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Editor's note: CuspAI raised a $100m Series A in September and is rumored to have reached a unicorn valuation. They have all-star advisors from Geoff Hinton to Yann Lecun and team of deep domain experts to tackle this next frontier in AI applications.In this episode, Max Welling traces the thread connecting quantum gravity, equivariant neural networks, diffusion models, and climate-focused materials discovery (yes, there is one!!!).We begin with a provocative framing: experiments as computation. Welling describes the idea of a “physics processing unit”—a world in which digital models and physical experiments work together, with nature itself acting as a kind of processor. It's a grounded but ambitious vision of AI for science: not replacing chemists, but accelerating them.Along the way, we discuss:* Why symmetry and equivariance matter in deep learning* The tradeoff between scale and inductive bias* The deep mathematical links between diffusion models and stochastic thermodynamics* Why materials—not software—may be the real bottleneck for AI and the energy transition* What it actually takes to build an AI-driven materials platformMax reflects on moving from curiosity-driven theoretical physics (including work with Gerard ‘t Hooft) toward impact-driven research in climate and energy. The result is a conversation about convergence: physics and machine learning, digital models and laboratory experiments, long-term ambition and incremental progress.Full Video EpisodeTimestamps* 00:00:00 – The Physics Processing Unit (PPU): Nature as the Ultimate Computer* Max introduces the idea of a Physics Processing Unit — using real-world experiments as computation.* 00:00:44 – From Quantum Gravity to AI for Materials* Brandon frames Max's career arc: VAE pioneer → equivariant GNNs → materials startup founder.* 00:01:34 – Curiosity vs Impact: How His Motivation Evolved* Max explains the shift from pure theoretical curiosity to climate-driven impact.* 00:02:43 – Why CaspAI Exists: Technology as Climate Strategy* Politics struggles; technology scales. Why materials innovation became the focus.* 00:03:39 – The Thread: Physics → Symmetry → Machine Learning* How gauge symmetry, group theory, and relativity informed equivariant neural networks.* 00:06:52 – AI for Science Is Exploding (Not Emerging)* The funding surge and why AI-for-Science feels like a new industrial era.* 00:07:53 – Why Now? The Two Catalysts Behind AI for Science* Protein folding, ML force fields, and the tipping point moment.* 00:10:12 – How Engineers Can Enter AI for Science* Practical pathways: curriculum, workshops, cross-disciplinary training.* 00:11:28 – Why Materials Matter More Than Software* The argument that everything—LLMs included—rests on materials innovation.* 00:13:02 – Materials as a Search Engine* The vision: automated exploration of chemical space like querying Google.* 01:14:48 – Inside CuspAI: The Platform Architecture* Generative models + multi-scale digital twin + experiment loop.* 00:21:17 – Automating Chemistry: Human-in-the-Loop First* Start manual → modular tools → agents → increasing autonomy.* 00:25:04 – Moonshots vs Incremental Wins* Balancing lighthouse materials with paid partnerships.* 00:26:22 – Why Breakthroughs Will Still Require Humans* Automation is vertical-specific and iterative.* 00:29:01 – What Is Equivariance (In Plain English)?* Symmetry in neural networks explained with the bottle example.* 00:30:01 – Why Not Just Use Data Augmentation?* The optimization trade-off between inductive bias and data scale.* 00:31:55 – Generative AI Meets Stochastic Thermodynamics* His upcoming book and the unification of diffusion models and physics.* 00:33:44 – When the Book Drops (ICLR?)TranscriptMax: I want to think of it as what I would call a physics processing unit, like a PPU, right? Which is you have digital processing units and then you have physics processing units. So it's basically nature doing computations for you. It's the fastest computer known, as possible even. It's a bit hard to program because you have to do all these experiments. Those are quite bulky, it's like a very large thing you have to do. But in a way it is a computation and that's the way I want to see it. You can do computations in a data center and then you can ask nature to do some computations. Your interface with nature is a bit more complicated. But then these things will have to seamlessly work together to get to a new material that you're interested in.[01:00:44:14 - 01:01:34:08]Brandon: Yeah, it's a pleasure to have Max Woehling as a guest today. Max has done so much over his career that I've been so excited about. If you're in the deep learning community, you probably know Max for his work on variational autocoders, which has literally stood the test of prime or officially stood the test of prime. If you are a scientist, you probably know him for his like, binary work on graph neural networks on equivariance. And if you're a material science, you probably know him about his new startup, CASPAI. Max has a long history doing lots of cool problems. You started in quantum gravity, which is I think very different than all of these other things you worked on. The first question for AI engineers and for scientists, what is the thread in how you think about problems? What is the thread in the type of things which excite you? And how do you decide what is the next big thing you want to work on?[01:01:34:08 - 01:02:41:13]Max: So it has actually evolved a lot. In my young days, let's breathe, I would just follow what I would find super interesting. I have kind of this sensor. I think many people have, but maybe not really sort of use very much, which is like, you get this feeling about getting very excited about some problem. Like it could be, what's inside of a black hole or what's at the boundary of the universe or what are quantum mechanics actually all about. And so I follow that basically throughout my career. But I have to say that as you get older, this changes a little bit in the sense that there's a new dimension coming to it and there's this impact. Going in two-dimensional quantum gravity, you pretty much guaranteed there's going to be no impact on what you do relative, maybe a few papers, but not in this world, this energy scale. As I get closer to retirement, which is fortunately still 10 years away or so, I do want to kind of make a positive impact in the world. And I got pretty worried about climate change.[01:02:43:15 - 01:03:19:11]Max: I think politics seems to have a hard time solving it, especially these days. And so I thought better work on it from the technology side. And that's why we started CaspAI. But there's also a lot of really interesting science problems in material science. And so it's kind of combining both the impact you can make with it as well as the interesting science. So it's sort of these two dimensions, like working on things which you feel there's like, well, there's something very deep going on here. And on the other hand, trying to build tools that can actually make a real impact in the world.[01:03:19:11 - 01:03:39:23]RJ: So the thread that when I look back, look at the different things that you worked out, some of them seem pretty connected, like the physics to equivariance and, yeah, and, uh, gravitational networks, maybe. And that seems to be somewhat related to Casp. Do you have a thread through there?[01:03:39:23 - 01:06:52:16]Max: Yeah. So physics is the thread. So having done, you know, spent a lot of time in theoretical physics, I think there is first very fundamental and exciting questions, like things that haven't actually been figured out in quantum gravity. So that is really the frontier. There's also a lot of mathematical tools that you can use, right? In, for instance, in particle physics, but also in general relativity, sort of symmetry space to play an enormously important role. And this goes all the way to gauge symmetries as well. And so applying these kinds of symmetries to, uh, machine learning was actually, you know, I thought of it as a very deep and interesting mathematical problem. I did this with Taco Cohen and Taco was the main driver behind this, went all the way from just simple, like rotational symmetries all the way to gauge symmetries on spheres and stuff like that. So, and, uh, Maurice Weiler, who's also here, um, when he was a PhD student, he was a very good student with me, you know, he wrote an entire book, which I can really recommend about the role of symmetries in AI and machine learning. So I find this a very deep and interesting problem. So more recently, so I've taken a sort of different path, which is the relationship between diffusion models and that field called stochastic thermodynamics. This is basically the thermodynamics, which is a theory of equilibrium. So but then formulated for out of equilibrium systems. And it turns out that the mathematics that we use for diffusion models, but even for reinforcement learning for Schrodinger bridges for MCMC sampling has the same mathematics as this theoretical, this physical theory of non-equilibrium systems. And that got me very excited. And actually, uh, when I taught a course in, um, Mauschenberg, uh, it is South Africa, close to Cape Town at the African Institute for Mathematical Sciences Ames. And I turned that into a book site. Two years later, the book was finished. I've sent it to the publisher. And this is about the deep relationship between free energy, diffusion models, basically generative AI and stochastic thermodynamics. So it's always some kind of, I don't know, I find physics very deep. I also think a lot about quantum mechanics and it's, it's, it's a completely weird theory that actually nobody really understands. And there's a very interesting story, which is maybe good to tell to connect sort of my PZ back to where I'm now. So I did my PZ with a Nobel Laureate, Gerard the toft. He says the most brilliant man I've ever met. He was never wrong about anything as long as I've seen him. And now he says quantum mechanics is wrong and he has a new theory of quantum mechanics. Nobody understands what he's saying, even though what he's writing down is not mathematically very complex, but he's trying to address this understandability, let's say of quantum mechanics head on. And I find it very courageous and I'm completely fascinated by it. So I'm also trying to think about, okay, can I actually understand quantum mechanics in a more mundane way? So that, you know, without all the weird multiverses and collapses and stuff like that. So the physics is always been the threat and I'm trying to apply the physics to the machine learning to build better algorithms.[01:06:52:16 - 01:07:05:15]Brandon: You are still very involved in understanding and understanding physics and the worlds. Yeah. And just like applications to machine learning or introducing no formalisms. That's really cool.[01:07:05:15 - 01:07:18:02]Max: Yes, I would say I'm not contributing much to physics, but I'm contributing to the interface between physics and science. And that's called AI for science or science or AI is kind of a super, it's actually a new discipline that's emerging.[01:07:18:02 - 01:07:18:19]Speaker 5: Yeah.[01:07:18:19 - 01:07:45:14]Max: And it's not just emerging, it's exploding, I would say. That's the better term because I know you go from investments into like in the hundreds of millions now in the billions. So there's now actually a startup by Jeff Bezos that is at 6.2 billion sheep round. Right. Insane. I guess it's the largest startup ever, I think. And that's in this field, AI for science. It tells you something that we are creating a new bubble here.[01:07:46:15 - 01:07:53:28]Brandon: So why do you think it is? What has changed that has motivated people to start working on AI for science type problems?[01:07:53:28 - 01:08:49:17]Max: So there's two reasons actually. One is that people have been applying sort of the new tools from AI to the sciences, which is quite natural. And there's of course, I think there's two big examples, protein folding is a big one. And the other one is machine learning forest fields or something called machine learning inter-atomic potentials. Both of them have been actually very successful. Both also had something to do with symmetries, which is a little cool. And sort of people in the AI sciences saw an opportunity to apply the tools that they had developed beyond advertised placement, right, or multimedia applications into something that could actually make a very positive impact in society like health, drug development, materials for the energy transition, carbon capture. These are all really cool, impactful applications.[01:08:50:19 - 01:09:42:14]Max: Despite that, the science and the kind of the is also very interesting. I would say the fact that these sort of these two fields are coming together and that we're now at the point that we can actually model these things effectively and move the needle on some of these sort of science sort of methodologies is also a very unique moment, I would say. People recognize that, okay, now we're at the cusp of something new, where it results whether the company is called after. We're at the cusp of something new. And of course that always creates a lot of energy. It's like, okay, there's something, it's like sort of virgin field. It's like nobody's green field. Nobody's been there. I can rush in and I can sort of start harvesting there, right? And I think that's also what's causing a lot of sort of enthusiasm in the fields.[01:09:42:14 - 01:10:12:18]RJ: If you're an AI engineer, basically if the people that listen to this podcast will be in the field, then you maybe don't have a strong science background. How does, but are excited. Most I would say most AI practitioners, BM engineers or scientists would consider themselves scientists and they have some background, a little bit of physics, a little bit of industry college, maybe even graduate school that have been working or are starting out. How does somebody who is not a scientist on a day-to-day basis, how do they get involved?[01:10:12:18 - 01:10:14:28]Max: Well, they can read my book once it's out.[01:10:16:07 - 01:11:05:24]Max: This is basically saying that there is more, we should create curricula that are on this interface. So I'm not sure there is, also we already have some universities actual courses you can take, maybe online courses you can take. These workshops where we are now are actually very good as well. And we should probably have more tutorials before the workshop starts. Actually we've, I've kind of proposed this at some point. It's like maybe first have an hour of a tutorial so that people can get new into the field. There's a lot out there. Most of it is of course inaccessible, but I would say we will create much more books and other contents that is more accessible, including this podcast I would say. So I think it will come. And these days you can watch videos and things. There's a huge amount of content you can go and see.[01:11:05:24 - 01:11:28:28]Brandon: So maybe a follow-up to that. How do people learn and get involved? But why should they get involved? I mean, we have a lot of people who are of our audience will be interested in AI engineering, but they may be looking for bigger impacts in the world. What opportunities does AI for science provide them to make an impact to change the world? That working in this the world of pure bits would not.[01:11:28:28 - 01:11:40:06]Max: So my view is that underlying almost everything is immaterial. So we are focusing a lot on LLMs now, which is kind of the software layer.[01:11:41:06 - 01:11:56:05]Max: I would say if you think very hard, underlying everything is immaterial. So underlying an LLM is a GPU, and underlying a GPU is a wafer on which we will have to deposit materials. Do we want to wait a little bit?[01:12:02:25 - 01:12:11:06]Max: Underlying everything is immaterial. So I was saying, you know, there's the LLM underlying the LLM is a GPU on which it runs. In order to make that GPU,[01:12:12:08 - 01:12:43:20]Max: you have to put materials down on a wafer and sort of shine on it with sort of EUV light in order to etch kind of the structures in. But that's now an actual material problem, because more or less we've reached the limits of scaling things down. And now we are trying to improve further by new materials. So that's a fundamental materials problem. We need to get through the energy transition fast if we don't want to kind of mess up this world. And so there is, for instance, batteries. That's a complete materials problem. There's fuel cells.[01:12:44:23 - 01:13:01:16]Max: There is solar panels. So that they can now make solar panels with new perovskite layers on top of the silicon layers that can capture, you know, theoretically up to 50% of the light, where now we're at, I don't know, maybe 22 or something. So these are huge changes all by material innovation.[01:13:02:21 - 01:13:47:15]Max: And yeah, I think wherever you go, you know, I can probably dig deep enough and then tell you, well, actually, the very foundation of what you're doing is a material problem. And so I think it's just very nice to work on this very, very foundation. And also because I think this is maybe also something that's happening now is we can start to search through this material space. This has never been the case, right? It's like scientists, the normal way of working is you read papers and then you come up with no hypothesis. You do an experiment and you learn, et cetera. So that's a very slow process. Now we can treat this as a search engine. Like we search the internet, we now search the space of all possible molecules, not just the ones that people have made or that they're in the universe, but all of them.[01:13:48:21 - 01:14:42:01]Max: And we can make this kind of fully automated. That's the hope, right? We can just type, it becomes a tool where you type what you want and something starts spinning and some experiments get going. And then, you know, outcome list of materials and then you look at it and say, maybe not. And then you refine your query a little bit. And you kind of do research with this search engine where a huge amount of computation and experimentation is happening, you know, somewhere far away in some lab or some data center or something like this. I find this a very, very promising view of how we can sort of build a much better sort of materials layer underneath almost everything. And also more sustainable materials. Our plastics are polluting the planet. If you come up with a plastic that kind of destroys itself, you know, after, I don't a few weeks, right? And actually becomes a fertilizer. These are things that are not impossible at all. These things can be done, right? And we should do it.[01:14:42:01 - 01:14:47:23]RJ: Can you tell us a little bit just generally about CUSBI and then I have a ton of questions.[01:14:47:23 - 01:14:48:15]Speaker 5: Yeah.[01:14:48:15 - 01:17:49:10]Max: So CUSBI started about 20 months ago and it was because I was worried about I'm still worried about climate change. And so I realized that in order to get, you know, to stay within two degrees, let's say, we would not only have to reduce our emissions to zero by 2050, but then, you know, another half century or even a century of removing carbon dioxide from the atmosphere, not by reducing your emissions, but actually removing it at a rate that's about half the rate that we now emit it. And that is a unsolved problem. But if we don't solve it, two degrees is not going to happen, right? It's going to be much more. And I don't think people quite understand how bad that can be, like four degrees, like very bad. So this technology needs to be developed. And so this was my and my co-founder, Chet Edwards, motivation to start this startup. And also because, you know, we saw the technology was ready, which is also very good. So if you're, you know, the time is right to do it. And yeah, so we now in the meanwhile, we've grown to about 40 people. We've kind of collected 130 million investment into the company, which is for a European company is quite a lot. I would say it's interesting that right after that, you know, other startups got even more. So that's kind of tells you how fast this is growing. But yeah, we are we are now at the we've built the platform, of course, but it's for a series of material classes and it needs to be constantly expanded to new material classes. And it can be more automated because, you know, we know putting LLMs in as the whole thing gets more and more automated. And now we're moving to sort of high throughput experimentation. So connecting the actual platform, which is computational, to the experiments so that you can get also get fast feedback from experiments. And I kind of think of experiments as something you do at the end, although that's what we've been doing so far. I want to think of it as what I would call a sort of a physics processing unit, like a PPU, right, which is you have digital processing units and then you have physics processing units. So it's basically nature doing computations for you. It's the fastest computer known as possible, even. It's a bit hard to program because you have to do all these experiments. Those are quite, quite bulky. It's like a very large thing you have to do. But in a way, it is a computation. And that's the way I want to see it. So I want to you can do computations in a data center and then you can ask nature to do some computations. Your interface with nature is a bit more complicated. But then these things will have to seamlessly work together to get to a new material that you're interested in. And that's the vision we have. We don't say super intelligence because I don't quite know what it means and I don't want to oversell it. But I do want to automate this process and give a very powerful tool in the hands of the chemists and the material scientists.[01:17:49:10 - 01:18:01:02]Brandon: That actually brings up a question I wanted to ask you. First of all, can you talk about your platform to like whatever degree, like explain kind of how it works and like what you your thought processes was in developing it?[01:18:01:02 - 01:20:47:22]Max: Yeah, I think it's been surprisingly, it's not rocket science, I would say. It's not rocket science in the sense of the design and basically the design that, you know, I wrote down at the very beginning. It's still more or less the design, although you add things like I wasn't thinking very much about multi-scale models and as the common are rated that actually multi-scale is very important. And the beginning, I wasn't thinking very much about self-driving labs. But now I think, you know, we are now at the stage we should be adding that. And so there is sort of bits and details that we're adding. But more or less, it's what you see in the slide decks here as well, which is there is a generative component that you have to train to generate candidates. And then there is a digital twin, multi-scale, multi-fidelity digital twin, which you walk through the steps of the ladder, you know, they do the cheap things first, you weed out everything that's obviously unuseful, and then you go to more and more expensive things later. And so you narrow things down to a small number. Those go into an experiment, you know, do the experiment, get feedback, etc. Now, things that also have been more recently added is sort of more agentic sort of parts. You know, we have agents that search the literature and come up with, you know, actually the chemical literature and come up with, you know, chemical suggestions for doing experiments. We have agents which sort of autonomously orchestrate all of the computations and the experiments that need to be done. You know, they're in various stages of maturity and they can be continuously improved, I would say. And so that's basically I don't think that part. There's rocket science, but, you know, the design of that thing is not like surprising. What is it's surprising hard to actually build it. Right. So that's that's the thing that is where the moat is in the data that you can get your hands on and the and actually building the platform. And I would say there's two people in particular I want to call out, which is Felix Hunker, who is actually, you know, building the scientific part of the platform and Sandra de Maria, who is building the sort of the skate that is kind of this the MLOps part of the platform. Yeah. And so and recently we also added sort of Aaron Walsh to our team, who is a very accomplished scientist from Imperial College. We're very happy about that. He's going to be a chief science officer. And we also have a partnerships team that sort of seeks out all the customers because I think this is one thing I find very important. In print, it's so complex to do to actually bring a material to the real world that you must do this, you know, in collaboration with sort of the domain experts, which are the companies typically. So we always we only start to invest in the direction if we find a good industrial partner to go on that journey with us.[01:20:47:22 - 01:20:55:12]Brandon: Makes a lot of sense. Over the evolution of the platform, did you find that you that human intervention, human,[01:20:56:18 - 01:21:17:01]Brandon: I guess you could start out with a pure, you could imagine two directions when you start up making everything purely automatic, automated, agentic, so on. And then later on, you like find that you need to have more human input and feedback different steps. Or maybe did you start out with having human feedback? You have lots of steps and then like kind of, yeah, figure out ways to remove, you know,[01:21:17:01 - 01:22:39:18]Max: that is the second one. So you build tools for you. So it's much more modular than you think. But it's like, we need these tools for this application. We need these tools. So you build all these tools, and then you go through a workflow actually in the beginning just manually. So you put them in a first this tool, then run this to them or this with sithery. So you put them in a workflow and then you figure out, oh, actually, you know, this this porous material that we are trying to make actually collapses if you shake it a bit. Okay, then you add a new tool that says test for stability. Right. Yeah. And so there's more and more tools. And then you build the agent, which could be a Bayesian optimizer, or it could be an actual other them, you know, maybe trained to be a good chemist that will then start to use all these tools in the right way in the right order. Yeah. Right. But in the beginning, it's like you as a chemist are putting the workflow together. And then you think about, okay, how am I going to automate this? Right. For one very easy question you can ask yourself is, you know, every time somebody who is not a super expert in DFT, yeah, and he wants to do a calculation has to go to somebody who knows DFT. And so could you start to automate that away, which is like, okay, make it so user friendly, so that you actually do the right DFT for the right problem and for the right length of time, and you can actually assess whether it's a good outcome, etc. So you start to automate smaller small pieces and bigger pieces, etc. And in the end, the whole thing is automated.[01:22:39:18 - 01:22:53:25]Brandon: So your philosophy is you want to provide a set of specific tools that make it so that the scientists making decisions are better informed and less so trying to create an automated process.[01:22:53:25 - 01:23:22:01]Max: I think it's this is sort of the same where you're saying because, yes, we want to automate, yeah, but we don't see something very soon where the chemists and the domain expert is out of the loop. Yeah, but it but it's a retreat, right? It's like, okay, so first, you need an expert to tell you precisely how to set the parameters of the DFT calculation. Okay, maybe we can take that out. We can maybe automate that, right? And so increasingly, more of these things are going to be removed.[01:23:22:01 - 01:23:22:19]Speaker 5: Yeah.[01:23:22:19 - 01:24:33:25]Max: In the end, the vision is it will be a search engine where you where somebody, a chemist will type things and we'll get candidates, but the chemist will still decide what is a good material and what is not a good material out of that list, right? And so the vision of a completely dark lab, where you can close the door and you just say, just, you know, find something interesting and then it will it will just figure out what's interesting and we'll figure out, you know, it's like, oh, I found this new material to blah, blah, blah, blah, right? That's not the vision I have. He's not for, you know, a long time. So for me, it's really empowering the domain experts that are sitting in the companies and in universities to be much faster in developing their materials. And I should say, it's also good to be a little humble at times, because it is very complicated, you know, to bring it to make it and to bring it into the real world. And there are people that are doing this for the entire lives. Yeah. Right. And it's like, I wonder if they scratch their head and say, well, you know, how are you going to completely automate that away, like in the next five years? I don't think that's going to happen at all.[01:24:35:01 - 01:24:39:24]Max: Yeah. So to me, it's an increasingly powerful tool in the hands of the chemists.[01:24:39:24 - 01:25:04:02]RJ: I have a question. You've talked before about getting people interested based on having, you know, sort of a big breakthrough in materials, incremental change. I'm curious what you think about the platform you have now in are sort of stepping towards and how are you chasing the big change or is this like incremental or is there they're not mutually exclusive, obviously, but what do you think about that?[01:25:04:02 - 01:26:04:27]Max: We follow a mixed strategy. So we are definitely going after a big material. Again, we do this with a partner. I'm not going to disclose precisely what it is, but we have our own kind of long term goal. You could call it lighthouse or, you know, sort of moonshot or whatever, but it is going to be a really impactful material that we want to develop as a proof point that it can be done and that it will make it into the into the real world and that AI was essential in actually making it happen. At the same time, we also are quite happy to work with companies that have more modest goals. Like I would say one is a very deep partnership where you go on a journey with a company and that's a long term commitment together. And the other one is like somebody says, I knew I need a force field. Can you help me train this force field and then maybe analyze this particular problem for me? And I'll pay you a bunch of money for that. And then maybe after that we'll see. And that's fine too. Right. But we prefer, you know, the deep partnerships where we can really change something for the good.[01:26:04:27 - 01:26:22:02]RJ: Yeah. And do you feel like from a platform standpoint you're ready for that or what are the things that and again, not asking you to disclose proprietary secret sauce, but what are the things generally speaking that need to happen from where we are to where to get those big breakthroughs?[01:26:22:02 - 01:28:40:01]Max: What I find interesting about this field is that every time you build something, it's actually immediately useful. Right. And so unlike quantum computing, which or nuclear fusion, so you work for 20, 30, 40 years and nothing, nothing, nothing, nothing. And then it has to happen. Right. And when it happens, it's huge. So it's quite different here because every time you introduce, so you go to a customer and you say, so what do you need? Right. So we work, let's say, on a problem like a water filtration. We want to remove PFAS from water. Right. So we do this with a company, Camira. So they are a deep partner for us. Right. So we on a journey together. I think that the breakthrough will happen with a lot of human in the loop because there is the chemists who have a whole lot more knowledge of their field and it's us who will help them with training, having a new message. And in that kind of interface, these interactions, something beautiful will happen and that will have to happen first before this field will really take off, I think. And so in the sense that it's not a bubble, let's put it that way. So that's people see that as actual real what's happening. So in the beginning, it will be very, you know, with a lot of humans in the loop, I would say, and I would I would hope we will have this new sort of breakthrough material before, you know, everything is completely automated because that will take a while. And also it is very vertical specific. So it's like completely automating something for problem A, you know, you can probably achieve it, but then you'll sort of have to start over again for problem B because, you know, your experimental setup looks very different in the machines that you characterize your materials look very different. Even the models in your platform will have to be retrained and fine tuned to the new class. So every time, you know, you have a lot of learnings to transfer, but also, you know, the problems are actually different. And so, yes, I would want that breakthrough material before it's completely automated, which I think is kind of a long term vision. And I would say every time you move to something new, you'll have to start retraining and humans will have to come in again and say, okay, so what does this problem look like? And now sort of, you know, point the the machine again, you know, in the new direction and then and then use it again.[01:28:40:01 - 01:28:47:17]RJ: For the non-scientists among us, me included a bit of a scientist. There's a lot of terminology. You mentioned DFT,[01:28:49:00 - 01:29:01:11]RJ: you equivariance we've talked about. Can you sort of explain in engineering terms or the level of sophistication and engineering? Well, how what is equivariance?[01:29:01:11 - 01:29:55:01]Max: So equivariance is the infusion of symmetry in neural networks. So if I build a neural network, let's say that needs to recognize this bottle, right, and then I rotate the bottle, it will then actually have to completely start again because it has no idea that the rotated bottle. Well, actually, the input that represents a rotated bottle is actually rotated bottle. It just doesn't understand that. Right. If you build equivariance in basically once you've trained it in one orientation, it will understand it in any other orientation. So that means you need a lot less data to train these models. And these are constraints on the weights of the model. So so basically you have to constrain the way such data to understand it. And you can build it in, you can hard code it in. And yeah, this the symmetry groups can be, you know, translations, rotations, but also permutations. I can graph neural network, their permutations and then physics, of course, as many more of these groups.[01:29:55:01 - 01:30:01:08]RJ: To pray devil's advocate, why not just use data augmentation by your bottle is in all the different orientations?[01:30:01:08 - 01:30:58:23]Max: As an option, it's just not exact. It's like, why would you go through the work of doing all that? Where you would really need an infinite number of augmentations to get it completely right. Where you can also hard code it in. Now, I have to say sometimes actually data augmentation works even better than hard coding the equivariance in. And this is something to do with the fact that if you constrain the optimization, the weights before the optimization starts, the optimization surface or objective becomes more complicated. And so it's harder to find good minima. So there is also a complicated interplay, I think, between the optimization process and these constraints you put in your network. And so, yeah, you'll hear kind of contradicting claims in this field. Like some people and for certain applications, it works just better than not doing it. And sometimes you hear other people, if you have a lot of data and you can do data augmentation, then actually it's easier to optimize them and it actually works better than putting the equivariance in.[01:30:58:23 - 01:31:07:16]Brandon: Do you think there's kind of a bitter lesson for mathematically founded models and strategies for doing deep learning?[01:31:07:16 - 01:31:46:06]Max: Yeah, ultimately it's a trade-off between data and inductive bias. So if your inductive bias is not perfectly correct, you have to be careful because you put a ceiling to what you can do. But if you know the symmetry is there, it's hard to imagine there isn't a way to actually leverage it. But yeah, so there is a bitter lesson. And one of the bitter lessons is you should always make sure your architecture is scale, unless you have a tiny data set, in which case it doesn't matter. But if you, you know, the same bitter lessons or lessons that you can draw in LLM space are eventually going to be true in this space as well, I think.[01:31:47:10 - 01:31:55:01]RJ: Can you talk a little bit about your upcoming book and tell the listeners, like, what's exciting about it? Yeah, I should read it.[01:31:55:01 - 01:33:42:20]Max: So this book is about, it's called Generative AI and Stochastic Thermodynamics. It basically lays bare the fact that the mathematics that goes into both generative AI, which is the technology to generate images and videos, and this field of non-equilibrium statistical mechanics, which are systems of molecules that are just moving around and relaxing to the ground state, or that you can control to have certain, you know, be in a certain state, the mathematics of these two is actually identical. And so that's fascinating. And in fact, what's interesting is that Jeff Hinton and Radford Neal already wrote down the variational free energy for machine learning a long time ago. And there's also Carl Friston's work on free energy principle and active entrance. But now we've related it to this very new field in physics, which is called stochastic thermodynamics or non-equilibrium thermodynamics, which has its own very interesting theorems, like fluctuation theorems, which we don't typically talk about, but we can learn a lot from. And I think it's just it can sort of now start to cross fertilize. When we see that these things are actually the same, we can, like we did for symmetries, we can now look at this new theory that's out there, developed by these very smart physicists, and say, okay, what can we take from here that will make our algorithms better? At the same time, we can use our models to now help the scientists do better science. And so it becomes a beautiful cross-fertilization between these two fields. The book is rather technical, I would say. And it takes all sorts of things that have been done as stochastic thermodynamics, and all sorts of models that have been done in the machine learning literature, and it basically equates them to each other. And I think hopefully that sense of unification will be revealing to people.[01:33:42:20 - 01:33:44:05]RJ: Wait, and when is it out?[01:33:44:05 - 01:33:56:09]Max: Well, it depends on the publisher now. But I hope in April, I'm going to give a keynote at ICLR. And it would be very nice if they have this book in my hand. But you know, it's hard to control these kind of timelines.[01:33:56:09 - 01:33:58:19]RJ: Yeah, I'm looking forward to it. Great.[01:33:58:19 - 01:33:59:25]Max: Thank you very much. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

That AI essay I shared with you yesterday sure got Wall Street's attention. Anthropic says Chinese models are training off of Claude. A significant new breakthrough in chip production technology. And as fun as that tri-fold phone might be, you probably want to wait for later iterations of the form factor. Software Stocks Are Having Another Ugly Day (WSJ) Anthropic Accuses Chinese Companies of Siphoning Data From Claude (WSJ) Meta and AMD Agree to AI Chips Deal Worth More Than $100 Billion (WSJ) Exclusive: ASML unveils EUV light source advance that could yield 50% more chips by 2030 (Reuters) Putting Samsung's $2,899 TriFold To the Test as a Phone, Tablet and Laptop (Bloomberg) Learn more about your ad choices. Visit megaphone.fm/adchoices

This is a recap of the top 10 posts on Hacker News on February 23, 2026. This podcast was generated by wondercraft.ai (00:30): The Age Verification Trap: Verifying age undermines everyone's data protectionOriginal post: https://news.ycombinator.com/item?id=47122715&utm_source=wondercraft_ai(01:55): Ladybird adopts Rust, with help from AIOriginal post: https://news.ycombinator.com/item?id=47120899&utm_source=wondercraft_ai(03:21): Americans are destroying Flock surveillance camerasOriginal post: https://news.ycombinator.com/item?id=47127081&utm_source=wondercraft_ai(04:47): Elsevier shuts down its finance journal citation cartelOriginal post: https://news.ycombinator.com/item?id=47119530&utm_source=wondercraft_ai(06:12): Pope tells priests to use their brains, not AI, to write homiliesOriginal post: https://news.ycombinator.com/item?id=47119210&utm_source=wondercraft_ai(07:38): Binance fired employees who found $1.7B in crypto was sent to IranOriginal post: https://news.ycombinator.com/item?id=47127396&utm_source=wondercraft_ai(09:04): Hetzner (European hosting provider) to increase prices by up to 38%Original post: https://news.ycombinator.com/item?id=47121029&utm_source=wondercraft_ai(10:29): Magical Mushroom – Europe's first industrial-scale mycelium packaging producerOriginal post: https://news.ycombinator.com/item?id=47119274&utm_source=wondercraft_ai(11:55): FreeBSD doesn't have Wi-Fi driver for my old MacBook, so AI built one for meOriginal post: https://news.ycombinator.com/item?id=47129361&utm_source=wondercraft_ai(13:21): ASML unveils EUV light source advance that could yield 50% more chips by 2030Original post: https://news.ycombinator.com/item?id=47125349&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Onderzoekers van ASML zeggen een manier te hebben gevonden om het vermogen van de lichtbron in hun chipmachine te verhogen van het huidige 600 watt tot 1000 watt. Dat maakt dat ASML verwacht nog voor 2030 hun chipproductie met 50 procent te kunnen verhogen. Dit moet ASML helpen zijn voorsprong te houden op Amerikaanse en Chinese concurrenten. Dat meldt persbureau Reuters. Rosanne Peters vertelt erover in deze Tech Update. Het gaat om EUV machines, wat staat voor ultraviolette lithografie (EUV). Doordat de lichtbron in de chipmachine verhoogd kan worden, maakt dat er meer chips per uur geproduceerd kunnen worden waardoor de kosten per chip dalen. De EUV-machines van ASML zijn zo belangrijk dat er hard wordt gewerkt deze machines uit verkeerde handen te houden. Door deze nieuwe kennis heeft ASML het technologisch meest uitdagende aspect van hun chipmachines weten te verbeteren. Verder in deze Tech Update: De nieuwe S26 toestellen van Samsung krijgen ook Perplexity als een van de AI-assistenten See omnystudio.com/listener for privacy information.

Wist je dat de chip in je smartphone is uitgevonden in een Belgisch lab waar zelfs een baardhaar een heel productieproces kan verpesten? Tweakers-redacteur Daan van Monsjou ging de cleanroom in en vertelt wat hij daar aantrof. IMEC in Leuven is het onderzoeksinstituut waar TSMC, ASML, Intel en Samsung samenwerken aan de chiptechnologie van morgen. Daan van Monsjou, PC-hardwareman bij Tweakers, was een van de weinige journalisten die de gloednieuwe NanoIC Pilot Line cleanroom mocht betreden. In deze aflevering legt hij uit hoe chips écht gemaakt worden, van tindruppeltjes die met 300 km/u worden beschoten tot spiegels die gladder zijn dan Duitsland vlak is. We ontdekken waarom 2 nanometer eigenlijk marketing is, hoe een chip maken drie maanden duurt, en waarom zelfs startups en studenten straks hun eigen 1,4nm-chips kunnen ontwerpen. Plus: wordt 2026 het jaar van Linux op de gaming-desktop? Over Daan van Monsjou Daan van Monsjou is redacteur bij Tweakers, waar hij al ruim zes jaar schrijft over PC-hardware, processors en chiptechnologie. Hij is de drijvende kracht achter de interne "Core"-nieuwsbrief over chipfabricage en interviewde onder meer de CTO van ASML. Tweakers: Daan van Monsjou Bluesky: Daan van Monsjou In deze aflevering 0:00:00 IMEC, kleine chips en een stukje geluk0:03:18 Wordt 2026 het jaar van Linux op de gaming-desktop?0:10:22 Wat is IMEC en waarom werkt de hele chipwereld daar samen?0:15:11 De cleanroom in: luchtdouches, kapjes en extreme schoonheid0:17:55 Waarom 2 nanometer niet echt 2 nanometer is0:20:04 Piu-piu-lasers: hoe EUV-licht wordt gemaakt met tindruppeltjes0:22:13 Transistors uitgelegd: van planar tot FinFET en gate-all-around0:27:05 Drie maanden voor één chip — en tienduizenden wafers per maand0:32:24 De nieuwe NanoIC Pilot Line: 1,4 nanometer voor startups en studenten0:35:01 Chipsoevereiniteit: waarom Europa meer zelf moet kunnen0:40:08 Een stukje geluk: waarom zelfs Intel vastliep op 10 nanometer0:44:14 De atomaire limiet: hoe ver kunnen chips nog krimpen?0:47:46 Worden onze thuiscomputers ooit quantum? Genoemd in deze aflevering IMEC (Belgisch onderzoeksinstituut voor nanoelectronica) ASML en hun EUV / High-NA EUV machines TSMC, Samsung, Intel — de drie grote chipfabrikanten NanoIC Pilot Line (nieuwe IMEC cleanroom, 2,5 miljard euro) Process Design Kit (PDK) voor 1,4nm chipdesign Steam Deck / Steam Machine en Linux gaming (Valve / SteamOS) Cadence en Synopsys (EDA-software) MNOT S04E21 – Lasers en zandpannenkoeken van ASML met Sander Blok Fedora KDE (Linux-distributie die Daan privé gebruikt) See omnystudio.com/listener for privacy information.

From new cancer drugs to batteries and robotics – China's top-tier growth companies are forging paths of their own rather than following in the west's footsteps. Investment manager Sophie Earnshaw names companies that have caught her eye and explains why being a long-term stock picker differs in China from elsewhere. Background:Sophie Earnshaw is a decision-maker on our China Equities Strategy and joint manager of the Baillie Gifford China Growth Trust. In this conversation, she tells Short Briefings… host Leo Kelion about a select group of Chinese companies breaking new ground, supported by the state's efforts to become self-sufficient in more of today's critical technologies and a leader in some of those of the future. Earnshaw also details how the “phenomenal rate” at which companies are born, scale and die in the country makes stock-picking a challenging task – making the access we have to company leaders, academics and other local expertise core to our mission of finding the best firms to invest in on behalf of our clients. Portfolio companies discussed include:- CATL – the battery maker whose products power electric vehicles worldwide and increasingly support the renewable energy sector- BeOne and Innovent Biologics – pharmaceutical firms developing the next generation of cancer drugs - AMEC and NAURA – semiconductor equipment makers enabling China to develop increased self-reliance in computer chips - Alibaba, ByteDance and Tencent – China's ‘big tech' companies, whose artificial intelligence tools are becoming embedded into people's daily lives- MiniMax – the AI startup rolling out video and agentic tools at a fraction of the cost of western counterparts- Horizon Robotics – the automated driving tech provider with its eye on an even bigger opportunity. Resources:Baillie Gifford podcastsChina: a tale of two storiesChina investment strategy hub (institutional clients only)House of HuaweiPrivate investor forum 2025: investing in great growth companiesTrip notes: on the road with Baillie Gifford China Growth Trust  Companies mentioned include:AlibabaAMECASMLBeOneByteDanceCATLHorizon RoboticsInnovent BiologicsJiangsu HengruiHuaweiMiniMaxSamsungNAURATencentTSMCXiaohongshu Timecodes:00:00  Introduction01:55   Joining the China Equities Strategy02:40  Intense competition04:00  The government's influence06:10   CATL, the electrification champion08:45  Investing with a 5-year time horizon10:25   Shanghai office, local expertise11:45   Regulations and geopolitics14:30   China's next Five-year Plan16:15   Innovent Biologics' new cancer drugs18:10   Lower-cost clinical trials19:45   Being selective in semiconductors21:25   Investing in chip equipment makers23:00  China's ‘big tech and AI'25:10   MiniMax making AI like ‘tap water'27:45  The road to robotics29:35  A market you can't ignore30:30  Book choice Glossary of terms (in order of mention): Third plenum: a major policy meeting of China's ruling Communist Party, often used to set big economic/political direction.Sovereign bond issuance: The government raising money by selling bonds (IOUs) to investors.Opportunity set: the range of investable companies available to choose from.Capex: capital expenditure – money spent on long-term assets like factories, equipment, or data centres.Fiscal deficit target: how much more the government plans to spend than it collects in revenue (taxes plus other income), expressed as a share of the economy.GDP: gross domestic product – the total value of goods and services a country produces in a year.Market capitalisation: the total value of a company's shares (share price × number of shares).ESG: environmental, social and governance – how a company manages environmental impact, people issues, and corporate oversight.Large-form batteries: big battery packs used in things like electric vehicles and grid storage.Energy storage systems: large batteries that store electricity for later use (helping balance the grid).Generic drugs: copies of medicines whose patents have expired; usually cheaper, same active ingredient.Bi-specific (bispecific) drugs: drugs designed to bind to two targets at once (often to direct immune cells to cancer).ADC drugs: antibody–drug conjugates – antibodies that deliver a toxic payload to cancer cells.Out-licensing: selling rights to your drug/technology to another company (often for upfront + milestone payments).EUV machines: extreme ultraviolet lithography equipment used to make the most advanced chips.Foundry: a factory business that manufactures chips for other companies.Etch and deposition: steps in chipmaking – etch removes material to form patterns, deposition adds thin layers.Picks and shovels: a metaphor for companies that sell essential tools to an industry (rather than end products).Digitalisation: moving processes and services from offline to software and data-driven systems.Compute: the processing power (chips and servers) used to train/run AI.Large language model (LLM): an AI trained on lots of text to generate and understand language.Margins: how much profit a company makes per pound/dollar of revenue (after costs).Cloud business: selling computing power/storage/software over the internet instead of on a local machine.Algorithm layer: the method or software logic that makes the AI work (as distinct from the hardware).Gross margin: revenue minus direct costs (before overheads), a rough measure of product profitability.Assisted driving: features that help a driver (lane-keeping, adaptive cruise control, etc) but don't fully replace them.Autonomous driving: a car driving itself with minimal or no human input.Software attachment rate: the percentage of customers who add paid software features and/or subscriptions.

Il y a 60 ans mourait le Père Pierre Teilhard de Chardin. Ce Jésuite tout à la fois paléontologue, théologien et philosophe fut autant vénéré que décrié de son vivant, manquant même de peu une condamnation de Rome. Aujourd'hui que peut-on dire de cette grande figure du catholicisme du XXème siècle ? Qui était Pierre Teilhard de Chardin ? Quelle influence a-t-il exercé sur la science ? Et que peut-on retenir de sa pensée ? Pour nous accompagner dans cette réflexion, nous recevons cette semaine Patrice Boudignon, historien et auteur en 2008 de la biographie " Pierre Teilhard de Chardin, sa vie son oeuvre, sa réflexion (Cerf Histoire), ainsi que le Père François Euvé, lui aussi Jésuite, théologien et scientifique de formation, auteur du livre " Sauver le cosmos dans les pas de Teilhard de Chardin " qui paraîtra courant novembre aux éditions Salvatore. Il est par ailleurs membre du comité de rédaction de la revue Recherches de sciences religieuses et rédacteur en chef de la revue Etvdes Emission du 11 octobre 2015.

00:00 Introducción 00:15 Mercado Libre junta 4 plataformas de streaming y promete hasta 50% de ahorro Con Meli Mega, la compañía integra Netflix, Disney+, HBO Max y Apple TV+ desde 249 pesos al mes, junto con envíos gratis y beneficios financieros. 01:33 El 48% de los expertos en ciberseguridad navega a ciegas ante la IA La adopción acelerada de la IA detonó una carrera armamentista silenciosa en las empresas, con más riesgos y nuevas vulnerabilidades. 03:14 ASML, el monopolio invisible detrás del boom de Nvidia Esta es la única empresa capaz de fabricar las máquinas que producen los chips más avanzados del mundo. Su dominio en la litografía EUV la convierte en el eslabón crítico detrás del boom de Nvidia y la IA.

Wat als het meest waardevolle bedrijf van Nederland geen economische troef meer is, maar een geopolitiek drukmiddel op het bord van Donald Trump? In aflevering 138 van Studio Tegengif ontrafelen we waarom ASML plots midden in een mondiale machtsstrijd staat — en waarom Nederland zich moet afvragen of het eigenaar is van een succesverhaal, of beheerder van een gevaarlijk geo-economisch chokepoint. Journalist Diederik Baazil, auteur van De belangrijkste machine ter wereld, neemt ons mee van een bijna mislukte Philips-afsplitsing naar de kern van de wereldwijde chipoorlog. We praten over EUV, exportrestricties, Trump, Taiwan en het fascinerende ecosysteem rond ASML — van Zeiss tot TSMC, van kennisinstellingen tot kabinetten. En vooral: wat moet Nederland doen als bondgenootschappen verschuiven, afhankelijkheden wapens worden en ASML letterlijk “op het menu” komt te staan? Zoals je van Studio Tegengif verwacht proberen we complexe zaken toegankelijk te bespreken. Deze aflevering werd gemaakt met ondersteuning van Wim Brons van remotepodcast.nl. Een aanrader voor als je op afstand een podcast wil maken met fantastische geluidskwaliteit. Wil je ons steunen? Dat kan: je kunt vriend van de show worden:
https://vriendvandeshow.nl/studio-tegengif ***SHOWNOTES*** Diederik Baazil, en Cagan Koc, ‘De Belangrijkste machine ter wereld: Hoe ASML verwikkeld raakte in een internationale machtsstrijd' (2025) https://uitgeverijprometheus.nl/boeken/belangrijkste-machine-ter-wereld-paperback/ Reuters, ‘Exclusive: How China built its ‘Manhattan Project' to rival the West in AI chips' https://www.reuters.com/world/china/how-china-built-its-manhattan-project-rival-west-ai-chips-2025-12-17/ Diederik Baazil, en Cagan Koc, Financieel Dagblad, ‘Wat doet Nederland als Trump ASML op het menu zet?' https://fd.nl/opinie/1583851/wat-doet-nederland-als-trump-asml-op-het-menu-zet

TSMC: extreem goede cijfers. Samsung: explosieve verwachtingen. Intel: kan niet aan de vraag voldoen. Kortom, het lijkt in de sterren geschreven te staan dat ook het laatste kwartaal van afgelopen jaar garant staat zéér goede cijfers van de chipmachinemaker uit Veldhoven. Ook analisten zijn enthousiast. Ze verhogen stuk voor stuk hun koersdoel voor het aandeel. Het lijkt er dus op dat je superlatieven te kort komt. Woensdag weten we ook of dat terecht is, want dan komt ASML met hun cijfers. Bob Homan van ING Investment Office vertelt je in hoeverre ASML nog teleur kan stellen. En naar welk cijfertje je woensdagochtend op zoek moet. Over de podcast: In Beurs in Zicht stomen we je klaar voor de beursweek die je tegemoet gaat. Want soms zie je door de beursbomen het beursbos niet meer. Dat is verleden tijd! Iedere week vertelt een vriend van de show waar jouw focus moet liggen. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro.See omnystudio.com/listener for privacy information.

1:57:05 – Frank in New Jersey, plus the Other Side. Topics include: Music selections, yacht rock, The Second Arrangement by Steely Dan, Tron: Ares (2025), poker chips, Stranger Things finale, Sorcerer (1977), Stones in Exile (2010), EUV microchip manufacturing, “The Ridiculous Engineering Of The World’s Most Important Machine”, Mixue pronounciation, Oh! Susanna, another Mullholland Drive synchronicity, and […]

1:57:05 – Frank in New Jersey, plus the Other Side. Topics include: Music selections, yacht rock, The Second Arrangement by Steely Dan, Tron: Ares (2025), poker chips, Stranger Things finale, Sorcerer (1977), Stones in Exile (2010), EUV microchip manufacturing, “The Ridiculous Engineering Of The World’s Most Important Machine”, Mixue pronounciation, Oh! Susanna, another Mullholland Drive synchronicity, and […]

Happy holidays from Sinica! This week, I speak with Paul Triolo, Senior Vice President for China and Technology Policy Lead at DGA Albright Stonebridge Group and nonresident honorary senior fellow on technology at the Asia Society Policy Institute's Center for China Analysis. On December 8th, Donald Trump announced via Truth Social that he would approve Nvidia H200 sales to vetted Chinese customers — a decision that immediately sparked fierce debate. Paul and I unpack why this decision was made, why it's provoked such strong reactions, and what it tells us about the future of technology export controls on China. We discuss the evolution of U.S. chip controls from the Entity List expansions under Trump's first term through the October 2022 rules and the Sullivan Doctrine, the role of David Sacks and Jensen Huang in advocating for this policy shift, whether Chinese firms will actually want to buy H200s given their heterogeneous hardware stacks and Beijing's autarky ambitions, what the Reuters report about China cracking ASML's EUV lithography code tells us about the choke point strategy, and whether selective engagement actually strengthens Taiwan's Silicon Shield or undermines it. This conversation is essential listening for understanding the strategic, technical, and political dimensions of the semiconductor competition.6:44 – What the H200 decision actually changes in the real world 9:23 – The evolution of U.S. chip controls: from Entity Lists to the Sullivan Doctrine 18:28 – How Jensen Huang and David Sacks convinced Trump 25:21 – The good-faith case for why export control advocates see H200 approval as a strategic mistake 32:12 – What H200s practically enable: training, inference, or stabilizing existing clusters 38:49 – Will Chinese companies actually buy H200s? The heterogeneous hardware reality 46:06 – The strategic contradiction: exporting 5nm GPUs while freezing tool controls at 16/14nm 51:01 – The Reuters EUV report and what it reveals about choke point technologies 58:43 – How Taiwan fits into this: does selective engagement strengthen the Silicon Shield? 1:07:26 – Looking ahead: broader rethinking of export controls or patchwork exceptions? 1:12:49 – What would have to be true in 2-3 years for critics to have been right about H200?Paying it forward: Poe Zhao and his Substack Hello China TechRecommendations: Paul: Zbig: The Life of Zbigniew Brzezinski, Amerca's Great Power Propheti by Ed Luce; Hyperdimensional Substack by Dean Ball Kaiser: Everything Is Tuberculosis by John Green; The Anthropocene Reviewed by John Green; So Very Small by Thomas LevensonSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Our 229th episode with a summary and discussion of last week's big AI news!Recorded on 12/19/2025Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Notable releases include OpenAI's GPT-5.2 Codex for advanced coding and Google's Gemini Free Flash for competitive AI application performance. Nvidia's new open-source Trion-3 models also showcase impressive benchmarks.Funding updates highlight Lovable's $330M Series B, valuing the AI coding startup at $6.6B, and Faya's $140M Series D for AI model hosting, valued at $4.5B.China makes significant strides in semiconductor technology with advances in EUV lithography machines, led by Huawei and SMIC, potentially disrupting global chip manufacturing dominance.Key safety and policy updates include OpenAI's GPT-5.2 system card focusing on biosecurity and cybersecurity risks, while Google partners with the US military to power a new AI platform with Gemini models.Timestamps:(00:00:10) Intro / Banter(00:02:09) News PreviewTools & Apps(00:02:56) Google launches Gemini 3 Flash, makes it the default model in the Gemini app | TechCrunch(00:10:13) ChatGPT launches an app store, lets developers know it's open for business | TechCrunch(00:13:35) Introducing GPT-5.2-Codex | OpenAI(00:19:23) Story about OpenAI release - GPT image 1.5(00:22:27) Meta partners with ElevenLabs to power AI audio across Instagram, Horizon - The Economic TimesApplications & Business(00:23:16) OpenAI to End Equity Vesting Period for Employees, WSJ Says(00:28:20) How China built its ‘Manhattan Project' to rival the West in AI chips(00:36:47) China's Huawei, SMIC Make Progress With Chips, Report Finds(00:41:03) OpenAI in Talks to Raise At Least $10 Billion From Amazon and Use Its AI Chips(00:43:32) Amazon has a new leader for its ‘AGI' group as it plays catch-up on AI | The Verge(00:47:27) Broadcom reveals its mystery $10 billion customer is Anthropic(00:49:12) Vibe-coding startup Lovable raises $330M at a $6.6B valuation | TechCrunch(00:50:38) Fal nabs $140M in fresh funding led by Sequoia, tripling valuation to $4.5B | TechCrunchProjects & Open Source(00:51:10) Nvidia Becomes a Major Model Maker With Nemotron 3 | WIRED(00:59:24) Meta introduces new SAM AI able to isolate and edit audio • The Register(00:59:54) [2512.14856] T5Gemma 2: Seeing, Reading, and Understanding Longer(01:03:10) Anthropic makes agent Skills an open standard - SiliconANGLEResearch & Advancements(01:03:47) Budget-Aware Tool-Use Enables Effective Agent Scaling(01:08:21) Rethinking Thinking Tokens: LLMs as Improvement Operators(01:10:50) What if AI capabilities suddenly accelerated in 2027? How would the world know?Policy & Safety(01:12:58) Update to GPdfT-5 System Card: GPT-5.2(01:18:04) Neural Chameleons: Language Models Can Learn to Hide Their Thoughts from Unseen Activation Monitors(01:20:47) Async Control: Stress-testing Asynchronous Control Measures for LLM Agents(01:24:37) Google is powering a new US military AI platform | The VergeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

「NordVPN X M觀點」: https://nordvpn.com/miula 專屬優惠碼「miula」 透過專屬優惠連結購買兩年方案加贈4個月好禮，還有30天內退款保證，完全零風險！ --- EP262. 馬斯克拿到薪水、TikTok 交易完成、中國 EUV 能打嗎 | M觀點 --- (00:40) EP262 預告 (03:07) 業配時間：NordVPN (05:22) 閒聊時間：台北隨機攻擊事件 (08:09) 第一個話題：馬斯克拿到薪水 (23:58) 第二個話題：TikTok 交易完成 (39:56) 第三個話題：中國 EUV 能打嗎 --- M觀點資訊 --- 科技巨頭解碼: https://bit.ly/3koflbU M觀點 Telegram - https://t.me/miulaviewpoint M觀點 IG - https://www.instagram.com/miulaviewpoint/ M觀點Podcast - https://bit.ly/34fV7so M報: https://bit.ly/345gBbA M觀點YouTube頻道訂閱 https://bit.ly/2nxHnp9 M觀點粉絲團 https://www.facebook.com/miulaperspective/ 任何合作邀約請洽 miula@outlook.com -- Hosting provided by SoundOn

Burnie and Ashley discuss the great Chinese catch up, EUV chips, research milestones, and all the Marvel teasers.

Has China cracked a major puzzle for chip parity? Trump Media merges with a… Fusion Energy startup? Coinbase continues its efforts to let you trade everything. OpenAI is turning on the fundraising afterburners. And how to catch a North Korean IT infiltrator. China may have reverse engineered EUV lithography tool in covert lab, report claims — employees given fake IDs to avoid secret project being detected, prototypes expected in 2028 (Tom's Hardware) Trump media group agrees $6bn merger with Google-backed fusion energy company (FT) Coinbase adds prediction markets and stock trading in push to be one-stop trading app (CNBC) OpenAI Has Discussed Raising Tens of Billions at Valuation Around $750 Billion (The Information) Amazon Caught North Korean IT Worker By Tracing Keystroke Data (Bloomberg) Learn more about your ad choices. Visit megaphone.fm/adchoices

Shenzhen scientists develop EUV lithography prototype, the FTC probes Instacart’s AI pricing tool, and Apple is modifying its iOS app store policies in Japan. MP3 Please SUBSCRIBE HERE for free or get DTNS Live ad-free. A special thanks to all our supporters–without you, none of this would be possible. If you enjoy what you seeContinue reading "Gemini 3 Flash Replaces 2.5 as Default in Google’s AI Tools – DTH"

Het is Tesla toch wéér gelukt. Het bedrijf stunt op de beurs. Misschien wel de comeback van het jaar. In het eerste kwartaal ging er nog 36 procent van de beurswaarde af, nu tikt het bedrijf van Elon Musk een all-time-high aan.Deze aflevering kijken we of dat logisch is. Er zijn nog steeds de nodige beren op de weg, maar toch lijken beleggers die niet te zien. Sterker nog: ze denken dat Tesla helemaal binnen gaat lopen met robotaxi's. Collega Noud Broekhof (Nationale Autoshow) legt uit waarom dat nergens op slaat.Verder hebben we het over dé overnamesoap van 2025. Die van Warner Bros. Netflix lijkt dan toch aan het langste eind te trekken, want Paramount moet twee klappen verwerken. De eerste is Jared Kusner, schoonzoon van Trump. Die loopt als financieerder weg van de deal. Tweede klap is de directie van Warner Bros zelf.Ook bespreken we Chinese zorgen voor ASML. Nee, geen exportrestrictie van de Amerikanen. De Chinezen zijn dit keer zelf het probleem. Volgens persbureau Reuters hebben ze zelf een EUV-machine in elkaar geknutseld... Justin Blekemolen van onlinebroker Lynx is te gast. Met hem hebben we het ook over de waarschuwing van De Nederlandsche Bank. Dat zich nu ook zorgen maakt over een AI-bubbel.See omnystudio.com/listener for privacy information.

Het is Tesla toch wéér gelukt. Het bedrijf stunt op de beurs. Misschien wel de comeback van het jaar. In het eerste kwartaal ging er nog 36 procent van de beurswaarde af, nu tikt het bedrijf van Elon Musk een all-time-high aan.Deze aflevering kijken we of dat logisch is. Er zijn nog steeds de nodige beren op de weg, maar toch lijken beleggers die niet te zien. Sterker nog: ze denken dat Tesla helemaal binnen gaat lopen met robotaxi's. Collega Noud Broekhof (Nationale Autoshow) legt uit waarom dat nergens op slaat.Verder hebben we het over dé overnamesoap van 2025. Die van Warner Bros. Netflix lijkt dan toch aan het langste eind te trekken, want Paramount moet twee klappen verwerken. De eerste is Jared Kusner, schoonzoon van Trump. Die loopt als financieerder weg van de deal. Tweede klap is de directie van Warner Bros zelf.Ook bespreken we Chinese zorgen voor ASML. Nee, geen exportrestrictie van de Amerikanen. De Chinezen zijn dit keer zelf het probleem. Volgens persbureau Reuters hebben ze zelf een EUV-machine in elkaar geknutseld... Justin Blekemolen van onlinebroker Lynx is te gast. Met hem hebben we het ook over de waarschuwing van De Nederlandsche Bank. Dat zich nu ook zorgen maakt over een AI-bubbel.See omnystudio.com/listener for privacy information.

ASML CEO Christophe Fouquet says the company's technical knowledge is essential for its work with customers, and he spends time studying the technical details of the company's chip-making machines. Fouquet's next big test is leading a transition from extreme ultraviolet lithography technology to high numerical aperture EUV, which aims to push chip geometries below 2-nm and make chips more capable of running advanced applications in AI and other fields. He speaks with Bloomberg's Tom MackenzieSee omnystudio.com/listener for privacy information.

- Marvell in AI, Celestial AI - Co-Packaged Optics, Photonics Interconnects - Lasers for EUV, xLight FEL Lasers, ASML Cymer's LPP Lasers - ASML, Canon, Nikon - Chinese efforts in chip manufacturing: SMEE, SiCarrier - Canon's Nano Imprint Lithography (NIL) - China's Xizhi Electron Beam Lithography - Okinawa Institute of Science and Technology (OIST)'s simplified optics in EUV - SDCS Research on Parkinson's Disease [audio mp3="https://orionx.net/wp-content/uploads/2025/12/HPCNB_20251208.mp3"][/audio] The post HPC News Bytes – 20251208 appeared first on OrionX.net.

VOV1 - Ngày 05/12, Thủ tướng Đức Friedrich Merz đến Thủ đô Bruxelles với mục tiêu thuyết phục người đồng cấp Bỉ Bart De Wever đồng ý với kế hoạch của Liên minh châu Âu (EU) Về việc sử dụng các tài sản bị đóng băng của Nga trong bối cảnh các ngân hàng lục địa già lo ngại bị trả đũa.

中國真的惹毛了日本，惹毛了高市早苗，惹毛了濱崎步！ 日本現在直接升級成台灣最強最派的盟友，一旦喚醒了沈睡的軍事巨人，習近平真的應該要怕！ 我們就告訴你，為什麼習大大會變成雖小小！為什麼不敢打日本和台灣？為什麼中共幾百次的「最後警告」，變成了笑話！ 高市內閣支持度不降反升，直接飆到75%。高市早苗本人現在是全日本年輕人的偶像！ 日本掌握全球製造業關鍵原材料，軍事重工潛力世界一流。中國官媒認證：當日本和平憲法解封時，日本一夜之間將可製造oooo枚核彈！ 開心嗎？分享影片給你的親友吧！ 還有台灣挺日本，日本愛台灣，茶茶王國「堀井七茗園」京都宇治抹茶買一送一！大家買起來！ 全台獨家的世界經濟追劇深入報導，精彩萬分，持續連載中！ （現在就加入會員支持我們，還可以看到更多專屬影片～） https://www.youtube.com/@emmytw/join 全民挺日本

In deze aflevering spreekt Jelle van Baardewijk met journalist Diederik Baazil over De Belangrijkste Machine ter Wereld – het boek over ASML, de chipoorlog en de geopolitieke strijd tussen China, de VS en Europa. Wat staat ons te wachten rond Taiwan, chips en de wereldorde?--

"Leadership is staying ahead of change without losing authenticity". "Trust is the real currency of sales, teams, and Japan's business culture". "Zeiss's foundation model is a rare advantage: patient capital reinvested into R&D". "Japan is less "risk-averse" than "uncertainty-avoidant" when decisions lack clarity and consensus". "Language is helpful for connection, but not the primary qualification for leading in Japan". Brief Bio Vincent Mathieu is the CEO of Carl Zeiss Japan, leading a multi-division portfolio spanning semiconductors, medical devices, microscopy, industrial quality solutions, ophthalmic lenses, and imaging optics. Originally from the south of France near the Basque Country, he studied business in Toulouse, then spent several years travelling and working across Morocco, Denmark, Ireland, Chile, and South America—discovering along the way that his core strength was building trust in sales. He first came to Japan in 2001 to launch and grow a new division, learning the realities of hiring, selling, and leading without fluency in Japanese. After returning to Europe for global and country leadership roles—including navigating a corporate receivership in the UK—he was recruited to Zeiss and returned to Japan for a second stint. There, he led a turnaround in the vision care business by rebuilding the team, premium positioning, and distribution strategy, then expanded to broader regional responsibilities before taking the top role in Japan, leading a larger organisation through compliance, regulatory, structural change, and remuneration reform. Carl Zeiss is often mistaken as "just cameras", yet the company's real gravity sits elsewhere: precision optics, industrial measurement, medical equipment, and the advanced semiconductor ecosystem that powers modern computing. Vincent Mathieu, CEO of Carl Zeiss Japan, uses that breadth as both a strategic advantage and a leadership test—because leading a portfolio business demands credibility across wildly different technical domains, from microscopy used by Nobel Prize-winning researchers to X-ray inspection systems supporting EV battery quality control. He also points to a structural difference that shapes Zeiss's long-term posture: the company operates as a foundation rather than a classic shareholder-led public entity, enabling sustained reinvestment into R&D and the patience required to develop complex innovations that may run at a loss for years before they become indispensable. In semiconductors, that mindset shows up in partnerships and breakthrough optics supporting lithography and EUV pathways tied to ever-smaller chips and AI-era demand. Mathieu's personal story mirrors the adaptive leadership he advocates. He describes an early uncertainty about career direction, a formative period of travel and "odd jobs", and a gradual shift into commercial roles where trust, not extroversion, became his sales engine. His first Japan assignment was a tough entry: conservative hiring conditions, limited language ability, and the slow build of distributor confidence—where one relationship took years to convert. Returning later via Zeiss, he expected a smoother "global" environment and instead found a familiar friction point: leadership without a shared language, competing internal politics, and the need to earn followership through visible effort. His approach was practical and gemba-oriented—going into the field with salespeople, learning enough Japanese to observe and debrief well, and leading by example rather than relying on title or hierarchy. In his current role, the leadership challenge is no longer a small turnaround team but a larger organisation navigating regulatory scrutiny, compliance expectations, talent gaps, and a shift from "box-moving" to workflow and digital solutions. He frames Japan's organisational reality as deeply sensitive to trust, transparency, and consistency—especially when change touches taboo areas such as pay. Whether the topic is performance-based remuneration, AI adoption, or organisation redesign, Mathieu returns to the same idea: leadership is change management plus authenticity. The most durable influence, in his view, comes from understanding who the leader is, then showing up coherently—because Japanese organisations may not offer immediate feedback, but they do evaluate whether words and actions match. Q&A Summary What makes leadership in Japan unique? Leadership in Japan is uniquely shaped by trust, time, and social proof. Decision-making often relies on nemawashi (pre-alignment), the ringi-sho approval flow, and a preference for consensus that reduces future friction. Feedback can be indirect, and the "real signals" may appear later, after relationships deepen. Why do global executives struggle? Global leaders often struggle when they arrive expecting predictable "rules" about Japan, or when they assume a corporate title will create followership. Without local credibility, language bridges, and contextual awareness of honne/tatemae dynamics, even good strategies can stall. Impatience can be read as shitsukoi (pushy), yet excessive patience can also lead to inertia—forcing leaders to balance consistency with restraint. Is Japan truly risk-averse? Japan is frequently labelled risk-averse, but a more useful lens is uncertainty avoidance. When ambiguity is high, organisations increase process and consensus to control outcomes. Once clarity exists—shared numbers, shared logic, shared stakeholders—Japanese teams can execute decisively and at high quality, often outperforming more improvisational cultures. What leadership style actually works? A field-based, trust-building style works: lead by example, show operational commitment, and invest in relationships. Mathieu's experience suggests credibility is built through visible contribution—being present with customers, coaching sales behaviours, and demonstrating consistency. Authenticity matters: employees may accept difficult change if the leader is transparent, coherent, and reliably delivers on commitments. How can technology help? Technology helps when framed as decision intelligence rather than novelty. AI tools, automation, and even "digital twins" for process and manufacturing can reduce reporting burden, strengthen compliance, and redirect scarce talent towards analysis and customer value. The warning is "AI for AI's sake": capability must be learned, prompts must be mastered, and use cases must be chosen with discipline. Does language proficiency matter? Language matters for connection and cultural nuance, but it should not be the primary criterion for leading in Japan. A leader can choose English for clarity at scale—especially when communicating strategy—while still building trust through effort, respect, and selective Japanese usage in day-to-day engagement. What's the ultimate leadership lesson? The ultimate lesson is that leadership is managing change while staying true to oneself. As confidence grows, leaders feel less pressure to perform to other people's expectations and more capacity to act with authenticity. That inner coherence becomes a stabiliser for teams navigating uncertainty, consensus-building, and transformation. Author Credentials Dr. Greg Story, Ph.D. in Japanese Decision-Making, is President of Dale Carnegie Tokyo Training and Adjunct Professor at Griffith University. He is a two-time winner of the Dale Carnegie "One Carnegie Award" (2018, 2021) and recipient of the Griffith University Business School Outstanding Alumnus Award (2012). As a Dale Carnegie Master Trainer, Greg is certified to deliver globally across all leadership, communication, sales, and presentation programs, including Leadership Training for Results. He has written several books, including three best-sellers — Japan Business Mastery, Japan Sales Mastery, and Japan Presentations Mastery — along with Japan Leadership Mastery and How to Stop Wasting Money on Training. His works have also been translated into Japanese, including Za Eigyō (ザ営業), Purezen no Tatsujin (プレゼンの達人), Torēningu de Okane o Muda ni Suru no wa Yamemashō (トレーニングでお金を無駄にするのはやめましょう), and Gendaiban "Hito o Ugokasu" Rīdā (現代版「人を動かす」リーダー). In addition to his books, Greg publishes daily blogs on LinkedIn, Facebook, and Twitter, offering practical insights on leadership, communication, and Japanese business culture. He is also the host of six weekly podcasts, including The Leadership Japan Series, The Sales Japan Series, The Presentations Japan Series, Japan Business Mastery, and Japan's Top Business Interviews. On YouTube, he produces three weekly shows — The Cutting Edge Japan Business Show, Japan Business Mastery, and Japan's Top Business Interviews — which have become leading resources for executives seeking strategies for success in Japan.

Pierre Teilhard de Chardin est rarement un nom parfaitement inconnu mais sa vie et sa pensée le sont davantage, et a fortiori peut-être encore sa foi. C'est le thème d'un cours que François Euvé propose ce semestre aux Facultés Loyola : l'occasion de mieux découvrir précisément la vie de ce jésuite paléontologue du début du XXème s. par une porte d'entrée plus intime, celle de sa foi. Page du cours "La foi de Teilhard" : https://www.loyolaparis.fr/agenda/la-foi-de-teilhard/ Page de François Euvé sur le site des Facultés Loyola : https://www.loyolaparis.fr/enseignant/francois-euve/Site de la revue Etudes : https://www.revue-etudes.com Interview menée par Isabelle de La Garanderie, doctorante en théologie aux Facultés Loyola ParisEnregistrement et montage : Cyprien Rigolot.  Musique : Improvisations au piano de Pascal Marsault  Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

PC World's Adam Patrick Murray stops by this week to discuss the trip he and Will recently took to visit Intel's new 18A chip fabrication facility in Arizona. Settle in for a wide-ranging chat about the upcoming Panther Lake architecture, why Intel won't have a new desktop part for a while longer, the future of next-gen chiplet interconnects, the difficulty of scheduling between big and little cores, suiting up to enter the fab, 30mph FOUPs whizzing around overhead, EUV machines the size of multiple school buses, getting served beer by tiny horses (??), and more. Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

This special midweek bonus episode of the pod features a conversation with special guest Marc Hijink. Marc is the author of Focus – The ASML Way, which charts the evolution of the Dutch company to its current position as the sole supplier of EUV lithography tools to the global semiconductor manufacturing industry. They start by discussing the book and the profound significance of ASML to the chip sector, before eventually moving on to explore its consequent position at the centre of the tech cold war between the US and China.

► Get a free fractional share!What's the tail risk for the stock market? Find out on this week's PlayingFTSE Show!A big underperformance from one Steve this week and a big outperformance from the other one. But who's managed to do what?October's Bank of America Fund Managers Survey suggests investors are worrying about an AI bubble. So we're revising this theme as a potential stock market crash. Steve W's been looking at Nvidia's unusual deals and the question of whether there are echoes of the dot-com bust. Maybe – but there are also some important differences.ASML looks like it's back on track with its EUV sales. And Steve D has been checking out the situation after the latest earnings report.A strong book-to-bill ratio close to 1 is a very positive sign. So with the company's moat still intact, is it too late to think about buying shares? LVMH shares are up after a surprise turnaround in revenues. The company is back to growth overall and some of its major divisions are showing positive signs. That means it's now trading at a P/E ratio of around 27. So is Steve W – who's well up on his investment – looking to move on to something else? Sartorius shares have been doing very well recently and that's good news for Steve D. The life sciences lab supplies company looks like it's on the up after a few disappointing years.Pfizer's deal with the US government to invest heavily in domestic production should be a big source of future revenues. But what's Steve's plan for his investment?Only on this week's PlayingFTSE Podcast!This show is sponsored by Trading 212! To get free fractional shares worth up to 100 EUR / GBP, you can open an account with Trading 212 through this link https://www.trading212.com/Jdsfj/FTSE. Terms apply.When investing, your capital is at risk and you may get back less than invested.Past performance doesn't guarantee future results.► Get 15% OFF Fiscal.ai:Huge thanks to our sponsor, Fiscal.ai, the best investing toolkit we've discovered! Get 15% off your subscription with code below and unlock powerful tools to analyze stocks, discover hidden gems, and build income streams. Check them out at Fiscal.ai!https://fiscal.ai/?via=steve► Follow Us On Substack:Sign up for our Substack and get light-hearted, info-packed discussions on everything from market trends and investing psychology to deep dives into different asset classes. We'll analyze what makes the best investors tick and share insights that challenge your thinking while keeping things engaging.Don't miss out! Sign up today and start your journey with us.https://playingftse.substack.com/► Support the show:Appreciate the show and want to offer your support? You could always buy us a coffee at: https://ko-fi.com/playingftse(All proceeds reinvested into the show and not to coffee!)► Timestamps:0:00 INTRO & OUR WEEKS10:45 MORE ON THE AI BUBBLE29:32 ASML45:24 LVMH57:24 SARTORIUS► Show Notes:What's been going on in the financial world and why should anyone care? Find out as we dive into the latest news and try to figure out what any of it means. We talk about stocks, markets, politics, and loads of other things in a way that's accessible, light-hearted and (we hope) entertaining. For the people who know nothing, by the people who know even less. Enjoy► Wanna get in contact?Got a question for us? Drop it in the comments below or reach out to us on Instagram: https://www.instagram.com/playing_ftse/► Enquiries: Please email - playingftsepodcast@gmail(dot)com► Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Always consult with a qualified financial professional before making any investment decisions.

本集由《經理人月刊》編輯團隊精選國際財經管理資訊、以及科技相關議題，採訪編輯李岱君提供導讀和解析，幫助讀者快速掌握最新的管理趨勢。 第十四屆ATD亞太年會暨展覽，將於10月28日～10月30日在台北國際會議中心隆重登場。本次大會主題為「人機協作共創永續職場」，邀請國內外60位以上重量級的講師分享他們的見解。 立即報名：https://www.atdapc.org.tw 在半導體世界裡，台積電是兵家必爭的前線戰場，而來自荷蘭小鎮的 ASML，則是背後唯一的「軍火商」。它的曝光機是製造最先進晶片不可或缺的工具，全球 90% 的晶片生產都要靠它。若沒有 ASML，智慧型手機、AI、資料中心，甚至電動車產業都將停擺。這集《接軌國際》，導讀荷蘭記者 Marc Hijink 的新書 《造光者》，帶你看懂： 1. ASML 的關鍵地位：為何全球最先進晶片的生產，全仰賴這台被稱為「世界上最複雜的機器」的曝光機？ 2.從 40 人到 4 萬人的逆襲：ASML 如何從「問題兒童」翻身，靠專注與冒險精神超越美日大廠？ 3.EUV 的不可能任務：在地球上建造「太陽」，只為了產生 13.5 奈米波長的極紫外光。 4.荷蘭式直率文化：ASML 如何用扁平、挑戰權威的文化，動員全球 144 國的工程師完成看似不可能的任務？ Powered by Firstory Hosting

ASML艾斯摩爾，這家以EUV極紫外光微影技術而聞名的全球最大半導體設備商，它不僅摩爾定律（Moore's Law）得以延續，更在美中科技戰的地緣政治角力上意外成為關鍵角色。而ASML艾斯摩爾的成就，則與前總裁暨技術長布令克（Martin van den Brink）的領導風格與其獨特的「萊茵模式」公司文化息息相關。 這集節目，我們特別邀請到荷蘭新鹿特丹商報的科技記者馬克・海因克（Marc Hijink），他在新冠疫情期間花了三年時間深入ASML艾斯摩爾採訪，寫出了很多大家都不知道的內幕故事。我們將深入聊聊布令克如何突破萬難，帶領ASML艾斯摩爾持續突破技術極限，以及在全球半導體競爭格局中，收購西盟科技（Cymer）與邁普（Mapper）背後的策略思考。 主持人：天下雜誌總主筆 陳良榕 來賓： 荷蘭《新鹿特丹報》（NRC）的財經記者與科技專欄作家 馬克・海因克（Marc Hijink） ＊9月限定《胡說科技》＋《造光者》，看懂晶片賽局： https://bit.ly/3HW1sCz ＊立即訂閱《胡說科技》電子報： https://bit.ly/3TuL8eb ＊意見信箱：bill@cw.com.tw -- Hosting provided by SoundOn

In this episode, Clay unpacks the extraordinary rise of ASML — a little-known Dutch company that quietly became the most important player in global technology. Since its IPO in 1995, ASML has compounded at 20% annually. ASML holds one of the most powerful monopolies on earth as it's the sole manufacturer of EUV lithography machines, which make the world's most advanced semiconductor chips. Without ASML, companies like Apple, NVIDIA, and TSMC couldn't power iPhones, AI data centers, or the modern digital economy. IN THIS EPISODE YOU'LL LEARN: 00:00 - Intro 04:56 - How ASML grew from a Philips spinoff into Europe's most important tech company. 13:31 - How ASML's partnership with TSMC shaped the global semiconductor industry. 32:16 - Why ASML holds a near-monopoly on EUV lithography machines. 41:01 - Why the geopolitical tension between the US and China place ASML at the center of technology power struggles. 53:30 - How investors can view ASML's growth, risks, and future opportunities. 01:00:23 - What makes ASML's moat nearly impossible for competitors to replicate. 01:05:08 - The dual leadership that propelled ASML's rise and built a culture of relentless focus. And so much more! Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Join Clay and a select group of passionate value investors for a retreat in Big Sky, Montana. Learn more ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to engage in meaningful stock investing discussions with Stig, Clay, Kyle, and the other community members. Marc Hijink's book: Focus: The ASML Way. Related Episode TIP727: 7 Powers by Hamilton Helmer. Related Episode TIVP024: TSMC: The Most Important Business in the World?. Follow Clay on ⁠⁠X⁠⁠ and ⁠⁠LinkedIn⁠⁠. Check out all the books mentioned and discussed in our podcast episodes ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy ad-free episodes when you subscribe to our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses in just a few minutes each week through our newsletter, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠We Study Billionaires Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X (Twitter)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Browse through all our episodes (complete with transcripts) ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance Tool⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠⁠⁠sponsors⁠⁠⁠: ⁠SimpleMining⁠ ⁠HardBlock⁠ ⁠AnchorWatch⁠ ⁠Human Rights Foundation⁠ ⁠Cape⁠ ⁠Unchained⁠ ⁠Vanta⁠ ⁠Shopify⁠ ⁠Onramp⁠ ⁠Abundant Mines⁠ HELP US OUT! Help us reach new listeners by leaving us a ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠rating and review⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠! It takes less than 30 seconds, and really helps our show grow, which allows us to bring on even better guests for you all! Thank you – we really appreciate it! Support our show by becoming a premium member! ⁠⁠⁠https://theinvestorspodcastnetwork.supportingcast.fm⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

中國在半導體研發領域，近期有好消息傳出，有大學自主研發出了電子束光刻機「羲之」，目前已經投入市場，也吸引了中國許多科技大廠採購。在國際出口管制之下，這樣的研發成果預計能為中國的晶片製造，帶來不少優勢。不過也有專家說了，電子束光刻機的製造效率，比目前最先進、艾斯摩爾大廠的EUV光刻機慢上數百倍，要想在國際市場中突破重圍，恐怕有不小難度。 留言告訴我你對這一集的想法： https://open.firstory.me/user/cku2d315gwbbo0947nezjmg86/comments YT收看《寰宇全視界》

At the 2024 SPIE Advanced Lithography and Patterning Symposium, Ann Kelleher, a top Intel manufacturing executive, mentioned something called Directed Self-Assembly or DSA. In that presentation, she called DSA a key innovation for cost-effective scaling, and said it had to be brought to High Volume Manufacturing. Intel later presented results of a DSA workflow that can "heal" errors in EUV lithography - potentially cutting costs by a dramatic amount. But what is DSA? DSA has been around for over 30 years. It was once positioned to be a replacement for photolithography, but now is seen as a complement to it. But will the fabs adopt it? In today's video, a bottoms-up way to pattern the ever-shrinking lines of our semiconductors: Directed Self Assembly, or DSA.

At the 2024 SPIE Advanced Lithography and Patterning Symposium, Ann Kelleher, a top Intel manufacturing executive, mentioned something called Directed Self-Assembly or DSA. In that presentation, she called DSA a key innovation for cost-effective scaling, and said it had to be brought to High Volume Manufacturing. Intel later presented results of a DSA workflow that can "heal" errors in EUV lithography - potentially cutting costs by a dramatic amount. But what is DSA? DSA has been around for over 30 years. It was once positioned to be a replacement for photolithography, but now is seen as a complement to it. But will the fabs adopt it? In today's video, a bottoms-up way to pattern the ever-shrinking lines of our semiconductors: Directed Self Assembly, or DSA.

Back from Vacation - refreshed? Fun with Coldplay! A quick look at how indices and sectors are doing Sugar in the news.... The REAL running of the bulls - bad things happen PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter INTERACTIVE BROKERS  Warm-Up - Back from Vacation - refreshed? - Fun with Coldplay! - Sugar in the news.... - The REAL running of the bulls - bad things happen Markets - Earnings Season is here - about to get some big results this week - Some earnings to discuss - A quick look at how indices and sectors are doing - Markets react to Trump/Fed comments - YUGE deal with Japan Market Update Top to Bottom in - Fed - April (2 Months) Bottom to Recovery of Loss -  (2 months) Now overshot and climbing to new ATH DJIA up 4.8% SP500 Up 7.69% NASDAQ up 9% Bitcoin Up 24% Emerging Markets up 24% USD down 10% Gold up 24% Copper up 35% Small Caps up 0.95% Apple DOWN 14% YTD Sectors Staring off: Will the TARIFFS actually go into play on August 1st, 2025??? What to do? - Here we go again... - I heard Adam and Tina coming to Florida next week and I have not heard from them.... -- Now maybe it is nowhere near me - Florida is a big state... (Am I being too sensitive?) - If JCD came to FLA - I would think you would call me.... right? Lessons - Astronomer, the tech company that found itself launched into the public eye after its CEO Andy Byron was spotted on a Jumbotron video at a Coldplay concert last embracing an employee, announced that Byron has been placed on leave. - Astronomer's cofounder and chief product officer Pete DeJoy is now serving as interim CEO, the company said in a statement Friday night. - “Our leaders are expected to set the standard in both conduct and accountability,” the statement said in part, adding that the company's board of directors “has initiated a formal investigation into this matter and we will have additional details to share very shortly.” - PEOPLE - think before you do stupid things! NETFLIX Earnings - Netflix posted second-quarter revenue growth of 16% on Thursday after the closing bell. - The company raised its full-year revenue guidance, citing “healthy” member growth and ad sales. - Netflix reported revenue of $11.08 billion for the second quarter, higher than Wall Street's estimates of $11.07 billion, according to data compiled by LSEG. -Stock was sold off after - priced to perfection - Netflix's higher forecast reflects the weakening of the U.S. dollar compared with other currencies as well as “healthy” member growth and ad sales, the company said in a statement. (What happened to constant currency?????) - Off 8% this month, Up 36% YTD Impressive - Taiwan Semiconductor Manufacturing Company on Thursday reported a near 61% year-on-year rise in second-quarter profit, hitting a record high and beating estimates, as demand for artificial intelligence chips stayed strong. - The world's largest contract chip manufacturer forecast third-quarter revenue between $31.8 billion and $33.0 billion — a 38% year-over-year increase and 8% higher from the prior quarter at the midpoint. - - Revenue: 933.80 billion new Taiwan dollars ($31.7 billion), vs. NT$931.24 billion expected - - Net income: NT$398.27 billion, vs. NT$377.86 billion On the Other Hand - ASML warned of the possibility of no growth in 2026, even as it beat top and bottom line expectations for the second quarter. - ASML's guidance for the current quarter missed expectations while it narrowed its own forecast for the rest of the year. - Shares of the firm ended the day 11.4% lower after the report -  ASML is the sole supplier of extreme ultraviolet (EUV) lithography systems,

Supercharge your analysis with AI! Get 15% of your membership with our special link here: https://fiscal.ai/csi/ASML stock had a "rough" week after Q2 2025 earnings. But customer upgrades to EUV (extreme ultraviolet) lithography are underway as a new generation of high-performance chips ramp up production. Despite the noise, ASML is going to be just fine. Chip Stock Investor explains the critical data points you need to know in this mid-year 2025 update.Join us on Discord with Semiconductor Insider, sign up on our website: www.chipstockinvestor.com/membershipSign Up For Our Newsletter: https://mailchi.mp/b1228c12f284/sign-up-landing-page-short-form********************************************************Affiliate links that are sprinkled in throughout this video. If something catches your eye and you decide to buy it, we might earn a little coffee money. Thanks for helping us (Kasey) fuel our caffeine addiction!Content in this video is for general information or entertainment only and is not specific or individual investment advice. Forecasts and information presented may not develop as predicted and there is no guarantee any strategies presented will be successful. All investing involves risk, and you could lose some or all of your principal.Timestamps:(00:00) Overview of ASML and Its Role in the Semiconductor Industry(03:48) ASML's Financial Performance in Q2 2025(04:52) Cash Flow and Stock Buybacks(08:54) Revenue Breakdown and Market Analysis(14:38) Future Outlook and Conclusion #ASML #EUVlithography #ASMLEUV #highna #lowna #semiconductors #chips #investing #stocks #finance #financeeducation #silicon #artificialintelligence #ai #financeeducation #chipstocks #finance #stocks #investing #investor #financeeducation #stockmarket #chipstockinvestor #fablesschipdesign #chipmanufacturing #semiconductormanufacturing #semiconductorstocks Nick and Kasey own shares of ASML

Beleggers en analisten droomden van het cijferseizoen, maar ASML maakt er een kleine nachtmerrie van. Ineens durft de directie niet meer te voorspellen. Ze weten niet of ze volgend jaar nog groeien. Iets waar ze drie maanden geleden nog wél vanuit gingen. Het zorgt voor een enorme klap voor het aandeel. Ruim 11 procent eraf. ASML trekt bovendien andere tech-aandelen mee in z'n val. Deze aflevering hebben we het over die gigantische tegenvaller. En over die onzekerheid. Waar komt die onzekerheid precies vandaan en wanneer is 'ie weg? Naast ASML hebben we het ook over de tegenaanval van Europa. Volgens persbureau Bloomberg zijn de Fransen namelijk helemaal klaar met Trump en zijn dreigementen en zetten ze zwaar geschut is. Meerdere landen willen meedoen, al is een deel enorm bang voor een escalerende handelsoorlog. Een slagveld, zo is de handelsdag voor Renault het best te omschrijven. De topman vertrekt en laat het bedrijf achter met beroerde cijfers. Beleggers schrikken zo erg van die resultaten (en de vooruitblik), dat het aandeel Renault een van de ergste dagen ooit beleefd. Verder hebben we het over Jensen Huang van Nvidia, die is zich aan het inlikken in China. Scott Bessent hoeft zich dan weer niet in te likken bij president Trump. Die ligt zo goed bij hem, dat hij hem openlijk noemt als de nieuwe Fed-baas. Sluiten we af met goed nieuws. Goldman Sachs overtreft alles en iedereen. Schrijft zelfs geschiedenis!See omnystudio.com/listener for privacy information.

In this episode, Jeremie and Edouard Harris, co-founders of Gladstone AI and national security advisors, join us to break down the real score in the U.S.–China AI race. We unpack what it actually means to “win” in AI: from cutting-edge model development and compute infrastructure to data center vulnerabilities, state-sponsored espionage, and the rise of robotic warfare. The Harris brothers explain why energy is the hidden battleground, how supply chains have become strategic liabilities, and why export controls alone won't save us. This is not just a geopolitical showdown - it's a race for superintelligence, and the clock is ticking. ------

Daniel Mahncke and Shawn O'Malley turn their focus to Taiwan Semiconductor Manufacturing Company (ticker: TSM) — the quiet engine powering nearly every device we touch and the global AI boom. Pioneering the pure-play foundry model, TSMC went from a government-backed experiment in the late 1980s to controlling more than 90 % of the world's leading-edge chip production, fabricating Apple's A- and M-series processors, Nvidia's AI GPUs, and virtually every 3nm part on the planet. In this episode, you'll learn how Morris Chang's radical bet rewired the semiconductor industry, why extreme scale and EUV mastery give TSMC a moat rival chipmakers can't cross, how its Arizona and Japan fabs fit into a strategy shaped by China-Taiwan tension, and how wafer volumes, node mix, and multi-billion-dollar capex translate into free-cash-flow and ROIIC. Daniel and Shawn also debate the right way to price the ever-present geopolitical “wipe-out” tail risk, and ask whether today's market price offers a margin of safety on what could be the most important company in the world. Prefer to watch? Click ⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠ to watch this episode on YouTube. IN THIS EPISODE, YOU'LL LEARN 00:00 - Intro 07:39 - How TSMC evolved from a government-backed project to a trillion-dollar company 07:45 - How the semiconductor industry works 16:10 - Where TSMC's dominance comes from 31:27 - What moat protects TSMC's business 39:54 - What the competitive landscape looks like 43:55 - Where future growth is coming from 49:45 - How geopolitical risk is impacting TSMC 1:03:25 - Whether TSMC is attractively valued at its current levels 1:09:18 - Whether Shawn & Daniel add TSM to The Intrinsic Value Portfolio And much, much more! *Disclaimer: Slight timestamp discrepancies may occur due to podcast platform differences. BOOKS AND RESOURCES Get smarter about valuing businesses in just a few minutes each week through our newsletter, ⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠. Sign Up for ⁠⁠The Intrinsic Value Community.⁠ Interview with TSMC Founder Morris Chang Quartr's Overview of TSMC NZS Semiconductor Whitepaper Check out our previous Intrinsic Value breakdowns: ⁠⁠⁠⁠⁠⁠⁠Nintendo⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠Airbnb⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠AutoZone⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠Alphabet⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠Ulta⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠John Deere⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠Madison Square Garden Sports⁠⁠⁠⁠⁠⁠⁠. Check out the books mentioned in the podcast ⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠. Enjoy ad-free episodes when you subscribe to our ⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠X (Twitter)⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠. Browse through all our episodes (complete with transcripts) ⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠. Try Shawn's favorite tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠TIP Finance⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠⁠sponsors⁠⁠: • Airbnb⁠ Connect with Shawn: ⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠Email⁠⁠⁠⁠⁠⁠⁠ Connect with Daniel: ⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠Email⁠⁠⁠⁠⁠⁠⁠ HELP US OUT! Help us reach new listeners by leaving us a ⁠⁠⁠⁠⁠⁠⁠rating and review⁠⁠⁠⁠⁠⁠⁠ on ⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠! It takes less than 30 seconds and really helps our show grow, which allows us to bring on even better guests for you all! Thank you – we really appreciate it!  Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

For nearly forty years, Chemically Amplified Resists or CARs (say it separately) dominated the semiconductor industry. But technological changes present opportunities for major industrial shifts. And the introduction of EUV is perhaps the biggest technological change in the past 10 years. EUV's rise has opened the door to a perhaps-revolutionary new photoresist, and two very interesting new players supporting them. In this video, we talk about the new metal oxide and dry resists, developed for the EUV era and beyond.

For nearly forty years, Chemically Amplified Resists or CARs (say it separately) dominated the semiconductor industry. But technological changes present opportunities for major industrial shifts. And the introduction of EUV is perhaps the biggest technological change in the past 10 years. EUV's rise has opened the door to a perhaps-revolutionary new photoresist, and two very interesting new players supporting them. In this video, we talk about the new metal oxide and dry resists, developed for the EUV era and beyond.

While attending an ASML presentation at SEMICON Korea, I noticed a tidbit on one of the slides. A key part of their future EUV roadmap has been growing the power source to 800 watts or more. But it had not been super clear to me how they were going to do that, other than maybe raising the speed of the tin pellets. Then I saw the slide and it showed something new, at least to me. Three laser pulses rather than two. Now how about that? No, this is not an Onion article. This looks to be real. In this video, I want to talk about lasers and EUV light sources. And how three is better than two.

While attending an ASML presentation at SEMICON Korea, I noticed a tidbit on one of the slides. A key part of their future EUV roadmap has been growing the power source to 800 watts or more. But it had not been super clear to me how they were going to do that, other than maybe raising the speed of the tin pellets. Then I saw the slide and it showed something new, at least to me. Three laser pulses rather than two. Now how about that? No, this is not an Onion article. This looks to be real. In this video, I want to talk about lasers and EUV light sources. And how three is better than two.

When the semiconductor industry first started on EUV lithography, almost everyone believed that the optics would be the hardest things to do. Wavefront error—deviations of a light wave from its ideal—comes from imperfections in mirrors and lenses. A good lens aims for lambda/10 error; EUV optics must hit lambda/50. With EUV's 13.5 nm wavelength, that's 260 picometers. For context, a water molecule is 275 pm wide. That's the total error budget—for the entire six-mirror system. Because errors add in quadrature, each mirror gets 106 pm rms. But mirrors double light deviation on reflection, so surface accuracy must be halved: 53 pm rms. That's the radius of a hydrogen atom. It is 20 times harder for an EUV system with six mirrors to achieve the same wavefront performance of a DUV system with 60 surfaces. In this video, we go back to the machine you guys all know and love (again) and the finest multilayer mirrors ever made in history.

Social media is passing around an announcement that Huawei is testing a China-domestically developed EUV machine. This machine uses an EUV light source known as Laser-induced Discharge Plasma, or LDP. This is in contrast to ASML's method, which is called the Laser Produced Plasma or LPP method. It is claimed that LDP is much more efficient than LPP. Smaller, simpler and with better energy efficiency. Has ASML just been DeepSeek'd? I have been asked to speak on this via email and Twitter. I guess I have to do it. There is so little out there about how this machine works, so I am not going to speculate. But people have tried LDP before and we can talk about that. Feel free to extrapolate from there. In today's cope video, let's take a look at the Laser-Induced Discharge Plasma EUV light source.

Our modern world depends on chips, and our most modern chips depend on complex machines that use a process called extreme ultraviolet lithography. All of these machines are made by one company: ASML. The WSJ's Ben Cohen met one of the machine's mechanics and got a rare, behind-the-scenes tour of a factory where one of them operates. He explains the nearly sci-fi tech behind EUV lithography and gives us a peek into the one tool responsible for all the tech in your life. Sign up for the WSJ's free Technology newsletter.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome back to another insightful clip of Market Mondays! In this clip, our hosts Rashad Bilal and Ian Dunlap dive deep into the fascinating world of semiconductor technology and its critical importance in the AI industry. We kick things off with a thorough discussion about Nvidia, one of the most innovative and high-performing companies in the tech world. Over recent years, Nvidia has made significant strides in the development of GPUs (Graphics Processing Units) and AI technologies. With their groundbreaking advancements, they have established themselves as one of the leading companies shaping the future of technology.Rashad provides a comprehensive explanation of the intricate relationship between Nvidia, TSM (Taiwan Semiconductor Manufacturing Company), and ASML. TSM is the manufacturer behind Nvidia's cutting-edge chips, but they rely on ASML for the production of crucial lithography machines — specifically the EUV (Extreme Ultraviolet Lithography) machines. These machines are essential for creating wafers with 3 and 5 nanometer counts, which are vital for the efficiency and speed of GPUs.Ian further elaborates on the technical intricacies, highlighting the importance of these nanometer measurements and why companies like Nvidia opt for them to ensure superior performance. Together, Rashad and Ian explain how this tight-knit supply chain functions, emphasizing the necessity for all parties involved to be operating smoothly for the continued success of tech giants like Nvidia and AMD.Rashad also raises a critical point about the geopolitical aspects of semiconductor manufacturing. He highlights that while TSM has a significant presence in Taiwan, the establishment of a new foundry in Arizona, USA, is an encouraging step towards reducing dependency on imports. This development, tied to the larger CHIP Act, shows how political actions are directly influencing the tech landscape.Towards the latter part of the discussion, Ian shares some investment tips. He advises viewers to set entry points for high growth tech stocks at 25% from the top, as well as the importance of patience in the investment process. Rashad concurs, offering a slightly different metric range of 15-20% and pointing out the potential mistakes new investors might need to avoid, particularly chasing stocks that have already peaked.In an exciting twist, Ian demonstrates the utility of using AI tools like ChatGPT for investment research. By querying ChatGPT about ASML's monopoly on EUV machines, viewers can clearly see the critical role ASML plays in the semiconductor industry and why it's a dominant force that's hard to compete with.If you're an investor, tech enthusiast, or someone interested in the future of AI and semiconductor technologies, this episode is packed with invaluable insights and practical tips. Don't miss out on understanding the nuances of the tech supply chain, the impact of geopolitical developments, and strategic investment advice!Be sure to LIKE, COMMENT, and SUBSCRIBE for more industry-shaping discussions every week. Share your thoughts with us in the comments below – what are your predictions for the future of semiconductors and AI? *Hashtags:*#MarketMondays #Nvidia #TSM #ASML #Semiconductors #AI #Technology #InvestmentTips #GPUs #EUV #TechIndustry #Investing #StockMarket #ChipsAct #TechTalk #QuantumComputingTune in to stay ahead of the curve with Market Mondays!Support this podcast at — https://redcircle.com/marketmondays/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Welcome to another insightful clip of Market Mondays! Today, we're diving deep into ASML, a pivotal player in the semiconductor industry that has remained somewhat under the radar despite its enormous influence. Join hosts Rashad Bilal, Ian Dunlap, and Troy Millings as they dissect the role of ASML in the chipmaking ecosystem and what the future holds for this tech giant.ASML is known for its near-monopoly on EUV (Extreme Ultraviolet) lithography technology, which is critical in creating the most advanced semiconductors. Although we frequently discuss industry giants like Nvidia and Taiwan Semiconductor Manufacturing Company (TSMC), it's crucial to understand the importance of the equipment that makes their success possible. And that's where ASML comes in.#### Key Points Discussed:- **ASML's Market Position**: Rashad introduces ASML as a gateway company vital to the technology ecosystem, highlighting its near-monopoly on EUV technology.- **Market Trends**: Ian and Troy discuss the current state of the semiconductor market, including a "chip recession," and how ASML is faring amidst these challenges.- **Price Predictions and Investment Strategy**: Ian provides an analysis of ASML's price fluctuations and offers insights on potential entry points for investors, emphasizing the value of being strategic, especially amidst market volatility.- **Technological Importance**: Troy elaborates on ASML's critical role in the semiconductor supply chain, particularly in creating nanometer chips that companies like Nvidia and TSMC rely on.- **Long-term Outlook**: The hosts discuss the future prospects of ASML, including the potential for geopolitical tensions affecting its market, but they also affirm its integral role in the continuing advancements in AI and chipmaking.#### Why ASML Matters:- **Monopoly on Cutting-edge Tech**: ASML's technology is indispensable for manufacturing the next generation of semiconductors, positioning it as a linchpin in the tech supply chain.- **Investment Potential**: Despite a 30% drop from its pandemic high, the hosts see significant long-term value in ASML, making it a compelling consideration for any tech-focused portfolio.- **Geopolitical Factors**: As a Dutch company, ASML operates within a complex landscape of international trade, which could either present opportunities or pose challenges, depending on the geopolitical climate.ASML is not a straightforward investment. Ian likens its volatility to that of Tesla, where the highs can be incredibly rewarding, but the lows can also be gut-wrenching. That's why knowing when to enter and exit positions is crucial.Moreover, the discussion highlights the broader implications of ASML's technology for the AI market and future chip innovations. For those interested in the mechanics of the tech supply chain and long-term investment strategies, this episode is a must-watch.Stay plugged into Market Mondays for more expert analyses and in-depth discussions on the key players and trends shaping the financial landscape. Make sure to like, comment, and subscribe for more valuable content.Hashtags:#MarketMondays #ASML #Investing #Semiconductors #TechStocks #Nvidia #TSMC #EUVTechnology #StockMarket #FinancialEducation #InvestFest #IanDunlap #RashadBilal #TroyMillings #AI #ChipRecession #LongTermInvestmentDon't miss this crucial episode as we peel back the layers on ASML, a company that may not be in the spotlight as much as its peers, but is undoubtedly a cornerstone in the world of technology and finance. Tune in to Market Mondays and stay ahead of the curve!Support this podcast at — https://redcircle.com/marketmondays/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy