Podcasts about cpus

In this episode of The Effortless Podcast, Dheeraj Pandey speaks with Dr. Abhishek Bhowmick about how quantum mechanics reshaped our understanding of determinism and why that shift matters for AI today.  From the Einstein–Bohr debates to the idea that nature is fundamentally probabilistic, they explore how the collapse of “if-then” thinking began nearly a century ago. The discussion draws parallels between quantum superposition and modern LLM behavior. At its core, the episode reframes AI as a rediscovery of how reality computes. The conversation then moves from physics to computing architecture, tracing the evolution from scalar CPUs to GPUs, TPUs, tensors, and eventually quantum computing. They examine why probabilistic systems and vector math feel more natural than purely deterministic software. Hybrid computing models show that classical systems still matter. The episode also unpacks what quantum computers are truly good at, especially in cryptography and simulation. Ultimately, it reflects on whether the future of computing lies in embracing probability rather than resisting it. Key Topics & Timestamps 00:00 – Welcome, context, and how Dheeraj & Abhishek met 04:00 – Abhishek's journey: IIT, Princeton, Apple, Snowflake 08:00 – The 1927 Solvay Conference and physics at a crossroads 12:00 – Einstein vs. Bohr: determinism vs. probability 16:00 – Superposition and the collapse of the wave function 20:00 – Fields vs. particles: what is an electron really? 25:00 – Matter particles, force particles, and the Standard Model 30:00 – Transistors, voltage, and the rise of deterministic computing 35:00 – From scalar CPUs to vectors and matrices 40:00 – Tensors, linear algebra, and modern AI systems 45:00 – Principle of Least Action and gradient descent parallels 50:00 – Hallucinations, probability mass, and LLM behavior 55:00 – Vector databases, embeddings, and KNN search 59:00 – GPUs vs. TPUs: matrix vs. tensor architectures 1:05:00 – What quantum computers are actually good at 1:10:00 – Post-quantum cryptography and the future of computing Host -  Dheeraj Pandey Co-founder & CEO at DevRev. Former Co-founder & CEO of Nutanix. A systems thinker and product visionary focused on AI, software architecture, and the future of work. Guest -  Dr Abhishek Bhowmick                                                                                                                                                                                                                Co-Founder and CTO of Samooha, a secure data collaboration platform acquired by Snowflake. He previously worked at Apple as Head of ML Privacy and Cryptography, System Intelligence, and Machine Learning, and earlier at Goldman Sachs. He attended Princeton University and was awarded IIT Kanpur's Young Alumnus Award in 2024. Follow the Host and Guest - Dheeraj Pandey: LinkedIn - https://www.linkedin.com/in/dpandey Twitter - https://x.com/dheeraj Abhishek Bhowmik  LinkedIn –  https://www.linkedin.com/in/ab-abhishek-bhowmick Twitter/X – https://x.com/bhowmick_ab Share Your Thoughts Have questions, comments, or ideas for future episodes?

What does it take to design a programming language from scratch when the target isn't just CPUs, but GPUs, accelerators, and the entire AI stack? In this episode, I sit down with legendary language architect Chris Lattner to talk about Mojo — his ambitious attempt to rethink systems programming for the machine learning era. We trace the arc from LLVM and Clang to Swift and now Mojo, unpacking the lessons Chris has carried forward into this new language. Mojo aims to combine Python's ergonomics with C-level performance, but the real story is deeper: memory ownership, heterogeneous compute, compile-time metaprogramming, and giving developers precise control over how AI workloads hit silicon. Chris shares the motivation behind Modular, why today's AI infrastructure demands new abstractions, and how Mojo fits into a rapidly evolving ecosystem of ML frameworks and hardware backends. We also dig into developer experience, safety vs performance tradeoffs, and what it means to build a language that spans research notebooks all the way down to kernel-level execution.

“I think that for geophysicists out there, people need to realize that it's an integrated career path. You can't separate the geophysics from the HPC anymore, if we ever did to begin with.” High-performance computing is becoming more important as seismic data grows in size and complexity. This episode highlights the January The Leading Edge special section on high-performance computing. Guest editors Madhav Vyas and Elizabeth L'Heureux share their perspective on GPUs, CPUs, AI tools, and better algorithms in geophysics, and they stress that future success depends on combining geophysical knowledge with strong computational skills. KEY TAKEAWAYS > Modern seismic imaging depends on both advanced physics and powerful, well-chosen computing hardware. > Data movement and system architecture can limit performance as much as raw processing speed. > Geophysicists increasingly need programming and computational science skills alongside domain expertise. LINKS * Read the January 2026 special section, High-performance computing in geophysics - https://pubs.geoscienceworld.org/tle/issue/45/1 * Introduction to this special section: High-performance computing in geophysics by Madhav Vyas; Elizabeth L'Heureux; Raj Gautam - https://doi.org/10.1190/tle-4501-SS01 ABOUT SEISMIC SOUNDOFF Seismic Soundoff showcases conversations addressing the challenges of energy, water, and climate. Produced by the Society of Exploration Geophysicists (SEG) and hosted by Andrew Geary of 51 features, these episodes celebrate and inspire the geophysicists of today and tomorrow. Three new episodes monthly. See the full archive at https://seg.org/resources/podcast/.

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

What actually happens when AI stops being a cloud-only experiment and starts running on desks, in labs, and inside real teams trying to ship real work? In this episode, I sit down with Logan Lawler, Senior Director at Dell Technologies, to unpack how AI workloads are really being built and supported on the ground today. Logan leads Dell's Precision and Pro Max AI Solutions business and hosts Dell's own Reshaping Workflows podcast, giving him a rare vantage point into how engineers, developers, creatives, and data teams are actually working, not how marketing slides suggest they should be. We start by cutting through the noise around AI PCs. At every conference stage, Logan breaks down what genuinely matters when choosing hardware for AI work. CPUs, GPUs, NPUs, memory, and software stacks all play different roles, and misunderstanding those roles often leads teams to overspend or underspec. Logan explains why all AI workstations qualify as AI PCs, but not all AI PCs are suitable for serious AI work, and why GPUs remain central for anyone doing real model development, fine-tuning, or inference at scale. From there, the conversation shifts to a broader architectural rethink. As AI workloads grow heavier and data sensitivity increases, many organizations are reconsidering where compute should live. Logan shares how GPU-powered Dell workstations, storage-rich environments, and hybrid cloud setups are giving teams more control over performance, cost, and data. We explore why local compute is becoming attractive again, how modern GPUs now rival small server setups, and why hybrid workflows, local for development and cloud for deployment, are becoming the default rather than the exception. One of the most compelling parts of the discussion comes when Logan connects hardware choices back to business reality. Drawing on real-world examples, he explains how teams use local AI environments to move faster, reduce cloud costs, and avoid getting locked into architectures that are hard to unwind later. This is not about abandoning the cloud, but about being intentional from the start, mainly as AI usage spreads beyond developers into marketing, operations, and everyday business roles. We also step back to reflect on a deeper challenge. As AI becomes easier to use, what happens to critical thinking, curiosity, and learning? Logan shares a candid perspective, shaped by his experiences as a parent, technologist, and podcast host, raising questions about how tools should support rather than replace thinking. If you are trying to make sense of AI PCs, local versus cloud compute, or how teams are really reshaping workflows with AI hardware today, this conversation offers grounded insight from someone living at the center of it. Are we designing systems that genuinely empower people to think better and build faster, or are we sleepwalking into decisions we will regret later? How do you want your own AI workflow to evolve? Useful Links TLDR AI newsletter and the Neurons. The Reshaping Workflows podcast Connect with Logan Lawler Follow Dell Technologies on LinkedIn

RAM, SSDs, GPUs, CPUs — the tech building blocks behind everything from laptops to media players — are getting harder to source and more expensive by the week. On What's Next, host Aki Anastasiou is joined by Craig Nowitz (CEO) and Ryan Martyn (Co-founder, Sales & Marketing Director) from Syntech Distribution to unpack what's really behind the supply crunch — and why it's not a “COVID-style” blip. They explain how hyperscalers racing to build AI infrastructure are soaking up global capacity, pushing manufacturers toward higher-margin enterprise memory, and triggering sharp price increases that filter straight into South Africa's consumer and corporate IT budgets. The conversation also gets practical: what procurement teams should prioritise in 2026, why Windows 11 hardware readiness has become a security issue, and how businesses can adapt by moving faster, planning smarter, and considering alternative hardware choices. If you're responsible for IT upgrades — or you're wondering whether to buy now or wait — this episode lays out the market reality and the smartest moves to avoid getting caught out.

Man hat schon damit gerechnet: Valve verschiebt die Steam Machine, natürlich wegen der anhaltenden Speicherkrise. Spätestens jetzt wäre ein konkreter Termin und ein Preis zu verkünden gewesen, stattdessen heißt es jetzt erste Jahreshälfte. Als kleiner Trost bestätigt Valve, weiter an VRR über HDMI zu arbeiten und daneben auch an einem verbesserten Upscaler. Damit kann eigentlich nur FSR 4 gemeint sein. Inoffizielle Implementierungen von FSR 4 (oder FSR AI wie es inzwischen heißt) für RDNA 2 und RDNA 3 gibt es auf Linux schon länger über Forks von Proton oder auf Windows über Optiscaler. Eine offizielle Version für die älteren Architekturen, oder zumindest für RDNA 3, wäre aber sehr begrüßenswert. Und überfällig. AMDs CEO Dr. Lisa Su hat im Conference Call zu den letzten Geschäftszahlen wohl Microsoft überrumpelt und verkündet, dass der SoC für die nächste Xbox 2027 bereit sei. Grundsätzlich sind bisher auch alle (bis auf André Peschke) davon ausgegangen, dass die nächsten Konsolen 2027 erscheinen würden. Aber dann kam die ganze Sache mit dem Speicher. Und jetzt ist vermutlich auch Microsoft nicht mehr sicher, ob die nächste Xbox 2027 erscheinen wird. Viel Spaß mit Folge 294! Sprecher:innen: Meep, Michael Kister, Mohammed Ali DadAudioproduktion: Michael KisterVideoproduktion: Mohammed Ali Dad, Michael KisterTitelbild: Mohammed Ali DadBildquellen: Valve/Bild von katermikesch auf PixabayAufnahmedatum: 07.02.2026 Besucht unsim Discord https://discord.gg/SneNarVCBMauf Bluesky https://bsky.app/profile/technikquatsch.deauf Youtube https://www.youtube.com/@technikquatsch https://www.youtube.com/@technikquatschgamingauf TikTok https://www.tiktok.com/@technikquatschauf Instagram https://www.instagram.com/technikquatschauf Twitch https://www.twitch.tv/technikquatsch RSS-Feed https://technikquatsch.de/feed/podcast/Spotify https://open.spotify.com/show/62ZVb7ZvmdtXqqNmnZLF5uApple Podcasts https://podcasts.apple.com/de/podcast/technikquatsch/id1510030975Deezer https://www.deezer.com/de/show/1162032 00:00:00 Herzlich willkommen zu Technikquatsch Folge 294! 00:02:52 Valve verschiebt Steam Machine, arbeitet an HDMI VRR und besserem Upscaler.https://store.steampowered.com/news/group/45479024/view/625565405086220583?l=english 00:10:50 Chinesische Hersteller von Speicherchips rücken in den Fokus.http://winfuture.de/news,156633.html 00:19:49 CPU-Tests als Vergleich zwischen RAM im Dual Channel und mit einem Riegelhttps://www.computerbase.de/artikel/arbeitsspeicher/ram-ein-modul-intel-core-ultra-200s-test.95998/ 00:24:55 SoC für neue Xbox werde 2027 bereit sein laut AMD. Ob Microsoft bereit sein wird, ist fraglich.https://www.computerbase.de/news/gaming/next-gen-konsole-amd-nennt-einen-moeglichen-starttermin-fuer-die-naechste-xbox.96024/ 00:30:29 Nachtrag zu Kernfusion und ITERhttps://www.simplyscience.ch/teens/wissen/strom-aus-kernfusionhttps://www.iter.org/ 00:38:03 Börse unruhig wegen Auswirkungen von AI auf SaaS-Unternehmen.https://www.reuters.com/business/media-telecom/global-software-stocks-hit-by-anthropic-wake-up-call-ai-disruption-2026-02-04/ 00:47:40 Kursstürze von Gaming-Unternehmen wie Take Two nach Release von Google Genie.https://bsky.app/profile/jasonschreier.bsky.social/post/3me7ii5loxs2z 01:02:27 Mo schaut: Es: Welcome to Derryhttps://www.imdb.com/title/tt19244304/ 01:11:54 Hinweis: Onimusha 2: Samurai’s Destiny auf Technikquatsch Gaminghttps://www.youtube.com/watch?v=-8iWiB3DxlM

There have been a lot of features added to the SQL Server platform over the years. Several of these features let us perform functions that are beyond what a database has traditionally been designed to handle. SQL Server has had the ability to send emails,  execute Python/R/etc. code, and in SQL Server 2025, we can call REST endpoints. Quite a few of these features (arguably) are more application-oriented than database-oriented. There's nothing inherently wrong with having a server perform some of these functions, and there have been some very creative implementations using these features. I recently ran into one of these examples from Amy Abel, where she shows how to use the new REST endpoint feature to call an AI LLM to generate and send emails from your database server. That's creative, and it's reminiscent of the numerous examples from various experts over the years who demonstrate how these features can be used to accomplish a task. Read the rest of Expensive CPUs

Nach ersten Vorab-Benchmarks zur CES haben sich Volker und Jan Intel Core Ultra 300 inzwischen im Detail angesehen und Fabian und Jan steigen diese Woche direkt mit diesem Test in den Podcast ein. Nicht zu unrecht, denn die Ergebnisse haben es stellenweise wirklich in sich. Oder war Intels Marketing nur einfach clever? Im Anschluss geht es um den Test des Ryzen 7 9850X3D und was das Takt-Upgrade für den 9800X3D zu leisten im Stande ist. Und auch das nächste Thema dreht sich um "X3D": Bekanntlich sind diese CPUs dank großem Cache weniger anfällig, was RAM-Bandbreite und -Latenz anbelangt. Kann man aktuell also Geld sparen und nur auf ein DDR5-Module setzen (Single-Channel)? Volker hat sich das angesehen und Fabian und Jan schließen sich seinem Urteil an. Mit einem Update zu den steigenden Grafikkarten-Preisen schließen die beiden diese Episode ab. Viel Spaß beim Zuhören!

Ryan Debenham, CEO of Grin, shares his unconventional journey from software engineer to leading a nearly billion-dollar creator management platform. In this candid conversation, Ryan reveals how he "accidentally" became a CEO by following challenges rather than titles, and why that mindset shift transformed how he builds products and companies.He discusses the critical disconnect between engineering and go-to-market teams, the revolutionary potential of AI agents in influencer marketing, and why democratizing influence could unlock a massive untapped market. Ryan also shares insights from his time at Qualtrics (acquired by SAP for $8B) and Route, offering practical wisdom on connecting product teams to revenue outcomes and building AI that feels "alive."Key Takeaways[4:30] - The Accidental CEO Path: Ryan explains how becoming a CEO was never his plan—he loved building products but never built companies around them. His career evolved by chasing challenges rather than titles or money.[10:30] - The Product-to-Company Graveyard: Ryan candidly shares how his early product ideas (including a ride-sharing concept 20 years ago and a photo categorization tool) died because he focused only on building, not on solving the hard business problems.[12:15] - The Mindset Shift: The biggest change from engineering to CEO? When revenue numbers became Ryan's responsibility, he finally understood what customers truly needed—not just what they said they wanted.[14:30] - Breaking Down Silos: Ryan discusses why the tension between product, engineering, marketing, and sales "will kill the business" and how he's connecting these departments at the hip.[19:30] - The Qualtrics Lesson: A powerful story about spending six months building the wrong text analytics product at Qualtrics, despite sitting next to customers repeatedly. The lesson: understanding business needs requires deeper connection than just listening to feature requests.[26:00] - AI as Electricity: Ryan's compelling analogy comparing LLMs to the development of electricity and CPUs—powerful building blocks that are worthless alone but transformational when paired with the right infrastructure.[28:30] - Mandatory AI Adoption: Ryan required all engineers at Grin to use AI coding tools. One engineer quit over the pressure but came back, realizing it was a mistake. His prediction: in a few years, you won't get hired as an engineer if you don't know AI tools.[32:00] - Building Software That's "Alive": Ryan describes Gia, Grin's AI agent that journals daily, runs standups with other agents, creates action items, and can discuss what she's learning and what features should be built next.[35:00] - The Influencer Marketing Problem: Why Grin's growth stalled—aspirational customers bought the software but failed at influencer marketing because the operational complexity was too high, leading to churn.[38:30] - The Two-Sided Platform Gap: Most influencer platforms built for merchants and forgot creators. Ryan explains why supporting creators is the most important part of the solution.[44:30] - Democratizing Influence: Ryan's vision that "everybody is an influencer"—the real opportunity is capturing and rewarding the micro-influence that happens in everyday conversations between millions of people.[49:00] - The Collision Course: Why affiliate marketing and influencer marketing are merging into something new—it's all about capturing word-of-mouth at different scales.Tweetable...

AI is moving fast. But the data foundations underneath it are not. That is the core theme of my latest interview with Rajan Goyal, Founder and CEO of DataPelago on The Ravit Show, recorded at their office in Mountain View.Here is what we unpacked -- Why now- Three shifts are colliding at once. Hardware acceleration is now mainstream. Generative AI has changed how data is created and consumed. Data complexity has exploded beyond what existing systems were designed to handle. The result is clear. Enterprises do not just need faster systems. They need a more unified data foundation.Where the tension is- AI models are advancing quickly, from multimodal systems to agents and domain-specific LLMs. But the data infrastructure beneath them is still built for an analytics-first world. Most companies spend more time moving data between systems than actually innovating with it.What DataPelago is building- We spent time breaking down DataPelago Nucleus, described as the world's first universal data processing engine. One engine that can handle batch, streaming, relational, vector, and tensor workloads together. The key idea is simple but powerful. Ingest, transform, and query data without constantly moving it across systems.We also talked about what makes their approach different.- A DataOS layer that intelligently maps workloads across CPUs, GPUs, and other accelerators.A DataApp layer that plugs into engines like Spark and Trino.And DataVM, a data-focused virtual machine that unifies execution across heterogeneous hardware.Why Spark acceleration matters- For teams running Spark today, we discussed the DataPelago Accelerator for Spark. It runs existing Spark workloads on accelerated compute with zero code changes. Faster joins, shuffles, preprocessing, and lower cost, without rewriting pipelines.Why today's stack is breaking- Warehouses, lakes, and lakehouses were built for SQL analytics. AI workloads need tight coupling between data and compute. The separation we see today leads to redundant pipelines, silos, and expensive data movement. Many teams are forced to optimize for analytics or AI, but not both.Why DataPelago was founded and what customers see- The founding insight was clear. Data systems were never designed for AI-scale throughput. Customers adopting this approach are unifying analytics and AI pipelines on one platform, simplifying infrastructure while improving performance, governance, and observability. Rajan made an interesting comparison. This shift for data processing is similar to what GPUs did for compute.What's next- We closed by talking about how the data and AI relationship will evolve over the next few years, and what this looks like in real-world deployments. That is what the next episode will dive into.If you are building AI systems and still relying on analytics-era data foundations, this one is worth your time.#data #ai #gpu #datapelago #lakehouse #sql #analytics #theravitshow

Recorded at CES 2026, this special episode of the Washington AI Network Podcast examines how quantum computing and AI tools are moving from theory into real-world use.Host Tammy Haddad interviews Pouya Dianat, chief revenue officer of Quantum Computing Inc., about what quantum computing is—and is not—and its implications for encryption, national security, finance, and data centers. Dianat explains why quantum systems are designed to complement classical computing and how quantum processing units will operate alongside CPUs and GPUs.The episode also features a conversation with the Las Vegas stars of YouTube's Iced Coffee Hour, Graham Stephan and Jack Selby.

Wilder Lopes is the CEO and Founder of Ogre.run, working on AI-driven dependency resolution and reproducible code execution across environments.How Universal Resource Management Transforms AI Infrastructure Economics // MLOps Podcast #357 with Wilder Lopes, CEO / Founder of Ogre.runJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractEnterprise organizations face a critical paradox in AI deployment: while 52% struggle to access needed GPU resources with 6-12 month waitlists, 83% of existing CPU capacity sits idle. This talk introduces an approach to AI infrastructure optimization through universal resource management that reshapes applications to run efficiently on any available hardware—CPUs, GPUs, or accelerators.We explore how code reshaping technology can unlock the untapped potential of enterprise computing infrastructure, enabling organizations to serve 2-3x more workloads while dramatically reducing dependency on scarce GPU resources. The presentation demonstrates why CPUs often outperform GPUs for memory-intensive AI workloads, offering superior cost-effectiveness and immediate availability without architectural complexity.// BioWilder Lopes is a second-time founder, developer, and research engineer focused on building practical infrastructure for developers. He is currently building Ogre.run, an AI agent designed to solve code reproducibility.Ogre enables developers to package source code into fully reproducible environments in seconds. Unlike traditional tools that require extensive manual setup, Ogre uses AI to analyze codebases and automatically generate the artifacts needed to make code run reliably on any machine. The result is faster development workflows and applications that work out of the box, anywhere.// Related LinksWebsite: https://ogre.runhttps://lopes.aihttps://substack.com/@wilderlopes https://youtu.be/YCWkUub5x8c?si=7RPKqRhu0Uf9LTql~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Wilder on LinkedIn: /wilderlopes/Timestamps:[00:00] Secondhand Data Centers Challenges[00:27] AI Hardware Optimization Debate[03:40] LLMs on Older Hardware[07:15] CXL Tradeoffs[12:04] LLM on CPU Constraints[17:07] Leveraging Existing Hardware[22:31] Inference Chips Overview[27:57] Fundamental Innovation in AI[30:22] GPU CPU Combinations[40:19] AI Hardware Challenges[43:21] AI Perception Divide[47:25] Wrap up

This week, we're joined by Ivan Burazin, co-founder of Daytona - a company rethinking developer environments for an AI-native world.We talk about how Daytona creates real value for developers, why the most advanced agent companies are emerging bottom up, and the idea that agents should be treated as first class users with reliable access to compute. Ivan also shares some of Daytona's most demanding use cases including AI scientists in chemistry and pharma running agents inside massive sandboxes with hundreds of CPUs.We also cover what reliability looks like when customers define success as whether the system works at all, how Daytona maintains sub 90 millisecond latency while spinning up millions of environments per day, and how they support always on usage with no fixed schedule. Finally, we dig into go to market lessons from putting engineers on the front lines early to why Daytona prioritizes in person engagement and intentional events as the most authentic way to build trust with developers.Episode chapters:1:43 – Market timing and why now3:18 – Building for developers and dev tools4:55 – Global developer communities7:55 – Computers as infrastructure for agents10:32 – Product-led growth12:17 – Enterprise use cases and adoption16:25 – Managing cost, performance, and latency19:20 – Hiring for resiliency at scale20:40 – Internal AI use cases at Daytona24:25 – Creating a bottom-up go-to-market motion27:42 – Hiring and scaling developer relations29:55 – Partnerships and ecosystem strategy31:00 – Quick-fire round This episode is brought to you by Grata, the leading deal sourcing platform for private equity. Grata's AI powered search, investment grade data, and intuitive workflows help you find and win the right deals faster. Visit grata.com to book a demo.This episode is also sponsored by Overlap, the AI powered app that uses LLMs to surface the best moments from any podcast. Overlap reads full transcripts, finds the most relevant clips, and stitches them into a personalized stream of insights. Tap into podcasts as a real information source with Overlap 2.0, now available on the App Store.

In this episode of the Crazy Wisdom podcast, host Stewart Alsop sits down with Peter Schmidt Nielsen, who is building FPGA-accelerated servers at Saturn Data. The conversation explores why servers need FPGAs, how these field-programmable gate arrays work as "IO expanders" for massive memory bandwidth, and why they're particularly well-suited for vector database and search applications. Peter breaks down the technical realities of FPGAs - including why they "really suck" in many ways compared to GPUs and CPUs - while explaining how his company is leveraging them to provide terabyte-per-second bandwidth to 1.3 petabytes of flash storage. The discussion ranges from distributed systems challenges and the CAP theorem to the hardware-software relationship in modern computing, offering insights into both the philosophical aspects of search technology and the nuts-and-bolts engineering of memory controllers and routing fabrics.For more information about Peter's work, you can reach him on Twitter at @PTRSCHMDTNLSN or find his website at saturndata.com.Timestamps00:00 Introduction to FPGAs and Their Role in Servers02:47 Understanding FPGA Limitations and Use Cases05:55 Exploring Different Types of Servers08:47 The Importance of Memory and Bandwidth11:52 Philosophical Insights on Search and Access Patterns14:50 The Relationship Between Hardware and Search Queries17:45 Challenges of Distributed Systems20:47 The CAP Theorem and Its Implications23:52 The Evolution of Technology and Knowledge Management26:59 FPGAs as IO Expanders29:35 The Trade-offs of FPGAs vs. ASICs and GPUs32:55 The Future of AI Applications with FPGAs35:51 Exciting Developments in Hardware and BusinessKey Insights1. FPGAs are fundamentally "crappy ASICs" with serious limitations - Despite being programmable hardware, FPGAs perform far worse than general-purpose alternatives in most cases. A $100,000 high-end FPGA might only match the memory bandwidth of a $600 gaming GPU. They're only valuable for specific niches like ultra-low latency applications or scenarios requiring massive parallel I/O operations, making them unsuitable for most computational workloads where CPUs and GPUs excel.2. The real value of FPGAs lies in I/O expansion, not computation - Rather than using FPGAs for their processing power, Saturn Data leverages them primarily as cost-effective ways to access massive amounts of DRAM controllers and NVMe interfaces. Their server design puts 200 FPGAs in a 2U enclosure with 1.3 petabytes of flash storage and terabyte-per-second read bandwidth, essentially using FPGAs as sophisticated I/O expanders.3. Access patterns determine hardware performance more than raw specs - The way applications access data fundamentally determines whether specialized hardware will provide benefits. Applications that do sparse reads across massive datasets (like vector databases) benefit from Saturn Data's architecture, while those requiring dense computation or frequent inter-node communication are better served by traditional hardware. Understanding these patterns is crucial for matching workloads to appropriate hardware.4. Distributed systems complexity stems from failure tolerance requirements - The difficulty of distributed systems isn't inherent but depends on what failures you need to tolerate. Simple approaches that restart on any failure are easy but unreliable, while Byzantine fault tolerance (like Bitcoin) is extremely complex. Most practical systems, including banks, find middle ground by accepting occasional unavailability rather than trying to achieve perfect consistency, availability, and partition tolerance simultaneously.5. Hardware specialization follows predictable cycles of generalization and re-specialization - Computing hardware consistently follows "Makimoto's Wave" - specialized hardware becomes more general over time, then gets leapfrogged by new specialized solutions. CPUs became general-purpose, GPUs evolved from fixed graphics pipelines to programmable compute, and now companies like Etched are creating transformer-specific ASICs. This cycle repeats as each generation adds programmability until someone strips it away for performance gains.6. Memory bottlenecks are reshaping the hardware landscape - The AI boom has created severe memory shortages, doubling costs for DRAM components overnight. This affects not just GPU availability but creates opportunities for alternative architectures. When everyone faces higher memory costs, the relative premium for specialized solutions like FPGA-based systems becomes more attractive, potentially shifting the competitive landscape for memory-intensive applications.7. Search applications represent ideal FPGA use cases due to their sparse access patterns - Vector databases and search workloads are particularly well-suited to FPGA acceleration because they involve searching through massive datasets with sparse access patterns rather than dense computation. These applications can effectively utilize the high bandwidth to flash storage and parallel I/O capabilities that FPGAs provide, making them natural early adopters for this type of specialized hardware architecture.

In this episode, Ben Bajarin and Jay Goldberg discuss the highlights from CES 2023, focusing on the significant advancements in robotics, AI infrastructure, and the competitive landscape among major tech companies like NVIDIA, AMD, and Intel. They explore the themes of modularity in data centers, the evolving role of CPUs, and the challenges posed by memory supply constraints. The conversation also touches on the future of autonomous vehicles and the integration of AI in everyday technology, emphasizing the rapid pace of innovation in the tech industry.

What if we told you that CES did not feature any new GPUs?  But it did feature more frames!  MSI with LIGHTNING and GPU safeguard, Phison's new controller, and that wily AMD with new Ryzen 7 9850X3D (and confirmed Ryzen 9 9950X3D2) - whee!  Remember the Reboot computer generated cartoon?  Remember D-Link Routers and Zero Days?  Remember Intel?  It's all here! That and everything old is new again with Old GPUs and CPUs coming back .. because RAM.Thanks again to our sponsor with CopilotMoney!  Get on your single pane of financial glass and bring order to your money and spending - it's even actually fun to save again.  Get the web version and use our code for 26% off at http://try.copilot.money/pcperTimestamps:0:00 Intro00:56 Patreon01:37 Food with Josh04:10 AMD announces Ryzen 7 9850X3D05:41 AMD sort of confirmed the 9950X3D207:00 NVIDIA DLSS 4.509:34 Intel was at CES12:50 MSI LIGHTNING returns14:54 MSI also launching GPU Safeguard Plus PSUs19:44 WD_Black is now Sandisk Optimus GX Pro21:54 Phison has the most efficient SSD controller26:11 ASUS ROG RGB Stripe OLED28:44 First computer-animated TV show restored33:29 Podcast sponsor - Copilot Money34:57 (In)Security Corner44:32 Gaming Quick Hits1:06:31 Picks of the Week1:24:08 Outro ★ Support this podcast on Patreon ★

In this episode, host Seth Earley welcomes Brandon Lucia, CEO of Efficient Computer, for a deep dive into how AI advancements are reshaping the future of computing—particularly with a focus on energy efficiency, sustainable infrastructure, and real-world applications.Brandon Lucia brings almost 20 years of experience in computer architecture, having served as an academic at Carnegie Mellon University and led significant research at the boundary of hardware and software innovation. He and his team have pioneered a new kind of hardware architecture designed to drastically reduce power consumption for AI workloads without sacrificing performance or versatility. Their work has far-reaching implications for data centers, edge AI, robotics, automotive, and large-scale infrastructure monitoring.Key Takeaways from this Episode:AI's energy demands are accelerating rapidly and require rethinking not just bigger models, but architectural efficiency at every level.Effective AI infrastructure goes beyond mathematical optimization (like linear algebra); it includes real-world complexity and physical deployment.Specialized hardware architectures (CPU, GPU) are evolving, but general-purpose solutions with built-in efficiency—like those from Efficient Computer—can unlock new application domains.Edge computing and “physical AI” (as distinguished from legacy IoT) require extremely efficient processing to enable long device lifetimes and advanced capabilities.Efficient Computer's chips offer exponential gains in energy efficiency compared to market-leading CPUs and embedded GPUs—sometimes up to hundreds of times better.Enterprises should focus on hardware-software co-design and apply principles like Amdahl's Law: you are limited by what you can't optimize, so balancing all types of computation is critical.Fine-grained personalization and retraining of AI at the edge will be increasingly important for future applications.Organizations that deal in manufacturing, logistics, automotive, infrastructure, or robotics stand to benefit greatly from advances in efficient hardware and architecture.Insightful Quote from the Show:"We're not going to meet these energy requirements with the existing hardware and software—we have to change." - Seth Earley"We are vastly ahead of our competition when it comes to energy consumption. Batteries last longer. You can do more under a power cap. You're not limited by thermal constraints. Those convert directly into capabilities into lifetime. So you can do more than you could do today." - Brandon LuciaTune in for a conversation that not only explores the technical side of AI hardware, but also the practical, business, and societal impacts of powering tomorrow's intelligent systems with greater efficiency.LinksLinkedIn: https://www.linkedin.com/in/brandon-lucia-0767792/Website: https://www.efficient.computer/Thanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

The AI supercycle is expanding beyond just GPUs. In our first episode of the 2026 series, we break down the critical infrastructure that acts as the "roads and freeways" for data: data center networking, optics, and silicon photonics.Logic chips (like CPUs and GPUs) are the "office" where work gets done, but the network is the "commute" that moves that data. Without advanced cabling, transceivers, and switches, AI clusters simply cannot function.Find out what companies are involved in this fast growing market and how to approach investing in them. Join us on Discord with Semiconductor Insider, sign up on our website: www.chipstockinvestor.com/membershipSupercharge your analysis with AI! Get 15% of your membership with our special link here: https://fiscal.ai/csi/Sign Up For Our Newsletter: https://mailchi.mp/b1228c12f284/sign-up-landing-page-short-formChapters:00:00 - Investing in Chip Stocks 2026 01:43 - The "Roads" of AI: What is Data Center Networking? 02:46 - Copper vs. Fiber Optics: The Differences 03:59 - Market Size: Logic vs. Optoelectronics Sales 05:32 - The Cable Kings: Amphenol, Corning & CommScope 08:12 - Light Sources: Coherent, Lumentum & Broadcom 11:15 - Signal Integrity: Re-timers (Astera Labs, Credo) & DSPs 15:16 - Transceivers: Nvidia, Jabil & Intel 17:18 - Switching, Routing & The Full Stack (Broadcom, Marvell) 18:48 - Investment Strategy: Niche Players vs. Supply Chain ControllersIf you found this video useful, please make sure to like and subscribe!*********************************************************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.#Semiconductors #ChipStocks #AIInvesting #DataCenter #SiliconPhotonics #Nvidia #Broadcom #OpticalNetworking #TechStocks #Investing2026Nick and Kasey own shares of a Nvidia, Broadcom, Credo, Amphenol and a number of others mentioned in the video.

Try OCI for free at http://oracle.com/eyeonai  This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload – where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking.  Join Modal, Skydance Animation, and today's innovative AI tech companies who upgraded to OCI…and saved. Why is AI moving from the cloud to our devices, and what makes on device intelligence finally practical at scale? In this episode of Eye on AI, host Craig Smith speaks with Christopher Bergey, Executive Vice President of Arm's Edge AI Business Unit, about how edge AI is reshaping computing across smartphones, PCs, wearables, cars, and everyday devices. We explore how ARM v9 enables AI inference at the edge, why heterogeneous computing across CPUs, GPUs, and NPUs matters, and how developers can balance performance, power, memory, and latency. Learn why memory bandwidth has become the biggest bottleneck for AI, how ARM approaches scalable matrix extensions, and what trade offs exist between accelerators and traditional CPU based AI workloads. You will also hear real world examples of edge AI in action, from smart cameras and hearing aids to XR devices, robotics, and in car systems. The conversation looks ahead to a future where intelligence is embedded into everything you use, where AI becomes the default interface, and why reliable, low latency, on device AI is essential for creating experiences users actually trust. Stay Updated: Craig Smith on X: https://x.com/craigss     Eye on A.I. on X: https://x.com/EyeOn_AI 

GPUs dominate today's AI landscape, but Google argues they are not necessary for every workload. As AI adoption has grown, customers have increasingly demanded compute options that deliver high performance with lower cost and power consumption. Drawing on its long history of custom silicon, Google introduced Axion CPUs in 2024 to meet needs for massive scale, flexibility, and general-purpose computing alongside AI workloads. The Axion-based C4A instance is generally available, while the newer N4A virtual machines promise up to 2x price performance.In this episode, Andrei Gueletii, a technical solutions consultant for Google Cloud joined Gari Singh, a product manager for Google Kubernetes Engine (GKE), and Pranay Bakre, a principal solutions engineer at Arm for this episode, recorded at KubeCon + CloudNativeCon North America, in Atlanta. Built on Arm Neoverse V2 cores, Axion processors emphasize energy efficiency and customization, including flexible machine shapes that let users tailor memory and CPU resources. These features are particularly valuable for platform engineering teams, which must optimize centralized infrastructure for cost, FinOps goals, and price performance as they scale.Importantly, many AI tasks—such as inference for smaller models or batch-oriented jobs—do not require GPUs. CPUs can be more efficient when GPU memory is underutilized or latency demands are low. By decoupling workloads and choosing the right compute for each task, organizations can significantly reduce AI compute costs.Learn more from The New Stack about the Axion-based C4A: Beyond Speed: Why Your Next App Must Be Multi-ArchitectureArm: See a Demo About Migrating a x86-Based App to ARM64Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.  Visit https://agntcy.org/ and add your support. Why is AI so powerful in the cloud but still so limited inside everyday devices, and what would it take to run intelligent systems locally without draining battery or sacrificing privacy? In this episode of Eye on AI, host Craig Smith speaks with Steve Brightfield, Chief Marketing Officer at BrainChip, about neuromorphic computing and why brain inspired architectures may be the key to the future of edge AI. We explore how neuromorphic systems differ from traditional GPU based AI, why event driven and spiking neural networks are dramatically more power efficient, and how on device inference enables faster response times, lower costs, and stronger data privacy. Steve explains why brute force computation works in data centers but breaks down at the edge, and how edge AI is reshaping wearables, sensors, robotics, hearing aids, and autonomous systems. You will also hear real world examples of neuromorphic AI in action, from smart glasses and medical monitoring to radar, defense, and space applications. The conversation covers how developers can transition from conventional models to neuromorphic architectures, what role heterogeneous computing plays alongside CPUs and GPUs, and why the next wave of AI adoption will happen quietly inside the devices we use every day. Stay Updated: Craig Smith on X: https://x.com/craigss  Eye on A.I. on X: https://x.com/EyeOn_AI  

In this episode, Zain Asgar, co-founder and CEO of Gimlet Labs, joins us to discuss the heterogeneous AI inference across diverse hardware. Zain argues that the current industry standard of running all AI workloads on high-end GPUs is unsustainable for agents, which consume significantly more tokens than traditional LLM applications. We explore Gimlet's approach to heterogeneous inference, which involves disaggregating workloads across a mix of hardware—from H100s to older GPUs and CPUs—to optimize unit economics without sacrificing performance. We dive into their "three-layer cake" architecture: workload disaggregation, a compilation layer that maps models to specific hardware targets, and a novel system that uses LLMs to autonomously rewrite and optimize compute kernels. Finally, we discuss the complexities of networking in heterogeneous environments, the trade-offs between numerical precision and application accuracy, and the future of hardware-aware scheduling. The complete show notes for this episode can be found at https://twimlai.com/go/757.

Cloudlflare asks Amazon to hold it's virtual "beer", HEVC and H.265 support being removed from CPUs ... sorta, Steam Machine priced like lobster and will Intel bLLC compete with AMD 3D-vCache?  But we mostly complain about DDR5 in this exciting episode!00:00 Intro00:30 Patreon01:44 Food with Josh04:10 We talk about the DDR5 problem for the third week in a row18:44 TSMC confirmed September power outage at Arizona fab23:01 Dell and HP removing HEVC from some laptops26:33 Unpowered SSDs slowly lose data32:32 Is Intel bLLC really an X3D competitor?34:13 (in)Security Corner42:01 Gaming Quick Hits46:18 Picks of the Week1:06:12 Outro ★ Support this podcast on Patreon ★

Much attention has been focused in the news on the useful life of GPUs. While the pervasive narrative suggests GPUs have a short lifespan, and operators are “cooking the books,” our research suggests that GPUs, like CPUs before, have a significantly longer useful life than many claim.

In this episode, we welcome Lead Principal Technologist Hari Kannan to cut through the noise and tackle some of the biggest myths surrounding AI data management and the revolutionary FlashBlade//EXA platform. With GPU shipments now outstripping CPUs, the foundation of modern AI is shifting, and legacy storage architectures are struggling to keep up. Hari dives into the implications of this massive GPU consumption, setting the stage for why a new approach is desperately needed for companies driving serious AI initiatives. Hari dismantles three critical myths that hold IT leaders back. First, he discusses how traditional storage is ill-equipped for modern AI's millions of small, concurrent files, where metadata performance is the true bottleneck—a problem FlashBlade//EXA solves with its metadata-data separation and single namespace. Second, he addresses the outdated notion that high-performance AI is file-only, highlighting FlashBlade//EXA's unified, uncompromising delivery of both file and object storage at exabyte scale and peak efficiency. Finally, Hari explains that GPUs are only as good as the data they consume, countering the belief that only raw horsepower matters. FlashBlade//EXA addresses this by delivering reliable, scalable throughput, efficient DirectFlash Modules up to 300 TB, and the metadata performance required to keep expensive GPUs fully utilized and models training faster. Join us as we explore the blind spots in current AI data strategies during our "Hot Takes" segment and recount a favorite FlashBlade success story. Hari closes with a compelling summary of how Pure Storage's complete portfolio is perfectly suited to provide the complementary data management essential for scaling AI. Tune in to discover why FlashBlade//EXA is the non-compromise, exabyte-scale solution built to keep your AI infrastructure running at its full potential. For more information, visit: https://www.pure.ai/flashblade-exa.html Check out the new Pure Storage digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 04:30 Primer on FlashBlade 11:32 Stat of the Episode on GPU Shipments 13:25 What is FlashBlade//EXA 18:58 Myth #1: Traditional Storage Challenges for AI Data 22:01 Myth #2: AI Workloads are not just File-based 26:42: Myth #3: AI Needs more than just GPUs 31:35 Hot Takes Segment

Shawn Tierney meets up with Mark Berger of Siemens to learn how Siemens integrates SIRIUS ACT devices (push buttons, selector switches, pilot lights) with PROFINET in this episode of The Automation Podcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 253 Show Notes: Special thanks to Mark Berger of Siemens for coming on the show and sending us a sample! Read the transcript on The Automation Blog: (automatically generated) Shawn Tierney (Host): Thank you for tuning back in to the automation podcast. My name is Shawn Tierney from Insights. And today on the show, we have a special treat. We have Mark Berger back on from Siemens to bring us up to speed on serious act. He’s gonna tell us all about the product, and then we’re even gonna do a small demo and take a look at it working live. So with that said, let’s go ahead and jump into this episode with Mark Burger from Siemens and learn all about their push buttons on PROFINET. Mark, it’s been a while since you’ve been on the show. Thank you for coming back on and agreeing to talk about this. Mark Berger (Siemens): Oh, thank you so much. I truly appreciate you letting me be on. I appreciate your channel, and I enjoy watching it. And I’m excited to show you some of this great technology. So I’ve got, the PowerPoint up here. We’ll just do a simple PowerPoint to kinda give you an overview, and then we’ll dive into the hardware. Shawn Tierney (Host): Appreciate it. Thank you. Mark Berger (Siemens): No problem. So as we stated, the Sirius X over PROFINET, let me emphasize that, the, actuators, the push buttons, the estops, the selector switches, they are all standard, when you use these. So if you have those on the shelf, the only thing that PROFINET does is that it adds, removes the normal contact blocks and adds the PROFINET, terminal blocks on the back. So every all the actuators that we’re showing are just standard actuators for the 22 millimeter push button line. So easy to use, modern design, performance and action, and extremely rugged and flexible. The, 22 millimeter is out of the box IP 69 k, which for those who are maybe in the food and beverage, verticals would understand what that is. And that’s for direct hose down, wash down, able to handle a high pressure washing and not able to leak past the actuator into the panel. So IP 69 k is a a great place for dust and wash down and hosing and where you’re having rain and so forth, to be able to protect for a keep of any, water passing into the panel. So introduction wise, it’s, the PROFINET push buttons for us. It it is, again, the same actuators, the same, connections, and so forth, but what we’re going to exchange is the terminal blocks, for it. So on there, I stated it’s, IP 69 k is standard. You don’t need any, extra covers forward or anything to fulfill that requirement, But it’s, it’s insensitive to dust and oil and caustic solutions, you know, like citric acid where you’re hosing down some stainless steel parts and so forth. Now what we have here is, changing out the terminal blocks that have wiring. So usually on a push button, you have two wires coming in, and then you have, for illuminated, you have two wires coming in and so forth and going out. And after you have 20 or 30 push buttons or 10 or 15 push buttons, you’ve got a substantial little bit of wiring or cabling that will be passing from the door over into the main cabinet of your control cabinet. What we’re going to do with PROFINET push buttons is we’re going to eliminate all that wiring. And then in addition, eliminate the input and output cards that you will need for your PLC and take it down to a pro, Ethernet cable, an r j r j 45 cable, and then down to a 24 volts. And that’s all that will pass from the cabinet onto the door where you’re mounting your push buttons. So, huge, safe and cost of wires. We’re reducing all the wire outlay. And, you know, back in the day when I build panels, it was an art how you got all the wires all nice and pretty and got them laid out and wire tied them down and so forth and just made the a piece of art on the backside. And then, it it was all done. You got it all wired. And then, of course, there was somebody that said, hey. We forgot to add another selector switch. So you had to go back and cut all that stuff and redo the whole layout and so forth. So with PROFINET, it’s extremely flexible and easily, to adapt to if you need something, more because you’re not taking all that wiring back to the panel, passing it across the hinge of the door and so forth. It is also with a safety PLC. You do have PROFIsafe, so we can do estops on the door as you can see here in the picture, but then we can do non safe applications also. So today, we’ll be just doing some non safe applications. And then the communications again is PROFINET. But then also just to touch real quick, we do have it on IO Link and on Aussie with our push buttons. So what is SiriusACT with PROFINET? There we go. So what you have is the first, block or interface module that you put on the back of your push button, that’s where the, Ethernet is plugged into and your 24 volts is plugged into. And then after that, subsequently, then the push buttons that you have is that you have what we call a terminal module. And in between the, the interface module to a terminal module or from terminal module to terminal module, you can go up to one meter of cabling, and it’s a ribbon cable. And we’ll show that here shortly. And then if you have up to we can do up to 20 push buttons, terminal modules, with a total of 21 push buttons. And then so from the first interface module all the way to the last push button, you can go up to 10 meters. And then it gives, again, 24 volt power supply for it. And we have, again, as I stated, as nonsafe, talking just PROFINET, and then the safety version, talking PROFISAFE on PROFINET. So serious act, we can go up on the the safety up to seal three and performance level e as an echo. We have, again, the the standard interface module without safety. You have the PLC, the interface module, and then the subsequent terminal modules for it. And then the cabling that goes from the interface module to out to the terminal modules is a simple ribbon cable that comes into the back of the terminal modules. The only tool that you need is simply it’s just a screwdriver, and, you, push it into the terminal module, push down. It uses, vampire connections, insulation displacement, vampire connections, and you push it down in. There’s no stripping of the wires. There’s no mix up. The indicator you can see on the wires here in a minute will show you that there’s a little red line that shows you, which way it, enters into the terminal, and then that’s it. It’s very straightforward. It’s, very simple with tools. And, as I stated, it’s, just like a normal push button that you’d put on, but then we’re gonna add, remove the contact block and add the terminal module or the interface module in the place of the contact block. Just to emphasize again, we can do PROFISAFE on, with a safety PLC and a safety controller, and we can give you all the safety, requirements for the either the ISO or the IEC specifications for safety out there in the field. Here’s some of the part numbers. First one, of course, is the interface module, and that has the ability to do PROFIsafe. It has also, additionally, four digital inputs, one digital output, and then one analog input. And we’ll talk about that a little bit more just in a few minutes. And then the non safe version, 24 volts. You have a, two versions of this one, one with just with just a standard, 24 volts input, but then there’s an additional one that has the four digital in, one digital out, and one analog in. So there’s two different part numbers. One where you don’t need the additional, digital inputs and outputs and analog, and then the and then the part number with the the additional inputs and outputs. But the safety one comes there’s no other version, just the one. Then you have what we call the terminal modules, and there’s three versions. One terminal module is just the command module only. It’s mounted with two mechanical signaling blocks to signal. So you have two contact blocks built in. Then you have one that’s a terminal module with the command, the terminal blocks, and then also an integrated LED. And then you can put what color you want the LED to be, and you can see there the the part number changed for red, blue, amber, so on. And then you have a just an LED module to where it’s no contactors. It’s just LED. And, I think with our demo we’re gonna show today, we’re just gonna show the contact block and LED module and only the LED module today. There’s some other, accessories with the safety. There’s a memory module to where that you, is all the configurations are put into the memory module, and something happens to that interface module. Everything’s put in there, the IP address, the configuration, and everything. If something gets broke and so forth or you have to replace it, you pull the memory module out, put the new terminal or interface module in, plug in the memory module, cycle the power, and it’s up and running. All the configurations, the IP address, everything’s already there. And then on the interface module, it does not come with an LED, so you’re required to buy this this, LED right here if you need it for it, and that’s what you use for the interface module. And then, of course, the ribbon cable that goes between the interface module to the terminal block or terminal module and terminal module and so forth come in five meter length and 10 meter length. K. So what’s it provide for you? Well, the benefits are, I’ll I’ll be very blunt. If it’s just one or two buttons on a panel, it won’t be that cost effective. Yes. We’re reducing the IO, the IO inputs and outputs, but for the savings, it’s not the best. Now when you get up to about three or four push buttons, then that cost saving is, very realized. Now when you go up to 20 push buttons, yes, you’re saving a lot of money, especially in the IO cards that you’re not gonna be required to have. And then, of course, all the wiring and the labor, getting it all wired up and doing all the loop checks to make sure that when you push this button, it’s wired into the right terminal block on the IO card, so on and so forth. So about, the break is about two to three push buttons to where it will become very cost effective for you to use it. But like I said yesterday, without PROFINET push buttons, it was all the wiring you brought across and putting them into all your IO cards and so forth. And now with PROFINET push buttons, all that goes away, and all you’re bringing across is an Ethernet cable and 24 volts positive and 24 volts negative across that hinge into the door. And that’s it. K. And then emphasizing again, we can do PROFIsafe and those, push buttons and estops. The estop can be part of your safety circuit and give you the, safety levels that you’re required from either sill and or performance level safeties depending on the specification, IEC, or ISO that you’re following within your plant. K? And then hardware configuration. Now this is where we step into reduction of engineering and helping you guys get going, quicker and making sure engineering is done properly. You know, back in the day, we’d wire up all the wires, coming from the push buttons, you know, a selector switch, a start button, stop button, indicator lights, and so forth. And and all those wires sometimes just, you know, the what we’re working with, all the wires look the same. You’ve put labels on them. You may have labeled it wrong, and you wired into an input card or an output card. So there’s some time where you’re over there doing some loop checks where you’re trying to say, yes. That’s coming into input byte dot bit, and that should be the selector switch. Well, with the PROFINET push buttons, we’re able to not have to worry about that, and we’re gonna demonstrate that just here in a minute. But you also have a full lineup of the push buttons coming into portal so that you can see the lineup and verify that it is the parts that you want. In TI portal, you can see that, of course, the first, button is the interface module, and then sequentially is the terminal modules that have either just contactors, LED and contactors, or just LEDs. And we’ll we’ll show that just here momentarily. But it’s all integrated into TIA portal. It has a visual representation of all the push buttons, and it’s simple and fast, to, configure. We’ll show you that here in just a moment. And there’s no addressing, for it. So some of the stuff that you have out there, you have addressing, making sure what the address is right, and so on. This is a standardized data management, and it’s extremely time saving and engineering saving for, the user. Shawn Tierney (Host): Well, let me ask you a question about that. If the snow addressing, do the items show up, in the order that they’re wired? In other words, you know, you’re daisy chasing the you’re you’re going cable to cable from device to device. Is that the order that they show up? Mark Berger (Siemens): That’s exactly right. Shawn Tierney (Host): Okay. Mark Berger (Siemens): So if you don’t know which ones are what, you just literally put run your hand from the interface module, follow that cable, and the next one that will be visually saw in portal will be the one that it lands on first. Perfect. And then there’s a cable that leaves that one and goes into the next one, daisy chained, and then that’s what’ll be represented in that lineup. And here in just a minute, we’ll we’ll show that. Alright. Thank you for that question. Okay. Now once I got it wired up, how do I know that I got it wired properly? And we’re gonna show that here in just a minute. But just graphically wise, you have the ability to see if it is all wired up. You do not need to plug it into the PLC. This all it needs is 24 volts. The PLC can come later and plugging it in later and so forth. There’s no programming. This all comes out of the box. So once you plug it in, if all on the backside looking at the terminal blocks and the daisy chain ribbon cable, if it’s all green, you wired it up properly, and it’s working properly. But then if you see a red light flashing either at the terminal module because that will that will bubble up to the terminal module. So if you have a problem somewhere pardon me, the interface module. If you have some problem with the terminal modules, a push button like number two or three or four, it will bubble up into the, interface module to let it know, hey. We got a problem. Can you look to see where it’s at? And as you see here, we have maybe a device that’s defective. And so it bubbles up into the interface module to let you know, and a red light lets you know that we have maybe a defective module. You know, something hammered it pretty hard, or, it may have been miswired. Then the second one down below, we’ve got a wiring error to where you don’t have the green lights on the back and everybody else’s there’s no green light shown. That means you have a wiring error. Or if everything works great, it’s green lights across, but then the next level of this is is my push button working? So then we you’ll push or actuate the push button or actuate the selector switch, and the green light will flash to let you know that that terminal module or interface module is working properly. And we’ve done our our, loop checks right there before we’ve even plugged it into the PLC or your programmer has come out and sat down and worked with it. We can prove that that panel is ready to roll and ready to go, and you can set it aside. And if you got four or five of the same panel, you can build them all up, power it up, verify that it’s all green lights across the board. It is. Great. Set it down. Build up another one and go on from there. So it shows you fast fault detection without any additional equipment or additional people to come in and help you show you that. When we used to do loop checks, usually had somebody push the button, then yell at the programmer, hey. Is this coming in at I zero dot zero? Yeah. I see it. Okay. Or then he pushed another one. Hey. Is this coming in on I 0.one? No. It’s coming in on i0. Three. So there was that two people and then more time to do that loop check or the ring out as some people have called it. So in this case, you don’t need to do that, and you’ll see why here in just a minute. And then, again, if we do have an interface module that, maybe it got short circuited or something hit it, it you just pull the ePROM out, plug it into the new one, bring in the ribbon cable, and cycle the power, and you’re up and running. Alright. And then this is just some of the handling options of how it handles the data, with the projects and so forth, with basic setups, options that you can be handling with this, filling bottles. What we wanna make sure to understand is that if maybe push buttons, you can pick push buttons to work with whatever project you want it to do. So if you have six push buttons out there, two of them are working on one, bottle filling, and then the rest of them are working on the labeling, you can separate those push buttons. Even though that they’re all tied together via PROFINET, you can use them in different applications across your machine. Shawn Tierney (Host): You’re saying if I have multiple CPUs, I could have some buttons in light work with CPU one, PLC one, and some work with PLC two? Mark Berger (Siemens): Yep. There’s handling there. There’s programming in the backside that needs to be done, but, yes, that can happen. Yep. Oh, alright. So conclusion, integrated into TI portal. We’re gonna show that here in a minute. So universal system, high flexibility with your digital in, digital outs, analogs, quick and easy installation, one man, one hand, no special tooling, and then substantially reducing the wiring and labor to get it going. And then, again, integrated safety if, required for the your time. So with that, let’s, switch over to TI portal. So I’ve already got a project started. I just called it project three. I’ve already got a PLC. I’ve got our, new g, s seven twelve hundred g two already in. And then what I’m gonna do is I’ve, already built up the panel. And, Shawn, if you wanna show your panel right here. Shawn Tierney (Host): Yeah. Let me go ahead and switch the camera over to mine. And so now everybody’s seeing my overhead. Now do you want me to turn it on at this point? It’s off. Yeah. Yeah. Mark Berger (Siemens): Let’s do it. Shawn Tierney (Host): Gonna turn it on, and all the lights came on. So we have some push buttons and pilot lights here, but the push buttons are illuminated, and now they’ve all gone off. Do you want me to show the back now? Mark Berger (Siemens): Yep. So what we did there is that we just showed that the LEDs are all working, and that’s at the initial powering up of the 24 volts. Now we’re gonna switch over and, you know, open up the cabinet and look inside, and now we’re looking on the backside. And if you remember in the PowerPoint, I said that we’d have all green lights, the everything’s wired properly. And as you look, all the terminal modules all have green lights, and so that means that’s all been wired properly. If you notice, you see a little red stripe on the ribbon cable. That’s a indication. Yep. To show you that. And then if you look on the on the out on the, the interface module, Shawn, there’s it says out right there at the bottom. Yeah. There’s a little dot, and that dot means that’s where the red stripe goes, coming out. So that little dot means that’s where the red stripe comes. Yep. Right there. And that’s how it comes out. And then if you look just to the left a little bit, there’s another, in, and there’d be a red dot underneath that ribbon cable showing you how the red the the red goes into it. Notice that everything’s clear, so you can see that the wire gets engaged properly all the way in. And then all you do is take a screwdriver and push down, and then the vent, comes in. The insulation displacement comes in and, and, makes the connections for you. So there’s no strip tie cable stripping tools or anything special for doing that. Another item, just while we’re looking, if you look in the bottom left hand corner of that terminal module, you see kind of a a t and then a circle and then another t. That’s an indicator to let you know that that’s two contactors and an LED that you have on the backside. Shawn Tierney (Host): We’re talking about right here? Mark Berger (Siemens): Yep. Yep. Right there. Shawn Tierney (Host): Okay. Mark Berger (Siemens): So that’s an indicator to tell you what type of terminal block it is a terminal, block that it is. That’s two contactors and LED. And then if you look at one in the bottom left hand corner, there’s just a circle. That means you just have an LED. So you have some indicators to show you what you’re looking at and so forth. So today, we’re just using the two, LED only, and then we’re doing the contactor and LED combination. I I don’t have one there on your demo that’s just the contactor. So Shawn Tierney (Host): Now you were telling me about these earlier. Yeah. Mark Berger (Siemens): So yeah. The so if you look there on that second row of the terminal blocks, you have a UV and an AI, and I’ll show that in the schematic here in just a little bit, but there, that is a 10 volt output. If you put a 250 ohm or 250 k ohm, potentiometer and then bring that signal back into AI, you have an analog set point that comes in for it that will automatically be scaled zero to 1,000 count or zero to 10 volts. Mhmm. And then you can use that for a speed reference for a VFD. And it’s already there. All you have to do, you don’t have to scale it or anything. You can put it towards, you know, okay. Zero to 1,000 count means zero to 500 PSI or or zero to 100 feet per second on a conveyor belt, and I’m I’m just pulling numbers out. But that’s the only real scalability scaling you have to do. So it’ll be a zero to 1,000 count is what you’ll see instead of, like, yep. Then you got four digital ins that you can use and then a one digital out. Now the four, I, kinda inquired wife just four, but let’s say that you have a four position joystick. You could wire all four positions into that interface module, and then the output could be something else for a local horn that you want or something to that case with it. So you in addition to the, push buttons, you also have a small, distribution IO block right there in the in your panel. Shawn Tierney (Host): Which is cool. Yeah. I mean, maybe yeah. Like you said, maybe you have something else on the panel that doesn’t fit in with, you know, this line of push buttons and pilot lights like a joystick. Right? And that makes a lot of sense. You were saying too, if I push the button, I can test to see if it’s working. Mark Berger (Siemens): Correct. So if you yep. Go right ahead. Shawn Tierney (Host): I’m pushing that middle one right there. You can see it blinking now. Mark Berger (Siemens): And that tells you that the contacts have been made, and it’s telling you that the contacts work properly. Shawn Tierney (Host): And now I’m pushing the one below it. So that shows me that everything’s working. The contacts are working, and we’re good to go. Mark Berger (Siemens): Yep. Everything’s done. We’ve done the loop checks. We know that this is ready to be plugged into the PLC and handed off to whomever is going to be, programming the PLC and bring it in, in which means that we’ll go to the next step in the TI portal. Shawn Tierney (Host): Yeah. Let me switch back to you, and we’re seeing your TI portal now. Mark Berger (Siemens): Awesome. Okay. So I’ve got the PLC. I’ve plugged it in to if if I needed an Ethernet switch or I’ve plugged it directly into the PLC. Now I have just built up that panel. I haven’t had anything, done with it for an IP address because it is a TCP IP protocol. So we need to do a IP address, but it’s on PROFINET. And then I’m gonna come here to online access, and I wanna see that I can see it out there that I’m talking to it. So I’m gonna do update accessible devices. It’s gonna reach out via my, Ethernet port on my laptop. And then there’s our g two PLC and its IP address. So that’s that guy right here. Mhmm. And then I have something out there called accessible devices, and then this is its MAC address. So what I and I just have those two items on the network, but, you know, you could have multiples as, you know, with GI portal. We can put an entire machine in one project. So I come here and drop that down, and I go to online diagnostics. I I go online with it, but I don’t have really a lot here to tell me what’s going on or anything yet. But I come here, and I say assign IP address. And I call one ninety two, one sixty eight, zero zero zero, and zero ten zero, and then our usual 255, two fifty five, two fifty five, and then I say assign IP address. Give it a second. It’s gonna go out and tell it, okay. You’re it. Now I wanna see if it took, and you look right there, it took. And I’m I’m kinda anal, so I kinda do it again just to verify. Yep. Everything’s done. It’s got an IP address. Now I’m gonna come up, and I’m going to go to my project, and I’m gonna switch this to new network view. Here’s my PLC. I’m gonna highlight my project. Now there’s two ways I can go about it, and I’m sure, Shawn, you’ve learned that Siemens allows you to kinda do it multiple ways. I could come in here and go into my field devices, and I could come into my commanding and interface modules, and I’d start building my push button station. But we’re gonna be a little oh and ah today. We’re gonna highlight the project. I’m gonna go to online, and I’m gonna come down here to hardware detection and do PROFINET devices from network. Brings up the screen to say, hey. I want you to go out and search for PROFINET industrial Ethernet. Come out via my, NIC card from my laptop, and I want you to start search. Shawn Tierney (Host): For those of you who watched my previous episodes doing the e t 200 I o, this is exactly the same process we used for that. Mark Berger (Siemens): Yep. And I found something out there that I know I gave the IP address, but it doesn’t have a PROFINET name yet. So that’s okay. I’ve I got the IP address. We’ll worry about the PROFINET name. So we’ll hide check mark this, and this could be multiple items. Shawn Tierney (Host): Mhmm. Mark Berger (Siemens): K. So now add device. Shawn Tierney (Host): And this is the sweet part. Mark Berger (Siemens): And right here, it’s done. It went out, interrogated the interface module, and said, okay. Are you there? Yep. I’m here. Here’s my IP address. And it also shared with it all of come in here, double click on it now. Shawn Tierney (Host): The real time saver. Yep. Mark Berger (Siemens): Yep. And then now here’s all the push buttons in your thing. And let me zoom that out. It’s at 200%. Let’s go out to a 100. And now it already interrogated the interface module and all the terminal modules to tell me what’s in my demo. Yep. And again, as you stated in your questions, how do I know which one’s the next one? You just saw the ribbon cable Mhmm. And then it brings you so forth and so on. So that’s done. We’re good. I’m gonna go back to my network view, and I’m gonna say, hey. I want you to communicate via PROFINET to there, which I’m done. And then it also gives you here’s the PLC that you’re gonna do because, you know, if we have a big project, we may have four or five of these stations, and you wanna know which PLC is the primary PLC on it. And then we’ve done that. I’m going to quickly just do a quick compile. And next, I’m gonna come here. I’m gonna click here. Now I could just do download and and let the PROFINET name, which is here, go into it. But I’m gonna right click, and I’m gonna say assign device name and say update list. It’s gonna go interrogate the network. Takes a second. No device name assigned. No PROFINET name. So this is how we do that time determinism with PROFINET. So I’m gonna highlight it, and I say assign the name, and it’s done. Close. So now it has a PROFINET name and IP address. So now I’m able to go in here and hit download and load. And we’re going to stop because we are adding hardware, so we are putting the CPU in stop and hit finish. Now I always make sure I’m starting the CPU back up and then hit finish. And then I’m gonna go online, go over here and show network view, and go online. And I got green balls and green check marks all over the board, so I’m excited. This works out. Everything’s done. But now what about the IO? So now your programmer is already talking to it, but now I need to know what the inputs and outputs are. So go back offline, double click here, and then I’m gonna just quickly look at a couple things. The interface modules IO tags are in a different spot than the terminal modules. So just a little note. It’s right here. If you double click on integrated I LED, you click here and then go to properties and say IO tags. There it lists all of the inputs and outputs. So it comes here. But if I do a terminal module, click here, then once you just click on it in general oops. Sorry. In general, it’s right here in the IO addressing. There’s where it starts start the bytes, but then I come here to tags, and then here’s the listing. So the the the programs automatically already allocated the byte and the bit for each of these guys. So if I click there, there, click there, there’s it there, onward and upward. Now notice that the byte so if I click on position four, it is three. So it’s one one less because the base zero versus here, it’s five. Just give me a little bit of a so if you look in here, all that starts at I four dot zero. I four dot zero. So k. So that’s there. So I’m gonna come here. I’m gonna go to the selector switch for this, and I’ve called it s s one, and that’s input two dot zero. Then I’m gonna click here, and I’m gonna call this green push button. Notice there’s two inputs because I have one contactor here, one contactor there, and 30 and 31. So then what I’m gonna do is that I’m going to go over here to the PLC, and I’m gonna go to and it’s updated my PLC tag table. There you go. It’s in there. So then I’m gonna grab that guy. I’m gonna because portal pushes you to use two monitors. I’m gonna come here, go to the main OB, and then I’m gonna just grab a normally open contact, drag it on, drop it, put it in there we go. And then I’m gonna grab selector switch and drop that right there, and grab green LED and drop that right there, and then close that out and compile. And everybody’s happy. I’m gonna download and say yes. Okay. And then I’m gonna go online. Alright. So it’s waiting in for me to switch that, and there you go. And if you wanna see my screen there, Shawn, that’s the green light is turned on. Shawn Tierney (Host): Yeah. Let me switch over to Okay. Bring up your, alright. And could you switch it back off now? Mark Berger (Siemens): Yeah. No problem. Yep. So there we go. We switch it off. We switch it on. Now I wanna show you something kinda cool. If I turn that off and I come back here and I go offline Mhmm. I have a indicator light that needs to flash to let the operator know that there’s something here I need you to attend to. So we used to put in some type of timer. Right? Mhmm. Shawn Tierney (Host): Mhmm. Mark Berger (Siemens): And so what we would do here instead of that, I’m gonna come back down here to my tab and go to the hardware config. I’m gonna double click here. I’m gonna go to module parameters, and I’m gonna drop this down, and I’m gonna put it at two hertz. Also, just to point out, I can also do a normally open contact and a normally closed contact and switch them. You see right here. Cool. And I can control the brightness of the LED if it has an LED, and it’s all hard coded into it. So once I’ve done that, do a quick compile. I’m I mean, you know, I’ve always compile and then do download. Mhmm. Mhmm. So we’re gonna download that and hit load and finish. K. Here we go. Turn that on, and now it’s flashing. Shawn Tierney (Host): That’s great. So you have a timer built in. If you need to flash, you don’t have to go get a clock bit or create your own timer. Plus, if it’s a button, you can change the contacts from normally open to normally closed. That is very cool. Mark Berger (Siemens): Yep. And that is PROFINET push buttons. As I stated let me quickly pull that up. Remember, you pointed out just a few minutes ago, here is the wiring diagram for that. So here’s the back of that with the terminal blocks. And you come down here, and it shows you that you just wire in that, variable resistor or a potentiometer. And you see m and you there’s the 10 volts, and then the signal comes into a. And then that guy is right here. Excellent. So if you come here, you go to properties and IO tags, and it comes in on I 60 fours and input and IO tags, and then I could call that a pot. Yeah. And now you have a potentiometer that you can use as a a speed reference for your VFD. That is very cool. Engineering efficiency, we reduced wiring. We don’t have all the IO cards that is required, and we have the diagnostics. Emphasize that each of these here, their names, you can change those if you would like because this is your diagnostic string. So if something goes wrong here, then it would come up and say commanding. So you double click here, and we go here to general, and it’ll say commanding and underscore LED module two, or you can you can call that start conveyor p b. And then that would change this. Now see this changed it. This would be your diagnostic string to let you know if if that button got damaged or is not working properly. Shawn Tierney (Host): You know, I wanted to ask you too. If I had, let’s say I needed two potentiometers on the front of the enclosure, could I put another interface module in the system? Even if it didn’t have any push buttons on it or pilots on it, could I just put it in there to grab, some more IO? Mark Berger (Siemens): Yep. Yes, sir. I have a customer that he uses these as small little IO blocks. Shawn Tierney (Host): Yeah. I mean, if you just needed a second pot, it might make sense to buy another interface module and bring it into that than buying an analog card. Right? Assuming the resolution and everything was app you know, correct for your application, but that’s very cool. I you know, it it really goes in line with all the videos we’ve done recently looking at e t 200 I o, all the different flavors and types. And when you walk through here, you know, I’m just so especially, thankful that it reads in all the push buttons and their positions and pilot lights. Because if you have this on your desk, you’re doing your first project, you can save a lot of dragging and dropping and searching through the hardware catalog just by reading it in just like we can read in a rack of, like, e t 200 SPIO. Mark Berger (Siemens): Yep. Engineering efficiency, reducing wiring, reducing time in front of the PC to get things up and running. You saw how quickly just a simple push button and a and, you know, again, a simple start and turn that on and off the races we went. Shawn Tierney (Host): Well, Mark, I really wanna thank you. Was there anything else that we wanted to cover before we close out the show? Mark Berger (Siemens): Nope. That’s just about it. I think, we got a little bit to have your your viewers, think about for it. So I appreciate the time, and I really appreciate you allowing me to show this. I think this is a a really engineering efficiency way of going about using our push buttons and and, making everybody’s projects in a timely manner and getting everything done and having cost savings with it. Shawn Tierney (Host): Well, and I wanna thank you for taking the time out of your busy day, not only to put together a little demo like you have for me to use here in the school, but also to come on and show our audience how to use this. And I wanna thank our audience. This was actually prompted from one of you guys out there at calling in or writing in. I think it was on YouTube somewhere and saying, hey. Could you cover the PROFINET push buttons from Siemens? I didn’t even know they had them. So thanks to the viewers out there for your feedback that helps guide me on what you wanna see. And, Mark, this would not be possible if it wasn’t for your expertise. Thank you for coming back on the show. I really appreciate it. Mark Berger (Siemens): Thank you, Shawn. All the best. Thank you. Shawn Tierney (Host): I hope you enjoyed that episode. And I wanna thank Mark for taking time out of his busy schedule to put together that demo and presentation for us and really bring us up to speed on Sirius X. And I wanna thank the user out there who put a comment on one of my previous videos that said, hey. Did you know Siemens has this? Because I wouldn’t have known that unless you said that. So thank you to all you. I try to read the comments every day or at least every two days, and so I appreciate you all wherever you are, whether you’re on YouTube, the automation blog, Spotify, iTunes, Google Podcasts, and wherever you’re listening to this, I just wanna thank you for tuning in. And now with next week being Thanksgiving, we’ll have a pause in the automation show, then we have some more shows in December, and we’re already filming episodes for next year. So I’m looking forward to, releasing all those for you. And if you didn’t know, I also do another podcast called the History of Automation. Right now, it’s only available on video platforms, so YouTube, LinkedIn, and the automation blog. Hopefully, someday we’ll also do it on, audio as well. But, we’re meeting with some of the really legends in automation who worked on some of the really, you know, just really original PLCs, original HMIs, up and through, like, more modern day systems. So it’s just been a blast having these folks on to talk about the history of automation. And so if you need something to listen to during Thanksgiving week or maybe during the holidays, check out the history of automation. Again, right now, it’s only available on YouTube, the automation blog, and LinkedIn, but I think you guys will enjoy that. And I wanna wish you guys, since I won’t be back next week, a very happy Thanksgiving. I wanna thank you always for tuning in and listening, and I also wanna wish you all good health and happiness. And until next time, my friends, peace. Until next time, Peace ✌️  If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

In today's Cloud Wars Minute, I delve into OpenAI's $38 billion partnership with AWS, giving Amazon a major role in powering and scaling OpenAI's AI workloads.Highlights0:03 — OpenAI and AWS have announced a multi‑year strategic partnership valued at $38 billion for AWS. This deal will enable AWS to provide the infrastructure necessary to support the operation and scaling of OpenAI's AI workloads. OpenAI is currently utilising computing resources through AWS, which include hundreds of thousands of NVIDIA GPUs and the capability to scale up to tens of millions of CPUs.01:02 — The infrastructure rollout for OpenAI includes architecture optimised for maximum AI processing efficiency and performance, with clusters designed to support a variety of workloads such as inference for ChatGPT and model training. This latest deal is yet another staggering example of the demand for AI services — a demand that companies like OpenAI must invest billions in to keep up with the pace.01:55 — OpenAI recently signed several significant deals with technology partners, including a remarkable $300 billion agreement with Oracle. While that figure might seem outrageous, it puts the $38 billion into a more relatable context. One thing is clear: wherever you stand in the AI revolution, whatever your role is — just make sure that you have one, because this unprecedented growth is touching every corner of the business world. Visit Cloud Wars for more.

** AWS re:Invent 2025 Dec 1-5, Las Vegas - Register Here! **Learn how Anyscale's Ray platform enables companies like Instacart to supercharge their model training while Amazon saves heavily by shifting to Ray's multimodal capabilities.Topics Include:Ray originated at UC Berkeley when PhD students spent more time building clusters than ML modelsAnyscale now launches 1 million clusters monthly with contributions from OpenAI, Uber, Google, CoinbaseInstacart achieved 10-100x increase in model training data using Ray's scaling capabilitiesML evolved from single-node Pandas/NumPy to distributed Spark, now Ray for multimodal dataRay Core transforms simple Python functions into distributed tasks across massive compute clustersHigher-level Ray libraries simplify data processing, model training, hyperparameter tuning, and model servingAnyscale platform adds production features: auto-restart, logging, observability, and zone-aware schedulingUnlike Spark's CPU-only approach, Ray handles both CPUs and GPUs for multimodal workloadsRay enables LLM post-training and fine-tuning using reinforcement learning on enterprise dataMulti-agent systems can scale automatically with Ray Serve handling thousands of requests per secondAnyscale leverages AWS infrastructure while keeping customer data within their own VPCsRay supports EC2, EKS, and HyperPod with features like fractional GPU usage and auto-scalingParticipants:Sharath Cholleti – Member of Technical Staff, AnyscaleSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

While all the focus is on the massive AI Data Center deals with OpenAI and Oracle, which could generate over $100 billion in revenue over ther course of 3 years , the near-term profit story is all about CPUs, PC/laptop stabilization, and EPYC. The shift to maximizing revenue growth—even with a potential 10% share dilution from the OpenAI equity award —is a big change for AMD investors. Chip Stock Investor breaks down the numbers, the massive GPU/MI400 series deployment risk , the surprising profit centers in Q3, and an updated Reverse DCF valuation. Join us on Discord with Semiconductor Insider, sign up on our website: www.chipstockinvestor.com/membershipSupercharge your analysis with AI! Get 15% of your membership with our special link here: https://fiscal.ai/csi/Sign Up For Our Newsletter: https://mailchi.mp/b1228c12f284/sign-up-landing-page-short-formChapters:0:00:00 Intro: The AMD AI Pivot and Q3 Earnings 0:00:58 Data Center Segment: CPU is the Biggest Profit Driver 0:01:54 CEO Lisa Su: Epyc CPU Revenue Triples YoY in Q30:03:36 The Big Shift: Share Dilution for OpenAI & MI400 GPU 0:05:32 Client & Gaming Segments Fixing Profit Margins 0:06:21 The MI400 Inflection Point and $100 Billion Revenue Potential 0:08:26 Updated Reverse DCF Valuation for AMD Stock 0:10:48 Our Final TakeIf you found this video useful, please make sure to like and subscribe!*********************************************************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.#amd #amdstock #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 AMD

  In this episode of Cybersecurity Today, host Jim Love dives into several shocking security lapses and emerging threats. Highlights include ransomware negotiators at Digital Mint accused of being behind attacks, a new AI vulnerability that exploits Windows' built-in stack, and a misuse of OpenAI's API for command and control in malware operations. Additionally, AMD confirms a flaw in its Zen 5 CPUs that could lead to predictable encryption keys, and the Louvre faces scrutiny after a major theft reveals poor password practices and maintenance failures. The episode underscores the importance of basic security measures like strong passwords and regular audits despite advanced technological systems in place. 00:00 Introduction and Sponsor Message 00:48 Ransomware Negotiators Turned Hackers 02:08 AI Stack Vulnerabilities in Windows 04:04 Backdoor Exploits OpenAI's API 05:24 AMD's Encryption Key Flaw 06:59 Louvre Heist and Security Lapses 08:24 Conclusion and Call to Action

Send us a textImagine if your computer could explore a landscape of possibilities all at once, using the same rules that make electrons behave in surprising ways. That's the mental pivot Farai, a quantum physicist and teacher, helps us make as we break down what quantum computing really is and where it actually wins. We trade hype for clarity, showing how superposition, entanglement, and interference become practical tools when classical methods hit walls.We walk through the real stakes: modeling complex materials to build safer batteries and corrosion-resistant coatings, accelerating drug discovery by simulating chemistry where properties emerge, and tackling massive optimization problems that govern airport gates, delivery routes, and supply chains. Farai explains why quantum machines are not replacements for CPUs or GPUs but new teammates in a hybrid stack, each part doing what it does best. The goal is targeted advantage, not universal speedups, and the payoff arrives when the search space explodes beyond classical reach.Along the way, we zoom out to nature as our design mentor. Bacteria that fix nitrogen more efficiently than factories, plants that capture sunlight better than our best solar cells, human brains that run powerful cognition on twenty watts—these examples aren't trivia; they are roadmaps for engineering. By learning from natural intelligence and combining it with quantum algorithms, we can cut energy waste, shorten R&D cycles, and unlock better outcomes across industry and public services. Farai also shares his work leading the Africa Quantum Consortium, proving that the next wave of innovation is global, collaborative, and grounded in education.If you care about the future of computing, climate tech, logistics, and medicine, this conversation will sharpen your lens. Listen, subscribe, and share with someone who still thinks quantum is just sci‑fi. Then tell us: which real-world problem would you optimize first?Thanks for tuning in to this episode of Follow The Brand! We hope you enjoyed learning about the latest trends and strategies in Personal Branding, Business and Career Development, Financial Empowerment, Technology Innovation, and Executive Presence. To keep up with the latest insights and updates, visit 5starbdm.com. And don't miss Grant McGaugh's new book, First Light — a powerful guide to igniting your purpose and building a BRAVE brand that stands out in a changing world. - https://5starbdm.com/brave-masterclass/ See you next time on Follow The Brand!

Episode 87: We revisit some discussion from last week as we've found more dodgy stuff Microsoft has done, before chatting about the current situation Intel is in with CPUs. They aren't anywhere near as competitive up against AMD now, as AMD were with Ryzen when Intel was dominant. (Note: This podcast was recorded before the recent AMD RDNA 2 driver decision, we'll discuss that in the future)CHAPTERS00:00 - Intro00:29 - Microsoft Does Dodgy Stuff Again11:34 - Intel CPUs when AMD is Dominant vs AMD CPUs when Intel is Dominant21:35 - The Discounts Aren't Enough34:53 - Platform Longevity is Crucial41:33 - Platform Support is Always Better1:04:34 - Updates From Our Boring LivesSUBSCRIBE TO THE PODCASTAudio: https://shows.acast.com/the-hardware-unboxed-podcastVideo: https://www.youtube.com/channel/UCqT8Vb3jweH6_tj2SarErfwSUPPORT US DIRECTLYPatreon: https://www.patreon.com/hardwareunboxedLINKSYouTube: https://www.youtube.com/@Hardwareunboxed/Twitter: https://twitter.com/HardwareUnboxedBluesky: https://bsky.app/profile/hardwareunboxed.bsky.social Hosted on Acast. See acast.com/privacy for more information.

Timestamps: 0:00 it's almost halloween i guess 0:19 AMD renames, rebadges older laptop CPUs 1:17 RedTiger-based Discord account hack 2:20 Australia sues Microsoft 3:31 War Thunder! 4:24 QUICK BITS INTRO 4:34 Fujitsu laptops with Blu-ray drives 5:21 Cooler Master walks back repair instructions 6:12 NHTSA investigate's Tesla's Mad Max mode 7:03 Microsoft files $4.7 in OpenAI losses under 'other' 8:03 Mushrooms as memristors! NEWS SOURCES: https://lmg.gg/mXOat Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Ben Bajarin and Jay Goldberg discuss Intel's recent earnings report, highlighting a sense of stability in the market compared to previous downturns. They explore the demand for CPUs, particularly in the enterprise sector, and the implications of upcoming product launches. The conversation shifts to Intel's foundry developments, where they express optimism about new manufacturing processes and customer engagement. They also analyze the competitive landscape of AI compute infrastructure, particularly focusing on Amazon's challenges with its Tranium chips and the implications of Anthropic's partnership with Google. Finally, they delve into the future of AI agents, discussing the current limitations and potential advancements needed for these technologies to become viable.

Send us a textARM Ascends, Oil Drifts, Queens EnduresI open on macro static and shutdown fog, a strange steadiness where the market beat goes on. The Beige Book whispers fractures, three Fed districts up, five flat, four softening, a recalibration more than a roar. The feature turns to ARM, where the data center bottleneck is power, not code. ARM sells the blueprint, cutting CPU energy use perhaps by half, and the live question is simple: can it win 50 percent of data center CPUs. Then energy's riddle: oil sits near $56, where it was in 2005, with about 1.5 trillion barrels of proven reserves setting the rough scale. I close with Queens County, a Tony Soprano thrift that became my acid test and first great trade, the name fades, but the fuse remains.If this hit the mark, tap 5 

In this episode, we're joined by Kunle Olukotun, professor of electrical engineering and computer science at Stanford University and co-founder and chief technologist at Sambanova Systems, to discuss reconfigurable dataflow architectures for AI inference. Kunle explains the core idea of building computers that are dynamically configured to match the dataflow graph of an AI model, moving beyond the traditional instruction-fetch paradigm of CPUs and GPUs. We explore how this architecture is well-suited for LLM inference, reducing memory bandwidth bottlenecks and improving performance. Kunle reviews how this system also enables efficient multi-model serving and agentic workflows through its large, tiered memory and fast model-switching capabilities. Finally, we discuss his research into future dynamic reconfigurable architectures, and the use of AI agents to build compilers for new hardware. The complete show notes for this episode can be found at https://twimlai.com/go/751.

-NVIDIA revealed its DGX Spark AI computer earlier this year and today is officially on for $3,999. Though relatively tiny, it hosts the company's entire AI platform including GPUs and CPUs, along with NVIDIA's AI software stack "into a system small enough for a lab or an office.” -Ofcom has slapped 4chan with a £20,000 fine, that's the equivalent of $26,700 here in the states, for failing to comply with the internet and telecommunications regulator's request for information under the UK's Online Safety Act of 2023. -Slack's new Slackbot is basically an AI chatbot like all the rest, but this one has been purpose-built to help with common work tasks. Folks can use natural language to converse with the bot and it can do stuff like whip up project plans, flag daily priorities and analyze reports. It can also help people find information when they only remember a few scant details. The company says it will "give every employee AI superpowers" so they can "drive productivity at AI speed." Learn more about your ad choices. Visit podcastchoices.com/adchoices

Bad Crypto, Blood Thirsty Zombie CPUs, Y2K38, Park Mobile, Palo Alto, Redis, Red Hat, Deloitte, Aaran Leyland, and more on the Security Weekly News. Visit https://www.securityweekly.com/swn for all the latest episodes! Show Notes: https://securityweekly.com/swn-518

Bad Crypto, Blood Thirsty Zombie CPUs, Y2K38, Park Mobile, Palo Alto, Redis, Red Hat, Deloitte, Aaran Leyland, and more on the Security Weekly News. Show Notes: https://securityweekly.com/swn-518

Bad Crypto, Blood Thirsty Zombie CPUs, Y2K38, Park Mobile, Palo Alto, Redis, Red Hat, Deloitte, Aaran Leyland, and more on the Security Weekly News. Visit https://www.securityweekly.com/swn for all the latest episodes! Show Notes: https://securityweekly.com/swn-518

Matching up the audio this week for a change of pace!  That Snapdragon X2 Elite Extreme sometimes compares favorably, there's a new Kindle Scribe and you will never guess who's coming to the Intel investment party.  Also Microsoft extends security updates for Windows 10 if you live in the right places, and the they're also looking into micro-channel cooling?  All this and so much more!00:00 Intro00:44 Patreon02:33 Food with Josh05:58 Snapdragon X2 Elite Extreme benchmarks11:32 Qualcomm wins final battle with Arm over Oryon14:23 Amazon Kindle Scribe lineup now bigger, offers first color model18:10 LG has world's first 6K TB5 display21:54 Apple might invest in Intel?26:48 Intel 13th and 14th Gen price hike32:03 Microsoft gives in on Windows 10 at the 11th hour - sort of36:42 Microsoft also exploring tiny channels on CPUs for microfluidic cooling42:16 Podcast sponsor Zapier43:36 (In)Security Corner53:52 Gaming Quick Hits1:06:25 Picks of the Week1:23:57 Outro ★ Support this podcast on Patreon ★

News and Updates: Apple iOS 26 delivers one of the biggest iPhone upgrades in years. The new Liquid Glass interface adds a translucent, holographic look, while Spatial Scenes uses AI to turn photos into dynamic 3D wallpapers. Major app redesigns include a cleaner Camera for one-handed use, a simplified Photos layout, customizable Messages with polls and chat backgrounds, and an upgraded Lock Screen. New Battery Settings now estimate charging times and debut Adaptive Power Mode (on iPhone 15 Pro+). But the flashy Liquid Glass design has drawn complaints of eye strain, dizziness, and legibility issues, with Apple offering accessibility tweaks as workarounds. Intel + Nvidia struck a $5B partnership that could reshape PCs. Nvidia bought a 4–5% stake in Intel, and the two are co-developing hybrid CPUs with Nvidia GPU chiplets connected via NVLink. These SoCs could boost AI PCs, power slimmer gaming laptops, and bring workstation-level performance to mini desktops — potentially blurring the line between integrated and discrete graphics. Nvidia + OpenAI announced a massive $100B investment deal. Nvidia will fund the buildout of 10 gigawatts of AI data centers using its upcoming Vera Rubin chips, more than doubling today's top AI hardware. The arrangement lets Nvidia recycle investment into chip sales while giving OpenAI infrastructure to push toward “superintelligence.” The deal lifted Nvidia's market cap to nearly $4.5T, the largest in the world. SpaceX Starlink filed to launch up to 15,000 new satellites to supercharge its direct-to-cell service. The move follows a $17B spectrum deal with EchoStar and will boost capacity 20-fold, enabling LTE-like performance for calls and messaging in dead zones. T-Mobile remains the US launch partner, but CEO Elon Musk hinted SpaceX could eventually sell mobile service directly, competing with carriers. Microsoft is injecting Copilot into all Microsoft 365 accounts, unless you manually use the Customization feature to stop the auto install.

When we think about what separates winning traders from those who struggle, we usually picture strategies, indicators, or a bit of insider know-how. But what if the biggest edge has been sitting on your desk all along? In this episode, I sit down with Eddie Z, also known as Russ Hazelcorn, the founder of EZ Trading Computers and EZBreakouts. With more than 37 years of experience as a trader, stockbroker, technologist, and educator, Eddie has built his career around one mission: helping traders cut through noise, avoid expensive mistakes, and get the tools they need to stay competitive in a fast-moving market. Eddie breaks down the specs that actually matter when building a trading setup, from RAM to CPUs to data feeds, and exposes which so-called “upgrades” are nothing more than overpriced fluff. We also dig into the rise of AI-powered trading platforms and bots, and what traders can do today to prepare their machines for the next wave. As Eddie points out, a lagging system or a missed feed isn't just an inconvenience—it can be the difference between a profitable trade and a costly loss. Beyond the hardware, we explore the broader picture. Rising tariffs and global supply chain disruptions are already reshaping the way traders access technology, and Eddie shares practical steps to avoid being caught short. He also explains why many experienced traders overlook their machines as a “secret weapon” and how quick, targeted fixes can transform reliability and performance in under an hour. This conversation goes deeper than specs and gadgets. Eddie opens up about the philosophy behind the EZ-Factor, his unique approach that blends decades of Wall Street expertise with cutting-edge technology to simplify trading and help people succeed. We talk about his ventures, including EZ Trading Computers, trusted by over 12,000 traders, and EZBreakouts, which delivers actionable daily and weekly picks backed by years of experience. For traders looking to level up—whether you're just starting out or managing multiple screens in a professional setting—this episode is packed with insights that can help you sharpen your edge. Eddie's perspective is clear: the right machine, the right mindset, and the right knowledge can make trading not only more profitable, but, as he likes to put it, as “EZ” as possible. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

Rate cut - rates up? Diet Stocks - losing weight Good news/bad news - all good for markets Bessent for Fed Chair and Treasury Secretary? 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 Warm-Up - BRAND New server - all provisioned - Much faster DH Site - Need a new CTP stock! - New Clear Stocks! - To the Sky - Money Tree Market - Tik Tok news Markets - Rate cut - rates up - Diet Stocks - losing weight - Good news/bad news - all good for markets - StubHub IPO Update SELL Rosh Hashanah - Buy Yom Kippur? Vanguard Issues? Got a call this morning..Gent in NY... NEW CLEAR - On Fire! - Have you seen the returns on some of these stocks? - YTD - - URA (Uranium ETF) Up 75% -- SMR (NuScale) Up 164% - - OKLO (OKL) up 518% - - CCJ (Cameco) up 65% TikTok Nonsense - President Donald Trump said in an interview that aired Sunday that conservative media baron Rupert Murdoch and his son Lachlan are likely to be involved in the proposal to save TikTok in the United States. -Trump also said that Oracle executive chairman Larry Ellison and Dell Technologies CEO Michael Dell are also likely to be involved in the TikTok deal. More TikTok - White House Press Secretary Karoline Leavitt says TikTok's algorithm will be secured, retrained, and operated in the U.S. outside of Bytedance's control; Oracle (ORCL) will serve as Tiktok's security provider; President Trump will sign TikTok deal later this week - What does that mean and will it be the same TikTok. - Who is doing the retraining??????? SO MANY QUESTIONS MEME ALERT! - Eric Jackson, a hedge fund manager who partly contributed to the trading explosion in Opendoor, unveiled his new pick Monday — Better Home & Finance Holding Co. - Jackson said his firm holds a position in Better Home but didn't disclose its size. - Shares of Better Home soared 46.6% on Monday after Jackson touted the stock on X. At one point during the session, the stock more than doubled in price. - The New York-based mortgage lender jumped more than 36% last week. Intel - INTC getting even more money. - Now, NVDA pouring in $5B - Nvidia and Intel announced a partnership to jointly develop multiple generations of custom data center and PC products. Intel will manufacture new x86 CPUs customized for Nvidia's AI infrastructure, and also build system-on-chips (SoCs) for PCs that integrate Nvidia's RTX GPU chiplets. - Both the US Government and NVDA got BELOW market pricing on their shares. NVDA $$ - Nvidia is investing in OpenAI. On September 22, 2025, Nvidia announced a strategic partnership with OpenAI, which includes an investment of up to $100 billion - The agreement will help deploy at least 10 gigawatts of Nvidia systems, which will include millions of its GPUs. The first phase is scheduled to launch in the second half of 2026, using Nvidia's Vera Rubin platform. Autism Link - Shares of Kenvue (KVUE) are trading lower largely due to reports from the White House and HHS suggesting a forthcoming warning linking prenatal use of acetaminophen (Tylenol's active ingredient) to autism risk. - Investors are concerned that such a warning could lead to regulatory action, changes in labeling requirements, litigation risk, or reduced demand for one of KVUE's key products. It's estimated that Tylenol accounts for approximately 7-9% of KVUE's total revenue. - The company has strongly denied any scientific basis for the link, but the uncertainty itself is hurting sentiment. - Finally, this also comes on top of recent weak financial performance: KVUE posted a Q2 revenue decline of 4% and cut its full-year guidance on August 7. - - Lawsuits to follow... Pfizer

NVIDIA is doubling down on AI dominance with massive investments across cloud, chips, and infrastructure. It struck a $6.3B deal with CoreWeave to secure long-term GPU demand, is investing $5B in Intel to co-develop custom CPUs and PC chips that pair Intel processors with NVIDIA GPUs, and is committing up to $100B with OpenAI to build data centers requiring 10 gigawatts of power. These moves lock in demand, expand NVIDIA's role across computing ecosystems, and cement its leadership in the race to scale global AI infrastructure. This and more on the Tech Field Day News Rundown with Alastair Cooke and guest host Scott Robohn. Time Stamps: 0:00 - Cold Open 0:36 - Welcome to the Tech Field Day News Rundown1:22 - Hugging Face Brings Open-Source Models to GitHub Copilot Chat3:52 - Pulumi Introduces AI Agents to Automate Infrastructure Management6:51 - Cisco DevNet is now Cisco Automation 9:12 - North Dakota to Test Portable Micro Data Centers for AI in Oil Fields12:14 - Sumo Logic Launches AI Agents to Streamline Cybersecurity Operations14:46 - Justice Department Moves to Break Up Google's Ad Business17:43 - NVIDIA's Multi-Billion-Dollar Moves Expand AI and Computing Leadership21:35 - The Weeks Ahead22:58 - Thanks for Watching the Tech Field Day News RundownGuest Host: Scott Robohn, CEO of SolutionalFollow our hosts ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Tech Field Day ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X/Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mastodon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

Ruby core team member Aaron Patterson (tenderlove) takes us deep into the cutting edge of Ruby's performance frontier in this technical exploration of how one of the world's most beloved programming languages continues to evolve.At Shopify, Aaron works on two transformative projects: ZJIT, a method-based JIT compiler that builds on YJIT's success by optimizing register allocation to reduce memory spills, and enhanced Ractor support to enable true CPU parallelism in Ruby applications. He explains the fundamental differences between these approaches - ZJIT makes single CPU utilization more efficient, while Ractors allow Ruby code to run across multiple CPUs simultaneously.The conversation reveals how real business needs drive language development. Shopify's production workloads unpredictably alternate between CPU-bound and IO-bound tasks, creating resource utilization challenges. Aaron's team aims to build auto-scaling web server infrastructure using Ractors that can dynamically adjust to workload characteristics - potentially revolutionizing how Ruby applications handle variable traffic patterns.For developers interested in contributing to Rails, Aaron offers practical advice: start reading the source code, understand the architecture, and look for ways to improve it. He shares insights on the challenges of making Rails Ractor-safe, particularly around passing lambdas between Ractors while maintaining memory safety.The episode concludes with a delightful tangent into Aaron's latest hardware project - building a color temperature sensor for camera calibration that combines his photography hobby with his programming expertise. True to form, even his leisure activities inevitably transform into coding projects.Whether you're a seasoned Ruby developer or simply curious about language design and performance optimization, Aaron's unique blend of deep technical knowledge and playful enthusiasm makes this an engaging journey through Ruby's exciting future.Send us some love. HoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.JudoscaleAutoscaling that actually works. Take control of your cloud hosting.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the show

Episode 81: We're back! Lots to discuss in this video, including YouTube weirdness, the future of AMD and Intel's CPU platforms, the good old CPU core debate, upcoming GPU rumors and more.CHAPTERS00:00 - Intro03:13 - Our YouTube views are down, this is what the stats say31:14 - Zen 7 on AM5 and Intel's competing platform54:13 - How important is platform longevity?1:07:58 - Six core CPUs are still powerful for gaming1:17:27 - Will Intel make an Arc B770?1:26:22 - No RTX Super any time soon1:29:14 - Updates from our boring livesSUBSCRIBE TO THE PODCASTAudio: https://shows.acast.com/the-hardware-unboxed-podcastVideo: https://www.youtube.com/channel/UCqT8Vb3jweH6_tj2SarErfwSUPPORT US DIRECTLYPatreon: https://www.patreon.com/hardwareunboxedLINKSYouTube: https://www.youtube.com/@Hardwareunboxed/Twitter: https://twitter.com/HardwareUnboxedBluesky: https://bsky.app/profile/hardwareunboxed.bsky.social Hosted on Acast. See acast.com/privacy for more information.

This week we talk about General Motors, the Great Recession, and semiconductors.We also discuss Goldman Sachs, US Steel, and nationalization.Recommended Book: Abundance by Ezra Klein and Derek ThompsonTranscriptNationalization refers to the process through which a government takes control of a business or business asset.Sometimes this is the result of a new administration or regime taking control of a government, which decides to change how things work, so it gobbles up things like oil companies or railroads or manufacturing hubs, because that stuff is considered to be fundamental enough that it cannot be left to the whims, and the ebbs and eddies and unpredictable variables of a free market; the nation needs reliable oil, it needs to be churning out nails and screws and bullets, so the government grabs the means of producing these things to ensure nothing stops that kind of output or operation.That more holistic reworking of a nation's economy so that it reflects some kind of socialist setup is typically referred to as socialization, though commentary on the matter will still often refer to the individual instances of the government taking ownership over something that was previously private as nationalization.In other cases these sorts of assets are nationalized in order to right some kind of perceived wrong, as was the case when the French government, in the wake of WWII, nationalized the automobile company Renault for its alleged collaboration with the Nazis when they occupied France.The circumstances of that nationalization were questioned, as there was a lot of political scuffling between capitalist and communist interests in the country at that time, and some saw this as a means of getting back against the company's owner, Louis Renault, for his recent, violent actions against workers who had gone on strike before France's occupation—but whatever the details, France scooped up Renault and turned it into a state-owned company, and in 1994, the government decided that its ownership of the company was keeping its products from competing on the market, and in 1996 it was privatized and they started selling public shares, though the French government still owns about 15% of the company.Nationalization is more common in some non-socialist nations than others, as there are generally considered to be significant pros and cons associated with such ownership.The major benefit of such ownership is that a government owned, or partially government owned entity will tend to have the government on its side to a greater or lesser degree, which can make it more competitive internationally, in the sense that laws will be passed to help it flourish and grow, and it may even benefit from direct infusions of money, when needed, especially with international competition heats up, and because it generally allows that company to operate as a piece of government infrastructure, rather than just a normal business.Instead of being completely prone to the winds of economic fortune, then, the US government can ensure that Amtrak, a primarily state-owned train company that's structured as a for-profit business, but which has a government-appointed board and benefits from federal funding, is able to keep functioning, even when demand for train services is low, and barbarians at the gate, like plane-based cargo shipping and passenger hauling, becomes a lot more competitive, maybe even to the point that a non-government-owned entity may have long-since gone under, or dramatically reduced its service area, by economic necessity.A major downside often cited by free-market people, though, is that these sorts of companies tend to do poorly, in terms of providing the best possible service, and in terms of making enough money to pay for themselves—services like Amtrak are structured so that they pay as much of their own expenses as much as possible, for instance, but are seldom able to do so, requiring injections of resources from the government to stay afloat, and as a result, they have trouble updating and even maintaining their infrastructure.Private companies tend to be a lot more agile and competitive because they have to be, and because they often have leadership that is less political in nature, and more oriented around doing better than their also private competition, rather than merely surviving.What I'd like to talk about today is another vital industry that seems to have become so vital, like trains, that the US government is keen to ensure it doesn't go under, and a stake that the US government took in one of its most historically significant, but recently struggling companies.—The Emergency Economic Stabilization Act of 2008 was a law passed by the US government after the initial whammy of the Great Recession, which created a bunch of bailouts for mostly financial institutions that, if they went under, it was suspected, would have caused even more damage to the US economy.These banks had been playing fast and loose with toxic assets for a while, filling their pockets with money, but doing so in a precarious and unsustainable manner.As a result, when it became clear these assets were terrible, the dominos started falling, all these institutions started going under, and the government realized that they would either lose a significant portion of their banks and other financial institutions, or they'd have to bail them out—give them money, basically.Which wasn't a popular solution, as it looked a lot like rewarding bad behavior, and making some businesses, private businesses, too big to fail, because the country's economy relied on them to some degree. But that's the decision the government made, and some of these institutions, like Goldman Sachs, had their toxic assets bought by the government, removing these things from their balance sheets so they could keep operating as normal. Others declared bankruptcy and were placed under government control, including Fannie Mae and Freddie Mac, which were previously government supported, but not government run.The American International Group, the fifth largest insurer in the world at that point, was bought by the US government—it took 92% of the company in exchange for $141.8 billion in assistance, to help it stay afloat—and General Motors, not a financial institution, but a car company that was deemed vital to the continued existence of the US auto market, went bankrupt, the fourth largest bankruptcy in US history. The government allowed its assets to be bought by a new company, also called GM, which would then function as normal, which allowed the company to keep operating, employees to keep being paid, and so on, but as part of that process, the company was given a total of $51 billion by the government, which took a majority stake in the new company in exchange.In late-2013, the US government sold its final shares of GM stock, having lost about $10.7 billion over the course of that ownership, though it's estimated that about 1.5 million jobs were saved as a result of keeping GM and Chrysler, which went through a similar process, afloat, rather than letting them go under, as some people would have preferred.In mid-August of this year, the US government took another stake in a big, historically significant company, though this time the company in question wasn't going through a recession-sparked bankruptcy—it was just falling way behind its competition, and was looking less and less likely to ever catch up.Intel was founded 1968, and it designs, produces, and sells all sorts of semiconductor products, like the microprocessors—the computer chips—that power all sorts of things, these days.Intel created the world's first commercial computer chip back in 1971, and in the 1990s, its products were in basically every computer that hit the market, its range and dominance expanding with the range and dominance of Microsoft's Windows operating system, achieving a market share of about 90% in the mid- to late-1990s.Beginning in the early 2000s, though, other competitors, like AMD, began to chip away at Intel's dominance, and though it still boasts a CPU market share of around 67% as of Q2 of 2025, it has fallen way behind competitors like Nvidia in the graphics card market, and behind Samsung in the larger semiconductor market.And that's a problem for Intel, as while CPUs are still important, the overall computing-things, high-tech gadget space has been shifting toward stuff that Intel doesn't make, or doesn't do well.Smaller things, graphics-intensive things. Basically all the hardware that's powered the gaming, crypto, and AI markets, alongside the stuff crammed into increasingly small personal devices, are things that Intel just isn't very good at, and doesn't seem to have a solid means of getting better at, so it's a sort of aging giant in the computer world—still big and impressive, but with an outlook that keeps getting worse and worse, with each new generation of hardware, and each new innovation that seems to require stuff it doesn't produce, or doesn't produce good versions of.This is why, despite being a very unusual move, the US government's decision to buy a 10% stake in Intel for $8.9 billion didn't come as a total surprise.The CEO of Intel had been raising the possibility of some kind of bailout, positioning Intel as a vital US asset, similar to all those banks and to GM—if it went under, it would mean the US losing a vital piece of the global semiconductor pie. The government already gave Intel $2.2 billion as part of the CHIPS and Science Act, which was signed into law under the Biden administration, and which was meant to shore-up US competitiveness in that space, but that was a freebie—this new injection of resources wasn't free.Response to this move has been mixed. Some analysts think President Trump's penchant for netting the government shares in companies it does stuff for—as was the case with US Steel giving the US government a so-called ‘golden share' of its company in exchange for allowing the company to merge with Japan-based Nippon Steel, that share granting a small degree of governance authority within the company—they think that sort of quid-pro-quo is smart, as in some cases it may result in profits for a government that's increasingly underwater in terms of debt, and in others it gives some authority over future decisions, giving the government more levers to use, beyond legal ones, in steering these vital companies the way it wants to steer them.Others are concerned about this turn of events, though, as it seems, theoretically at least, anti-competitive. After all, if the US government profits when Intel does well, now that it owns a huge chunk of the company, doesn't that incentivize the government to pass laws that favor Intel over its competitors? And even if the government doesn't do anything like that overtly, doesn't that create a sort of chilling effect on the market, making it less likely serious competitors will even emerge, because investors might be too spooked to invest in something that would be going up against a partially government-owned entity?There are still questions about the legality of this move, as it may be that the CHIPS Act doesn't allow the US government to convert grants into equity, and it may be that shareholders will find other ways to rebel against the seeming high-pressure tactics from the White House, which included threats by Trump to force the firing of its CEO, in part by withholding some of the company's federal grants, if he didn't agree to giving the government a portion of the company in exchange for assistance.This also raises the prospect that Intel, like those other bailed-out companies, has become de facto too big to fail, which could lead to stagnation in the company, especially if the White House goes further in putting its thumb on the scale, forcing more companies, in the US and elsewhere, to do business with the company, despite its often uncompetitive offerings.While there's a chance that Intel takes this influx of resources and support and runs with it, catching up to competitors that have left it in the dust and rebuilding itself into something a lot more internationally competitive, then, there's also the chance that it continues to flail, but for much longer than it would have, otherwise, because of that artificial support and government backing.Show Noteshttps://www.reuters.com/legal/legalindustry/did-trump-save-intel-not-really-2025-08-23/https://www.nytimes.com/2025/08/23/business/trump-intel-us-steel-nvidia.htmlhttps://arstechnica.com/tech-policy/2025/08/intel-agrees-to-sell-the-us-a-10-stake-trump-says-hyping-great-deal/https://en.wikipedia.org/wiki/General_Motors_Chapter_11_reorganizationhttps://www.investopedia.com/articles/economics/08/government-financial-bailout.asphttps://www.tomshardware.com/pc-components/cpus/amds-desktop-pc-market-share-hits-a-new-high-as-server-gains-slow-down-intel-now-only-outsells-amd-2-1-down-from-9-1-a-few-years-agohttps://www.spglobal.com/commodity-insights/en/news-research/latest-news/metals/062625-in-rare-deal-for-us-government-owns-a-piece-of-us-steelhttps://en.wikipedia.org/wiki/Renaulthttps://en.wikipedia.org/wiki/State-owned_enterprises_of_the_United_Stateshttps://247wallst.com/special-report/2021/04/07/businesses-run-by-the-us-government/https://en.wikipedia.org/wiki/Nationalizationhttps://www.amtrak.com/stakeholder-faqshttps://en.wikipedia.org/wiki/General_Motors_Chapter_11_reorganization This is a public episode. 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