvent pas exploser. Fukushima était d'abord un tsunami, pas un accident nucléaire au sens strict. Depuis, toutes les centrales françaises ont été équipées de générateurs diesel en hauteur et de récupérateurs (les "cendriers") pour le cas où le coeur fondrait.41:42 Les SMR (Small Modular Reactors) : l'avenir du nucléaire ou juste une promesse ? Aucun SMR n'est encore construit à ce jour. L'idée : des petits réacteurs plus rapides à déployer, moins coûteux, qui peuvent remplacer une centrale charbon en plug and play. Les Américains y croient surtout pour décarboner leur vieux parc charbon.45:13 Cybersécurité des centrales : isolées d'internet par principe physique Les systèmes qui font fonctionner les centrales nucléaires ne sont pas connectés à internet. C'est une barrière physique, pas logicielle. EDF mobilise plusieurs centaines de personnes à temps plein sur la cybersécurité.46:45 IA et consommation d'énergie : une vraie menace ou un raisonnement trop statique ? Il y a 15 ans, on prédisait que les data centers allaient représenter 10% de la consommation mondiale d'électricité. On en est à 2,2%. Les projections en ligne droite tombent toujours à côté parce qu'elles ignorent les gains d'efficacité technologique. En France, la marge est très large : EDF a exporté un record historique de 90 TWh l'année dernière.51:33 La panne en Espagne-Portugal : l'analogique contre le numérique L'électricité est analogique : production doit en permanence égaler consommation. Un écart provoque l'effondrement. En Espagne, une suite de problèmes improbables arrivés en même temps (un "orage parfait") a déstabilisé le réseau. La France s'est déconnectée pour éviter d'être entraînée dans la chute.56:41 Géothermie : pourquoi elle n'a pas décollé en France La géothermie dépend des choix de subvention publique. L'Allemagne l'a financée, la France non. En France, l'électricité est peu chère et faiblement carbonée, donc l'incentive est quasi nul. Installer de la géothermie en retrofit exige de tout creuser. La géothermie profonde pose en plus des risques sismiques.1:01:58 Fusion nucléaire : le Graal énergétique, entre 2035 et 2070 La fusion produirait une énergie quasi-illimitée, décarbonée, peu coûteuse et presque sans déchets. Les scientifiques parlent de premiers prototypes vers 2060-2070, les start-ups d'une dizaine d'années plus tôt. On a récemment réussi pour la première fois à produire plus d'énergie qu'on n'en consomme dans une réaction de fusion. Même si ça arrive, les premières centrales en production seront probablement vers 2080-2100.1:06:40 Hydrogène : blanc, vert, gris. Ce que chacun veut dire vraiment L'hydrogène gris (produit industriellement) est très polluant. Le vert (via électrolyse) est très cher. Le blanc (naturel, dans le sol) est encore expérimental. EDF ne croit pas à l'hydrogène pour les voitures particulières : trop dangereux, trop cher, réseau à construire from scratch. Pour les bus et camions sur station centralisée, ça peut avoir du sens.1:11:23 Aviation à hydrogène et SAF : ce qu'on peut espérer vraiment Airbus a repoussé son projet d'avion hydrogène à 2050. L'aviation mise aujourd'hui sur les SAF (Sustainable Aviation Fuels) : des carburants produits à partir de CO2 capté dans l'air, déjà présents dans les réservoirs des avions Air France. C'est l'horizon réaliste, avant peut-être un avion électrique pour les courtes distances (Paris-Berlin, lignes régionales), d'ici 2030.1:15:50 Pourquoi il y a quand même des raisons d'espérer Julien conclut sur une conviction : en regardant sur le temps long, le monde va mieux. The World in Data le montre sur 200 ans. Dans l'énergie, on est passé des voitures à particules des années 50 à l'électricité bas carbone d'aujourd'hui, en 60-70 ans. Et on surestime toujours les transformations à court terme tout en les sous-estimant à long terme.1:19:44 Clap de fin : ouvrir la porte à la nuance Suggestion d'autres épisodes à écouter : #391 L'indépendance énergétique est-elle sous nos pieds? Avec Pierre Brossolet (https://audmns.com/fcRUEpN) #187 Energy Observer: envisager le futur de l'énergie avec Louis Noel Viviès (https://audmns.com/vJdRdXI) Vlan #131 Transition énergétique: ce qu'un adulte devrait savoir avec Matthieu Auzanneau (https://audmns.com/SPHszOf)Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Julien Villeret dirige l'innovation du groupe EDF, on s'est retrouvés un jour de pluie, ce qui tombait plutôt bien pour parler d'énergie. Julien est l'un de ces rares interlocuteurs capables de parler du mix énergétique français sans perdre la nuance ni tomber dans le discours institutionnel. Il connaît le sujet de l'intérieur, et il n'a pas peur d'aller là où ça grince.Dans cet épisode, nous parlons de nucléaire, bien sûr, mais aussi de ce qu'on ne comprend pas sur l'électricité en général. J'ai questionné Julien sur les déchets nucléaires (leur volume réel vous va surprendre), sur les compétences qu'on a perdues en arrêtant de construire des centrales, sur pourquoi une voiture électrique en Allemagne, c'est techniquement une voiture au charbon, et sur l'hydrogène, qu'on nous vend comme la grande révolution alors que la réalité est beaucoup plus complexe.On parle aussi de fusion nucléaire, de SMR, de la panne en Espagne, du compteur Linky, de l'IA et de sa consommation d'énergie, et des SAF, ces carburants d'aviation qui permettent de voler à neutralité carbone dès aujourd'hui.Ce qui m'a frappé dans cet échange, c'est la posture. Julien ne survend pas, il ne minimise pas. Il essaie juste de remettre des faits là où il y a trop souvent des fantasmes.CITATIONS MARQUANTES1. "Si on n'a plus d'énergie, on n'a plus de plastique. Et si on n'a plus de plastique, on n'a plus d'hôpitaux." (Julien Villeret, ~0:03:44)2. "Tous les déchets nucléaires produits par le parc français depuis les années 60, c'est en gros deux piscines olympiques en volume." (Julien Villeret, ~0:17:25)3. "Une centrale nucléaire, ça ne peut pas exploser. C'est un fantasme." (Julien Villeret, ~0:36:26)4. "Les plus grandes batteries du monde aujourd'hui, ce sont des barrages." (Julien Villeret, ~0:11:15)5. "On a arrêté de construire des centrales pour des raisons idéologiques. Les gens qui savaient faire sont partis à la retraite." (Julien Villeret, ~0:26:25)IDÉES MARQUANTES1. L'énergie est consubstantielle à la civilisation, pas optionnelle Timestamp : ~0:02:51 L'énergie n'est pas un confort ou un luxe, c'est le socle de tout : la santé, la nourriture, la fabrication industrielle, la vie moderne dans son entier. Le rejet d'une écologie radicale par les populations vient en partie de là : on leur demande de renoncer à quelque chose qui est aussi fondamental que l'air qu'ils respirent. Pourquoi c'est important : tant qu'on ne pose pas ce cadre, on ne peut pas avoir un débat énergétique honnête.2. L'électricité propre ou sale dépend de comment elle est produite, pas de comment elle est consommée Timestamp : ~0:07:00 Une voiture électrique en France est l'une des plus propres au monde. La même voiture en Allemagne fonctionne au charbon. Ce n'est pas l'usage qui définit l'empreinte carbone, c'est la chaîne de production entière. Pourquoi c'est important : ça remet en question beaucoup de discours simplistes sur la mobilité électrique et force à penser en systèmes.3. Les barrages hydrauliques sont les plus grandes batteries du monde Timestamp : ~0:10:18 L'eau stockée dans un barrage, c'est de l'électricité en réserve. On ouvre ou on ferme selon le besoin. C'est une batterie géante, naturelle, disponible immédiatement. La France l'utilise pour réguler son réseau depuis des décennies. Pourquoi c'est important : cette réalité physique remet en question l'idée que le stockage d'électricité est un problème sans solution.4. Les compétences nucléaires se perdent quand on arrête de construire Timestamp : ~0:26:08 La France a arrêté de construire des centrales pour des raisons politiques. Résultat : les ingénieurs et soudeurs spécialisés ont vieilli et pris leur retraite, et les jeunes ne se sont pas formés sur des métiers qu'on disait sans avenir. Aujourd'hui, EDF recrute 10 000 personnes par an pour rattraper le retard. Pourquoi c'est important : les décisions politiques sur l'énergie ont des conséquences industrielles qui prennent des décennies à corriger.5. Penser l'énergie en statique est une erreur de raisonnement Timestamp : ~0:47:53 Il y a 15 ans, on prédisait que les data centers représenteraient 10% de la consommation mondiale d'électricité. Aujourd'hui on est à 2,2%. Pourquoi ? Parce que les technologies deviennent plus efficaces au fur et à mesure. Tirer la droite et extrapoler lineairement est une erreur systématique dans tous les grands débats énergétiques. Pourquoi c'est important : c'est le même réflexe qu'on applique aujourd'hui à l'IA, et probablement avec les mêmes erreurs de projection.6. La fusion nucléaire : entre le Graal et la promesse impossible Timestamp : ~1:01:58 La fusion produirait une énergie presque illimitée, décarbonée, peu coûteuse et quasi sans déchets. C'est la centrale nucléaire idéale sur le papier. Sauf qu'on ne sait pas encore si on arrivera à la construire, et que les horizons varient de 2035 (optimistes) à 2070 (scientifiques). Les premières centrales en production : probablement 2080-2100. Pourquoi c'est important : ça relativise les discours apocalyptiques sur l'énergie et rappelle qu'on a des décennies pour construire, pas juste quelques années.7. L'hydrogène vert : trop cher, trop dangereux pour la mobilité légère Timestamp : ~1:07:41 EDF ne croit pas à l'hydrogène pour les voitures particulières. Trop cher à produire, trop dangereux à stocker sous pression, infrastructure à construire from scratch. En revanche, pour les bus et les camions approvisionnés depuis une station centralisée, ça peut faire du sens. Les avions, eux, se tournent vers les SAF (Sustainable Aviation Fuels), qui sont opérationnels dès aujourd'hui. Pourquoi c'est important : l'hydrogène est massivement sur-promu dans le débat public, et la réalité industrielle est beaucoup plus about de niche use cases que de révolution générale. QUESTIONS POSÉES DANS L'INTERVIEWQu'est-ce que les gens ne comprennent pas sur l'énergie, et ce serait bien qu'ils comprennent ?Est-ce que le rejet de l'écologie radicale vient du fait qu'on demande aux gens d'arrêter quelque chose de consubstantiel à leur vie ?Comment chez EDF observez-vous l'évolution de la consommation d'énergie, notamment la tension entre développement des usages et efficacité énergétique ?Quelle est l'intermittence réelle des éoliennes et des panneaux solaires, en chiffres concrets ?Qu'est-ce que le compteur Linky exactement, et pourquoi a-t-il généré autant de fantasmes ?Où en est-on de l'innovation sur les déchets nucléaires, et peut-on les recycler ?La France a-t-elle perdu des compétences nucléaires en arrêtant de construire ? Lesquelles ?Est-ce que les SMR (Small Modular Reactors) peuvent accélérer le déploiement du nucléaire ?Est-ce que l'IA et la blockchain vont créer une pénurie d'électricité, ou est-ce une projection trop statique ?Pourquoi l'hydrogène ne fonctionnera probablement pas pour la mobilité légère, et où peut-il avoir du sens ?RÉFÉRENCES CITÉESSites / DonnéesOur World in Data (mentionné comme "The World in Data") : site recommandé par Julien pour visualiser l'évolution du bien-être mondial sur 100-300 ans. (~1:16:20)Agence mondiale de l'énergie (AIE) : citée sur les prévisions de consommation électrique liée à l'IA. (~0:49:30)Institutions / OrganismesANDRA (Agence nationale pour la gestion des déchets radioactifs) : gestion des déchets nucléaires en France. (~0:17:25)Autorité de Sûreté Nucléaire (ASN) : régulation et surveillance du parc nucléaire français. (~0:17:25)ITER : projet international de fusion nucléaire basé en France. (~1:03:30)Enedis : opérateur du réseau de distribution électrique, gestionnaire du compteur Linky (distinct d'EDF). (~0:14:44)RTE : réseau de transport d'électricité française. (~0:44:12)ANSI / ANSSI : agence nationale de sécurité des systèmes d'information, mentionnée pour la cybersécurité des infrastructures. (~0:46:45)Projets / TechnologiesCIGEO : projet d'enfouissement des déchets nucléaires dans des couches géologiques profondes, mené par l'ANDRA. (~0:18:30)Flamanville 3 : prochain réacteur nucléaire français, sur le point d'être raccordé au réseau. (~0:21:03)Hinkley Point C : réacteur en construction au Royaume-Uni par EDF. (~0:28:18)Sizewell : projet de réacteur au Royaume-Uni. (~0:28:18)New World (projet EDF) : SMR développé par EDF. (~0:42:17)SAF / e-fuel (Sustainable Aviation Fuels) : carburant d'aviation bas carbone, obligation réglementaire croissante en Europe. (~1:12:32)ÉvénementsAccident de Fukushima : analysé en détail comme tsunami avant d'être un accident nucléaire, utilisé comme base d'apprentissage mondial. (~0:19:00)Panne électrique en Espagne et Portugal : analysée comme "orage parfait" lié à la nature analogique de l'électricité. (~0:51:33)Record d'exportation d'électricité EDF : 90 TWh exportés, record historique. (~0:48:11)Découverte scientifiqueHydrogène blanc : gisement potentiellement record découvert en France, hydrogène naturel présent dans le sol. (~1:06:40)TIMESTAMPS CLÉS (YouTube)00:00 Introduction : et si on se réjouissait à nouveau du futur ? 01:55 Présentation de Julien Villeret, directeur de l'innovation EDF02:05 L'énergie, c'est quoi au fond ? Ce que les gens ne comprennent pas L'énergie est consubstantielle à la civilisation depuis toujours. Sans électricité aujourd'hui, on perd tout : la santé, la nourriture, la fabrication industrielle. C'est le cadre que pose Julien avant d'aborder quoi que ce soit.04:18 Pourquoi l'écologie radicale ne passe pas dans l'opinion publique Le rejet du discours radical vient d'une réalité simple : on ne peut pas demander aux gens d'arrêter quelque chose d'aussi fondamental que l'énergie. La vraie question n'est pas d'arrêter, c'est comment produire et consommer différemment.06:29 Le pic du charbon et la réalité du mix énergétique mondial On continue de brûler beaucoup de charbon pour produire de l'électricité, notamment en Allemagne et en Pologne. Ce qui explique directement le sujet suivant.06:51 Voiture électrique en Allemagne = voiture au charbon ? Si l'électricité est produite au charbon, une voiture électrique n'est pas vertueuse. La chaîne complète de production compte, pas seulement le mode de transport. La France à 98% sans CO2 est une exception mondiale.08:37 Peut-on imaginer 100% d'énergie renouvelable ? Techniquement oui, économiquement non. Le problème de l'intermittence (les renouvelables produisent environ 25-30% du temps) et du coût du stockage rendrait la facture 10 à 20 fois plus élevée qu'aujourd'hui.10:18 Les barrages : les plus grandes batteries du monde L'eau stockée dans un barrage, c'est de l'électricité en réserve. Un lac, c'est une batterie géante naturelle. Les barrages hydroélectriques sont aussi des outils de régulation du réseau, activés ou coupés selon les besoins du moment.13:30 L'intermittence des renouvelables en chiffres concrets Éoliennes et panneaux solaires produisent à pleine puissance environ 25 à 30% du temps. Le pic de production solaire est autour de midi, soit rarement au moment des pics de consommation (matin, soir).14:34 Le compteur Linky : derrière les fantasmes, la réalité Linky ne surveille personne. Il envoie l'index de consommation une fois par jour, pendant 10 secondes, via les fils électriques, sans aucune émission d'ondes. Le détail au quart d'heure est opt-in. Ce sont surtout des fraudeurs que Linky a gênés.17:05 Les déchets nucléaires : vraiment deux piscines olympiques depuis les années 60 Tout le parc nucléaire français depuis le début des années 60 a produit environ 4 000 m3 de déchets à longue vie, soit deux piscines olympiques. Ils sont stockés à La Hague dans de l'eau (meilleur protecteur contre les radiations), avec un projet d'enfouissement géologique profond (CIGEO).21:47 Peut-on recycler les déchets nucléaires ? Oui, une partie du combustible usé est retraitée et réinjectée dans les centrales. Des recherches sont en cours pour fermer complètement le cycle : des réacteurs qui réutilisent en permanence le même combustible sans presque générer de déchets. Horizon : 2050-2070.22:53 Dépendances géopolitiques : uranium, gaz, pétrole, panneaux solaires Le pétrole et le gaz viennent du Moyen-Orient, de Russie et des États-Unis. Les panneaux solaires viennent quasi-exclusivement de Chine. L'uranium, lui, est présent dans de nombreux pays, n'est pas cher, et est stocké sur plusieurs années par sécurité.26:08 Les compétences nucléaires perdues et les 10 000 recrutements par an En arrêtant de construire des centrales pour des raisons politiques, la France a perdu des savoir-faire spécifiques : béton nucléaire, générateurs de vapeur, soudure qualifiée. EDF recrute maintenant 10 000 personnes par an pour reconstruire ces compétences. Un soudeur nucléaire gagne entre 3 000 et 4 000 euros par mois.32:04 Où seront construits les 6 nouveaux réacteurs français ? Sur les terrains déjà acquis à côté des centrales existantes (ex : Penly). Les riverains d'une centrale sont généralement très favorables : emplois, taxes locales, vie locale développée. Une centrale qui ne tourne pas, c'est un million d'euros de pertes par jour.36:21 Une centrale peut-elle exploser ? Les accidents nucléaires démystifiés Non, les centrales françaises ne peuvent pas exploser. Fukushima était d'abord un tsunami, pas un accident nucléaire au sens strict. Depuis, toutes les centrales françaises ont été équipées de générateurs diesel en hauteur et de récupérateurs (les "cendriers") pour le cas où le coeur fondrait.41:42 Les SMR (Small Modular Reactors) : l'avenir du nucléaire ou juste une promesse ? Aucun SMR n'est encore construit à ce jour. L'idée : des petits réacteurs plus rapides à déployer, moins coûteux, qui peuvent remplacer une centrale charbon en plug and play. Les Américains y croient surtout pour décarboner leur vieux parc charbon.45:13 Cybersécurité des centrales : isolées d'internet par principe physique Les systèmes qui font fonctionner les centrales nucléaires ne sont pas connectés à internet. C'est une barrière physique, pas logicielle. EDF mobilise plusieurs centaines de personnes à temps plein sur la cybersécurité.46:45 IA et consommation d'énergie : une vraie menace ou un raisonnement trop statique ? Il y a 15 ans, on prédisait que les data centers allaient représenter 10% de la consommation mondiale d'électricité. On en est à 2,2%. Les projections en ligne droite tombent toujours à côté parce qu'elles ignorent les gains d'efficacité technologique. En France, la marge est très large : EDF a exporté un record historique de 90 TWh l'année dernière.51:33 La panne en Espagne-Portugal : l'analogique contre le numérique L'électricité est analogique : production doit en permanence égaler consommation. Un écart provoque l'effondrement. En Espagne, une suite de problèmes improbables arrivés en même temps (un "orage parfait") a déstabilisé le réseau. La France s'est déconnectée pour éviter d'être entraînée dans la chute.56:41 Géothermie : pourquoi elle n'a pas décollé en France La géothermie dépend des choix de subvention publique. L'Allemagne l'a financée, la France non. En France, l'électricité est peu chère et faiblement carbonée, donc l'incentive est quasi nul. Installer de la géothermie en retrofit exige de tout creuser. La géothermie profonde pose en plus des risques sismiques.1:01:58 Fusion nucléaire : le Graal énergétique, entre 2035 et 2070 La fusion produirait une énergie quasi-illimitée, décarbonée, peu coûteuse et presque sans déchets. Les scientifiques parlent de premiers prototypes vers 2060-2070, les start-ups d'une dizaine d'années plus tôt. On a récemment réussi pour la première fois à produire plus d'énergie qu'on n'en consomme dans une réaction de fusion. Même si ça arrive, les premières centrales en production seront probablement vers 2080-2100.1:06:40 Hydrogène : blanc, vert, gris. Ce que chacun veut dire vraiment L'hydrogène gris (produit industriellement) est très polluant. Le vert (via électrolyse) est très cher. Le blanc (naturel, dans le sol) est encore expérimental. EDF ne croit pas à l'hydrogène pour les voitures particulières : trop dangereux, trop cher, réseau à construire from scratch. Pour les bus et camions sur station centralisée, ça peut avoir du sens.1:11:23 Aviation à hydrogène et SAF : ce qu'on peut espérer vraiment Airbus a repoussé son projet d'avion hydrogène à 2050. L'aviation mise aujourd'hui sur les SAF (Sustainable Aviation Fuels) : des carburants produits à partir de CO2 capté dans l'air, déjà présents dans les réservoirs des avions Air France. C'est l'horizon réaliste, avant peut-être un avion électrique pour les courtes distances (Paris-Berlin, lignes régionales), d'ici 2030.1:15:50 Pourquoi il y a quand même des raisons d'espérer Julien conclut sur une conviction : en regardant sur le temps long, le monde va mieux. The World in Data le montre sur 200 ans. Dans l'énergie, on est passé des voitures à particules des années 50 à l'électricité bas carbone d'aujourd'hui, en 60-70 ans. Et on surestime toujours les transformations à court terme tout en les sous-estimant à long terme.1:19:44 Clap de fin : ouvrir la porte à la nuanceHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

This episode is a wide-ranging conversation between Jacob and Swyx (Shawn Wang), an AI engineer, podcaster, and now operator at Cognition, who sits at a uniquely informed intersection of builder, investor, and community organizer in the AI world. The two cover the current state of the AI engineering zeitgeist: from the stabilization of agent infrastructure and the surprising stickiness of Claude Code, to the competitive dynamics of the AI coding wars, the rise of open models, the threat to traditional SaaS, and the frontier questions around world models, memory, and what it actually means for AI to "understand" something. The episode is grounded in practitioner-level candor, with Swyx offering real takes from running AIE conferences, working inside Cognition, and thinking deeply about what the next wave of AI-native software development looks like.   (0:00) Intro (1:17) What the Top AI Engineers Are Thinking About (2:13) Has AI Infra Finally Stabilized? (6:39) When Does Doing RL In-House Make Sense? (11:26) Why Selling Dev Tools to Agents is Different (17:18) AI Coding Wars (29:04) Consumer AI Plateau (30:22) Codex vs Claude Code (44:52) Future of Open Models   With your co-hosts:  @jacobeffron  - Partner at Redpoint, Former PM Flatiron Health  @patrickachase  - Partner at Redpoint, Former ML Engineer LinkedIn  @ericabrescia  - Former COO Github, Founder Bitnami (acq'd by VMWare)  @jordan_segall  - Partner at Redpoint

Early bird discounts for the San Francisco World's Fair, the biggest AIE gathering of the year, end today - prices will go up by ~$500 tonight so do please lock in ASAP!From near-universal AI tool adoption inside Shopify to internal systems for ML experimentation, auto-research, customer simulation, and ultra-low-latency search, Mikhail Parakhin joins us for a deep dive into what it actually looks like when a 20-year-old, $200B software company goes all-in on AI. We cover why Shopify has become much more vocal about its internal stack, what changed after the December model-quality inflection, and why the real bottleneck in AI coding is no longer generation, but review, CI/CD, and deployment stability.We also go inside Tangle, Tangent, SimGym, which are three major AI initiatives that Shopify is doing to make experimentation reproducible, optimization automatic, customer behavior simulatable, and search and catalog intelligence faster and cheaper at scale. Along the way, Mikhail explains UCP, Liquid AI, and why token budgets are directionally right but often measured badly, why AI-written code can still increase bugs in production, what makes Shopify's customer simulation defensible, and what he learned from the Sydney era at Bing.We discuss:* Mikhail's path from running a major Microsoft business unit spanning Windows, Edge, Bing, and ads to becoming CTO of Shopify* Why Shopify is talking more publicly about AI now, and why staying at the frontier has become necessary for the company* Shopify's internal AI adoption curve, the December inflection, and why CLI-style tools are rising faster than traditional IDE-based tools* Why Jensen Huang is directionally right on token budgets, but raw token count is still the wrong way to evaluate engineering output* Why the real unlock is not more agents in parallel, but better critique loops, stronger models, and spending more on review than generation* Why AI coding can still lead to more bugs in production even if models write cleaner code on average than humans* Why Shopify built its own PR review flow, and why Mikhail thinks most off-the-shelf review tools miss the point* How PR volume, test failures, and deployment rollback are becoming the real bottlenecks in the agent era* Why Git, pull requests, and CI/CD may need a new metaphor once code is written at machine speed* What Tangle is, and how Shopify uses it to make ML and data workflows reproducible, collaborative, and production-ready from the start* Why Tangle is different from Airflow, and why content-addressed caching creates network effects across teams* What Tangent is, and how Shopify is using auto-research loops to optimize search, themes, prompt compression, storage, and more* Why Tangent is becoming a democratizing tool for PMs and domain experts, not just ML engineers* Why AutoML finally feels real in the LLM era, and where auto-research still falls short today* Why Tangle, Tangent, and SimGym become much more powerful when combined into one system* What SimGym is, why simulated customers only work if you have real historical behavior, and why Shopify's data gives it a moat* How SimGym evolved from comparing A/B variants to telling merchants what to change on a single live storefront to raise conversions* Why customer simulation is so expensive, from multimodal models to browser farms to serving and distillation costs* How Shopify models merchant and buyer trajectories, runs counterfactuals, and thinks about interventions like discounts, campaigns, and notifications* Why category-level behavior is so different across commerce, and why ideas like Chinese Restaurant Processes are showing up again in practice* Shopify's new UCP and catalog work, including runtime product search, bulk lookups, and identity linking* Why Shopify is using Liquid AI, and why Mikhail sees it as the first genuinely competitive non-transformer architecture he has used in practice* Where Liquid already works inside Shopify today, from low-latency query understanding to large-scale catalog and Sidekick Pulse workloads* Whether Liquid could become frontier-scale with enough compute, and why Shopify remains pragmatic and merit-based about model choice* Who Shopify is hiring right now across ML, data science, and distributed databases* The Sydney story at Bing, why its personality was not an accident, and what Mikhail learned from deliberately shaping AI character early onMikhail Parakhin* LinkedIn: https://www.linkedin.com/in/mikhail-parakhin/* X: https://x.com/MParakhinTimestamps00:00:00 Introduction: Mikhail Parakhin, Microsoft, and Shopify00:01:16 Why Shopify Is Talking More About AI00:02:29 Internal AI Adoption at Shopify and the December Inflection00:06:54 Token Budgets, Jensen Huang, and Why Usage Metrics Can Mislead00:10:55 Why Shopify Built Its Own AI PR Review System00:12:38 AI Coding, More Bugs, and the Real Deployment Bottleneck00:14:11 Why Git, PRs, and CI/CD May Need to Change for Agents00:18:24 Tangle: Shopify's Reproducible ML and Data Workflow Engine00:21:19 Why Tangle Is Different from Airflow00:26:14 Tangent: Auto Research for Optimization and Experimentation00:30:07 How Tangent Democratizes Experimentation Beyond ML Engineers00:33:06 The Limits of Auto Research00:36:36 Why Tangle, Tangent, and SimGym Compound Together00:37:20 SimGym: Simulating Customers with Shopify's Historical Data00:42:47 The Infra Behind SimGym00:46:00 Why SimGym Gets Better with Real Customer History00:47:30 Counterfactuals, HSTU, and Modeling Merchant Trajectories00:51:55 CRPs, Clustering, and Category-Level Customer Behavior00:53:30 UCP, Shopify Catalog, and Identity Linking00:55:07 Liquid AI: Why Shopify Uses Non-Transformer Models00:59:13 Real Shopify Use Cases for Liquid01:03:00 Can Liquid Scale into a Frontier Model?01:09:49 Hiring at Shopify: ML, Data Science, and Databases01:10:43 Sydney at Bing: Personality Shaping and AI Character01:13:32 Closing ThoughtsTranscript[00:00:00] swyx: Okay. We're here in the studio, a remote studio, with Mikhail Parakhin, CTO of Shopify. Welcome.[00:00:08] Mikhail Parakhin: Thank you. Welcome.[00:00:10] swyx: I don't even know if I should introduce you as CTO of Shopify. I feel like you have many identities. Uh, you led sort of the, the Bing ML team, I guess, uh, uh, or ads team. I, I don't know, I don't know, uh, you know, it's, uh, people va-variously refer you as like CEO or, or, uh, I don't know what that, that, that said previous role at Microsoft was.[00:00:29] Mikhail Parakhin: Uh, that was... Yeah, my previous role w- at Microsoft was the-- I actually was the CEO of one of Microsoft's business units, which included, as I, you know, as we discussed, all the things that people like to laugh about, uh, including Windows and Edge and Bing and ads and everything.[00:00:47] swyx: Yeah, yeah. What a, what a, what a wild time.You've obviously, uh, done a lot since you landed at Shopify. Uh, one of the reasons I reached out was because you started promoting more sort of internal tooling, uh, primarily Tangle, but also a lot of people have seen and adopted Tobi's QMD, uh, and obviously, I think, uh, Shopify has always been sort of leading in terms of, uh, engineering.I think more-- it's just more recent that you guys have been more vocal about your sort of AI adoption. Is that, is that true?[00:01:16] Mikhail Parakhin: Well, I think AI tools in general are fairly recent development, uh, and we've-- Shopify, you know, at this stage of its development, we're developing AI in-in-house and other, uh, building tools that use AI and, you know, interfacing with the wider AI community, uh, you know, are on the sort of the, uh, runaway trajectory.So it just did by sort of natural byproduct. We, we talk about it more also. We just, uh, just even yesterday, Andrej Karpathy was famous in tweeting about, oh, are there some, uh, ways, uh, that, that you can organize your agents to store the data and then, uh, look up the data so that you don't have to research or, or lose context every- Yestime. And a little bit tongue in cheek, I tweeted that, “Hey, we've, we've done it much earlier, and we even have different approaches, Tobi and I.” Tobi, of course, is a big fan of QMD, and I'm more of a SQL, SQLite fan. But, uh, yeah, very similar things that we've already done here. The point is, yeah, we're very dynamic, you know, explosively growing company, and we have to be at the forefront of AI adoption, obviously.[00:02:29] swyx: Yeah. Yeah. Um, you, your team kindly prepared some slides actually that we were gonna bring up on to, uh, the screen. I think I can, I can screen share, and then we can kind of go through some of the shocking stats that maybe, maybe put some numbers to what exactly is going on. So here we have, uh- An internal AI tool adoption chart.What are we looking at here? What ?[00:02:54] Mikhail Parakhin: Yeah, this is very interesting statistics. Uh, this is number of daily active workers, you know, think of, uh, DAO, basically the active users of-[00:03:05] swyx: Yeah ...[00:03:05] Mikhail Parakhin: AI tool as a percentage of all the people in the company, right? And then- Yeah ... different AI tools. And, uh, you could see two things here is that one is the green is total.Uh, green is just total. So you could see that it approaches really % by now. It's hard not to do your job now without interacting deeply, at least with one tool. You could see another interesting thing is just as many people commented in December was the phase transition when suddenly models gotten good enough that, that everything took off and started growing.Uh, it, it was many people noticed that the thing is that small improvements accumulated into this big change in Sep- December roughly timeframe.[00:03:52] swyx: Yeah.[00:03:52] Mikhail Parakhin: The other thing I would claim you could see is that, uh, CLI-based tools and tools that don't require you to look at the code becoming more popular, and you could see, yeah, various versions of, uh, Cloud Code and Codex and Pi and internal development tools taking off.Uh, exactly, yeah, uh, and blue is our River, just internal agent for coding, where tools, uh, that require IDEs such as, uh, GitHub, Copilot or Cursor, they're not exactly shrinking, but they're not growing as fast. Like, uh, red, red line is, is the IDE kind of tools. So you could see that they're, they're not experiencing as, as fast of a growth.[00:04:37] swyx: As I understand it, basically, every employee has their choice, right? Of choose whatever tool you use, and then you're just kind of doing a, a daily sur-survey or something.[00:04:47] Mikhail Parakhin: Exactly. And, uh, we- Yeah ... the, the push is to get your job done, you can use any tool, and we effectively fund unlimited tokens for everybody.Uh, we, we do, we do try to control the models that, uh, people use, but from the bottom, not from top. Like we basically say, “Hey, please don't use anything less than Opus four point six.”[00:05:09] swyx: Oh .[00:05:10] Mikhail Parakhin: Some people, some people end up using GPT five point four extra high. Some people use Opus four point six. Um, uh, you know, uh, there are some, uh, there are plus and minuses in going for full one million context window versus not.But, uh, we try to discourage people from using anything less than that.[00:05:28] swyx: Yeah, yeah. Got it, got it. Uh, I mean, uh, that's, you know... The, the next chart here, it really kind of shows the expansion and the sort of December twenty twenty-five inflection, right? That, uh, people are using a lot of tokens. I think it's also really interesting that no one was kind of abusing it in twenty twenty-five.Like it was- Had comparatively, uh, to this year, there was almost no growth. I mean, it's still like, you know, probably, probably gave fifty percent.[00:05:56] Mikhail Parakhin: Yeah. This is just a different scale. It's still exponential- Yeah, yeah ...growth at just a different- ...rate of expansion. Uh, there was inflection point, and Sean, I would claim the, the super interesting part here is that you could see that the distribution becoming more and more skewed.Yes. The top percentiles grow faster. So that means- Yeah ...the people in the top ten percentile, they, their consumption grows faster than seventy-five and so forth. So, uh, the distribution skews more and more towards the highest users, which is... I don't know what it tells me. It's like it feels not ideal, to be honest.Or maybe it's okay. We'll see.[00:06:36] swyx: Why does it feel not ideal? Is, is it because of, um, quantity over quality, or what's the concern?[00:06:42] Mikhail Parakhin: Because take it to the limit. That means, you know, if, if this rate of separation continued- Ah, yes ...a year, there will be one person consuming all the tokens. So it's just, it's kinda strange.[00:06:54] swyx: Yeah, I mean, um, uh, I, I think internal like teaching and all that, uh, will, will help sort of distribute things more widely. But in, in the early days, of course, the people who are sort of more AI-pilled will obviously find more ways to use it than the people who are less AI-pilled. Maybe let's, let's call it that.I'll just, I'll just kinda quickly, uh, pause from the, the... You know, we will go back to the rest of the slides, but I just wanna, um, review, you know, there are a lot of CTOs of, of large companies like yourself where they're all considering some kind of token budget, right? Like I think it's something, something that Jensen Huang has been talking about, where like if your 200K engineer is not using 100K of tokens every year, like they're, they're underutilizing coding agents.Of course, Jensen Huang would say that, but like it seems a very quantity over quality approach and like some, some people are basically saying like, well, is this comparable to judging engineer quality by lines of code, right? Which we also know is like kind of flawed, but better than nothing. So I, I don't know if you have like a sort of management take here on, on how to view this kind of, uh, metrics.[00:08:02] Mikhail Parakhin: Well, I mean, you're, you're baiting me. I, I like... This is my favorite topic. Uh, if you let me, I'll probably talk for two hours on just this. I have a lot of things to say. Like I do think Jensen gotten a lot of bad press saying, “Oh, of course you're, you know, this, uh, the- ...the cake seller says you don't need enough cakes.”You know? Like, of course. Uh, but, uh, I actually, uh, think that's undeserved. I think he, he's actually right. Uh, I do think- He,[00:08:33] swyx: he's directionally correct.[00:08:35] Mikhail Parakhin: Yeah. Yeah. He's directionally correct for sure. Uh-[00:08:37] swyx: Who knows what the right number is? Yeah.[00:08:39] Mikhail Parakhin: The thing that I do Uh, want to say, and this is something that we learned through trial and error and very important is like two things.One is that it's not about just consuming tokens. Uh, you can consume tokens and, and in fact, the anti-pattern is running multiple agents, too many agents in parallel that don't communicate with each other. That's almost useless, uh, compared to just fewer agents and burns tokens very efficiently. Uh, setting up the right critique loop, especially with the high quality models, where one agent does something, the other one, ideally with a different model, critiques it, uh, suggests ways to improve it, the agent redoes it with this critique and, and so it takes much longer.So people don't like it because latency goes up. You know, they, they have to wait until this debate is happening. But, uh, the quality of the code is much higher. And another thing, just since you mentioned like, look, uh, uh, yeah, the overall budget is just like, uh, lines of codes. Lines of codes are exploding for everybody right now, or partially because AI is really mover balls, but partially just because AI can write a lot more code, you know, doesn't get tired.And so you have to have to have a very strong narrow waist during PR review. Otherwise, just the number of bugs will go through the roof. It's, uh, it's this unexpected consequence of the just volume trumping everything. I would claim by now good model writes code on average with fewer bugs than, than the average human.But since they write so much more of it, like more of it will make it into production. So you have to- You still[00:10:26] swyx: have[00:10:26] Mikhail Parakhin: more bugs. Yeah. Have to have a very rigorous PR reviews, also automated of course. But, uh, yeah, that to spend a lot budget there. Like this, this for me, for me, actually, the important metric is the ratio of budget spent during code generation versus, uh, spent, uh, expensive tokens like GPT, uh, five point four Pro or, uh, uh, Deep Think from Gemini, you know, checking on PR reviews.[00:10:55] swyx: Yeah, totally. Uh, I noticed in your chart you didn't have any review tools. Do you just use like, like let's say a Claude code to review tools? Or do you have another set of review tools like the Greptiles, the Code Rabbits, uh, Devin Reviews has a review tool. I don't know if you've had those specialist review tools.[00:11:13] Mikhail Parakhin: You are a little bit jumping on my store tool right now because the graphs I was only showing public tools. Uh, uh, the-- I haven't found a good PR review tool that, that does what I think should be done. And, uh, partially my, my thinking is because it's so... It just goes against both what people feel like emotionally they prefer and, uh, some of the, uh, you know, frankly Even business models that, that the companies run.At peer review tool, uh, time, you want to run the largest models. That means, I don't know, Codex or, or, uh, Cloud Code is not gonna cut it. You need to have pro-level models if you really want to, uh, stand the tide of bots from going into production. And you need us to spend a lot of time, the models taking turns, but you don't want, like, a big swarm of, uh, of, uh, agents.So in fact, you end up in a different dual-dualistic world where you generate not that many tokens. You, in fact, generate few tokens, but it takes f-a long time because these are expensive models taking turns rather than many, many agents trying to do many things in parallel. So that's, that's why I feel like I haven't found good tools, so we are using our own for peer review for now.[00:12:33] swyx: Yeah. Yeah. I mean, uh, I think a lot of companies are building their own, uh, especially to their needs, right?[00:12:38] Mikhail Parakhin: Mm-hmm.[00:12:38] swyx: Um, I, uh, you also have a chart here going back to the slides on, uh, PR merge growth, where we're now at thirty percent, uh, month on month rather than ten percent. Uh, and also the, the estimated complexity is going up.You know, this is productivity, right? ‘Cause y- presumably there's more stuff going into the code base and more, more features getting worked on. I'm curious about the backlog, right? Like the, the, the-- I actually don't mind a pro-level model taking an hour or two hours to review my PR, because I've dealt with humans who take a week to review my PR, right?And I keep pinging them on Slack, “Hey, hey, review my PR.” So, you know, I think there's some trade-off here where, like, it still doesn't make sense.[00:13:18] Mikhail Parakhin: Exactly. That, that's exactly m-my point. Uh, that on one hand, you can tolerate longer latencies at, uh, PR. On the other hand, like right now, the real problem is not in spending time waiting for PR.It's real problem is since there's so much more code than- Yeah ... uh, probability of at least some tests failing going up, and then you, like, keep de-failing, then you have to find the offending PR, evict it, retest it without that PR, and so deployment cycle becomes much longer. Uh, so it actually, in terms of the overall time to deploy, it's total time savings if you spend more time on a longer model, like thinking for an hour, because then, then you, you don't have to spend all that time during testing and rolling, you know, rolling back the deployment.[00:14:03] swyx: Yeah, totally. That's still worth it. You know, you don't look at the individual, look at the aggregate, and look at the, the, the change in the aggregate system.[00:14:11] Mikhail Parakhin: Exactly.[00:14:11] swyx: I'm kind of curious if, like, there's this PR mentality and, like, c-- the, the, the CICD paradigm will be changed eventually. Some people are like, obviously a lot of people want new GitHub, but I even wonder if, like, Git is the problem, right?Like, is that the bottleneck? Is the concept of a PR a bottleneck? Do you guys use stack diffs? I don't know if, uh, that's a, like, a merge queue stack diff type of thing.[00:14:34] Mikhail Parakhin: We, we use, we use Stacks, we u- we use Graphite. We worked with, uh, Graphite a lot. Uh, so we use Stack, uh, PRs. I think, uh, like that's clearly the overall CICD in general, and the interaction with the code repository right now is the, clearly the sort of the, the main issue and the bottleneck for us, uh, and highest top of mind.I would say we probably need a different metaphor or different whole design of how to process it in new agentic world. I haven't seen anything dramatically better yet. I, I think everybody right now is just trying to keep their head above the water ‘cause, ‘cause there, there's so many PRs and then everybody's CICD pipelines start creaking, the, the times are increasing, the number of bugs slipping by increasing, and you have to, have to clap on down.And so we are a little bit in this situation when we need to first stabilize that story and then start thinking, hey, what, what it could be a completely different and new world, which I haven't... I know some people working on it. I haven't seen something, like anything super compelling yet, but clearly the old thing were designed for humans will need to be morphed into something new.[00:15:53] swyx: One of the thing that I, I think about is kind of like the merge conflict is basically a global mutex on the whole system, right? And in, in hu- in human organizations, we do have something like that. It's the company standup. But like, other than that, it's like it's actually fitting for us to be somewhat decentralized, somewhat plugged into one stream of information source, but somewhat lossy.Like it's okay, you know, that, that not every delivery is like atomic consistency. Like we're not dealing with a database sometimes.[00:16:27] Mikhail Parakhin: This is a very good point, uh, because since humans don't write code too fast, you know that global mutex is not too bad. Once you-[00:16:36] swyx: Yes ...[00:16:37] Mikhail Parakhin: start writing code at the speed of machine, it becomes the, you know, the bottleneck.Then what do you do? Maybe, and I can't believe I'm saying this because I, I'm long-- lifelong opponent of, uh, microservices, and I always thought that was, like, a really bad idea. And now that you're saying it, like, maybe in new guys like microservices will make a comeback, you know, because then you, you can ship things independently in tiny things and, and the managing all that complexity automatically will be much easier.I don't know. Like, we'll s-- we'll have to see.[00:17:10] swyx: Yeah. I mean, I don't know what the Microsoft or, or Shopify thing is, but I, I read this paper from Google where they have a monorepo that deploys into microservices, right? And then, uh, the other concept that I think about a lot is the Chaos Monkey concept from, from Netflix.Being able to create, like, this robust system where, um, uh, you know, you, you have the service discovery, you have the, uh, the independent, independent microservices discovery and, and, uh, you know, probably going to be a fair amount of duplication. That's how an organic system sort of scales, uh, that, that you have that...I don't know how you call it. Slack? Robustness? Depend-- uh, d-duplication. I, I, I forget the-- I, I'm-- And this-- those-- these are not exactly the terms- Hmm ... I'm looking for, but I c-can't really think of the words. Okay. I was gonna go into Tangent and Tangle. Uh, so, uh, we, we sort of discussed the overall stats that, uh, Shopify has.Uh, but, you know, I, I think some, some pretty cool stuff that you guys are working on is your ML experimentation, uh, and your, your sort of auto tr-research training pipeline. Presumably you're much closer to this one because it's, it's a sort of personal hobby of yours. How, how would you explain them in, together?I thought we have a slide that, like, uh, has the s- the system diagram.[00:18:24] Mikhail Parakhin: Yeah. Tangle first and then Tangent as a-[00:18:27] swyx: Yeah ...[00:18:28] Mikhail Parakhin: as a thing on top of Tangle. And, uh, Tangle is the third generation, I claim, of, uh, systems of, uh, running any data processing, but a bit with a skew for ML experiments, but not necessarily. Any sort of data processing tasks where you need to iterate, share, and you have scale so that you want maximum efficiency.You know how, like, normally you would work, you would-- Imagine you're a data scientist or an ML practitioner, you would get Jupiter notebooks or, or maybe you would get, uh, you know, Pyth- your Python scripts, and you would manage the data, and you produce those TSV files, and you put them in some JFS or something.Then you would notice that, oh, it has this, uh, weird missing values. You go and write another script that, uh, goes and replaces them with, uh-[00:19:20] swyx: Ah ...[00:19:21] Mikhail Parakhin: dash S. And then, then you, then you run some, some, uh, “Oh, I need to filter bots.” And so you run some light GBM model that, uh, removes the bots. And then, then you like-- And then you, you kind of like get into shape, and then you start experimenting, and you run multiple experiments, and then you're like, “Oh my God,” like, “this experiment is worse.”You undo, and you cannot get to previous result. And like, “Ah, what did I do?” Like that. Again, then, then you finally like get everything working. Then you like start throwing it over the fence to production. You, you replicate it, those things don't work, and then sometimes you like don't notice that you forgot some feature naming and the, the features don't match.But then, like imagine you, you did everything, and then six months later you're like, have to repeat it because now there's more data, or you wanted to do another pass, and you're like, “What, what did I do?” Or like, or like, “This script crashes now,” or the, “the path has changed.” And then, then you're trying to, like you spend another month just doing ar- digital archeology on your own, you know, history, right?Now multiply that by many, many teams. Now imagine you got an intern that you wanna ramp up. Now you have to show that intern, “Oh, you know, look, here's the folder, there's the scripts, you know, ask your cloud agent to do, and then, uh, to, to figure it out.” And then cloud agent does something, and then you're, “Ah, yeah, right, right, it was the wrong folder.I forgot to tell you, I actually have this other thing I forgot myself.” And, and that's, that's the, like, the daily life we all, uh, all know it, uh, if, if you're a data scientist, machine practitioner, ma- machine learning practitioner or, uh, or even like any data managing, uh, person.[00:21:00] swyx: Yeah. So I, I used to do this, uh, f- uh, on the quant finance side, uh, in, in my hedge fund.So we did this before Airflow, and then, uh, obviously Airflow came along and, uh, then more recently Dagster, uh, I would say is like, in my mind, what I would use for that shape of problem, uh, where you had to materialize assets and create a pipeline.[00:21:19] Mikhail Parakhin: And that's, that's very good segue because... So Airflow is great, but Airflow is more about you, you have something and you wanna repeatedly run it in production on schedule.It's less about you as a team developing things and being able to share, and you grabbing the standard pipeline and saying, “Hey, I wanna change this tiny little component in the huge sea of data processing, and I don't wanna-- I wanna run ten experiments on this, and I wanna do hyperparameter optimization.”All that is very hard to do with Airflow. It's very easy to do with Tango. Tango is m- more about, it's everything about group of people Running experiments, it might be agents too nowadays. Uh, running experiments cheaply, collaborating, sharing results. Uh, you don't need to understand fully. You, you grab-- you clone somebody else's experiment or somebody else's pipeline, uh, run, uh, change small piece, run it, be, like, get it to production state, and then ship in one click.So then the... You don't have to port it into any other system to, to run in production. You can just run the same experiment. It's, it's fully production ready. And, and it's, uh, it has lots of... Again, as I said, it's third generation system. The original one was, I would claim there was Ether and then, uh, at least in my career, Ether was the first, first, uh, that pioneered this type of approach.And then there was, uh, Nirvana, which, uh, uh, at Yandex, which did kind of sec-second take on this. And now this one aggregates the, the learnings from all of those and, and Airflow as well to, to get to the state where you try it, it, it feels kind of magical. Uh, ‘cause now everything is based on content, uh, hashes.So even if the version changed, but if the output didn't change, nothing is being rerun. It's very efficient. If you... Multiple people start experiment that needs the same sort of data preprocessing, it's not repeated multiple times. It's automatically done only once. If you start ten experiments that all require, you know, some, some data preparation first as the first step, and you don't have to coordinate for that.Like, you don't have to know that other people are starting it. You now, it's very easy compos-, uh, composability, any language you can u- uh, you wanna use, and it's very visual. So you can see immediately, you can edit it easily, you can assemble small things with just even mouse clicks if you want to, and, uh, share, clone.And everybody knows also it's fully kind of static in the sense that we rerun it second time, it will exactly have the same results. Like, you will never have to do digital archeology. So full versioning and everything is also there.[00:24:06] swyx: Uh, so, so people can, uh... It's open source. Go to the GitHub repo and, and, uh, check it out.Uh, and it is also a really good, uh, blog post about it. I think all these is, like, really appealing. The, the, the, the thing that I think sells me the most about it is that, um, sort of development to production transition, right? Which I think, um, a lot of people haven't really solved that, uh, strictly, right?Like, we develop really, really well in, in Python notebooks, but then, you know, that's obviously not a sort of production ready process. I think that, like, any way in which that is solved, I think is, is very appealing. Then the other thing that you mentioned, which also raised my eyebrows, was content-based caching, which you mentioned is, is, um, you know, is ve-very much, uh, um, a sort of efficiency measure about, uh, you know, just like recalculation only on, on sort of content addressing Which I think makes sense.Uh, it surprised me that the savings could be this much, but maybe I just haven't worked at your scale where there's so much duplication, uh, that people just rerun because they change a single ID upstream.[00:25:10] Mikhail Parakhin: It does, yeah. But it's not only you rerun. The, the main savings are coming from the fact that you ran it, you got your job done, and you moved on.Then- Yeah ... somebody else in some department you don't know existed runs the same task, but on a newer version.[00:25:27] swyx: Yeah.[00:25:27] Mikhail Parakhin: Like right now, you can't, in, in most of the organizations, you can't even find out about it so that you can't even measure that you're spending that time twice, right? Here- Yeah ... if everybody's on Tango, that's detected automatically and detected that the output is the same.And then for that person, all it looks like is like experiment just suddenly moved, jumped forward, right? Uh, uh- Yeah ... so that's because, because the, there's network effect of multiple people helping each other.[00:25:51] swyx: Yeah. This is one of those things where it's designed to be a platform from the beginning rather than an individual developer's tool from the beginning, right?And, and everything's gonna streams down from there. That is the sort of Tango, uh, orchestrator, and it's, it manages jobs. We've seen a few versions of this, and this is obviously, uh, uh, the sort of, uh, unique approaches that you guys have, have, uh, figured out. And then there's Tangent.[00:26:14] Mikhail Parakhin: Yeah. And Tangent is basically an automatic auto research loop that can help and kind of do your work for you.Uh- ... you know, uh, effectively, effectively, Andrej Karpathy recently popularized it with auto research. Yes. Remember he said like he was, uh, speed running this, uh... Yeah, uh, you know the story. The, here we're basically bringing the same capability into Tango so that, uh, the, uh, Tangent can analyze it. It's just an agent that can run multiple experiments, figure out what can be changed, and keep on rerunning it, keep on modifying until, uh, maximizing some goal, some loss function, whatever you need to, to achieve.And in general, I would say if you're not using auto research-like approach in whatever you do, like literally whatever you do, then you're missing out. We saw at Shopify that taking like a wildfire, anything where you can put measurements can be done dramatically better. Our-[00:27:19] swyx: Mm-hmm ...[00:27:20] Mikhail Parakhin: uh, speed of, uh, templatization HTML, uh, completely new UX tem- uh, templatization of, uh, reducing latency for liquid themes.Uh, we-- Our, uh, search, uh, recently we moved from It's hard even, uh, quote from eight hundred QPS to forty-two hundred QPS with the same quality just by pure optimizations and not a research loop that kept running and changing code in our index serve on the same number of machines, just increasing the throughput.We, we managed to improve the quality of gisting and machine learning process. Uh, you know, gisting is the prompt compression technique that[00:27:59] swyx: allows for[00:28:00] Mikhail Parakhin: lower latency and, and lower and, uh, actually higher quality slightly. So like literally whatever different walks of life, and it doesn't have to be AI related.Uh, we, we had a reduction in, uh, storage because the agents would go and find data sets that clearly are derivative, uh, and then you don't need to store things twice. You know, we, we, we found somewhat embarrassingly that it was one of the largest tables was hashing random IDs into another random ID, and we literally- Oofput only one. So it was translating, yeah, two random IDs hashed[00:28:36] swyx: into[00:28:37] Mikhail Parakhin: each. So, so[00:28:37] swyx: it has access to the code as well, so it can, it can check the, like what, what the hell is it doing?[00:28:42] Mikhail Parakhin: So there, there cou- it could be run in two levels. You, uh, you know, at the superficial level, it could just use ex-existing components and, uh, reshuffle them.Uh, you know, like you can grab- Yeah ... uh, XGBoost, and you can grab some, some Py- PyTorch module, and then can grab some, you know, grab another tools and, and combine them. At a deeper level, since Tangle is all sort of CLI based underneath you, every, every component is a wrapped really CLI, uh, call and a YAML file, it can analyze code and create new components and, and, uh, keep on iterating as well.So, so you can, you can both have quick modifications of existing t- uh, pipelines with the, with components that are already there pre-baked, or you can create new components, uh, and-[00:29:29] swyx: Yeah ...[00:29:29] Mikhail Parakhin: keep iterating on those. So auto research is, again, this is probably the, the thing I was excited the most in the last two months happening, and we see it taking like, like totally like a wildfire.Just, uh, everybody, every day, every... well, every day, every minute, I would, uh, have somebody Slack message saying, “Oh, look how much better I made it.” And, uh, it's all throughout the research.[00:29:53] swyx: Is this democratized in some way in, in the sense that like is it your ML, uh, engineers and researchers doing this, or is it your regular PMs and software engineers also have the ability to auto-- to use Tangent?[00:30:07] Mikhail Parakhin: This is an awesome question. Like, Tango in general and Tangent in particular are extremely democratizing. Like they- Yeah ... they are the main tools for- ‘Cause I don't[00:30:15] swyx: need the details.[00:30:16] Mikhail Parakhin: Yeah. Exactly. Initially used by ML and AI engineers, but then literally, as you said, PMs are like the highest user right now is one of PMs on our org, uh, Sartak and he was, he was number one by, by usage of, of this ‘cause they're just, uh, energetic and knowledgeable, and now it, it unlocks a lot of capability where you don't have to co-change code manually.[00:30:39] swyx: I mean, I mean, because it kind of cuts out the ML, ML engineer from the process because the, the, the PMs have the domain knowledge and the ability to think about, uh, from first principles about, okay, what, what results do I want? And they can-- they even have the access to the data that, that needs to go in.So it's like in some ways, like this is the magic black box that we've always wanted for, for training and, and for, uh, I guess, uh, uh, hill climbing, whatever.[00:31:04] Mikhail Parakhin: It's basically cloud code for your AI development- ... uh, situation, right? Like now, now you don't have to know exactly how algorithms work. You can just, uh, bring your domain knowledge and expertise and product knowledge and iterate within Tangent until you've gotten the results that you need.[00:31:21] swyx: In my previous roles, every time that someone has pitched AutoML, you know, I've always been like, “Uh, this is not, this is not gonna work. It's, you know, it's, it's always gonna be a flop.” Somehow it's working now. I mean, presumably the answer is now we have LLMs and it's good enough, right? It's, it's an emergent property that we can do auto research, but like, it doesn't feel that satisfying that how come we didn't do this before, right?Like we just did like parameter search and like, I don't know. That's maybe that's it.[00:31:48] Mikhail Parakhin: Yeah. Bayesian optimization and hyperparameter optimization was, was the one that, or facet of AutoML that was used very actively, which incidentally also built into, uh, Tango. But, you know, I know Patrice Simard very well, and, uh, he was such a, uh, such a proponent of AutoML, and he put, like literally spent careers trying to democratize it.Without LLMs, it just turned out to be very hard. Like it, you, you would have flexibility within certain narrow domain, but it was hard to wider scale, and now with LLMs suddenly it's like magic wand, and so suddenly everybody- ... is an AutoML expert.[00:32:28] swyx: Yeah, I, I think it's multiple things, right? Like I'm, I'm just gonna bring up the, the, the chart again, right?Like LLMs can do the monitoring very well. That is the very potentially unbounded, super unstructured. It can do the analysis very well, it can do the... Uh, and basically it is much more intelligence poured into every single step. Uh, there's maybe nothing structurally changed about AutoML, but this is just m-more intelligent and more unstructured.[00:32:53] Mikhail Parakhin: Exactly.[00:32:54] swyx: Any flaws that you've run into? Like everyone is like drinking the Kool-Aid, oh my God, time savings, uh, you know, performance improvements. Like what, what, uh, issues have you have, uh, come up?[00:33:06] Mikhail Parakhin: This is really cool. It's not a solution to all the world's problems for sure. The limitations are usually the ones I-- And this is where we get into a bit of a subjective territory.Uh, I can only share what I've, I've seen so far, and I'm sure the situation, uh, is changing, and, you know, maybe after I say it, like many people will reach out and say, “Hey, what about this?” And you don't know that, and then, then we'll be probably right. But what I've seen is auto research is very good at doing kind of obvious things that you don't have bandwidth to do or you didn't notice or maybe you're not aware of like the-- some standard practices.It is not good at doing something completely out of distribution, something that, you know, you have to think for, for multiple days, uh, and, and do something like none of this. So, so it's, uh, I, uh, set an experiment once, uh, on, on my sort of, uh, hobby thing, and I let it run for, uh, ended up, uh, several weeks run, uh, you know, it's like full production kind of scale, so it, you know, slow runs and, and it ex-- it performed in the end, uh, over four hundred experiments, and only one was successful.I'm like, “Okay, that's, that's good.” But-[00:34:18] swyx: But it saved time.[00:34:19] Mikhail Parakhin: Yeah, I saved time. Like it, it was the, that thing. Yeah, if I, if I were doing four hundred experiments myself, my betting average, as I said, would have been much higher, I'm sure. But also, first of all, it would take me like three years to do four hundred experiments.And, uh, I didn't have to do them. Like the machines were just, uh, the price of electricity did that. So, and I got one improvement, uh, that in, uh, my, my-- Honestly, when I was starting that experiment, my thinking was to go and show that, “Hey, Andre, maybe you just don't know how to optimize.” And I was super smart because in, in my pro-problem, it was optimized for many years, and it was like fully improved.Uh, and I didn't expect it, you know, auto research to find anything at all. Yet it did. So instead of making fun of Andre, I ended up, uh, a big, big supporter. Yeah, that's exactly the tweet. Yes.[00:35:10] swyx: You and Toby really, really go back and forth on-online a lot, which is really funny. Uh, think of it as, as an eval for the optimalness of the code it's running on.Uh, it's almost like it reminds me of like a Kolmogorov complexity thing, but, uh, I guess it's-- there's some optimal thing that you're trying to sort of reduce down to, I guess. Um, and so, so you, you, you know, you should congratulate yourself that you had, uh, you know, uh, ninety-nine percent, uh, optimality.[00:35:36] Mikhail Parakhin: Exactly, yeah. I think Andre really deserves a lot of credit for popularizing this approach. This is, uh, this is incredibly, I think, powerful and cool and You know, the, uh, even him, him just mentioning it led to a lot of gains in a lot of places in the industry, so we should be thankful.[00:35:56] swyx: Yeah. I think he also has a just...I don't know what it is. Like, um, you know, it, it is a simple self-contained project that people can take and apply to other things, which is, is, is one thing, but also just the name. Just like somehow no one, no one managed to call their thing auto research. It's just naming things is very important. I think that that is mostly, uh, our coverage of Tango and, and, uh, Tangents.I think obviously, you know, there's a lot of, uh, ML infra at, at Shopify that people can, uh, dive into. We're about to go into SimGym, but before I do that, any, any other sort of broader comments around this whole effort? Like where is it, where is it leading to?[00:36:36] Mikhail Parakhin: As a segue to SimGym, like all those things start composing strongly.And, uh, you could see a huge unlock when you can look at each one of the tools and, and you see, oh, they're extremely useful. Uh, Tango is useful by itself. Auto Research is useful by itself. SimGym is useful by itself. If you combine all three, you create like synergetic effect. I think that's why we wanted to even, uh, cover them today is because this is something that if you go back even, you know, five years ago, would've been unthinkable.Uh, replicating that, uh, would, would be either incredibly costly or impossible, right? With probably thousands of people are required.[00:37:20] swyx: Well, we have serverless human, uh, serverless intelligence, right? Like, uh, so yes, you do have thousands of hu-- of, of intelligences, not just, not humans. And that's, that's close enough, right?Even if they're not AGI, they're, they're close enough to do the, the task that you need them to do. And, and, you know, that's, there's plenty for, for a lot of routine work, knowledge work. Okay, let's get into SimGym. Um, this is one of those things I, I was surprised to see actually it's apparently your, uh, one of your most popular launches, and I think something that, uh, I think Sim AI, I think Yunjun Park, who did the Smallville thing, there's a very small cottage industry of people trying to do like the simulate customer thing.I think a lot of people maybe don't super trust this yet because they're like, well, obviously they would just do what you prompt them to do, right? But maybe just think, uh, tell us about the sort of inspiration or origin story.[00:38:10] Mikhail Parakhin: That's exactly actually the thing I wanted to cover, because if you don't have the historical data, all you can do is prompt a-agents in a vacuum, and they will do exactly what you prompt them to do.In fact, when I first proposed it, and this is a bit of, um, my brainchild initially, if I, I can boast, even Toby said like, “But wouldn't they, they just repeat what, what you tell them?” And, uh, but I'm like, “Yes, except Shopify has decades of history of how people made changes and what there is, uh, there, what it resulted in terms of sales.”So now what we can do is we can-- we have this... It's not, it's a noisy data. There's a small, usually websites, uh, you know, like things, things are never in isolation. It's almost never AB experiment. It's always AA experiment when there's has two meanings, but basically, you know, in different time you run two different things.But if you aggregate in general, uh, like everything together, and you apply, uh, denoising and collaborative filtering like approach, you can extract a very clear signal. And then you can optimize your agents. And that's why it took so long. It took almost a year of that optimization of just us sitting and fiddling, and, and we had this internal goals of correlation of hitting-- internal goal was to hit zero point seven correlation with, uh, add to cart events, for example.Like that, that if we run real AB test experiment, that it should, it should go and, and rep-uh, replicate, uh, same sort of success that, that humans had or lack thereof. And it, it took forever, and I don't think that's easily replicatable because, uh, like who else would have that data? You have to have this historic, you know, decades, uh, worth of data.And now, now the, like the other thing you need is in-infrastructure and the scale, right? Because, uh, w- again, what we found, uh, stat sig results, you need to run a lot of simulations, a lot of agents, and, and it's-- Those are expensive things. Like you're, you're making actions in the browser because you want a real friction.You want to, to be able to get the image like of what humans will see because you wanna, uh, detect effects like, “Hey, if I make my images larger, will I have more sales or l- uh, fewer sales?” And like usually people's intuition here, by the way, is that I increase my images, I will have more because they look nicer.You know, designers all look sparse and big images. Like usually your sales tank, right? But, but, uh, you know, from HTML, all the characters look the same only the, the size tag looks different, right? So it's very hard. So you have to take visual information, you have to run this in simulated browser environment on the big farm and, and of course, you have to have, uh, like very, very expensive model, good model with multi-model model.So all this it's-- is what's taken so long and, uh, to share my personal fail a little bit there, Sean, is like, you know, we always had this bias to-- for like large company bias. You know, we always, uh, whenever you-- we do, we're like, “Hey, we'll run an experiment,” right? We make, make a change, and we will run an experiment and then, uh, see, uh, see which one's better or like, “No, this is worse,” and most of them are worse, so you discard it and keep iterating, hill climbing.And we're like, “Oh, like smaller merchants, they cannot get stat sig results. They cannot really run experiments simply because, you know, in a week there would be not enough data for them.” So we thought from this perspective. What we didn't realize is that most people don't have A and B, they just have one thing, and they need suggestions of What A and B should be.So, uh, we first build this, hey, we run simulation on two separate teams and, and, uh, say, “Hey, which one is better?” We then morphed it into, and very recently just released it, when you have just your site, your theme, we run over it and we say, “Hey, here's what predicted values of, of, uh, uh, conversions are, and here's how we think you should modify it to increase your conversions.”And then circling back to what you started with, the proof is in the pudding. Like, if we are not correlating with reality, like, people will not be using it. And, uh, thankfully, we see literally every day more users than the previous day. So, so right now, uh, right now- It's working. Yeah. I'm-- Right now my problem is how to pay for it all because the so our major thing is how to optimize the LLMs, do distillation, how to run the headless browsers, uh, and handful browsers, uh, uh, cheaper so that we can accommodate the increase in traffic.[00:42:47] swyx: Yeah. I, I understand that you, uh, you published a lot of technical detail at GTC, so I was just gonna bring it up a little bit. I think s- was this in, in con-conjunction with some kind of GTC presentation? Or something like that, right?[00:42:59] Mikhail Parakhin: Well, we, yeah, we, we did it in several place, but yeah, we had the engineering- Yeahblog, uh, as well. Yeah.[00:43:05] swyx: Yeah. So you're running, uh, GPT OSS. Uh,[00:43:08] Mikhail Parakhin: the, this is an older version. You know, now we run multimodal model. But yeah- Yeah ... GPT OSS, we still run GPT OSS as well for[00:43:15] swyx: And then you have the VMs, and you also have browser-based. I really like this one where it you said, “It violates almost every assumption that standard LLM serving is designed for.”And then you had like, basically orders of magnitude differences between everything.[00:43:29] Mikhail Parakhin: Exactly. Which is, which, uh, which was, you know, a bit of a challenge to implement, like when, like even simple things. Uh, be- since it violates all the assumptions, for example, multi-instance GPUs, like MIGs don't work as well.But we needed, uh, to get MIG to work because, ‘cause otherwise it's way too expensive. And so we had to deal with the, yeah, with, uh, lots of infrastructure and, and, uh, work with, uh, uh, Fireworks and CentML, uh, you know, to help with optimizations and browser-based, as you mentioned. Yeah, like, takes a village.[00:44:04] swyx: Okay. So there's a lot of like, I guess, experimentation in the infrastructure so far, and you've published more or less what you have here. I guess I'm, I'm less familiar with CentML. I, I don't do, uh, that much work in this, this part of the stack. But why was it the sort of preferred instance platform?[00:44:22] Mikhail Parakhin: There are really three probably top companies. There used to be, uh, uh- Three top companies, uh, at least I was aware of that did, uh, LM optimization. You know, together Fireworks and Santa ML, not necessarily in that order. Santa ML recently got acquired by NVIDIA. Uh, what they did is if you have a model and you want to optimize it to a specific prof-- uh, profile of usage, uh, they would go and do it.And, uh, we work with, with those companies, uh, this was work particularly in with Santa ML and NVIDIA to get them the best possible results out of it. And, and sometimes you, you have to retune depending on, like sometimes you want the maximum throughput, sometimes you want minimal latency, sometimes you want like the cheapest, right?And, yeah, or some combination. And so yeah, these are people who would come and help you.[00:45:14] swyx: I see. I see. Yeah, yeah. I'm familiar with these people for the LLM, you know, autoregressive stack. But the other interesting category of these optimizers is also the diffusion people, whereas like Fel and, you know, uh, Pruna recently has come up a lot as well, which I think is like really underappreciated, uh, at least by myself, because I, I thought, oh, all the workload would be LLMs, but actually there's a lot of diffusion as well.[00:45:38] Mikhail Parakhin: Exactly.[00:45:38] swyx: There's a lot here, so I, I, I... it's, it's, uh, it's, it's, it's hard to cover. But I, I do think like people underappreciate the importance of customer simulation, basically. I think this is something that I'm candidly still getting to terms with. Uh, you know, uh, you also-- your team also like prepared this, like, really nice diagram.Uh, I, I assume this is AI generated.[00:46:00] Mikhail Parakhin: Yeah, it looks-[00:46:01] swyx: Maybe it's not.[00:46:01] Mikhail Parakhin: Yeah, it looks, uh, Gemini-ish. Yeah, but, uh, uh, honestly, I, I don't know where, where the hell they generated. It looks, look, uh, looks like it's, uh, Google. But the interesting part, John, that, that, uh, we haven't covered, but I, I wanted to mention is if your store had previous customers, rather than it's a new store, you're like new merchant just launching things, it helps tremendously in just correlation and forecast.Yeah, we take your previous, uh, customer's behavior, and we create agents that replicate those specific distribution of, of customers that you get, and then we a- we apply those to your changes, and then that, that raised raw, you know, the re-- uh, just correlation with the add to cart events or to-- with conversion or whatever it, it, it may be, uh, quite dramatically.So, uh, replicating humans in general seems like an interesting, cool challenge.[00:46:58] swyx: As a shareholder, I think this is the-- like if people are Shopify shareholders, they should really deeply understand this because this is basically the moat. The, the more you use Shopify, the more it will just automatically improve, right?Like you're, you're doing the job for them.[00:47:13] Mikhail Parakhin: Yeah, that's what we started with. Like, uh- ... uh, otherwise, if you're just a startup, I wouldn't do it if, uh, you know, if it was my startup because Without the data, it, yeah, as, as you said, it's, it's exactly the case that, uh, whatever you say in prompt, that's, that's what the agents will be doing.[00:47:30] swyx: The statistician in me wants to like really satisfy the sort of, um, statistical intuition, I guess. Um, to me it's kind of, uh, the, the word that comes to mind is, um, ergodicity. Uh, so let's say a, a customer takes this path, customer takes this path, customer takes this path, right? Um, the... In my mind, the way I explain it is like, okay, here, here's the ninety-five percentile, here's the five percentile, and here's the median, right?Um, but to me, what SimGym is potentially doing is that it can, uh, modify... It can sort of model the sort of in-between sort of journeys as well, that, that maybe are dependent on the previous states. This may be like a very RL-type conclusion where like basically the summary statistics, if you only did naive AB testing, you only have the, the statistics at, at, at a certain point, and you only judge based on the sort of overall summary statistics.But here you can actually model trajectories. Does that make sense? Or-[00:48:31] Mikhail Parakhin: That makes total sense because like, well, that, that makes even more sense that maybe even you realize bec- because-[00:48:38] swyx: Okay. Please,[00:48:38] Mikhail Parakhin: please. Yes ... we do-- Yeah. The, so internally, uh, we have this system, we talked about it briefly once at NeurIPS.We have a huge HSTU-based system that models the whole companies, uh, and their possible paths. And like- Yeah ... what you are, what you are showing, like actually at any point of time, you can either model the user's behavior or you mo- can also think about, uh, the whole merchant as a company, as the entity that acts in the world.You can model that as well. And then you can do, can do counterfactuals. In your graph, like in your blue graph, uh, if you're... Imagine in the center there, uh, somewhere in the middle, you would have an intervention. I give that person a coupon, or I don't know, I send a personal thank you card, or give a discount in some- somewhere.And then you can, uh, then you can do forward rollouts from that counterfactual. So what would have happened with that intervention or without the intervention? And you can even ch- change where that intervention, uh, in time can happen, right? Like some- where, where in this journey. So we, we do this at the Shopify scale for our merchants, and then if we notice that something that they can be fixing, like there's a strong counterfactual, like we have Shopify policy, they basically get a notification like, “Hey, we think your...something is wrong with your-” I don't know, Canadian sales. Like, uh, it looks like it's misconfigured. Here's what you need to do. Or do you think like, uh, you have to set up this campaign with these parameters? And we do that at the buyer level to literally offer discounts or cashback or, or things to buyers.So this is-- I'm getting very excited. Like this is my sort of area of, uh, interest, I guess, and, and hobby. But being able to m-model something complex as human beings or companies and model counterfactuals on it, where you can have interventions in the future and optimize when to make intervention, what kind inter-- uh, what kind of intervention to make.It's such an unlock that previously was completely impossible. Like the-- it was, it was always dreamed of, but never... Like how would you even simulate it without LLMs or HTUs? I think very, very exciting times.[00:50:59] swyx: I just wanted to, uh, to maybe illustrate this. I, I'm not the best illustrator, but I, I am a conceptual statistics guy.And y-you know, you cannot just do this. Like this is a dimensionality AB test doesn't do, right? Like, uh, because it doesn't have the, the, the change over time, uh, stochastic nature, uh, and it doesn't have the sort of contextual like... Here's all the context to this point. Um, okay, cool. Um, that's SimGym.You're, you're gonna burn a lot of tokens on this thing. But you're, you're one of the, the only scale platforms in the world that can, uh, that can do this across a huge variety of workloads, right? I'm even curious on a sort of human, uh, research level of like, well, do, does retail behave d-differently from like clothing sales?D-does that behave differently from electronic sales? I, I don't know. I don't know what else you guys... The Kardashian shoppers, do they differ from like people who buy, uh, I don't know, cars and, uh, whatever.[00:51:55] Mikhail Parakhin: Well, very different, and different sensitivities and different modes of, uh, shopping and, and different levels of what's important.Now, to-totally, you can do aggregations at, uh, at a store level. You can do aggregations at a different, uh, category level. I don't know if, uh, you know, for our statisticians among us, I couldn't believe, but we-- recently we're looking at it, and we had to bring back, uh, CRPs, you know, Chinese restaurant process.It's a, like, way of aggregating and, like, naturally grow clustering. So across... Specifically to answer questions that, uh, like you were just posing on how, how if, if buyers behave different categories. And I'm like, “I haven't seen CRP since two thousand and one.” It's[00:52:37] swyx: so What? It's so- What is... No, I haven't, I haven't seen this.No. This is not in my training. Uh,[00:52:44] Mikhail Parakhin: but, but yeah, it, uh, uh, it actually, like the, the-- there was a very popular kind of theory, popular neurips HTML circles in early two thousands, uh, kind of nice. And now, now it has practical applications, uh- Yeah ... that we were resurrecting.[00:53:03] swyx: Yeah, amazing. Uh, I, I can see, I can see how this is like a, uh, a fun job for you where you get to apply all these things.Um, yeah, yeah, so super cool. Super cool. So, okay, so, so anyone who, who knows what CRPs are and has always wanted to use them at work, uh, they should, they should definitely join Shopify. Okay, so w-we have a lot and but I, I'm, I'm being mindful of the time. I, I do wanted to, to sort of cover some other things.Um, I-I'll give you a choice, UCP or Liquid?[00:53:30] Mikhail Parakhin: Liquid. I think, I think on UCP, you know, like UCP is very important for us and, and it just we are-- UCP, we have a structured, uh, discussions, and you can read about them, and we have, uh, blog posts, and we have a big release this week, in fact, like with our catalog.Oh,[00:53:46] swyx: okay.[00:53:46] Mikhail Parakhin: Uh, yeah,[00:53:46] swyx: but- Le-I mean, we, we can, we can discuss the, the, the release briefly because we'll release this after the-- after it's already announced so whatever. There's a catalog that you guys are doing?[00:53:55] Mikhail Parakhin: Yeah. So we are, we are- Okay ... we are bringing in capabilities of a whole, uh, Shopify catalog.Basically, you now you can search for products, you can do lookups by specific ID, you can do bulk lookups when you need to bring m-multiple products. You don't need to know in ad-in advance what you're trying to show or to sell or check out. Like, you can now, you can now have this decided at, at runtime, and this big area for investment for us for both non-personalized and personalized searches, trying to provide basically a win-window into whole universe of products that are being sold everywhere in the world.And Shopify is really not exactly, but almost like a super set of any-anything being sold. Now we are bringing it into UCP and, uh, and, uh, identity linking is another big thing for us, uh, so that you, you can use, uh, like Google or whatever, whatever identity you have, uh, they're minimizing friction.[00:54:56] swyx: Yeah. So[00:54:57] Mikhail Parakhin: yeah, big release for us.But Liquid AI of course we never talk about, and the problem might be more, more aligned with what we d-discussed previously on this chat.[00:55:07] swyx: Sure. The main thing that everyone understands about Liquid is that it is inspired by Worm, and I still don't know why. I'm curious on your explanation. I think you, you, uh, you can make things very approachable.And also I think like what is the potential of like the, the level of efficiency that you get out of Liquid?[00:55:23] Mikhail Parakhin: You- we all familiar with transformer architectures. And, uh, for the longest time, there was a competing architecture, it's called the state space models. So, so Sams, uh, you know, Chris, Chris Reyes, one of the pioneers and, and lots of startups, uh, trying to make those realities.They have, uh, significant benefits being main being, uh, being much faster and, uh, lower footprint and not quadratic in length, you know, sort of, uh, linear in, in, uh, in your context length. But with state space models- They never quite made it. Like they're used-- They have, uh, certain niches when they thrive, their hybrid architectures are useful, but they never quite made it.And liquid neural networks are, you can think of them as a next step, like, uh, sort of, uh, state-space model square. It's non-transformer architecture that's more complicated than sta-state space and really difficult to code if you-- if I'm being honest. But it's, um, very efficient. It's, uh, subline-- sub, uh, quadratic in, in length of your context.Uh, it's very compact way to represent things, and that's a liquid AI company. They... Their goal is to productize it, and very often you have this need, uh, when you need to have long context and small model, and you want to have low latency. Like in general, it's basically on par with transformers, and if you do hybrids with transformers, it's, it's even better.That's why we at Shopify, when we tried multiple and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and/or if you need longer context lengths, liquid was the best. And so we still use the whole zoo and always like obviously test and use everything, uh, every open source model and, you know, it feels l

durée : 00:08:57 - Le sept neuf - par : Simon Le Baron - Fatih Birol, directeur de l'Agence internationale de l'énergie, redoute des "conséquences terribles pour tout le paysage mondial de l'économie" après plusieurs semaines de guerres au Moyen-Orient, de blocage du détroit d'Ormuz et d'augmentation du prix du pétrole. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

durée : 00:08:57 - Les interviews d'Inter - par : Simon Le Baron - Fatih Birol, directeur de l'Agence internationale de l'énergie, redoute des "conséquences terribles pour tout le paysage mondial de l'économie" après plusieurs semaines de guerres au Moyen-Orient, de blocage du détroit d'Ormuz et d'augmentation du prix du pétrole. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

Greetings Glocal Citizens! This week on the podcast community builidng is a central theme. For twenty years, my guest, Bakajika Tshinanga, has operated at the exact intersection where culture generates economic value. His journey started in 2004, when he founded a student-led program at the University of Georgia Atlanta. Georgia Daze still runs today and has improved enrollment yield conservatively valued at $150M+ in incremental revenue for the university. That experience shaped how he thinks about infrastructure: who builds it, who benefits, and who owns the upside. He went on building hospitality programming delivering the cultural insight that prompted AT&T to sponsor Drake's inaugural headline tour and leading the campaign that introduced the Lyft ridesahre app to Atlanta. He's produced work with cultural figures including Michael Jordan, and while an undergraduate he producing concerts featuring Outkast, Lauryn Hill, and Dave Chappelle. Also of note, he owns a publishing catalog featuring songs from Lil Wayne, The Neptunes, Sevyn Streeter, Pusha T, and Usher. Coding since age 11, technology is lens he's always looking through. His latest endeavor, the lOlŌ's platform is the direct expression of technology deployed as infrastructure to close the gap between who creates cultural value and who captures it. He's lived the innovator's dilemma firsthand: operating at the riskiest part of the curve, where the work is undeniable but the capital isn't designed to find you. That experience is why lOlŌ exists—a collective ownership vehicle for cultural capital, community-capitalized, community-governed, designed to compound for generations. Where to fine Bakajika? On LinkedIn On Instagram On Threads What's Bakajika reading? The Science of Scaling by Dr. Benjamin Hardy adn Blake Erickson What's Bakajika watching? Dreaming Whilst Black on Showtime How's Bakijika listening to? Who is Joy Leone? Other topics of interest: Kinshasa, DRC About Georgia Daze University of Michigan Supreme Court Affirmative Action Case Atlanta Influences Everything Lɔlɔ̃ means loveSpecial Guest: Bakajika Tshinanga.

KEXP and BIME present Live on KEXP in Bilbao with Los Punsetes performing at Iglesia de la Encarnación. Recorded October 31, 2025. ¡Viva!MabuseArsenal de excusasTu Puto Grupo Ariadna Paniagua - VocalsAnntona - GuitarFiletillo - GuitarLuis Fernandez - BassChema Gonzalez - Drums Live on KEXP in Bilbao is a partnership with BIME. Recorded at Iglesia de la Encarnación in Bilbao, Spain. Host: Albina CabreraAudio Engineer: Kevin SuggsGuest Audio Engineer: BIME crew and Marco MorgioneAdio Mixer: Kevin SuggsMastering Engineer: Matt Ogaz BIMEJulen MartínLeire GoienetxeaAnne Salazar Guillermo Royo (NOIZ Lab Production) BIME Audio Team Israel Monzoncillo (Izarblue)Iñigo Escauriaza Juanjo Mediavilla Brayan ChacónPatxi Gabilondo (Call & Play backline assistant) Live on KEXP in Bilbao has the support of Sounds from Spain, ICEX, AIE and BASQUE.MUSIC. https://lospunsetes.comhttps://bime.org/http://kexp.org Photo by: Carlos CruzSupport the show: https://www.kexp.org/donateSee omnystudio.com/listener for privacy information.

En Capital Intereconomía, la Tertulia Capital reúne a David Henche, José Luis Fernández Santillana y Antonio Díaz Morales en un contexto marcado por la incertidumbre energética y el impacto de la guerra en la economía global. El debate arranca con el contraste entre el optimismo de Donald Trump sobre la fortaleza económica de EE.UU. y las advertencias de organismos internacionales como la AIE, que alerta de que Europa cuenta con reservas de combustible de aviación para apenas seis semanas. A ello se suma la tensión en el sector aéreo, con el enfrentamiento entre aerolíneas y Aena por la subida de tasas. En la Entrevista Capital, Joan Batalla, presidente de Sedigas, analiza el papel del gas en este escenario de volatilidad geopolítica. Batalla destaca que el gas natural sigue siendo un elemento clave para garantizar la seguridad energética y la competitividad industrial en España, especialmente en un entorno condicionado por conflictos como el de Irán. Además, pone el foco en las oportunidades de inversión que podrían superar los 10.000 millones de euros hasta 2031, así como en la necesidad de un marco regulatorio adecuado para impulsar el sector. También aborda cómo las tensiones en rutas estratégicas como Ormuz influyen en el mercado europeo del gas y si este recurso sigue actuando como factor de estabilidad o empieza a mostrar una mayor vulnerabilidad ante los shocks internacionales.

Reino Unido y Francia reúnen a los llamados países “no beligerantes” para intentar reabrir el estrecho Ormuz. El presidente de EEUU, Donald Trump, afirma que el acuerdo con Irán tiene "buena pinta” y que podrían reunirse el sábado. Europa podría quedarse sin combustible para aviones en unas seis semanas si persisten las actuales interrupciones en el suministro, es la última advertencia del director de la AIE, Fatih Birol.

C dans l'air du 15 avril 2026 - Ormuz: le blocus se reserre...les craintes s'étendentSi Donald Trump assure que la guerre est « presque finie » et évoque une reprise imminente des négociations avec l'Iran, sur le front économique, les signaux restent au rouge. « Il s'agit de la plus importante crise énergétique de l'histoire », s'est alarmé lundi le directeur exécutif de l'Agence internationale de l'énergie (AIE). Selon lui, le mois d'avril « devrait être encore pire que mars » pour le secteur de l'énergie, même dans l'hypothèse d'une fin rapide du conflit.De son côté, le Fonds monétaire international (FMI) a abaissé mardi sa prévision de croissance mondiale et a relevé ses prévisions d'inflation : la hausse mondiale des prix pourrait atteindre 6%. Il met également en garde contre le risque d'une récession si la guerre au Moyen-Orient devait se prolonger. Faut-il redouter un tel scénario ? La crise est-elle appelée à durer ?Pour les ménages, l'impact sur le pouvoir d'achat, déjà sensible avec la hausse des prix à la pompe, pourrait encore s'accentuer. Face à ces tensions, le gouvernement peine à trouver des réponses. Outre la réforme des tickets-restaurants, en chantier depuis plusieurs années, l'exécutif privilégie des mesures ciblées à destination des secteurs les plus exposés. Un paquet de 70 millions d'euros d'aides a déjà été débloqué, fin mars, pour les transporteurs routiers, les pêcheurs et les agriculteurs.« L'aide massive financée par la dette ne peut pas être la solution », a affirmé dimanche le nouveau ministre du Commerce et du Pouvoir d'achat, tout en assurant que « d'autres options sont à l'étude » pour soutenir ménages et entreprises. Alors que les effets du cessez-le-feu entre les États-Unis et l'Iran tardent à se faire sentir à la pompe, le gouvernement envisage désormais d'encadrer les marges des distributeurs afin « d'éviter des effets d'aubaine ».En seulement quarante-quatre jours de conflit, la facture des importations de combustibles fossiles a bondi de plus de 22 milliards d'euros dans l'UE. Alors que l'Europe s'enlise dans la crise, tous les pays n'optent pas pour la même stratégie en matière de carburant. L'Espagne a décidé de baisser les taxes et a été suivie par l'Allemagne, tandis que le Royaume-Uni a opté pour une réduction de la vitesse.Entre blocus et possible reprise des négociations, Donald Trump évoque une issue prochaine au conflit. Mais au-delà des déclarations politiques, quelle est réellement la situation dans le Golfe ? Prix à la pompe, inflation, pouvoir d'achat, emploi… Quelles sont les conséquences de la guerre au Moyen-Orient ? Quels sont les scénarios envisagés par le FMI pour les prochains mois ? Enfin quel rôle la Chine joue-t-elle dans la guerre en Iran ? Tire-t-elle profit de la situation ?Nos experts :- Philippe Dessertine – Économiste, directeur de l'Institut de Haute Finance, professeur à l'IAE Paris Panthéon-Sorbonne, auteur de L'horizon des possibles- Patrice Geoffron – Professeur d'économie à Paris Dauphine, directeur du Centre de géopolitique de l'énergie- Marie Bellan – Journaliste, service économique des Échos, en charge de l'environnement et de l'énergie - Richard Werly – Éditorialiste international, Blick.ch, auteur de Cette Amérique qui nous déteste - Céline Schoen ( en duplex de Bruxelles) – Correspondante à Bruxelles pour La Croix et Le Bulletin Quotidien

PVEM rompe alianza con Morena y PT rumbo al 2027 en SLPInvitan a foro sobre Protección Animal en Desastres en CDMX  AIE reduce previsión de demanda mundial de petróleo para 2026  Más información en nuestro podcast#grc

On this episode of The AIE Podcast… We have a WoW Mini Raid Wrap-up! Lots of patches in lots of games Happy First Contact to all who celebrate And, Happy Birthday to LOTRO! We are chatting amongst ourselves today All that and more coming up right now… Podcast Audio Raw Video http://youtu.be/fvUn580cqSQ Open Welcome to episode # 443 of the podcast celebrating you, the Alea Iacta Est gaming community, the die has been podcast. This is Mkallah: To my left is Mewkow: – (catch phrase here). And to my right is Tetsemi: (catch phrase here). This week we are filling you in on everything going on with AIE. Welcome! Ok, we'll be chatting amongst ourselves shortly, but first, let's cover this week's news… AIE News Community Mandatory Fun Nights Where the fun is mandatory but the attendance is not. Sunday – Destiny 2 8:30 pm Eastern Monday – GW2 9:30 pm Eastern Monday – STO 10:00 pm Eastern Tuesday – SWTOR 9:00 pm Eastern Wednesday – HFO Mythic+ Mayhem (WoW) 8:00 pm Eastern Friday – ESO 9:00 pm Eastern Saturday – LotRO 8:30 pm Eastern Saturday – FFXIV (Maps) 9:30 pm Eastern Saturday – Noob Raid (WoW) 11:00 pm Eastern Streaming and Guild Podcast News We have a ton of AIE member podcasts! Want to know where to find them? Look no further than here- New Overlords Podcast (Max and Sema) https://www.newoverlords.com Boards and Swords (Chris and Philip) https://boardsandswords.com/blog?category=Boards%20%26%20Swords Dr. Gameology ( Dr. Daniel Kaufmann ) https://drgameology.com/ STO – Fleet Action Report (Grebog and Nikodas) https://www.youtube.com/@fleetactionreport A Podcast Reborn: A FFXIV Community Podcast (Brandon aka Old Man Franks, Meagan, and Rho) – NSFL https://www.bonusroll.gg/directory/a-podcast-reborn/ NOMADS New World • Will be sunset in January 2027 • Might do a going away party before it goes away D4 • New expansion this month WoW We have a WoW mini raid wrap up! In Raid Team news, Alliance Team Hell Froze Over (HFO) defeated final boss Crown of the Cosmos (aka Xalatath) in The Voidspire – a victory with unexpected consequences – and then Beloren, first boss in March on Quel’Danas, both on Normal difficulty. On Horde side, Raid Team Kiddie Aggro is now 6/9 in Heroic Voidspire, having out punched the Double Dragons and then sending the Lightblinded Paladin trio bubble hearthing to the Shadowlands. Noob Raid completed the first three bosses in The Voidspire on Normal, and Dankinia’s death count is up to 11. Tetsemi- The next patch for Midnight arrives April 21, bringing new World content such as Void Assaults in Eversong Woods and Zul’Aman as well as Decor Duels – a Hide and Seek challenge that players will queue for. It’s also a busy time for WoW Holidays. Noblegarden ends April 13, Children’s Week arrives April 27 – which may be awkward as Outland orphan Salandria is now an adult NPC in Silvermoon – and Volunteer Guard Duty takes place April 28. Player Housing Endeavors have reset. Fortuna now has Mechagnomes (with miscalibrated hens that jump on your head), Salus has Drakthyr, and Virtus has Ethereals. Consortium won the Alliance side vote. Horde-Fortuna has Mechagnomes. Both got flashy zappy decor rewards this season. And, Gusty is running around with malfunctioning mechachickens on her head. SWTOR In swtor, the guild has some special plans coming for May the 4th, including a trivia contest and a community event. Stay tuned to discord for dates and times. The current Galactic Season is bringing back 2 of the original companions, so if you missed them from GS1 and GS3, now is your chance. MFN is on Tuesdays and everyone is welcome. We promise no more talk about peeps or the proper pronunciation of Coruscant! ESO MFN on Friday still happens, but the group is small FFXIV Next Liveletter is 4/17 North American Fanfest from 4/24-4/25 New major story patch at the end of the month (4/28) STO Lots of stuff going on with STO! All Platforms: Empersa Lance Updates: Damage output reduced, self-damage increased First Contact Day Celebrations! April 7th to May 7th 20 days of progress to earn the grand prize: Nobel Intel Batlecruiser Lobi Sale April 7th to April 12th 20% off all items! Bonus Marks Event April 10th to April 14th 50% bonus for all content that rewards marks For PC- Gral Class command Scout added to Infinity Lock Box starting April 7th Key Sale 15% off Keys from April 7th to April 12th Key Ring Bundle returns as well Includes ultimate tech upgrade LOTRO Lotro is turning 19!!!!! The new gifts will arrive on April 23, with the anniversary event running from April 23 at 10am eastern through May 11. There has been a letter from Orion with an updated development schedule for quarters 2 and 3 of this year. Some of the highlights arriving in the near future is new content with update 48 “A Glorious Hunt” as well as Rivendell Housing. A link to the letter is in the lotro channel. Also Saturday Night Lotro will kick back off this coming Saturday after an unexpected hiatus. And with that, let's see what we have been doing in and out of game GAME NEWS CLOSE And that's our show for tonight. While the chatroom begins suggesting show titles, we want to thank us for joining us. If you have a question or comment about our show, you can email us at podcast@aie-guild.org You can find us on the AIE Discord and BlueSky https://bsky.app/profile/aiepodcast.bsky.social. We record live with video once a month on Sunday at 8pm Eastern/5pm Pacific. You can join the chatroom and play along with us on our website at https://aie-guild.org/podcast-live-stream/ and look for the link to our discord server at https://aie-guild.org. And for past episodes, you can see them on our Youtube channel, https://www.youtube.com/c/TheAIEPodcast ! Our theme was composed by the amazing Andrew Allen, follow him at @keyswithsoul! And now it's time to play all of the great AIE member segments we received this week, including… Next time, we'll be talking to Guest in Game. So until then, AIE… – This is Mkallah – This is Mewkow – This is Tetsemi

Lo scalo di Brindisi era senza jet fuel. A Reggio Calabria sono state introdotte limitazioni per la carenza di cherosene e Pescara per un guasto si trova con una sola cisterna. Lo si legge nei bollettini pubblicati nel lunedì di Pasquetta e che fanno seguito ai quattro arrivati 48 ore fa. In quel caso si trattava degli aeroporti di Milano Linate, Bologna, Treviso e Venezia, per le difficoltà del fornitore Air Bp Italia.Intanto oggi la situazione sembra rientrata: procede regolarmente questa mattina la fornitura di carburante per gli aerei in partenza nell'aeroporto del Salento di Brindisi, gestito da Aeroporti di Puglia. Alcune autobotti sono già giunte nello scalo e altre ne arriveranno nel corso della giornata. La fornitura, peraltro, non è stata mai sospesa. Ieri sera, di fronte alla eventualità che lo scalo rimanesse nella giornata di oggi senza carburante, dopo la diffusione di alcuni bolllettini aeronautici, il presidente di Aeroporti di Puglia Antonio Maria Vasile aveva precisato che non c'era "alcuna emergenza" e che la situazione era "sotto controllo, anche e soprattutto a Brindisi", invitando a evitare "preoccupazioni o allarmismi. Le forniture di carburante continuano regolarmente e non c'è alcun rischio di carenza imminente", aveva aggiunto. Facciamo il punto con Ugo Arrigo professore associato presso l'Università Bicocca di Milano.L'allarme di DimonWall Street oggi ha aperto negativa con l'avvicinarsi della scadenza dell'ultimatum di Donald Trump all'Iran. Ieri, sul piano delle esternazioni pubbliche, a prendere la parola è stato Jamie Dimon. Nella consueta lettera agli azionisti, l amministratore delegato di JP Morgan ha sottolineato senza giri di parole come la guerra in Iran metta l intero mondo finanziario «di fronte all insorgenza di shock significativi per i prezzi del petrolio e delle materie prime, che potrebbero comportare un livello di inflazione più persistente e tassi di interesse più elevati rispetto alle attese dei mercati».«Non abbiamo avuto una recessione creditizia da molto tempo, e sembra che alcune persone diano per scontato che non accadrà mai», ha detto. Un monito netto, che squarcia il velo di compiacenza di Wall Street con una previsione inequivocabile: «Non appena il ciclo del credito si invertirà, cosa che accadrà un giorno, le perdite su tutti i prestiti a leva in generale saranno superiori al previsto». In ogni caso - ha aggiunto - non è "probabilmente" un problema sistemico. Il commento è di Alessandro Plateroti, Direttore editoriale UCapital.comAgenzia Internazionale dell'Energia, 'crisi per Hormuz la peggiore di quelle del '73, '79 e 2002 messe insieme'Il conto alla rovescia verso le 20 americane (le 2 di notte in Italia) è iniziato: nel giro di poche ore si capirà se il presidente americano Donald Trump darà seguito alla minaccia di "cancellare in una notte un intero Paese" o se concederà ancora tempo ai negoziati in corso attraverso i mediatori internazionali. Poche ore fa l'Iran ha chiuso tutti i canali di comunicazione diplomatici e indiretti con gli Stati Uniti, secondo quanto riportato dal quotidiano controllato dallo Stato Tehran Times su X. "Sono stati sospesi anche tutti gli scambi di messaggi". Il Teheran Times è considerato come strettamente legato alle fazioni più intransigenti all'interno del governo iraniano.Intanto per il direttore dell'Agenzia Internazionale dell'Energia (Aie), Fatih Birol, l'attuale crisi di petrolio e gas, innescata dal blocco dello stretto di Hormuz da parte dell'Iran, è "più grave di quelle del 1973, 1979 e 2002 messe insieme". Il mondo, ha affermato Birol, "non ha mai sperimentato un'interruzione dell'approvvigionamento energetico di tale portata". Il direttore dell'Aie ha aggiunto che i Paesi più a rischio sono quelli in via di sviluppo, le cui popolazioni saranno prevedibilmente colpite in modo duro dall'aumento dei prezzi dei combustibili. Il mese scorso, i Paesi membri dell'Aie hanno concordato di rilasciare parte delle riserve strategiche: Birol ha spiegato che tale processo è già stato avviato, ma attualmente resta ancora in corso. Ne parliamo con Davide Tabarelli, presidente Nomisma Energia.

KEXP and BIME present Live on KEXP in Bilbao with TANXUGUEIRAS performing at Iglesia de la Encarnación. Recorded October 30, 2025. 1. Todo amaina2. O Querer3. As que tiñan que estar4. Quen é a que canta?5. Pedindo Perdón Aida Tarrío - Vocal, PercussionOlaia Maneiro - Vocal, PercussionSabela Maneiro - Vocal, PercussionIago Pico - Percussions, Synths Live on KEXP in Bilbao is a partnership with BIME. Recorded at Iglesia de la Encarnación in Bilbao, Spain. Host: Albina CabreraAudio Engineer: Kevin SuggsGuest Audio Engineer: BIME crew and Andrés Luca de Tena López-EscobarAdio Mixer: Iago Pico Mastering Engineer: Matt Ogaz BIMEJulen MartínLeire GoienetxeaAnne Salazar Guillermo Royo (NOIZ Lab Production) BIME Audio Team Israel Monzoncillo (Izarblue)Iñigo Escauriaza Juanjo Mediavilla Brayan ChacónPatxi Gabilondo (Call & Play backline assistant) Live on KEXP in Bilbao has the support of Sounds from Spain, ICEX, AIE and BASQUE.MUSIC. https://tanxugueiras.comhttps://bime.org/http://kexp.org Join this channel to get access to perks:https://www.youtube.com/channel/UC3I2GFN_F8WudD_2jUZbojA/join Photo by Carlos CruzSupport the show: https://www.kexp.org/donateSee omnystudio.com/listener for privacy information.

C dans l'air du 24 mars 2026 - Ormuz, choc mondial : Trump cherche la sortie ?Le détroit d'Ormuz reste une carte maîtresse pour l'Iran dans sa guerre avec l'Amérique de Donald Trump, qui n'arrive pas à imposer son déblocage. Après avoir menacé « d'anéantir dans les 48 heures » les centrales électriques iraniennes si cette voie stratégique pour l'approvisionnement mondial en hydrocarbures n'était pas rouverte, le président américain a finalement repoussé, lundi, son ultimatum — rejeté par Téhéran — en affirmant que des discussions étaient en cours avec le régime iranien.La réaction des marchés ne s'est pas fait attendre : les Bourses ont vivement fluctué et les prix du pétrole ont reculé. Mohammad-Bagher Ghalibaf, figure majeure du régime iranien, a toutefois rapidement démenti l'existence de tels échanges, dénonçant de « fausses informations utilisées pour manipuler les marchés ». D'autres sources évoquent néanmoins un possible rôle d'intermédiaire du Pakistan.Que pourraient négocier les deux camps ? Une issue diplomatique est-elle envisageable ? Et les capacités militaires iraniennes ont-elles été sous-estimées ? Vendredi dernier, l'Iran a lancé deux missiles en direction de la base militaire américano-britannique de Diego Garcia, située à 4 000 km de ses côtes. Sans atteindre leur cible, ces tirs suggèrent que Téhéran dispose de vecteurs capables de dépasser les 2 000 km de portée jusqu'ici avancés.Donald Trump affirme vouloir mettre à profit les prochains jours pour ouvrir une voie de désescalade. Celle-ci reste toutefois incertaine. Israël poursuit en effet ses frappes en Iran, mais aussi au Liban. L'armée israélienne entend s'emparer d'une zone s'étendant du sud du pays jusqu'au fleuve Litani et cible désormais des ponts. Paris appelle « les autorités israéliennes à s'abstenir de telles opérations terrestres, qui auraient des conséquences humanitaires majeures et aggraveraient une situation déjà dramatique » au Liban, a déclaré mardi le chef de la diplomatie française, Jean-Noël Barrot. La veille, Emmanuel Macron a martelé qu'aucune « occupation » ne saurait garantir « la sécurité de qui que ce soit ».Dans ce contexte, le conflit s'enlise et ses répercussions énergétiques s'accentuent. Les prix des hydrocarbures s'envolent et l'Agence internationale de l'énergie (AIE) met en garde contre le risque d'une crise énergétique mondiale parmi les plus graves de ces dernières décennies. Les Philippines ont déclaré l'état d'urgence énergétique. L'Ukraine redoute une pénurie de diesel dès avril. Le Japon a déjà puisé une seconde fois dans ses réserves stratégiques. Au Chili, les automobilistes se ruent sur les stations-service face à une hausse annoncée d'un dollar par litre de gasoil. En France, le gouvernement demande aux raffineurs de produire plus rapidement.Nos experts :- Dominique TRINQUAND - ancien chef de la mission militaire française auprès de l'ONU, auteur D'un monde à l'autre chez Robert Lafont- Alain BAUER - Professeur émérite au CNAM, Fondateur du pôle Sécurité, Défense et Renseignement, auteur de Trump, le pouvoir des mots, publié aux éditions First- Mariam PIRZADEH - rédactrice en chef à France 24 et ancienne correspondante à Téhéran de 2014 à 2019- Nicole BACHARAN - Historienne et politologue, spécialiste des Etats-Unis, autrice de Requiem pour le monde libre, aux Éditions de l'Observatoire

“El mundo podría enfrentar su peor crisis energética en décadas”, es la advertencia del director de la AIE, Fatih Birol. El consenso de PWC advierte de que la guerra en Irán restará medio punto al crecimiento de España este año. La presidenta de la CE, Ursula von der Leyen, llega a Camberra para cerrar las negociaciones de un TLC entre la Unión y Australia.

Aie. Mais Aie… Aujourdʹhui Travelling se veut mordant et vous propose un film saignant à souhait avec beaucoup de viande dans le bouillon. Ce film cʹest Piranhas de Joe Dante sorti en 1978 dont nous extrayons les arrêtes et les biftecks. Piranhas raconte, après le succès des Dents de la Mer, une histoire de dents de rivière. Profitant du fabuleux succès de Steven Spielberg sorti en 1975, le producteur de série B et de films Z, Roger Corman, propose à un tout jeune réalisateur de faire ses preuves en tournant un pastiche de Jaws, les Dents de la Mer. On reprend peu ou prou les mêmes éléments en y ajoutant des bestioles fort méchantes, en bande cette fois, et en eau douce, en saupoudrant le tout de dénonciation politique, anti-armée, et dʹune pointe dʹhumour. Film dʹhorreur à petit budget, Piranhas est ce quʹon appelle un mockmuster. Un film inspiré dʹun autre ce qui demande toutefois du talent. Car réaliser un film de genre avec peu de moyens, ce nʹest pas si facile. Heureusement pour Joe Dante, quelques acteurs de série B, relativement prestigieux, acceptent de tourner notamment Keenan Wynn, Kevin McCarthy et Barbara Steele, la Scream Queen des seventies. Et puis, il y a les talents de monteur de Joe Dante et des évolutions technologiques et les compétences de concepteurs dʹeffets visuels, comme Phil Tippett et Adam Beckett qui venaient de concevoir et réaliser certaines créatures de la guerre des étoiles. Piranhas plait à un public averti et devient culte. Accessoirement, il nʹest pas trop égratigné par la critique. Normal, les journaux étaient grèves au moment de sa sortie. REFERENCES Images du tournage commentées par Joe Dante : https://www.youtube.com/watch?v=OYpq867wRC0 Joe Dante on "Piranha" : https://www.youtube.com/watch?v=6Fr-0Y9KeSM POST MORTEM: Joe Dante : https://www.youtube.com/watch?v=Xem4_Rs8Ank Joe Dante Q&A | Nightmare Weekend Richmond 2023: https://www.youtube.com/watch?v=iyTwOjTsk7o FANTASY FILM FESTIVAL: Joe Dante, Barbara Steele & Paul Bartel: https://www.youtube.com/watch?v=pukpsGMIvdM The Making of Piranha (1978) : https://www.youtube.com/watch?v=_4XJBkT8jxw

En 2026, la IA es un doble filo para el ESG: consume energía masiva (demanda de centros de datos crecerá entre el 165-220% para 2030, según Goldman Sachs y AIE), pero también algo extraordinario que acelerará la sostenibilidad como nunca. Maersk: IA optimiza rutas y puertos : reduce emisiones 9-15% en shipping (3% de emisiones globales). Unilever: Monitorea 24M hectáreas de palma con IA + satélites: cero deforestación y menos desperdicio en helados (~10%). PepsiCo: Lay's Smart Farm con IA : reduce el uso en -15% pesticidas, -5% agua, +5% productividad en cultivos regenerativos. Rio Tinto (minería): Computer vision detecta especies amenazadas en Weipa (Australia) y minimiza el impacto ambiental en la extracción de minerales críticos.

KEXP and BIME present Live on KEXP in Bilbao with Queralt Lahoz performing at Iglesia de la Encarnación. Recorded October 30, 2025. 1.La FUENTE L'AMAPOLA2.YNEPN3. 19/174. En Otro Lugar5. VUELVES6. LA FE Queralt Lahoz - VocalsLuis Federico Castro Hernández - DJAnna Bosch Sierra - KeyboardsMarc Soto Guasch - DrumsAna Navas Sánchez - Backing VocalsSilvia Merchán Bustos - Backing Vocals Live on KEXP in Bilbao is a partnership with BIME. Recorded at Iglesia de la Encarnación in Bilbao, Spain. Host: Albina CabreraAudio Engineer: Kevin SuggsGuest Audio Engineer: BIME crew and Manuel Larrosa MadurgaAudio Mixer: Kevin SuggsMastering Engineer: Matt Ogaz BIMEJulen MartínLeire GoienetxeaAnne Salazar Guillermo Royo (NOIZ Lab Production) BIME Audio Team Israel Monzoncillo (Izarblue)Iñigo Escauriaza Juanjo Mediavilla Brayan ChacónPatxi Gabilondo (Call & Play backline assistant) Live on KEXP in Bilbao has the support of Sounds from Spain, ICEX, AIE and BASQUE.MUSIC. https://queraltlahoz.comhttps://bime.org/http://kexp.org Join this channel to get access to perks:https://www.youtube.com/channel/UC3I2GFN_F8WudD_2jUZbojA/join Photo by Carlos CruzSupport the show: https://www.kexp.org/donateSee omnystudio.com/listener for privacy information.

Le prix de l'or noir flambe ! Depuis le début de la guerre au Moyen-Orient, les principales références du marché du pétrole ont augmenté de plus de 40%. La raison : le blocage par l'Iran du détroit d'Ormuz par où transite 1/5è du trafic pétrolier mondial. Face à cela, l'Agence internationale de l'énergie (AIE) accepte de débloquer des stocks stratégiques. Mais jusqu'où cette situation est-elle tenable ? Quelles seront les conséquences de la crise pétrolière ? Quelles solutions ? - Peut-il y avoir un virage à 180 degrés en faveur des énergies propres ? Pour en débattre - Marin Gillot, analyste des politiques énergétiques au sein du Think tank européen Stratégic Perspectives - Alexis Gléron, spécialiste des marchés de l'électricité et co-fondateur d'Augmented Energy. Il dispose d'une longue expérience dans l'analyse des marchés de l'électricité - Matthieu Auzanneau, ancien directeur du think tank The Shift Project, un centre de réflexion qui travaille sur la transition énergétique et la sortie des énergies fossiles. Auteur du livre Or noir, la grande histoire du pétrole, éditions La Découverte.

Le prix de l'or noir flambe ! Depuis le début de la guerre au Moyen-Orient, les principales références du marché du pétrole ont augmenté de plus de 40%. La raison : le blocage par l'Iran du détroit d'Ormuz par où transite 1/5è du trafic pétrolier mondial. Face à cela, l'Agence internationale de l'énergie (AIE) accepte de débloquer des stocks stratégiques. Mais jusqu'où cette situation est-elle tenable ? Quelles seront les conséquences de la crise pétrolière ? Quelles solutions ? - Peut-il y avoir un virage à 180 degrés en faveur des énergies propres ? Pour en débattre - Marin Gillot, analyste des politiques énergétiques au sein du Think tank européen Stratégic Perspectives - Alexis Gléron, spécialiste des marchés de l'électricité et co-fondateur d'Augmented Energy. Il dispose d'une longue expérience dans l'analyse des marchés de l'électricité - Matthieu Auzanneau, ancien directeur du think tank The Shift Project, un centre de réflexion qui travaille sur la transition énergétique et la sortie des énergies fossiles. Auteur du livre Or noir, la grande histoire du pétrole, éditions La Découverte.

Dans cet épisode, nous vous proposons de partir vers un site stratégique, situé à l'entrée du Golfe persique : le détroit d'Ormuz, point de passage par où transite près de 20% de la production mondiale de pétrole et de gaz. Aujourd'hui, en raison de la guerre au Moyen-Orient, le trafic est presque à l'arrêt. Des dizaines de tankers et de porte containers sont stationnés de part et d'autre du détroit. Ils n'osent plus bouger car une vingtaine de navires commerciaux ont été attaqués dans le détroit ou à proximité : des attaques de drones, des explosions qui ne sont pas toujours revendiquées mais qui interviennent alors que l'Iran a clairement indiqué que toute exportation de pétrole au profit de ses ennemis serait bloquée.Depuis l'offensive lancée par les Etats-Unis et Israël contre l'Iran le 28 février, la République islamique a donc choisi de riposter en utilisant le levier le plus puissant à sa disposition : ce détroit, dont la fermeture durable peut gravement déstabiliser l'économie mondiale. Selon l'Agence internationale de l'énergie (AIE), cette stratégie a entraîné la plus importante perturbation de l'approvisionnement en or noir de l'histoire. Pour comprendre comment l'Iran se sert du détroit pour faire plier ses adversaires et en quoi cela peut être dangereux pour une économie mondiale déjà fragile... Sur le Fil a sollicité Fabien Zamora, journaliste au sein du Pôle international de l'AFP à Paris et deux spécialistes de la zone, Emmanuel Hache, adjoint scientifique à l'institut de recherche IFP Énergie Nouvelle et Paul Tourret, directeur de l'Institut supérieur d'économie maritime.Réalisation : Maxime Mamet et Michaëla Cancela-KiefferExtraits sonores reccueillis par l'AFPTV sur le terrainLa Semaine sur le fil est le podcast hebdomadaire de l'AFP. Vous avez des commentaires ? Ecrivez-nous à podcast@afp.com. Si vous aimez, abonnez-vous, parlez de nous autour de vous et laissez-nous plein d'étoiles sur votre plateforme de podcasts préférée pour mieux faire connaître notre programme. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

A punto de cumplirse dos semanas del inicio de la guerra de Irán, el estrecho de Ormuz es ahora el enclave estratégico más importante del conflicto, que amenaza, incluso, con crear una crisis global y una subida de precios, casi sin precedentes. En las últimas 48 horas, Irán ya ha amenazado con subir el precio del barril hasta los 200 dólares, lo que supondría un récord histórico. Donald Trump, por su parte, acaba de autorizar la compra de crudo ruso para contener la inflación, al tiempo que ha celebrado esa misma escalada de precios: "Vamos a hacer mucho dinero". En el estrecho de Ormuz se juega todo y como prueba, un botón: por él pasan unos 20 millones de barriles diarios de petróleo -según datos de la Agencia Internacional de la Energía (AIE)-, además del 20% del comercio de gas natural licuado. Así, el bloqueo actual de esta vía marítima -ubicada entre Irán y Omán- ha generado que hasta 32 países hayan acordado la mayor liberación de reservas de petróleo de la historia. Entre ellos, está España. Los expertos ya llevan días avisando de las graves consecuencias que todo esto podría suponer a nivel mundial y es que la clave está ahora en el "tiempo". Así lo explica Gonzalo Pérez-Maura, que es el Consejero Delegado de Grupo Pérez y Cía, empresa líder en el sector naviero y logístico: "Se va a producir un bloqueo durante un periodo de tiempo y si se prolonga va a afectar a la cadena de suministro". · Canal de Telegram: https://t.me/lacontracronica · “Contra el pesimismo”… https://amzn.to/4m1RX2R · “Hispanos. Breve historia de los pueblos de habla hispana”… https://amzn.to/428js1G · “La ContraHistoria del comunismo”… https://amzn.to/39QP2KE · “La ContraHistoria de España. Auge, caída y vuelta a empezar de un país en 28 episodios”… https://amzn.to/3kXcZ6i · “Contra la Revolución Francesa”… https://amzn.to/4aF0LpZ · “Lutero, Calvino y Trento, la Reforma que no fue”… https://amzn.to/3shKOlK Apoya La Contra en: · Patreon... https://www.patreon.com/diazvillanueva · iVoox... https://www.ivoox.com/podcast-contracronica_sq_f1267769_1.html · Paypal... https://www.paypal.me/diazvillanueva Sígueme en: · Web... https://diazvillanueva.com · Twitter... https://twitter.com/diazvillanueva · Facebook... https://www.facebook.com/fernandodiazvillanueva1/ · Instagram... https://www.instagram.com/diazvillanueva · Linkedin… https://www.linkedin.com/in/fernando-d%C3%ADaz-villanueva-7303865/ · Flickr... https://www.flickr.com/photos/147276463@N05/?/ · Pinterest... https://www.pinterest.com/fernandodiazvillanueva Encuentra mis libros en: · Amazon... https://www.amazon.es/Fernando-Diaz-Villanueva/e/B00J2ASBXM #FernandoDiazVillanueva #iran #ormuz Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

En el Edomex aseguran predio con hasta 100 mil litros de huachicol  AIE advierte fuerte caída en el flujo petrolero por guerraEl caló mexicano surgió como una forma de expresión en los barrios de la CDMXMás información en nuestro podcast

Ce jeudi 12 mars, l'impact sur les marchés de la décision de l'Agence internationale de l'énergie (AIE) de libérer 400 millions de barils de stocks stratégiques pétroliers ainsi que la stratégie de gestion de l'État face à la hausse des prix à la pompe ont notamment été abordés par Charlotte de Montpellier, économiste senior chez ING, Patrice Geoffron, professeur à Paris-Dauphine et membre du Cercle des Économistes, et Roland Gillet, professeur d'économie financière à l'Université Paris 1 Panthéon-Sorbonne et à l'ULB, dans l'émission Les Experts, présentée par Raphaël Legendre sur BFM Business. Retrouvez l'émission du lundi au vendredi et réécoutez la en podcast.

Face à la flambée des prix du pétrole, les grandes puissances économiques envisagent de mobiliser leurs réserves stratégiques. Les ministres des Finances du G7 ont évoqué cette possibilité pour tenter de stabiliser le marché et éviter un choc pétrolier majeur. Mais ces stocks d'urgence ne constituent qu'une solution temporaire face aux tensions qui secouent l'approvisionnement mondial. La hausse du prix du baril de pétrole fait la Une de l'actualité économique. Le brut a récemment atteint les 110 dollars, signe d'une forte tension sur les marchés énergétiques. Cette flambée des prix et les menaces sur les flux d'approvisionnement inquiètent particulièrement les grandes puissances importatrices de pétrole. Dans ce contexte, les ministres des Finances du G7 se sont réunis pour évoquer l'utilisation de leurs réserves stratégiques de pétrole. L'objectif est clair : injecter du pétrole sur le marché afin d'augmenter l'offre et ainsi éviter, autant que possible, un nouveau choc pétrolier mondial. Ces discussions interviennent alors que les tensions autour du détroit d'Ormuz font craindre des perturbations importantes. Ce passage maritime est crucial pour l'économie mondiale : près de 20% du pétrole mondial y transite. Toute perturbation dans cette zone peut donc avoir des répercussions immédiates sur les prix du brut. Des réserves créées après le choc pétrolier de 1973 Les réserves stratégiques de pétrole trouvent leur origine dans la crise énergétique de 1973. À l'époque, plusieurs pays producteurs avaient décidé de réduire leurs exportations vers les États occidentaux, provoquant une explosion des prix et des pénuries d'essence dans de nombreux pays. Pour répondre à cette crise, l'Agence internationale de l'énergie (AIE) a été créée. Sa mission est limpide : renforcer la sécurité énergétique des pays consommateurs. Chaque pays membre doit ainsi disposer d'un stock d'urgence équivalant à au moins 90 jours d'importations de pétrole. Ces réserves constituent une forme d'assurance énergétique. En cas de crise majeure, les États peuvent puiser dans ces stocks afin d'injecter du pétrole sur le marché et compenser temporairement une pénurie. Dans le contexte actuel, ce mécanisme pourrait justement être activé. Avec les tensions autour du détroit d'Ormuz, l'offre de pétrole pourrait être perturbée, ce qui contribue à faire grimper les prix. Le fonctionnement du marché reste en effet simple. Lorsque l'offre diminue alors que la demande reste stable, les prix augmentent mécaniquement. Libérer une partie des réserves stratégiques permet donc d'augmenter l'offre disponible et de tenter de stabiliser les prix. Parfois même, l'annonce d'une possible utilisation de ces stocks suffit à calmer les marchés. C'est ce qui s'est produit ce lundi. Lorsque les pays du G7 ont évoqué la possibilité d'une action coordonnée, les cours du pétrole ont commencé à se stabiliser. Un outil utile mais limité dans le temps Ces réserves stratégiques restent toutefois une solution temporaire. Elles ne peuvent pas résoudre une crise énergétique durable. La raison est simple : les stocks sont limités. Même si les pays disposent d'environ trois mois de pétrole en réserve, cela représente en réalité seulement quelques semaines de consommation à l'échelle mondiale. Si une crise énergétique devait durer plusieurs mois, ces réserves pourraient donc s'épuiser relativement rapidement. Leur rôle est surtout de gagner du temps. Le temps nécessaire pour permettre aux producteurs d'augmenter leur production, pour réorganiser les routes commerciales ou encore pour trouver des solutions diplomatiques à la crise. Au-delà de la question des réserves stratégiques, cette situation rappelle surtout la forte dépendance de l'économie mondiale au pétrole. Une hausse brutale des prix peut en effet avoir plusieurs conséquences économiques majeures : une augmentation de l'inflation, car l'énergie renchérit les transports et la production, mais aussi un ralentissement de la croissance, les entreprises voyant leurs coûts augmenter. Malgré les efforts de transition énergétique, le pétrole reste aujourd'hui la colonne vertébrale du système énergétique mondial. Des transports à l'aviation en passant par la pétrochimie, une grande partie de l'économie en dépend encore. C'est pour cette raison que les réserves stratégiques restent un outil essentiel pour préserver l'équilibre des marchés énergétiques, même si leur efficacité atteint rapidement ses limites lorsque les crises se prolongent. À lire aussiGuerre au Moyen-Orient: au Nigeria et en Sierra Leone, la hausse du pétrole fait déjà grimper les prix à la pompe

On this episode of The AIE Podcast… World of Warcraft is now on Zillow Congrats to Sethos on 70! Spring Abundance is in the air Nifty things are happening in LOTRO And, we have our dear friends Gusty and Shojo here to talk all about AIE in World of Warcraft All that and more coming up right now… Podcast Audio Raw Video http://youtu.be/dWQ9wXpGYyY Open Welcome to episode #442 of the podcast celebrating you, the Alea Iacta Est gaming community, the die has been podcast. This is Mewkow: To my left is Tetsemi: – (catch phrase here). And to my right is Mkallah: (Hey folks, there is a homemade chicken pot pie in the Guild Kitchen). This week we are joined by special guests Gusty and Shojo who are here to talk to us about AIE in World of Warcraft Welcome! Ok, we'll be digging into World of Warcraft shortly, but first, let's cover this week's news… AIE News Community Mandatory Fun Nights Where the fun is mandatory but the attendance is not. Sunday – Destiny 2 8:30 pm Eastern Monday – GW2 9:30 pm Eastern Monday – STO 10:00 pm Eastern Tuesday – SWTOR 9:00 pm Eastern Wednesday – HFO Mythic+ Mayhem (WoW) 8:00 pm Eastern Friday – ESO 9:00 pm Eastern Saturday – LotRO 8:30 pm Eastern Saturday – FFXIV (Maps) 9:30 pm Eastern Saturday – Noob Raid (WoW) 11:00 pm Eastern Streaming and Guild Podcast News We have a ton of AIE member podcasts! Want to know where to find them? Look no further than here- New Overlords Podcast (Max and Sema) https://www.newoverlords.com Boards and Swords (Chris and Philip) https://boardsandswords.com/blog?category=Boards%20%26%20Swords Dr. Gameology ( Dr. Daniel Kaufmann ) https://drgameology.com/ STO – Fleet Action Report (Grebog and Nikodas) https://www.youtube.com/@fleetactionreport A Podcast Reborn: A FFXIV Community Podcast (Brandon aka Old Man Franks, Meagan, and Rho) – NSFL https://www.bonusroll.gg/directory/a-podcast-reborn/ NOMADS Rust on a PvE server since the new naval update came out. Boats are fun! New World will officially shutdown its servers January 31, 2027. We are still there! Keep an eye on the channel in nomads for a possible farewell as the end draws near. Valheim 5 Year Celebration and Regular Update. AIE Valheim Community Server Information is pinned in the AIE Discord #Nomads channel WoW Noob Raid had a great final run of Manaforge Omega on pre-Midnight launch – clearing both Normal and Heroic! Dankinia will be taking a break until the first raid in Midnight is released, around early April. Midnight launched on 3/2, with early access for folks who prepurchased expected to begin 2/27. Lots more about Midnight from our lovely guests later in the show. Player housing. This new feature is time consuming but a great deal of fun. The creativity of our Guildies is amazing to behold! WoW Classic As Jehdai pointed out, we have our first 70 (Sethos) and light activity on the 20th Anniversary TBC launch on Dreamscythed Horde side. We have the guild available with the guild bank open and invite others to join us in Outland. In other Classic news: Reminder that there are now 3 launchers for Classic (‘Original’, ‘Anniversary’, ‘Era’) “Original” Classic is progressing with Mists of Pandaria. The guild is available on Horde side on Galakras. If I have this right, MoP classic uses the modern guild interface so we have the new interface showing 651 members. “Anniversary” Classic which is now at TBC as mentioned above. “Era” classic is a separate launcher with 3 game modes: ‘Classic Era’ which is forever frozen in the original game (pre-TBC). We were on Mankrik here but really no activity or guild. “Season of Discovery” which is where experiments were performed by the devs which some expect may lead to the anticipated “Classic Plus” many hope for. We have no real activity there currently. “Hardcore” is the 3rd game mode in the Era client and probably our 2nd most active within the classic modes. We have an Alliance side guild on Doomhowl and some activity on the Horde side of Doomhowl but no guild currently. Lars plans to return to his attempt at a “Solo Self-Found” run to 60 in Hardcore but that has been on pause for quite some time. We did have guild members make it to 60 on the hardcore server previously. Ravisant is ProffessorWC or Sethos one of the raid leaders from HoG SWTOR In Swtor this week, we are ready for Update 7.8.1, Master’s Enigma. It will bring a story piece, Galactic Seasons 10, new date nights, and the Spring Abundance Festival. The story finale is coming in the June timeframe to make room for 8.0 at the end of the year. We continue to do our MFN on Tuesday nights. Please join us. There are no gear or level requirements, but you may need to have gotten your personal ship as we do tend to travel about. If you want to read about an awesome trip to Galaxy’s Edge, with not only pics but also tips and tricks for your own visit, check out guildie Strykezone’s blog post. You’ll find a link in the pinned messages in the swtor channel of the AIE discord. The Kanjiklub House https://kanjiklubhouse.com/2026/02/18/my-star-wars-adventure-to-galaxys-edge-at-disney/ ESO The guild has been working on endeavors and in game events. FFXIV This past Tuesday we got the new Variant dungeon with the newer middle level difficulty The next live letter is on March 13th at 4am PST, and they'll probably be discussing the next story patch LOTRO We had an update that brought us some nifty things. The new raid “The Folly of Nagakhêdi” (Nah-gah-KAY-dee) for tier 1 is live, tiers 2 and 3 are coming this week and next week. There is a new legendary reward track with some great rewards such as a rose-colored mûmak calf cosmetic pet. With other class changes, mainly with guardian, we also got a great change. It is now possible to start Private Encounters from inside of a Housing Neighborhood. Looking forward to seeing you in game and doing some of these awesome new instances. And with that, let's get back to Gusty and Shojo and find out what's going on in World of Warcraft. GAME NEWS CLOSE And that's our show for tonight. While the chatroom begins suggesting show titles, we want to thank Gusty and Shojo for joining us. If you have a question or comment about our show, you can email us at podcast@aie-guild.org You can find us on the AIE Discord and BlueSky https://bsky.app/profile/aiepodcast.bsky.social. We record live with video once a month on Sunday at 8pm Eastern/5pm Pacific. You can join the chatroom and play along with us on our website at https://aie-guild.org/podcast-live-stream/ and look for the link to our discord server at https://aie-guild.org. And for past episodes, you can see them on our Youtube channel, https://www.youtube.com/c/TheAIEPodcast ! Our theme was composed by the amazing Andrew Allen, follow him at @keyswithsoul! Next time, we'll be talking to Guest in Game. So until then, AIE… – This is Gusty – This is Shojo – This is Mewkow – This is Tetsemi – This is Mkallah And this has been… The AIE Podcast

China sets its lowest economic growth target since the 1991, but AIE's Derek Scissors says it's actually on the "high end of reality." The hiring bifurcation between blue- and white-collar jobs.  Plus, stocks under pressure as oil prices keep climbing, but software names buck the downtrend. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

KEXP and BIME present Live on KEXP in Bilbao with Derby Motoreta’s Burrito Kachimba performing at Iglesia de la Encarnación. Recorded October 29, 2025. 1. La Fuente2. Prodigio3. Gitana Dandy Piranha - VocalsGringo - GuitarBacca - Guitar, Backing VocalsSoni - BassPapi Pachuli - DrumsMachete Carrasco - Keyboards, Synths Live on KEXP in Bilbao is a partnership with BIME. Recorded at Iglesia de la Encarnación in Bilbao, Spain. Host: Albina CabreraAudio Engineer: Kevin SuggsGuest Audio Engineer: BIME crew and Edu RuizAudio Mixer: Jordi GilMastering Engineer: Matt Ogaz BIMEJulen MartínLeire GoienetxeaAnne Salazar Guillermo Royo (NOIZ Lab Production) BIME Audio Team Israel Monzoncillo (Izarblue)Iñigo Escauriaza Juanjo Mediavilla Brayan ChacónPatxi Gabilondo (Call & Play backline assistant) Live on KEXP in Bilbao has the support of Sounds from Spain, ICEX, AIE and BASQUE.MUSIC. https://dmbk.bandcamp.comhttps://bime.org/http://kexp.org Join this channel to get access to perks:https://www.youtube.com/channel/UC3I2GFN_F8WudD_2jUZbojA/join Photo by Carlos CruzSupport the show: https://www.kexp.org/donateSee omnystudio.com/listener for privacy information.

KEXP and BIME present Live on KEXP in Bilbao with Triángulo de Amor Bizarro performing at Iglesia de la Encarnación. Recorded October 31, 2025. 1. Robo tu tiempo2. Barca quemada3. Mi catedral4. Canción de la fama5. De la monarquía a la criptocracia6. Vigilantes del espejo Isabel Cea Álvarez - Bass, VocalsRodrigo Camaño Díaz - Guitar, VocalsRafael Mallo García - Drums Live on KEXP in Bilbao is a partnership with BIME. Recorded at Iglesia de la Encarnación in Bilbao, Spain. Host: Albina CabreraAudio Engineer: Kevin SuggsGuest Audio Engineer: BIME crew and Carlos Hernández NombelaAudio Mixer: Carlos Hernández NombelaMastering Engineer: Matt Ogaz BIMEJulen MartínLeire GoienetxeaAnne Salazar Guillermo Royo (NOIZ Lab Production) BIME Audio Team Israel Monzoncillo (Izarblue)Iñigo Escauriaza Juanjo Mediavilla Brayan ChacónPatxi Gabilondo (Call & Play backline assistant) Live on KEXP in Bilbao has the support of Sounds from Spain, ICEX, AIE and BASQUE.MUSIC. https://triangulodeamorbizarro.comhttps://bime.org/http://kexp.org Join this channel to get access to perks:https://www.youtube.com/channel/UC3I2GFN_F8WudD_2jUZbojA/join Photo by Carlos CruzSupport the show: https://www.kexp.org/donateSee omnystudio.com/listener for privacy information.

KEXP and BIME present Live on KEXP in Bilbao with Depresión Sonora performing at Iglesia de la Encarnación. Recorded October 28, 2025. 1. La Ley del Pobre2. Domingo Químico3. No te Hables Mal4. Cómo Será Vivir en el Campo5. Vacaciones para Siempre Marcos Crespo García - Guitar, VocalsGonzalo López Abad - GuitarRené Del Hoyo Gil - BassDavid Chamizo Cózar - DrumsJuan Gonzalez Cabello - KeysMimetiz Eskolako Abesbatza - Children’s Choir Live on KEXP in Bilbao is a partnership with BIME. Recorded at Iglesia de la Encarnación in Bilbao, Spain. Host: Albina CabreraAudio Engineer: Kevin SuggsGuest Audio Engineer: BIME crew and Miguel GrimaldoAudio Mixer: HartoMastering Engineer: Matt Ogaz BIMEJulen MartínLeire GoienetxeaAnne Salazar Guillermo Royo (NOIZ Lab Production) BIME Audio Team Israel Monzoncillo (Izarblue)Iñigo Escauriaza Juanjo Mediavilla Brayan ChacónPatxi Gabilondo (Call & Play backline assistant) Live on KEXP in Bilbao has the support of Sounds from Spain, ICEX, AIE and BASQUE.MUSIC. https://depresionsonora.comhttps://bime.org/http://kexp.org Join this channel to get access to perks:https://www.youtube.com/channel/UC3I2GFN_F8WudD_2jUZbojA/join Photo by Carlos CruzSupport the show: https://www.kexp.org/donateSee omnystudio.com/listener for privacy information.

KEXP and BIME present Live on KEXP in Bilbao with Alcalá Norte performing at Iglesia de la Encarnación. Recorded October 28, 2025. La Sangre del Pobre 420N La Calle Elfo Westminster La Vida Cañón Álvaro Rivas - VocalsJaime Barbosa - DrumsCarlos Elías - GuitarPablo Prieto - BassLaura de Diego - KeysRené del Hoyo - Guitar Live on KEXP in Bilbao is a partnership with BIME. Recorded at Iglesia de la Encarnación in Bilbao, Spain. Host: Albina CabreraAudio Engineer: Kevin SuggsGuest Audio Engineer: BIME crew and John BadiAudio Mixer: Pablo FergusMastering Engineer: Matt Ogaz BIMEJulen MartínLeire GoienetxeaAnne Salazar Guillermo Royo (NOIZ Lab Production) BIME Audio Team Israel Monzoncillo (Izarblue)Iñigo Escauriaza Juanjo Mediavilla Brayan ChacónPatxi Gabilondo (Call & Play backline assistant) Live on KEXP in Bilbao has the support of Sounds from Spain, ICEX, AIE and BASQUE.MUSIC. https://alcalanorte.bandcamp.comhttps://bime.org/http://kexp.org Join this channel to get access to perks:https://www.youtube.com/channel/UC3I2GFN_F8WudD_2jUZbojA/joinSupport the show: https://www.kexp.org/donateSee omnystudio.com/listener for privacy information.

On this episode of The AIE Podcast… We will see you at Nerdtacular Renegades is giving a galaxy far far away vibe SWTOR celebrates with 4 crazy nights the STO Starbase is expanding And, we have our first show of 2026! All that and more coming up right now… Podcast Audio Raw Video http://youtu.be/RNmHGBUf5gU Open Welcome to episode 441 of the podcast celebrating you, the Alea Iacta Est gaming community, the die has been podcast. This is Mkallah: To my left is Tetsemi – (catch phrase here). And to my right is Mewkow: (catch phrase here). Happy 2026, folks! The first week and a half has felt like a year and we are all getting back into the swing of things. Ok, we'll be digging into what we've all been up to shortly, but first, let's cover this week's news… AIE News Community Nerdtacular 2026 has been announced- the event that we affectionately refer to as the Nerd Family Reunion is back for the first time since 2017. Lots of information on Frogpants.com, including tickets and hotel details. The event is scheduled for June 11-13 and will be held at the Salt Lake Marriott Downtown at City Creek in Salt Lake City. All three of us have tickets and have been chatting about how we can record a live show. Mandatory Fun Nights Where the fun is mandatory but the attendance is not. Sunday – Destiny 2 8:30 pm Eastern Monday – GW2 9:30 pm Eastern Monday – STO 10:00 pm Eastern Tuesday – SWTOR 9:00 pm Eastern Wednesday – HFO Mythic+ Mayhem (WoW) 8:00 pm Eastern Friday – ESO 9:00 pm Eastern Saturday – LotRO 8:30 pm Eastern Saturday – FFXIV (Maps) 9:30 pm Eastern Saturday – Noob Raid (WoW) 11:00 pm Eastern Streaming and Guild Podcast News We have a ton of AIE member podcasts! Want to know where to find them? Look no further than here- New Overlords Podcast (Max and Sema) https://www.newoverlords.com Boards and Swords (Chris and Philip) https://boardsandswords.com/blog?category=Boards%20%26%20Swords Dr. Gameology ( Dr. Daniel Kaufmann ) https://drgameology.com/ STO – Fleet Action Report (Grebog and Nikodas) https://www.youtube.com/@fleetactionreport A Podcast Reborn: A FFXIV Community Podcast (Brandon aka Old Man Franks, Meagan, and Rho) – NSFL https://www.bonusroll.gg/directory/a-podcast-reborn/ NOMADS Destiny 2 The new expansion Renegades is chugging along- it is a very”Star Wars themed” expansion. We get lightsabers! It’s got a very cool Darth Revan/Kylo Ren feel to the new bad guy. We get to be kinda like bounty hunters and work for the criminal syndicates doing contract jobs as part of the core activity for the expansion. Earning reputation with each syndicate gains new abilities to use during the contract runs. Not much going on Guild wise. Things are pretty slow overall in Destiny 2, the community is waiting on the new Roadmap to see where things are going. Rumors and hopes are high that a Destiny 3 gets announced. If you are in Destiny 2 and Just_Wargus online feel free to reach out for a hand with anything. ARK – got a pretty dedicated group in there right now WoW Resting up for prepatch of Midnight. Housing is going well. Remix winding down. Noob Raid started up again after its holiday break. A new addon, XFaction for cross guild communication is working well. The last days of Legion Remix are upon us. We are in the final 7 days, so get those last minute runs done now. There is still transmog gear to grind, pets, mounts, and exclusive legion remix only housing items to grab before they are gone. If you ever wanted those legion artifact appearances, this is it. SWTOR In SWTOR, we closed out 2025 with our Winter Nights celebration which was 4 evenings of guild activities including a tour of World Bosses where we took a piece of armor off after each successive fight, stronghold tours, dressing up in our Life Day finest, a clothing drive on the starter planets, a parade of Life Day mounts, and a cracking good trivia night. Great way to spend the last gaming week of 2025. On to 2026! Next week is Total Galactic War! Again! This time we are ready for it. Mostly. We will be maximizing conquest points this week by doing group activities, crafting, and class stories. GSF and pvp are also good ways to rack up the points, if you are so inclined. Keep an eye on discord for any last minute group stuff that may pop up. ESO Nothing new really. It's been quiet due to the holidays. FFXIV Just had a new patch and things are going well. New MSQ, Savage raid tier is open and the world first race is already over. New trial with a train, new Unreal trial with everyone's favorite lady to hate and feel sorry for, Tsukuyomi’s Pain. The cosmic crafter and gatherer area has new updates, and crafters have pink heart themed items to make new. Hildebrand has more story, and the Air Force One GATE has a new place to fly around and pew pew.. The Strategy Board is live, and everyone is clearly using it for helpful raid explanations and mechanics demonstrations. Totally. Free login campaign has started. Up to 96 hours from when you start it. Ends February 9th. STO KDF fleet has made some big strides with the fleet holdings. The Starbase will complete the Tier IV shipyard in just about a week. Which means after that we can start tossing resources in for a tier IV Starbase. Also the Research lab is closing in on Tier III. What all this means is more fleet ships will be accessible. And once the research lab hits Tier III, the purchasable trait slots will be available. Fleet Action Report has been doing playthroughs of the missions showcasing accolades and recruit special tasks, while letting you know what rewards could be handy for your ships or on ground. We will be completing the final mission of the Iconian War arc this week. Which you can catch us live recording the episode Thursday 10pm eastern. Fun nights are Monday 10pm eastern. If you want to group up and run any STO content, looking for help with something, or just want to hang out, come join us. LOTRO Not much is happening. In general players are leveling, working on the epic quest, and enhancing their legendary items. If anyone is looking for help leveling or enhancing their legendary items please reach out to Maelllung and they'd be happy to help! And with that, let's get back to what we have been doing in and out of game! GAME NEWS HOST CHAT Question of the show- what was the geekiest gift you received for the holidays? CLOSE And that's our show for tonight. While the chatroom begins suggesting show titles, we want to thank everyone for joining us. If you have a question or comment about our show, you can email us at podcast@aie-guild.org You can find us on the AIE Discord and BlueSky https://bsky.app/profile/aiepodcast.bsky.social. We record live with video once a month on Sunday at 8pm Eastern/5pm Pacific. You can join the chatroom and play along with us on our website at https://aie-guild.org/podcast-live-stream/ and look for the link to our discord server at https://aie-guild.org. And for past episodes, you can see them on our Youtube channel, https://www.youtube.com/c/TheAIEPodcast ! Our theme was composed by the amazing Andrew Allen, follow him at @keyswithsoul! And now it's time to play all of the great AIE member segments we received this week, including… We will see you all in February. So until then, AIE… – This is Mkallah – This is Mewkow – This is Tetsemi And this has been… The AIE Podcast.

don't miss George's AIE talk: https://www.youtube.com/watch?v=sRpqPgKeXNk —- From launching a side project in a Sydney basement to becoming the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities—George Cameron and Micah Hill-Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is "open" really? We discuss: The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard) The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding "I don't know"), and Claude models lead with the lowest hallucination rates despite not always being the smartest GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias) The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron) The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents) Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions) V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models) — Artificial Analysis Website: https://artificialanalysis.ai (https://artificialanalysis.ai ("https://artificialanalysis.ai")) George Cameron on X: https://x.com/grmcameron (https://x.com/grmcameron ("https://x.com/grmcameron")) Micah Hill-Smith on X: https://x.com/_micah_h (https://x.com/_micah_h ("https://x.com/_micah_h")) Chapters 00:00:00 Introduction: Full Circle Moment and Artificial Analysis Origins 00:01:08 Business Model: Independence and Revenue Streams 00:04:00 The Origin Story: From Legal AI to Benchmarking 00:07:00 Early Challenges: Cost, Methodology, and Independence 00:16:13 AI Grant and Moving to San Francisco 00:18:58 Evolution of the Intelligence Index: V1 to V3 00:27:55 New Benchmarks: Hallucination Rate and Omissions Index 00:33:19 Critical Point and Frontier Physics Problems 00:35:56 GDPVAL AA: Agentic Evaluation and Stirrup Harness 00:51:47 The Openness Index: Measuring Model Transparency 00:57:57 The Smiling Curve: Cost of Intelligence Paradox 01:04:00 Hardware Efficiency and Sparsity Trends 01:07:43 Reasoning vs Non-Reasoning: Token Efficiency Matters 01:10:47 Multimodal Benchmarking and Community Requests 01:14:50 Looking Ahead: V4 Intelligence Index and Beyond

On this episode of The AIE Podcast… We have floor tanking and other WoW noobs… um, news Happy Lifeday, AIE December, where the attendance is optional and the fun is mandatory LOTRO brings YULE Kingdoms of Harad And, join us for our last show of the year All that and more coming up right now… Podcast Audio Raw Video http://youtu.be/TZ8A0NVJjUM Open Welcome to episode #440 of the podcast celebrating you, the Alea Iacta Est gaming community, the die has been podcast. This is Tetsemi: To my left is Mewkow: – (catch phrase here). And to my right is Mkallah: (hey guys, there is leftover caramel apple pie in the guild kitchen). This week, we have a wrap up of what is going on for December in AIE and gaming in general. Ok, we'll be digging into what we have been up to shortly, but first, let's cover this week's news… AIE News Community Mandatory Fun Nights Where the fun is mandatory but the attendance is not. Sunday – Destiny 2 8:30 pm Eastern Monday – GW2 9:30 pm Eastern Monday – STO 10:00 pm Eastern Tuesday – SWTOR 9:00 pm Eastern Wednesday – HFO Mythic+ Mayhem (WoW) 8:00 pm Eastern Friday – ESO 9:00 pm Eastern Saturday – LotRO 8:30 pm Eastern Saturday – FFXIV (Maps) 9:30 pm Eastern Saturday – Noob Raid (WoW) 11:00 pm Eastern Streaming and Guild Podcast News We have a ton of AIE member podcasts! Want to know where to find them? Look no further than here- New Overlords Podcast (Max and Sema) https://www.newoverlords.com Boards and Swords (Chris and Philip) https://boardsandswords.com/blog?category=Boards%20%26%20Swords Dr. Gameology ( Dr. Daniel Kaufmann ) https://drgameology.com/ STO – Fleet Action Report (Grebog and Nikodas) https://www.youtube.com/@fleetactionreport A Podcast Reborn: A FFXIV Community Podcast (Brandon aka Old Man Franks, Meagan, and Rho) – NSFL https://www.bonusroll.gg/directory/a-podcast-reborn/ WoW Noob Raid is on winter break until January 3, 2026 so Dankinia can get a bit of relief from the horrible jokes the group likes to torment her with. During 2025, Dankinia demonstrated her floor tanking abilities 172 times. While she is out, Celindre will be leading Noob Raid – After Dark during the normal 11pm EST time slot each Saturday. In WoW, Midnight – the next expansion – has a release date of March 2nd with the pre-patch release on Jan 13. Anyone with an Epic Edition pre-order will have early access – expected Feb 27 according to WoWHead. Players with early access will also be able to use the new Player Housing feature as of Dec 2. This will be a bit complicated for AIE and our co-guilds, which are one of several ways to join “neighborhoods” for player housing. Every player’s Warband gets two houses – one Horde and one Alliance, but any alt can access either home. There will be open neighborhoods that are server wide, and Guilds can create private neighborhoods open to anyone in that guild. Groups of friends (such as Raid Teams) can also create charters for their own neighborhoods. We will have neighb orhoods for all the co-guilds (probably) but haven’t assigned which officer will be assigned to which yet. Currently, in addition to the Legion Remix event (speed leveling/transmog/mounts etc), the WoW Anniversary is active in Retail WoW, with a rep & xp boost. Visiting the Anniversary Celebration at the Caverns of Time will provide an additional buff. Rep is good – because it’s one of the ways to obtain decor for Player Housing. SWTOR SWTOR will see a holiday release of update 7.8 entitled “Pursuit of Ruin”. It will include a new story chapter, a new type of Dynamic Encounter, and a new stronghold (player housing). That’s not to mention Life Day festivities, with the snow balls and the wookiee hugging. Also, for the first year ever, we will be trying out a new guild event during the evenings between Christmas and New Years. Stay tuned to discord for more info on that! ESO MFN in ESO generally takes the month of December off, but expect to find a couple of people hanging out at the normal time on Friday nights though the holidays. Attendance will be sporadic but feel free to reach out if anyone would like company while running the holiday events in game. FFXIV Patch 7.4 comes December 16th. Main Scenario – Into the Mist Raid Dungeon – The Arcadion: Heavyweight Division Variant Dungeon – The Merchant’s Tale New Dungeon – Mistwake New Trial – Hell on Rails New Unreal Trial – Tsukuyomi’s Pain (Unreal) New Frontline Campaign – Worqor Chirteh Weapon Enhancement Quests – Phantom Weapons Inconceivably Further Hildibrand Adventures Manderville Gold Saucer GATE – Air Force One (The Cieldalaes) Cosmic Exploration: Oizys and Tool Enhancement Quests – Cosmic Tools Strategy Board – Raid explanations but better Glamour Update – Unlocked all types! New Theme Settings – Clear Green and Clear White Mac support (Ventura ends Jan 27, Tahoe now supported) Save 50% on Collector's Edition Digital Upgrades Until December 7 New ridiculous hotpot mount (It flies) And irregular tomestones are active now until 7.4 is live. Fanfest is happening where Blizzcon is normally, April 24/25th. Cookbook #2 is out! LOTRO New expansion “Kingdoms of Harad” as of right now is scheduled to be released on December 3rd. The instance cluster for the expansion is expected to be delayed, which means this gives a chance to level up to the new level cap and get a group together and look at it as a kinship. Also looking forward to the Yule Festival that starts on December 11th. Join us as we look to have fun in Frostbluff as well as protecting it in “The Battle of Frostbluff”. STO Yes, it’s true, Cryptic studios has once again been sold. However, this is not a time for doom and gloom. The new owners have no intentions of sunsetting STO. In fact, STO has simply returned to its original owners who want to see the game succeed. There are ongoing sales in both the Zen Store and Mudd’s Market. Some of these are as high as 50% off! Lifetime Subscriptions are 33% off until Dec 14th Q’s Winter Wonderland will be returning on Dec 9th. While we don’t have any specific details on what the rewards will be this year, it’s sure to be something you don’t want to miss! And with that, let's get to what we have been doing in and out of the game! And, Mkallah has a question for everyone! GAME NEWS Okay, friends! What geeky thing are you planning to give for the holidays, and are there any geeky gifts you hope to receive? CLOSE And that's our show for tonight. While the chatroom begins suggesting show titles, we want to thank us, for joining us. If you have a question or comment about our show, you can email us at podcast@aie-guild.org You can find us on the AIE Discord and BlueSky https://bsky.app/profile/aiepodcast.bsky.social. We record live with video once a month on Sunday at 8pm Eastern/5pm Pacific. You can join the chatroom and play along with us on our website at https://aie-guild.org/podcast-live-stream/ and look for the link to our discord server at https://aie-guild.org. And for past episodes, you can see them on our Youtube channel, https://www.youtube.com/c/TheAIEPodcast ! Our theme was composed by the amazing Andrew Allen, follow him at @keyswithsoul! And now it's time to play all of the great AIE member segments we received this week, including… We will see you all in January of 2026!. So until then, AIE… – This is Tetsemi – This is Mewkow – This is Mkallah And this has been… The AIE Podcast.