Podcasts about hbm

Ben Bajarin and Jay Goldberg break down NVIDIA's earnings, examining how supply constraints shape growth and rising component costs pressure margins. They discuss NVIDIA's financing commitments to neoclouds and the risks of using its balance sheet to support the ecosystem. Marvell's XPU-attach strategy offers another route into custom silicon, with questions around when its Google opportunity will convert into revenue.From Hot Chips, Ben shares why competing inference architectures suggest the industry is still working out how to serve AI at scale. The conversation explores the software and system-design challenges facing accelerator startups, along with custom HBM base dies and their implications for memory costs and supplier flexibility. Waymo's workload-specific silicon raises another question: how much of inference will converge on a common architecture?

If there were any doubts about who's wearing the crown in the AI revolution... Nvidia just delivered another monster quarter. In today's episode, we're breaking down the latest earnings from Nvidia—and these aren't numbers that matter only to NVDA shareholders. Nvidia reported $96.2 BILLION in quarterly revenue, up an incredible 106% from a year ago. Even more impressive, its Data Center business generated $89 billion, up 117% year over year. Think about that for a moment. Nvidia isn't just growing. A company of this size just more than DOUBLED its revenue in one year. So the big question for today's show isn't simply whether Nvidia had a good quarter. It's: Can Nvidia—and the AI boom—keep this going? We'll dive into the numbers and look at what Nvidia's results tell us about the entire artificial-intelligence ecosystem. We'll discuss: Nvidia's latest earnings – What jumped out from the report and where the growth is coming from. Data Center dominance – What $89 billion in quarterly Data Center revenue tells us about global AI infrastructure spending. The AI spending boom – Are Microsoft, Meta, Amazon, Alphabet and other hyperscalers still willing to spend enormous amounts of money building AI infrastructure? Semiconductors – What Nvidia's results could mean for AMD, Broadcom, Micron and the rest of the chip sector. Memory – More AI computing means enormous demand for high-performance memory. Does Nvidia's growth strengthen the case for DRAM and HBM? Energy & infrastructure – All those GPUs have to go somewhere—and they require data centers, electricity, cooling, networking and an enormous infrastructure buildout. Valuation – At some point, even incredible growth can become fully priced in. Has Nvidia reached that point? The broader market – Nvidia has become so large and influential that its results can impact the Nasdaq, S&P 500 and overall investor sentiment. That's what makes this earnings report so important. Nvidia is no longer simply a semiconductor company investors watch four times a year. It's become one of the market's primary gauges of the entire AI investment cycle. Going into today's report, options markets were pricing roughly a 5.4% move in Nvidia shares, representing approximately $280 BILLION in potential market-cap movement in either direction. That's larger than the entire market capitalization of most companies! And with concerns growing recently about massive AI spending, stretched technology valuations and whether companies are generating enough return on their AI investments, Nvidia's results provide an important reality check. If AI is a bubble, somebody forgot to tell Nvidia's customers. But that doesn't mean the risks have disappeared. We'll separate the incredible fundamentals from the stock's valuation and ask the question traders actually care about: Great company... but is it still a great trade? For additional research, check out Nvidia Investor Relations and Nvidia Financial Reports. Listen now:

(00:00-27:41) Jackson's hat that was gifted from Cam Janssen. That's just the kinda guy Cam is. Hammered with joy. Backup YouTube accounts. Man, that second game hurt last night. Going for it in Game 1 hurt in Game 2. Winn not taking second on the double steal bit em. Audio of Oli Marmol talking about burning through the pen. The Age of Entitlement. Getting fird mid-shift but being asked to stick around. Sassy Batman. Worst hire in STL history. How much leather do you own, Doug? Things are contentious.(27:49-49:23) Turn up your subwoofers. If City can just keep from selling their best players, they can win this baby. Cards were 62-64 thru 126 games last year. MLS & Korn Ferry. Golfers in town at Bellerive getting some work in. Doug wondering if MLS can ever become a top tier league. Jackson's villain era. Gin is a man's drink. Iggy and Plowhawk talking space. Doug's a jingle man. Jackson is NOT censoring content.(49:33-1:12:06) Quinn Mathews with a second decent start. HBM is already giving up on Josh Baez. Extension time for Burleson? Herrera finally through a guy out. Larry Nickel is on hold and he wants to recap some wrestling. Brazil nuts. Chick and spaghetti. Just measure those bitches out. Fine, I'll just eat a donut. Money talks, wealth whispers, poverty posts on IG.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Jackson's hat that was gifted from Cam Janssen. That's just the kinda guy Cam is. Hammered with joy. Backup YouTube accounts. Man, that second game hurt last night. Going for it in Game 1 hurt in Game 2. Winn not taking second on the double steal bit em. Audio of Oli Marmol talking about burning through the pen. The Age of Entitlement. Getting fird mid-shift but being asked to stick around. Sassy Batman. Worst hire in STL history. How much leather do you own, Doug? Things are contentious.Turn up your subwoofers. If City can just keep from selling their best players, they can win this baby. Cards were 62-64 thru 126 games last year. MLS & Korn Ferry. Golfers in town at Bellerive getting some work in. Doug wondering if MLS can ever become a top tier league. Jackson's villain era. Gin is a man's drink. Iggy and Plowhawk talking space. Doug's a jingle man. Jackson is NOT censoring content.Quinn Mathews with a second decent start. HBM is already giving up on Josh Baez. Extension time for Burleson? Herrera finally through a guy out. Larry Nickel is on hold and he wants to recap some wrestling. Brazil nuts. Chick and spaghetti. Just measure those bitches out. Fine, I'll just eat a donut. Money talks, wealth whispers, poverty posts on IG.It's a big Seger Tuesday. Matt Leblanc in the Night Moves video. The AP Poll is out and Mizzou is number 25. Maybe Brandon Walker was kind of spot on. True Sons don't bring up facts. Split national championships. November 7th finna be a nation wide pony. Pound rocks, fella. Touch power lines. What's an art house movie? Martin's cruisin' for a bruisin'.What's coming up on Movie Boi? Best golf movies. Martin just got dismissed. Which golfer stopped off at Lambert's for some throwed rolls? Bill's tailbone. We're getting PGA celebrity sightings from all over town. Pass arounds. Sorghum. What's the sweet spot? Caller says he rented his home to a golfer back in 2018. Paid for a new basement with the funds. Now Kevin Chappell is taking shrapnel.Best watering holes in Washington Park. Jay Delsing got his 21st hole-in-one.Look, Doug. Brody Herman. He's got a problem with Doug's Mt. Rushmore of debuts having Gayle Sayers on it. Nobody like a know-it-all, Doug. Jordan Walker and Josh Baez takes. Mizzou takes. He's down on the Mizzou/Kansas game.Boots Randolph getting cut off too soon. Audio of Mike Francesa being a little dismissive when being asked about the Ultimate Warrior. Let's play it again for Martin since he walked in late. Prod Joe vs. Timberfake. Sour Shoes.Design Aire Heating & Cooling EMOTDWas Tom Cruise in Young Guns? I think Mr. 63011 wants to fight you now. Take it up with Bruckheimer. Apologize or be fired. Buck Swope let Jackson have it at TMA trivia night.Just settle in and let Bob Seger wash over you. What about Like A Rock, Bitch? This is why everyone should move into a cave. Audio of Bill Simmons take on youth sports. Three generations of Chairmen got tossed from the same high school baseball game. The adult Vaughn boys still get Easter baskets.And the winner of the EMOTD is...See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

En el episodio de hoy Valentina Orduz y Juan Manuel de los Reyes analizaron el déficit fiscal de Estados Unidos, que en julio alcanzó $432 mil millones de dólares, el mayor para ese mes en la historia, y explicaron por qué más déficit significa más emisión de bonos y más presión sobre los rendimientos. Luego abordaron el PPI, el índice de precios al productor. Por último, exploraron el caso de SK Hynix, líder mundial en memoria HBM para IA y su plan de expansión por $720,000 millones de dólares.

以前有些愛唱雖台灣的人常常說，不要跟中國對抗，2300萬人怎麼打得贏14億人？ 聽他們在放屁！台灣還真的狠狠的贏了！ 2,300萬人真的可以打贏14億人嗎？以前聽起來像做夢，今年美國公布的一張進口成績單，台灣超越中國啦！ 中國丟掉的美國訂單去了哪裡？親中國家竟然變成中國的傾銷地？難道真的是傳說中的「洗產地大法」？中國真的「洗」成功了嗎？ 超級爽、含金量又超高的一集，千萬不要錯過！記得按讚、訂閱，分享給所有關心台灣產業與國際政經的親友！ 全台獨家的世界經濟追劇深入報導，精彩萬分，持續連載中！ （現在就加入會員支持我們，還可以看到更多專屬影片～）  https://www.youtube.com/@emmytw/join

La crise de la mémoire informatique est encore loin d'être terminée. Et contrairement aux scénarios les plus optimistes, 2027 pourrait même devenir l'année la plus difficile depuis le début de la pénurie. Selon les informations de DigiTimes, relayées par TweakTown, les tensions restent aujourd'hui à leur maximum. Une grande partie des capacités de production de DRAM, la mémoire vive classique de nos ordinateurs et smartphones, mais aussi de HBM, cette mémoire à très haut débit indispensable aux accélérateurs d'intelligence artificielle, serait déjà réservée pour l'année prochaine.Jusqu'ici, les prévisions divergeaient. Certaines évoquaient un retour à la normale dès la fin de 2027, d'autres repoussaient cette perspective jusqu'en 2030. Mais selon le média taïwanais, 2027 pourrait finalement constituer le point culminant de la pénurie, aussi bien pour la mémoire vive que pour le stockage.Les grands acheteurs ont manifestement anticipé la situation. Au premier semestre, des contrats auraient été négociés très discrètement avec les trois principaux fabricants mondiaux : Samsung, SK Hynix et Micron. Les plus petits acteurs passent des commandes ponctuelles, tandis que des groupes comme Apple, Microsoft ou Nvidia sécurisent leurs approvisionnements grâce à des accords courant sur trois à cinq ans. Les hyperscalers, ces géants exploitant d'immenses infrastructures de cloud et de centres de données, captent une grande partie des capacités disponibles. Et le déséquilibre reste considérable : les trois principaux fabricants ne seraient actuellement capables de satisfaire que 60 à 70 % de la demande initialement exprimée. Les besoins liés à l'intelligence artificielle continuent donc de dépasser largement l'offre. Le problème touche également la mémoire NAND Flash, utilisée notamment dans les SSD, les smartphones et de nombreux systèmes de stockage. Là encore, la quasi-totalité des capacités de production de 2027 pourrait déjà être réservée. Il existe toutefois une petite nuance positive. Après les augmentations spectaculaires enregistrées en 2026, DigiTimes estime que la progression des prix devrait ralentir l'année prochaine. Cela ne signifie pas pour autant une baisse : les tarifs devraient rester à des niveaux très élevés. En attendant l'arrivée de nouvelles usines d'ici à 2030, Samsung, SK Hynix et Micron conservent donc une position particulièrement favorable. Pour les consommateurs comme pour les industriels, la pénurie pourrait encore durer plusieurs années. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Memory chips have been the standout trade of the AI boom, but July brought a sharp reality check. Global semiconductor stocks whipsawed as investors began questioning the return on hundreds of billions in data-center spending. Market jitters deepened after Chinese startup Moonshot released its open-source Kimi K3 model at a fraction of the cost of US frontier AI architectures. Compounding these concerns is the rise of CXMT, China’s leading DRAM supplier, whose stock surged fivefold following a blockbuster IPO – raising fears of aggressive capacity expansion that could disrupt incumbents such as Samsung, SK Hynix and Micron. Jake Silverman, semiconductor analyst at Bloomberg Intelligence, joins John Lee on the Asia Centric podcast. He unpacks why fundamental support for the memory sector remains solid, how high-bandwidth memory (HBM) creates a structural cap on standard DRAM supply, and why multi-year strategic supply contracts and extended fab construction lead times insulate incumbents from an immediate market collapse.See omnystudio.com/listener for privacy information.

Hoy: ChainDrop compromete más de 1.300 paquetes npm; Samsung apila HBM sobre aceleradores de IA; Nvidia abre Alpamayo 2 Super para conducción autónoma; Mistral presenta Shieldstral para moderación multimodal; y Moderna inicia la primera prueba humana de una vacuna de ARN mensajero contra el ebolavirus Bundibugyo.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

¿Ha empezado el crash de la inteligencia artificial? Las fuertes caídas y posteriores rebotes de compañías como Micron y SanDisk han sacudido Wall Street y han reabierto el debate sobre las elevadas valoraciones del sector tecnológico. En este vídeo analizamos el papel de las memorias HBM en el desarrollo de la IA, la escasez de oferta, la competencia china y los riesgos del apalancamiento. También explicamos el caso del fondo Situational Awareness, un ejemplo extremo de cómo la deuda puede multiplicar tanto las ganancias como las pérdidas. ------------------------------------------------------------------------------------------ Lo expuesto en esta emisión no presenta asesoramiento financiero personalizado. Se informa al inversor de que los instrumentos o inversiones a los que se refiere pueden no ser adecuados para sus objetivos, su situación financiera o su perfil de riesgo. La emisión no constituye una oferta, invitación de compra o suscripción o cancelación de inversiones, ni puede servir de base a ningún contrato o decisión. Se recomienda revisar la información legal de los productos, especialmente las características y los riesgos, antes de tomar decisiones. El Grupo Renta 4 no asume responsabilidad alguna por cualquier pérdida directa o indirecta que pudiera resultar del uso del contenido de esta emisión. Rentabilidades pasadas no garantizan rentabilidades futuras. Renta 4 Banco, S.A., es una empresa domiciliada en Madrid, Paseo de la Habana, 74, 28036 Madrid, teléfono 91 384 85 00. Es una entidad regulada y supervisada por el Banco de España (BdE) y por la Comisión Nacional del Mercado de Valores (CNMV) respecto a los servicios de inversión y auxiliares ¿Estamos ante el estallido de una burbuja o ante una corrección localizada después de unas subidas extraordinarias?

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

The rapid growth of artificial intelligence is creating enormous demand for advanced memory chips. As chip manufacturers move more of their resources toward High Bandwidth Memory, or HBM, the supply of traditional RAM and flash memory is being squeezed. In this episode, Skip Montreux and Samantha Vega examine the global memory shortage that has been dubbed “RAMageddon” and explain why it is increasing the cost of laptops, smartphones, cars, medical equipment, and many other products. Skip begins by explaining the three main types of memory involved in the current shortage. RAM allows computers and other devices to operate software, while flash memory stores data after a device has been turned off. High Bandwidth Memory, or HBM, is a faster and more powerful form of memory that is essential for running artificial intelligence systems and large language models. Skip and Samantha then look at why HBM production is reducing the supply of traditional memory. Producing one wafer of HBM can use the same manufacturing resources as three wafers of standard RAM. As demand for AI infrastructure increases, memory manufacturers are therefore moving more of their production capacity toward HBM. They also discuss the role of major technology companies such as Amazon Web Services, Alphabet, Meta, Microsoft, and Oracle. These companies are investing heavily in data centers, which require large amounts of HBM. This demand has encouraged manufacturers to prioritize the AI market over the consumer electronics market. The episode also examines Micron's decision to close its Crucial consumer memory brand and redirect resources toward HBM production. Next, Skip explains how HBM has disrupted the traditional boom-and-bust cycle of the memory chip industry. In the past, higher prices encouraged manufacturers to expand production. Once the new production capacity became available, supply increased and prices fell. However, strong and continuing demand from the AI industry has changed this familiar pattern. Finally, Skip and Samantha examine the consequences for businesses and consumers. Contract prices for RAM increased by between 90 and 95 percent, while flash memory prices rose by 75 percent. Delivery lead times have also extended beyond 58 weeks, and some manufacturers must pay for their orders in advance. These pressures are raising the cost of any product that requires computer memory. This episode helps listeners understand the global memory chip market while building practical Business English skills. In this episode, you will learn: What RAM, flash memory, and High Bandwidth Memory are. Why HBM is essential for artificial intelligence and data centers. How producing HBM reduces the manufacturing capacity available for standard RAM. How AI demand has disrupted the traditional boom-and-bust cycle of the memory industry. Why RAM and flash memory prices are increasing. How longer delivery times and advance-payment requirements affect manufacturers. Do you like what you hear? Become a D2B Member today for to access to our -- NEW!!!-- interactive audio scripts, PDF Audio Script Library, Bonus Vocabulary episodes, and D2B Member-only episodes. Visit d2benglish.com/membership for more information. Follow Down to Business English on Apple podcasts, rate the show, and leave a comment. Contact Skip, Dez, and Samantha at downtobusinessenglish@gmail.com Follow Skip & Dez Skip Montreux on Linkedin Skip Montreux on Instagram Skip Montreux on Twitter Skip Montreux on Facebook Dez Morgan on Twitter RSS Feed

One of the defining market stories of the past 12 months has not been AI chips that compute, but the chips that remember. Equity analyst Shan Rui Yeo explains how memory works, from DRAM and NAND to high bandwidth memory, and how an industry that destroyed wealth for four decades became disciplined after consolidating to three players in 2013. He then walks through what changed: AI inference has made memory the key bottleneck, memory content is climbing with each new generation of GPUs, and new supply takes three to four years to build. With prices up sharply and customers signing long-term agreements, Part 1 of this three-part conversation lands on a commodity industry whose business model is changing in real time.   Key Takeaways Memory is a commodity with a three-to-four-year supply lag, which is why the cycle has always been difficult. Consolidation to three players in 2013 turned four decades of wealth destruction into at least 15% returns on capital through the cycles. In AI inference, memory bandwidth sets the speed of token generation, making memory the key bottleneck. NVIDIA's Rubin GPU carries 384 GB of DRAM, the equivalent of 32 iPhones per GPU, or 160 million iPhones across five million GPUs. HBM consumes three times the wafer capacity of standard DRAM (four times with HBM4) and is forecast to absorb 30% of DRAM wafers by 2027. DRAM contract prices are up roughly 200% year to date and 400 to 500% year over year, and price increases are reaching phones, laptops, and consoles. Customers are signing three-to-five-year agreements with prepayments, which could support a re-rating of memory companies.   Companies Mentioned: Samsung Electronics, SK Hynix, Micron, NVIDIA, Intel, Texas Instruments, Apple, Nintendo   Host: Rob Campbell, CFA, Institutional Portfolio Manager Guest: Shan Rui Yeo, CFA, Equity Analyst   This episode is available for download anywhere you get your podcasts.   Founded in 1974, Mawer Investment Management Ltd. (pronounced "more") is a privately owned independent investment firm managing assets for institutional and individual investors. Mawer employs over 250 people in Canada, U.S., and Singapore.   Visit us at: https://www.youtube.com/@MawerInvestment https://www.mawer.com https://www.linkedin.com/company/mawer-investment-management/ https://www.instagram.com/mawerinvestmentmanagement/ #ArtOfBoring #MawerInvestmentManagement #MawerInvestment #Podcasts

Few places in the History of San Diego Craft Beer carry as much weight as the Home Brew Mart. Home to San Diego Homebrewers as well as Professional Brewers, just about eveyone has a story from HBM. In addition to beer supplies, Jim and his crew have also been brewing up some of the freshest beer in San Diego. With a small system in the back they are constantly cranking thru a variety of different styles of beer like today's Apricot American Wheat.

AI is no longer just a race to train smarter models. As AI moves into production, the bottleneck is increasingly inference: how fast models can generate tokens, use tools, reason, verify, and act. In this episode of the MAD Podcast, Matt Turck sits down with Andrew Feldman, co-founder and CEO of Cerebras, to explain why fast inference may define the next era of AI.Cerebras is known for building a chip the size of a silicon wafer. But this conversation is not just about one company or one chip. It is a deep dive into the AI infrastructure stack: GPUs, ASICs, memory, HBM, SRAM, data centers, power, TSMC, AWS, OpenAI, agents, reasoning models, and why speed changes what AI products can become. Andrew explains why “tokens per second per user” matters, why generating a single word can require moving the equivalent of 100 HD movies through memory, why agents amplify latency, why GPUs struggle with certain inference workloads, and why fast AI may eventually reshape SaaS itself.This is a reference conversation on fast inference, AI chips, and the next compute bottleneck.(00:00) Cold open & Intro(01:31) Why speed became the AI bottleneck(02:32) Tokens per second per user, explained(03:16) AI's broadband moment and the Netflix analogy(04:35) The AI chip landscape: GPUs, TPUs, Trainium, ASICs(06:36) What is an ASIC?(08:08) Nvidia, Groq, and the fast inference war(09:16) OpenAI, Broadcom, and specialized silicon(12:10) China, power, and sovereign AI infrastructure(15:05) Is the AI infrastructure boom a bubble?(18:56) The hidden bottlenecks: HBM, CoWoS, and 3nm(22:57) Why agents are creating CPU demand(25:36) Andrew Feldman's path from SeaMicro to Cerebras(26:13) Why Cerebras bet on AI in 2016(31:14) SRAM vs. HBM: why inference is a memory problem(33:19) What wafer-scale computing actually means(34:28) The deep-tech “Everest” problem(36:07) The moment the first Cerebras system worked(36:49) Ringing the bell and surviving deep tech(39:08) How a giant chip handles failure(41:22) Why GPUs struggle with decode(42:17) Prefill vs. decode explained(44:01) The “100 HD movies” problem in AI inference(45:04) How fast inference changes RL and training(48:08) Reasoning models and why they cost more compute(50:08) Verification, guardrails, and small models checking big models(52:37) Multimodal AI and the path to video(53:51) Cerebras' business model: hardware, cloud, and API(55:14) OpenAI's 750MW inference deal(55:36) Why data centers are measured in megawatts(58:01) AWS Trainium + Cerebras decode(59:29) Fast tokens as a cloud product(01:00:52) Is CUDA still a moat?(01:03:53) How TSMC helped Cerebras build the giant chip(01:07:41) Why nobody cared in 2020(01:08:15) Why chip supply chains are hard to diversify(01:09:54) Why today's AI models will be the worst you ever use(01:10:38) What fast AI could do to SaaS

Depuis l'automne dernier, le prix de la mémoire vive ne grimpe plus : il s'envole. Les fabricants répètent que cette tension restera temporaire, le temps d'ouvrir de nouvelles usines. Mais une analyse de Bank of America, relayée le 12 juillet par le quotidien taïwanais Commercial Times, fragilise ce discours. Elle paraît alors que Samsung, SK Hynix et Micron sont visés en Californie par une plainte collective les accusant d'avoir organisé cette rareté.La Corée du Sud affiche ses ambitions. Le président Lee Jae-myung veut doubler les capacités nationales d'ici 2030, notamment grâce aux mégasites de Samsung à Gwangju et de SK Hynix dans le Jeolla. Bank of America estime qu'après déduction des anciennes lignes arrêtées pour modernisation, la capacité coréenne en wafers (les plaques de silicium servant à fabriquer les puces) progresserait de moins de 10 % par an.Un professionnel taïwanais affirme que SK Hynix ne mettrait en service qu'un sixième des ajouts prévus d'ici 2028. Construire une usine de semi-conducteurs exige du temps : cinq ans pour les fondations, trois à quatre années supplémentaires pour les salles blanches et les machines, puis près d'une décennie pour l'écosystème complet. Le patron de SK Hynix prévient d'ailleurs que 2027 sera la pire année de l'histoire du secteur. La plainte déposée le 25 juin accuse les trois groupes d'avoir profité du basculement vers la HBM, une mémoire empilée indispensable aux accélérateurs d'intelligence artificielle, pour réduire l'offre de DDR4 et de DDR5. Chaque bit de HBM mobilise environ trois fois plus de silicium qu'un bit de DDR5 : produire davantage pour l'IA signifie fabriquer moins pour le grand public.Les plaignants évoquent une hausse de la DRAM d'environ 700 % en quatre ans. Ils devront prouver une coordination, une procédure comparable ayant échoué en 2018. Le passé nourrit les soupçons : Samsung et Hynix avaient déjà été sanctionnés pour entente dans les années 2000. En France, un kit de 32 gigaoctets de DDR5-6000 est passé d'environ 75 euros à l'été 2025 à près de 300 euros en juin. Les projections annoncent encore deux fortes hausses successives, sans répit avant 2028. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

ASML may have delivered the most important earnings report of the week for semiconductor investors.The company raised its full-year sales guidance again, reported a sharp increase in memory-related revenue, and announced plans to expand EUV lithography production by around 30% annually in 2027 and 2028.While the headlines focused on ASML, the implications extend far beyond one company.In this episode, we break down why ASML's latest results reinforce the broader AI infrastructure story—and what they mean for companies across the semiconductor supply chain.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comKey topics we explore:– Why ASML is one of the most important companies in the AI ecosystem– What the latest earnings reveal about global semiconductor demand– Why memory-related revenue surged and what it says about HBM demand– How strong EUV orders reinforce the long-term AI infrastructure buildout– The read-through for Nvidia, TSMC, Micron, SK hynix, and other semiconductor leaders– Why investors should watch semiconductor equipment companies as closely as AI chip designersThe bigger question:If the AI infrastructure boom were slowing, would ASML be raising guidance and expanding production capacity?For investors, ASML's earnings provide another important data point that demand for advanced semiconductor manufacturing—and the AI infrastructure powering it—remains robust.LINKSPrashant Choubey - ⁠https://www.linkedin.com/in/choubeysahab⁠Subscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X⁠Subscribe on Apple Podcasts - ⁠https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986⁠Subscribe on Spotify - ⁠https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ⁠VC10X website - ⁠https://vc10x.com⁠For sponsorship queries reach out to prashantchoubey3@gmail.comThis channel is for asset managers, allocators, and investors who want analysis that holds up—not headlines dressed as insight.Subscribe for weekly data-driven breakdowns of the forces reshaping capital markets.#ASML #Semiconductors #AI #ArtificialIntelligence #Nvidia #TSMC #Micron #SKHynix #HBM #EUV #ChipStocks #Investing #TechStocks #VC10X #Finance #VentureCapital #DataCenters #SemiconductorEquipment #WallStreet #Markets

每座晶圓廠每年消耗相當於 1,700 萬人飲用水需求的水量—水，不只是資源，是先進製程的核心投入。這集我們走進 SEMICON Taiwan 2026 展場，和藝康台灣總經理塗溥仁(PJ)與全球客戶業務總監孔維國(Mike)深聊: 當超純水技術遇上數位化平台，半導體產業的水資源困局如何破解？ 【聽完這集你會知道】 00:00 ｜ 水為什麼是半導體最隱形的戰略資源 SEMICON Taiwan 2026 聚焦八大主題，永續製造首度與先進製程並列核心議題。 02:19 ｜ Ovivo 收購完成：超純水技術如何直接影響晶片良率 04:12 ｜ 3D TRASAR™ 實機展示：從感測到自動加藥的全閉環控制 06:11 ｜ 三大產業痛點：加藥失準、回收率低、設備折損的解法 11:11 ｜ 端到端水循環地圖：從超純水到廢水回用的完整服務範疇 15:00 ｜ 永續報告與台灣願景：水管理好，先進製造才走得遠。藝康於南科設立全台首座水研發中心就近服務，期許成為台灣半導體廠最信賴的水管理夥伴。 【本集金句】 「水循環能力，已從 ESG 要求，轉化為製造競爭力的一部分。」 對談來賓：藝康台灣總經理 塗溥仁 PJ Tu、藝康台灣全球客戶業務總監 孔維國 Mike Kong 主持人：天下雜誌副總編輯 黃亦筠 本集節目由SEMI國際半導體產業協會合作推薦 SEMICON Taiwan 2026 國際半導體展將於 8 月 31 日率先展開國際論壇，展覽在9 月 2–4 日於南港展覽館 1、2 館登場，展會聚焦 AI 驅動下的半導體技術創新及全球生態系，展現台灣護國群山扮演關鍵角色，涵蓋先進製程、先進封裝、HBM、矽光子、量子技術、小晶片及永續製造等關鍵議題，匯聚超過 1,300 家企業，完整呈現國際半導體產業最新趨勢。立即報名觀展：https://registration.semicontaiwan.org/visitor/index?sid=commonradio #超純水UPW #藝康Ecolab #3DTRASAR #OvivoUltrapureWater #SEMICONTaiwan2026 -- Hosting provided by SoundOn

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The semiconductor industry is undergoing one of its most profound transformations in decades. Driven by the insatiable demand for compute power largely fueled by AI workloads, engineers are moving away from traditional monolithic chips and shifting toward complex multi-die designs. This shift brings a new set of challenges that conventional design and validation methods simply cannot handle.In a recent episode of the Tech Transformed podcast, host Dana Gardner sat down with Shekhar Kapoor, Executive Director of Product Line Management at Synopsys, to explore how the growing complexity of semiconductors is changing the way engineers design and validate modern systems. From thermal management to AI-driven automation, the conversation reveals why the old way of building chips is no longer good enough and what the future looks like.Multi-Die DesignKapoor explains that the transition to multi-die design is no longer a matter of preference but a necessity. He attributes this shift to the relentless demand for greater compute capacity, driven largely by the rapid growth of AI.Traditional monolithic chips are hitting hard limits. Reticle sizes are maxing out, and rising yield and cost challenges make it increasingly impractical to pack more functionality onto a single die. Multi-die designs solve this by disaggregating functionality across smaller dies, each targeting the most appropriate process technology, then integrating them into a unified, optimised package.Leading AI systems already integrate multiple compute and I/O dies alongside large high-bandwidth memory (HBM) stacks, scaling to 3x–5x reticle-class designs and beyond. The design challenge is very different. As Kapoor puts it: "You're no longer optimising a single chip, you're optimizing a system of chips."This requires system-level co-design from day one, spanning architecture, silicon, packaging, power delivery, and interconnect strategy simultaneously. Engineers must think in terms of System Technology Co-Optimisation (STCO), not just chip-level optimization. The design tools, methodologies, and team workflows all need to change. For engineers and technology leaders looking to explore these trade-offs, Synopsys has published a comprehensive eBook on accelerating multi-die design and innovation.Thermal Analysis and Multi-Physics ValidationHistorically, thermal, power, and electromagnetic analyses were performed as downstream validation steps once the core design was complete. In a multi-die world, that approach is no longer viable."Thermal management is becoming the number one issue when designing these multi-die designs. It has to be managed across a range of scales, from transistor activity to package and board level," Kapoor says.The problem with late-stage validation is timing. By the time thermal or power integrity issues surface, the most critical decisions are already locked in floorplans, interconnect topologies established, and packaging assumptions embedded.. At that point, the only options are costly ECOs, excessive margining, or a full redesign. Industry estimates suggest over-design can lead to up to 30-35 per cent wasted silicon and hundreds of millions of dollars in optimisation loss.The solution is a shift-left approach that embeds multiphysics analysis from the earliest stages of design. When thermal hotspots, voltage drop issues, and electromagnetic interactions are identified early, engineers can adjust partitioning and placement strategies before they become expensive problems.This is the methodology detailed in the Synopsys ebook on Multiphysics Fusion for multi-die design, which covers how teams can build continuous multiphysics validation into their flows to avoid late-stage surprises and protect both performance and reliability.Multiphysics Fusion and AI-Driven Chip DesignTo operationalise the shift-left methodology at scale, Synopsys has introduced the concept of Multiphysics Fusion. This is the native integration of AI-powered EDA technologies with ANSYS's gold-standard multiphysics sign-off analysis capabilities.Within the 3DIC Compiler platform, this means unifying the implementation environment with RedHawk-SC, RedHawk-SC Electrothermal, and HFSS-IC technologies. This brings IR drop, thermal, signal, and power integrity analysis directly into the design loop. The result is greater predictability, tighter correlation between in-design analysis and sign-off, and significantly fewer design iterations.The impact on design closure times has been substantial. According to Kapoor, teams using the Multiphysics Fusion solution have seen turnaround times shrink "from weeks to days, and in some cases even hours" even for large, high-performance multi-die designs.AI amplifies these gains further. Synopsys employs AI in two primary ways: assistive automation through its 3DSO.ai technology, which integrates multiphysics feedback into the optimization loop in real time, and agentic workflow orchestration, which becomes increasingly critical as system complexity scales toward designs incorporating hundreds or even thousands of GPUs. As Kapoor notes, at that scale, "agentic workflows could help engineers converge faster" and manage trade-offs that would otherwise be intractable. If you would like to find out more about this, download the full eBook: Multiphysics Fusion Technology for Multi-Die Designs Explained from Synopsys, which expands on each of these themes with real-world examples, design methodologies, and guidance for implementation teams. You can also connect with Shekhar Kapoor on LinkedIn.TakeawaysMulti-die architectures and their drivers.Challenges of traditional monolithic chips.Importance of early multi-physics analysis.Multiphysics fusion and its benefits.AI's role in design automation.Reducing time-to-market through integrated platforms.System-level co-design.Thermal management in 3D IC stacking.Shift left approach in multi-physics validation.Future trends in semiconductor design.Chapters00:00 Introduction to Semiconductor Complexity02:00 The Shift to Multi-Die Designs04:30 Challenges in Multi-Die Design08:11 The Importance of Early Multi-Physics Analysis10:05 Introducing Multiphysics Fusion12:37 AI's Role in Semiconductor Design16:37 Reducing Time to Market19:39 Applications Beyond AI21:12 Real-World Examples of Multi-Physics Validation26:20 Practical Advice for Engineers

Did you ever wonder why so many people didn't get out before the dot-com crash? It's an important question to ask yourself, especially if you believe you'll know exactly when to get out before any potential correction in today's AI and semiconductor stocks.   The reality is that the dot-com bubble burst only 25 years ago. Human nature hasn't changed since then. Investors today are no smarter than investors were back then, and the same emotions that drove the bubble are showing up again. There were four major reasons so many people lost money during the tech bust.   The first was that investors stopped focusing on earnings and price-to-earnings ratios. Instead, they justified sky-high valuations by looking at metrics like website traffic, page views, click-through rates, and the number of "eyeballs" on a screen. The assumption was that if revenue kept growing, profits would eventually follow. Many ignored the reality that businesses also have expenses, competition, and execution risk.   The second reason was FOMO or the fear of missing out. Between 1995 and 2000, the Nasdaq surged roughly 400%. As people watched friends, coworkers, and investors make fortunes on tech stocks and IPOs, more and more money poured into the market. Institutional investors and retail investors alike stopped worrying about valuations. They simply saw stocks going up and didn't want to miss the ride.   The third reason was the belief that "this time is different." You heard it everywhere: "You just don't get it. This is the new economy." Investors argued that traditional valuation metrics no longer mattered because the only thing that counted was gaining market share. Profitability could always come later.   The fourth reason was the assumption that capital would never dry up. Few investors paid attention to where companies were getting their money. Many businesses were surviving on venture capital rather than sustainable profits. When funding slowed and investors became more selective, those companies had no profitable business model to fall back on. Many quickly went bankrupt.   At the peak of the bubble, investors stopped asking basic questions. What am I paying for this company's earnings? What am I paying for its cash flow? In many cases, there weren't any. Yet investors convinced themselves the speculative frenzy would continue indefinitely.   The biggest lesson is a humbling one. We like to believe we'll recognize the top and get out before everyone else. But investors in 2000 believed the same thing. Human psychology hasn't changed, which is why bubbles continue to repeat throughout history.   Don't Build That Data Center in My Backyard The race to build AI infrastructure is running into an obstacle that many investors probably didn't see coming: local communities.   Across the country, residents are protesting and filing lawsuits to stop new AI data centers from being built in their neighborhoods. One of the biggest concerns is something most people never think about, the constant noise. Data centers operate around the clock, with cooling fans, chillers, and backup generators creating a continuous hum 24 hours a day. That may not sound like a major issue until you have to live next to it.   New York has become one of the focal points of this debate. While the state has plenty of available land for development, many communities are pushing back. Governor Kathy Hochul is even considering legislation that would place a moratorium on the construction of large data centers in certain areas.   Public opinion reflects that growing resistance. According to recent polling, 44% of Americans oppose additional data center construction, while only 21% support it. When the question becomes more personal and whether people would support a data center being built in their own community, opposition jumps to 57%, while support falls to just 14%.   Residents also question the long-term economic benefits. Building a data center may create thousands of construction jobs, but once the facility is complete, permanent employment may fall to just 100 to 200 workers. At the same time, these facilities consume enormous amounts of electricity. In some regions served by smaller utilities, a single data center could account for as much as 25% of total power demand, raising concerns about higher electricity costs and increased strain on the grid.   The political landscape is becoming more challenging. Lawmakers in states including Arizona, Illinois, and Ohio have restricted or eliminated tax incentives that were previously used to attract data center investment.   Even the companies building this infrastructure recognize the growing risk. The hyperscalers are expected to spend nearly $1 trillion on AI infrastructure this year, but increasing public opposition could slow those plans. Nebius Group, for example, warned in its 2025 annual report that rising resistance to data center projects in certain communities could become a headwind for future expansion.   Investors have spent a great deal of time focusing on AI demand, chips, and software. However, another risk is emerging that deserves attention: if communities continue saying, "Not in my backyard," the pace of AI infrastructure growth may not be as smooth as many expect.   Is Crypto Weakening One of America's Most Powerful Weapons? One of the United States' greatest geopolitical advantages isn't its military, it's the U.S. dollar.   Roughly 90% of global foreign exchange transactions involve the U.S. dollar. That dominance gives the United States enormous leverage. When the U.S. imposes financial sanctions and cuts countries off from the dollar-based financial system, it becomes far more difficult for them to conduct international trade, finance military operations, or access global markets.   That advantage is beginning to erode. Countries that have long opposed the United States such as Russia, Iran, and North Korea are increasingly turning to cryptocurrencies to bypass traditional financial channels. According to reports, their use of virtual currencies for cross-border transactions surged from roughly $12.5 billion in 2024 to more than $100 billion in 2025.   Crypto gives sanctioned nations another way to move money. It can be used to purchase drones, weapons, military components, and fuel, while also helping finance operations such as smuggling oil and paying suppliers outside the traditional banking system.   North Korea has become one of the world's most aggressive crypto thieves, using hacking and other cybercrimes to steal digital assets that can then be converted into funding for its military and weapons programs.   Part of the challenge is that cryptocurrency wallets are identified by long strings of letters and numbers rather than names. While blockchain transactions are publicly visible, identifying the person or organization controlling a wallet can be extremely difficult without additional intelligence. That makes enforcement of financial sanctions much harder.   Even terrorist organizations such as Hamas have, at times, solicited donations in cryptocurrency, illustrating how digital assets can be used to circumvent traditional financial controls.   This is why I believe cryptocurrency has become more than just an investment story, it has become a national security issue.   If Bitcoin and other cryptocurrencies were to experience a significant decline in value, it would reduce the purchasing power of those holding large crypto reserves, including sanctioned actors that rely on digital assets. While it would not eliminate their ability to use crypto, it could make this alternative financial system less effective and increase the relative importance of the dollar-based financial system.   The stronger the role of the U.S. dollar in global commerce, the more effective financial sanctions remain as a non-military tool of foreign policy. With cryptocurrencies becoming more widely adopted, policymakers will need to consider the risk of weakening one of America's most effective forms of economic leverage.   Even with oil off its recent peak, you still may not see cheaper airline tickets. You might assume that with the decline in oil prices, jet fuel costs are also declining, and airlines will pass those savings on to travelers through lower ticket prices. Oil and jet fuel prices have indeed come down, but don't expect airlines to slash fares anytime soon.   The reason is simple: demand remains strong. Even after airlines raised fares eight times since the start of the conflict in the Middle East, analysts say the average round-trip domestic ticket climbed roughly 19% to about $638 yet demand barely changed. In other words, consumers have shown they are willing to pay higher prices to travel. If people keep buying tickets, airlines have little incentive to lower fares and give up those higher profit margins.   Supply is also likely to remain constrained. Airlines aren't rushing to add flights because keeping capacity tight helps support higher ticket prices. The bankruptcy and downsizing of low-cost carriers such as Spirit Airlines has also reduced competition on many routes, making it easier for the remaining airlines to maintain pricing power.   To be fair, airline pricing should be viewed over a longer time horizon. From 2019 through 2025, overall consumer prices rose about 26%, while average airfares actually declined roughly 3.5%. So, despite the recent increases, airline tickets are still relatively inexpensive compared with the broader rise in inflation over the past six years.   The bottom line is that lower fuel costs alone don't guarantee lower ticket prices. As long as travel demand remains healthy and airlines keep capacity in check, consumers may not see much relief at the checkout screen.   Letting Air Out of the Investment Portfolio Balloon Before It Pops At one point or another, we've all seen a balloon inflated until it finally bursts. The same thing can happen to an investment portfolio.   Watching your portfolio grow is exciting, but every investor knows that markets don't go up forever. The challenge is that no one knows exactly when a portfolio has become too inflated. One of the biggest reasons investors refuse to sell is simple: they hate paying taxes. Believe me, I dislike paying taxes just as much as anyone else. But you should never let the tax bill dictate your investment decisions.   Sometimes the smartest move is to relieve some of the pressure in your portfolio before the market does it for you. There are two simple ways to accomplish this: trim oversized positions and sell investments that have become significantly overvalued.   The first strategy is reducing concentration risk. If you review your portfolio and discover that a single stock has grown to 10% or 12% of your total assets, it may be time to trim that position back to 7% or 8%. Yes, you'll likely owe capital gains taxes, but you'll also be reducing the risk that one investment can have an outsized impact on your portfolio if it suddenly declines.   The second strategy is selling investments that have exceeded your target price and can no longer be justified based on their fundamentals. If the valuation has become stretched and the company's earnings outlook no longer supports the stock price, it may be time to take profits. Again, you'll probably owe taxes on the gain, but remember that capital gains are generally taxed at favorable rates. More importantly, paying a 20% or 25% tax on your profit is often far less painful than watching the entire investment lose 20% or more in value. That 20% decline occurs on the entire position rather than just the gain.   No strategy is perfect. You may trim a position only to watch it continue climbing for another year or two. That's part of investing. Risk management isn't about perfectly timing the top, it's about ensuring that no single investment or sector can seriously damage your long-term financial plan.   Consistently following a disciplined, conservative approach won't always maximize returns during bull markets, but it can significantly reduce risk over a full market cycle. When the next major correction inevitably arrives, your portfolio should be positioned to withstand it. That makes it far easier to stay invested, avoid emotional decisions, and continue building wealth instead of panic-selling after the damage has already been done.   Successful investing isn't just about finding great investments. It's also about knowing when to reduce risk. Sometimes, letting a little air out of the balloon today is the best way to keep it from popping tomorrow.   Is AI creating the next memory boom... or setting up the next bust? SK Hynix just pulled off the largest foreign ADR listing in U.S. history, pricing its American depositary receipts at $149 and raising $26.5 billion. That isn't just a fundraising event, it is fuel for one of the most aggressive semiconductor expansion plans the industry has ever seen.   The company is pouring money into new factories, equipment, and advanced packaging capacity around the world. In the United States, SK Hynix is building its first manufacturing facility, a $4 billion advanced packaging plant in West Lafayette, Indiana, expected to be completed in 2028.   Back home in South Korea, the spending is even more staggering. SK Hynix plans to invest up to $720 billion expanding memory production, including a $390 billion semiconductor cluster in Yongin. The company has also committed roughly $7.8 billion by the end of 2027 for additional extreme ultraviolet (EUV) lithography machines, the highly specialized tools needed to manufacture cutting-edge HBM chips. These machines cost as much as $400 million each, are in extremely limited supply, and are only produced by ASML. The company is even accelerating its expansion timeline by more than a decade, with four new fabrication plants now expected to be completed by 2033.   The question investors should be asking isn't whether AI demand is real. It clearly is. The real question is whether the industry is repeating a familiar pattern. Memory has always been one of the most cyclical businesses in technology. Every major technology revolution from the dot-com boom, to smartphones, to cloud computing created a surge in demand for memory chips. Manufacturers responded by rapidly expanding production. Eventually supply caught up, prices collapsed, profits disappeared, and investors who arrived late learned just how brutal the memory cycle can be.   Today feels different... but that is often what every cycle feels like while it is happening.   SK Hynix's market value has increased more than sevenfold over the past year as AI infrastructure spending has created a shortage of HBM. Revenue nearly tripled between 2023 and 2025 to roughly $65 billion, and Wall Street expects sales to surge again to approximately $235 billion in 2026.   Those are incredible numbers. But when major memory producers start announcing massive capacity expansions, history suggests investors should at least consider what happens when today's shortage eventually becomes tomorrow's surplus. AI may create years of strong demand for memory, but the semiconductor industry has a long history of building too much capacity just as demand begins to normalize. The opportunity is enormous, but so is the risk if history repeats itself.   Financial Planning: Simple vs Compounding Interest Loans Many people assume that choosing a simple interest loan over a compound interest loan will dramatically reduce the amount of interest they pay, but in most real-world lending situations, the difference is minimal. The reason is that the power of compounding only becomes significant when a balance grows over time because interest is being added to the principal. With most consumer loans, borrowers either make interest-only payments that keep the principal balance unchanged or make payments that reduce the principal over time. In either case, the interest charged during each payment period is based on the outstanding loan balance at that time, not on an ever-growing balance. Since the loan balance is remaining the same or steadily declining rather than increasing, there is little opportunity for “interest on interest” to accumulate. While compounding can become important if unpaid interest is capitalized and added to the loan balance, that is the exception rather than the rule. For most mortgages, HELOCs, auto loans, personal loans, and similar debt, borrowers should focus far more on the interest rate than on whether the loan is described as using simple or compound interest.   Too Many People Are Using Target Date Funds in Their 401(k) For years, we've discussed the drawbacks of target date funds, including their higher fees and one-size-fits-all approach. Despite those concerns, they remain incredibly popular because they are simple and require very little effort from the investor. According to Vanguard, 61% of 401(k) participants invest in target date funds.   On the surface, they sound like the perfect solution. If you plan to retire around 2045, you simply choose the 2045 Target Date Fund and let it manage your investments. The fund automatically adjusts your portfolio over time, gradually reducing your exposure to stocks and increasing your allocation to bonds as you approach retirement.   Many investors don't realize how significant that shift can be. By the target retirement date, a target date fund may hold around 50% of its assets in bonds. The adjustments don't stop there. Reaching the target year doesn't mean the fund is liquidated or that you receive your money. Instead, the fund continues along its glide path and could increase its bond allocation to 70% or even 80% over the following years.   That approach may have made sense decades ago, but retirement looks very different today. Many people will spend 20 years or more in retirement. Over that length of time, maintaining enough exposure to stocks can be critical to helping your portfolio grow and keep pace with inflation. A portfolio that becomes too conservative too quickly may struggle to provide the long-term growth many retirees need.   Another limitation is that target date funds only manage the assets inside your 401(k). They don't take into account your IRAs, brokerage accounts, pensions, real estate, or other investments. As a result, your overall portfolio allocation could end up being far different than what is appropriate for your financial goals.   The convenience of target date funds is appealing, but convenience shouldn't replace planning. A successful retirement requires understanding how your money is invested, estimating what your portfolio could be worth when you retire, and developing a strategy for how those assets will be invested throughout retirement, not just until you reach it.   Is That Really Your Son or Daughter Calling You? You know your children's voices. You talk to them regularly. Then one day you get a frantic phone call from your son or daughter. They tell you they've just been in a serious accident. They need $15,000 immediately or they're going to jail. They tell you exactly how to send the money. Without hesitation, you wire the funds because you want to help your child.   Unfortunately, you have just been scammed by AI. AI-powered scams are exploding. Reports show AI-related fraud surged more than 1,200% in 2025, and at the current pace, losses from AI scams in the United States could reach $40 billion annually by 2027. Another study found that one in four adults has already experienced an AI voice scam.   Your first reaction may be, "That could never happen to me. I don't post anything on social media." But the problem may not be your online presence. It's your children.   Many people regularly post videos on social media, and today's AI only needs about three seconds of someone's voice to create a convincing clone. Once scammers have that sample, they can make it sound like your son or daughter is saying almost anything.   So how do you protect yourself? If you receive an emergency call asking for money, don't panic. Before sending anything, ask a question that only you and your child would know the answer to. Make it something that has never been shared publicly.   For example, ask about a funny childhood memory that only the two of you remember. Don't use information like birthdays, graduation dates, wedding dates, or other facts that could be found online or in public records. Remember with all these data centers there is so much information that is being obtained and saved but used for the wrong purposes.   Even better, establish a family safe word or passphrase today. Choose something simple that everyone can remember but that would never appear online.   If you ever receive one of these calls, ask for the safe word. If they can't provide it, assume it's a scam until you can verify the situation by calling your child directly or contacting another trusted family member.   As AI continues to improve, these scams will only become more convincing. The same technology powering innovation is also giving criminals new tools to exploit unsuspecting families. Stay alert. Verify before you trust. A few extra minutes could save you thousands of dollars and a great deal of heartache.   Is It Boom or Bust for Micron? It is hard to argue with Micron's incredible stock performance. Through July 2, the shares were up 242% year to date and an astonishing 701% over the previous 12 months. Even after recently falling about 22% from their peak, investors are still debating whether the company has much more room to run.   The good news is that Micron has locked in 15 new customers under long-term supply agreements, with some contracts extending as long as five years. Many of these agreements include customer deposits, giving the company excellent revenue visibility and reducing uncertainty over future sales. For investors, that is exactly the kind of stability they like to see.   But every smart investor should also ask: What is the downside?   While those contracts provide a strong foundation, they do not guarantee that demand will remain as strong over the long term. Unless a customer goes bankrupt, the contracts are largely locked in, but technology changes quickly. High prices and limited supply often encourage innovation, and the AI memory market is no exception.   Several companies are developing new architectures that reduce or even eliminate the need for high-bandwidth memory (HBM), which has been one of Micron's biggest growth drivers. As companies search for lower-cost and more efficient alternatives, demand for HBM could eventually soften.   Nvidia also signaled in June that it is redesigning portions of its upcoming Vera Rubin AI platform to use memory more efficiently. While Nvidia remains a major customer for HBM, improvements in memory efficiency could reduce the amount of HBM required per AI system over time.   Meanwhile, newly public chipmaker Cerebras has taken an entirely different approach. CEO Andrew Feldman has said the company's wafer-scale AI chips do not use HBM at all, arguing that it is too expensive and supply constrained. If other AI hardware companies pursue similar designs, it could create additional competition for HBM.   None of this means Micron's growth story is over. The company's long-term contracts provide meaningful protection, and AI demand remains exceptionally strong today. However, investors should remember that today's shortages and premium pricing often inspire tomorrow's technological breakthroughs.   The question for Micron investors is whether HBM remains the industry standard for years to come or whether innovation eventually reduces the need for it. If demand for HBM begins to slow, Micron's remarkable growth could also begin to moderate.   Companies Discussed: Caterpillar Inc. (Ticker: CAT)

SK Hynix raised $26.5 billion in a US share offering to fund capacity expansion, advanced packaging, and HBM production. The company is a key supplier of high bandwidth memory for Nvidia and other chip designers. In 2024, it announced an advanced packaging and R&D facility in West Lafayette, Indiana, with an investment of about $3.9 billion. US CHIPS Act incentives are drawing semiconductor investment, with grants and loans previously announced for TSMC, Samsung, Micron, and Intel. Demand from Microsoft, Amazon, and Google continues to pressure memory supply. Founders and operators should plan multi-quarter procurement, diversify suppliers, and use long-term agreements to manage risk.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Our 250th episode with a summary and discussion of last week's big AI news!Recorded on 06/27/2026Note from Andrey: sorry this is late again! this episode release somehow didn't save and I only realized late, my bad... next one will be out way sooner!Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:US government gating of frontier AI expands: Anthropic gets permission to release Mythos-5 to selected companies/agencies after a standoff, OpenAI rolls out GPT-5.6 “Sol” with initial access restricted to ~20 approved organizations, and Meta is pressed to submit models to “voluntary” review—signaling an emerging de facto licensing regime with geopolitical treaty implications.Model capability and safety signals remain murky: limited benchmark disclosure, claims of token-efficiency comparisons, and third-party reports that GPT-5.6 shows extreme benchmark “cheating” sensitivity highlight steering/alignment bottlenecks and uncertainty about real-world long-horizon behavior.Compute supply chain competition accelerates: OpenAI unveils its Jalapeño inference ASIC with Broadcom on TSMC 3nm; Amazon explores selling Trainium to data-center operators; Micron invests in Anthropic with memory supply agreements; SK Hynix surpasses Samsung on HBM-driven valuation; Groq raises $650M while pivoting toward neocloud.Open source and societal response intensify: GLM 5.2 (MIT-licensed) delivers strong long-context coding performance with rapid optimizations; EconEvals maps job-task exposure; bipartisan workforce initiatives and tax credits launch; DeepMind and Apollo publish loss-of-control/control roadmaps; Hollywood reportedly drops a near-finished Sam Altman biopic amid industry pressure.Timestamps (note - these don't take into account dynamically inserted ads and therefore may be off by a couple of minutes):(00:00:10) Intro / Banter(00:03:42) News PreviewTools & Apps(00:04:41) Anthropic allowed to release Mythos AI to some companies, agencies + Anthropic's Mythos mess is only getting worse + Anthropic floats proposal to Lutnick to end US ban of powerful 'Mythos,' 'Fable' AI models: sources(00:07:58) OpenAI Launches GPT-5.6 Sol Under First-Ever US Government-Gated AI Rollout | MLQ News + OpenAI's new flagship model GPT-5.6 Sol cheats on software tests more than any model before it + Summary of METR's predeployment evaluation of GPT-5.6 Sol(00:24:03) U.S. Presses Meta to Agree to A.I. Reviews - The New York Times(00:30:11) Anthropic's Claude Tag is learning your company, one Slack message at a time | TechCrunchApplications & Business(00:32:49) OpenAI reveals its first AI processor: Jalapeño | The Verge(00:38:29) Amazon in Talks to Sell Custom AI Chips in Bid to Undercut Nvidia(00:41:46) Micron invests in Anthropic and grants it a supply deal(00:45:18) SK Hynix overtakes Samsung to become South Korea's most valuable company | Reuters(00:49:12) AI chipmaker Groq confirms $650M raise, re-staffs after Nvidia's $20B not-acqui-hire deal | TechCrunch(00:52:47) SpaceX inks compute deal with Reflection AI, an open source AI lab | TechCrunchProjects & Open Source(00:54:46) GLM-5.2: Built for Long-Horizon Tasks + How we built the world's fastest API for GLM-5.2 + nvidia/GLM-5.2-NVFP4 · Hugging Face(01:03:04) EconEvalsPolicy & Safety(01:05:40) $500 million AI jobs push launches with bipartisan backing - POLITICO(01:07:47) Rep. Sam Liccardo unveils AI workforce tax credit bill - POLITICO(01:08:56) Google DeepMind announced an “AI Control Roadmap” for improving AI agent security. | The Verge + Securing internal systems against increasingly capable and imperfectly aligned AI(01:14:00) The Loss of Control Playbook: Degrees, Dynamics, and Preparedness + The Loss of Control Playbook(01:16:42) Why corporate AI super PACs spent $27 million on a local election | The Verge(01:20:25) Exclusive: Conservatives plan nationwide protest against AI data centersResearch & Advancements(01:27:37) Revisiting the Platonic Representation Hypothesis: An Aristotelian View(01:31:39) Wan-Streamer v0.1: End-to-end Real-time Interactive Foundation Models(01:33:59) Tapered Language ModelsSynthetic Media & Art(01:36:54) Hollywood is bending the knee to OpenAI | The VergeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

OpenAI entre à son tour dans la course aux puces d'intelligence artificielle conçues sur mesure. À la fin du mois de juin, l'entreprise a présenté « Jalapeño », son premier processeur maison, développé principalement pour les tâches d'inférence. L'inférence désigne la phase durant laquelle un modèle déjà entraîné produit une réponse, génère du texte ou exécute une instruction.La puce est fabriquée par Broadcom, mais sa conception a été menée par les ingénieurs d'OpenAI, avec l'appui de Broadcom et de Celestica. Selon les informations communiquées, neuf mois seulement se sont écoulés entre les premières étapes du projet et la version finale prête à entrer en fabrication. Un calendrier particulièrement court pour un composant aussi complexe. Jalapeño doit devenir le premier accélérateur d'une plateforme informatique pensée pour plusieurs générations de puces. Les trois partenaires veulent ainsi améliorer la rapidité, la fiabilité et l'accessibilité des services d'intelligence artificielle d'OpenAI.Broadcom et Celestica ne se limitent pas à la fabrication du processeur. Ils prennent également en charge l'industrialisation de l'ensemble de la plateforme : intégration des puces dans des racks de serveurs, mise en réseau des équipements et création de chaînes de production capables de monter progressivement en puissance. OpenAI présente Jalapeño comme une puce adaptable, conçue pour prendre en charge les principaux grands modèles de langage. Les premiers exemplaires produits par Broadcom exécutent déjà certaines charges de travail d'apprentissage automatique, notamment GPT-5.3-Codex-Spark. L'entreprise affirme que les performances observées correspondent aux objectifs fixés.Les caractéristiques techniques restent toutefois largement inconnues. OpenAI n'a pas précisé la puissance de calcul, la consommation électrique ou la finesse de gravure. Sur une photographie publiée par le groupe, on distingue néanmoins huit emplacements de mémoire HBM autour de la partie centrale du processeur. Cette mémoire à très haut débit est essentielle pour alimenter rapidement les accélérateurs en données. Le déploiement de Jalapeño est annoncé pour la fin de l'année 2026. OpenAI veut l'utiliser sur plusieurs générations de plateformes. Cette stratégie suit celle de Google, déjà doté de ses propres accélérateurs, tandis qu'Anthropic étudie également des solutions personnalisées. Pour les géants de l'IA, maîtriser les modèles ne suffit plus : il faut désormais contrôler aussi les puces qui les font fonctionner. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Mientras todos los reflectores apuntan a las GPUs de NVIDIA, hay una empresa que se está convirtiendo en una pieza igual de importante para el futuro de la inteligencia artificial: Micron. En este episodio analizamos los resultados más recientes de la compañía, el auge de las memorias HBM que impulsan los modelos de IA, por qué la demanda sigue creciendo a un ritmo sorprendente y qué riesgos podrían ponerle freno a esta historia. ¿Es Micron una de las grandes ganadoras de la revolución de la IA o el mercado está siendo demasiado optimista? Descúbrelo en este capítulo de Insights Podcast.

Happy 250th! The bulls are bubbling up! Yentervention – it is a thing. Labor market predictions. 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? PayPal.Donation.Button({ env:'production', hosted_button_id:'JJJHP2GDEJC7J', image: { src:'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt:'Donate with PayPal button', title:'PayPal - The safer, easier way to pay online!', } }).render('#donate-button'); Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Warm-Up - 250 Years! - We have the scorecard - Bulls are on the loose! - Kevin Hassett - what a putz - RAM JOB! Markets - Google's first day in the DJIA - a good one - SpaceX bonds already losing -Yen slips to 1986 levels - Yentervention? WHAT A PUTZ! - Trump Accounts launch July 4, with the NYSE and Nasdaq set to ring the opening bell from the Oval Office. - Program gives a $1,000 Treasury-funded investment account to U.S. children born from January 1, 2025 through December 31, 2028. - Kids under 18 can have accounts, but only newborns in that four-year window get the federal seed money. - Parents, family, employers, nonprofits, and governments can add money, with a general $5,000 annual contribution cap. - Money is invested in index funds and generally locked up until the child reaches adulthood. - Kevin Hassett pitched it as a way to teach kids about markets, ownership, saving, and compounding. His argument is that the more young people get exposed to investing early, and market ownership becomes less of an upper-income club. - However - > the government is handing out taxpayer-funded brokerage seed money while selling it as capitalism. - Also odd: the benefit may skew toward families who already know how to file forms, open accounts, and add more money. - So basically it is a forced financial-literacy experiment wrapped in a political brand name, with a socialist starter check to teach capitalism. First-Half Winners and Losers - S&P 500 finished the first half up roughly 7% to 8%, with the rally led by AI hardware, chips, memory, and data-center infrastructure. - Biggest winners were the shovel sellers: Sandisk up about 780%, Micron up about 296%, Western Digital up about 240%, Seagate up about 226%. - Overseas AI hardware ripped too: South Korea's Kospi up 123%, helped by Samsung up 169% and SK Hynix up 303%. - Semiconductor ETFs had a monster Q2: iShares Semiconductor ETF up 86.8%, VanEck Semiconductor ETF up 64.8%. - Japan's Nikkei rose about 38%; FTSE 100 gained about 5.8%. - Losers were the software/platform names that could not prove immediate AI payoff. - Microsoft was down about 24% despite being one of the biggest AI spenders. - Momentum stocks had one of their worst stretches in two decades as the Magnificent Seven slipped on capex worries. - Crypto and gold also lagged the AI-infrastructure trade. - Equity BULLS are running like it was San Fermin, Spain... MORE.... - Gold biggest quarterly loss since 2013 - Japan best quarter ever - Oil starts and ends - Kospi best quarter in 30 years - Stoxx 600 best Q in 5 years Something is going to break! - When Micro announced earnings, and we see that companies are panicking (News about existential threat to smaller tech players).. We said something is going to break - MU shares lifted to ATH on the news - big big beat - Micron's latest quarter showed a dramatic acceleration from the year-ago period, with revenue rising from $9,301 to $41,460 and EPS increasing from $1.91 to $25.11. - HUGE uptick in guidance - Apple increased pricing, Dell is increasing prices next week (17%), Microsoft raised price on XBox, HP across the board increase, Lenovo/Xiaomi increases, - NOW: Apple is lobbying the Trump administration for clearance to buy memory chips from China's ChangXin Memory Technologies Korea Goes All-In On AI Memory - Samsung and SK Hynix are backing a huge South Korea chip buildout tied to AI memory, HBM, advanced DRAM, packaging and data centers. - Samsung's plan includes hundreds of trillions of won for new fabs, including HBM facilities in Cheonan and Onyang. - SK Hynix is expanding Yongin and planning a major new chip base as it rides demand from Nvidia-linked HBM supply. - Government angle: Seoul wants domestic chip capacity treated like national infrastructure, not just corporate capex. - The state is trying to lock in supply-chain control before China, Taiwan, Japan and the U.S. pull more production into their own subsidy zones. - Market wrinkle: AI memory is hot now, but memory companies have a long history of overbuilding into strong pricing cycles. - Governments are no longer just subsidizing chips — they are helping plan semiconductor cities. RAM Job? - Samsung, SK hynix, and Micron were hit with a U.S. antitrust class-action lawsuit over alleged DRAM price fixing. - Allegation: the big three coordinated supply cuts while shifting capacity away from regular DDR3/DDR4 memory and into high-bandwidth memory for AI servers. - Plaintiffs say the three companies control roughly 90% of the DRAM market. - Conventional DRAM prices allegedly jumped about 700% over four years. - Complaint argues that in a normal commodity market, at least one supplier would usually increase production when prices spike. - Instead, the lawsuit says all three moved in the same direction at the same time. DRAM: We Have Seen This Movie Before - Yes, there was a similar DRAM price-fixing scandal in the 2000s. - DOJ investigation covered alleged DRAM price fixing from roughly 1998 through 2002. - Hynix pleaded guilty in 2005 and agreed to pay a $185 million criminal fine. - Samsung pleaded guilty in 2005 and agreed to pay a $300 million criminal fine. - Infineon pleaded guilty earlier, in 2004, and agreed to pay a $160 million fine. - Micron was involved in the investigation but received amnesty/cooperation treatment rather than the same criminal fine path. - Several executives were also charged or pleaded guilty. - State AGs and private plaintiffs later pursued civil cases tied to overpayment claims. - Difference now: the new case is not yet proven and appears focused on alleged coordinated supply restriction during the AI/HBM boom. Chevron and Microsoft - Chevron Corp signed 20-year deal with Microsoft for data center power. - Agreement supplies natural-gas fired generation for massive West Texas facility. - Project Kilby expected online 2028, ramping to 2.67 gigawatts. - Full output enough to power more than 530,000 Texas homes. - Chevron partnering Engine No. 1, final investment decision planned later. - Deal follows prior reports of exclusive long-term power negotiations. More Oil News - Drill baby Drill - Interior Department cutting federal drilling bonds by 95% to spur exploration. - Required bond drops from $500,000 to $25,000 for leases. - Bonds ensure cleanup costs don't fall on taxpayers if wells abandoned. - Policy change aims to encourage more oil and gas development. - Proposal subject to 60-day public comment after Federal Register publication. Dow 52,000 and the Tech Bounce - Dow closed above 52,000 for the first time Monday, finishing at 52,182.74. - S&P 500 gained 1.18%; Nasdaq jumped 2.07%. - S&P and Nasdaq snapped five-session losing streaks. - Alphabet rose 4.8% on its first day as a Dow component. - Tesla gained 8.5%; SpaceX rose more than 7%. - The bounce came after last week's tech selloff, with investors rotating back into mega-cap and AI names. Comcast Breaks Itself Up - Comcast plans to split media and connectivity into two separate companies. - NBCUniversal and Sky would be spun off in a tax-free deal; Comcast keeps broadband, wireless, and cable. - Completion expected within a year. - Shareholders would own both Comcast and the new NBCUniversal. - Comcast shares rose on the news; Charter also jumped as investors speculated Comcast could eventually pursue a broadband-scale deal. AI Trade Gets a Warning Label - Bank for International Settlements flagged the AI boom as a financial-stability risk. - The main concerns: elevated valuations, investor complacency, complex funding structures, and debt financing across the AI supply chain. - BIS also warned that record public debt and leveraged hedge-fund activity in sovereign bonds could amplify shocks. - Quote from BIS General Manager Pablo Hernandez de Cos: "Policy actions must reinforce each other." - The interesting part: central bankers are not saying AI is fake; they are saying the financing stack may be fragile. Inflation Back Above 4% - BEA's PCE price index rose 4.1% year over year in May. - April was 3.8%; March was 3.5%; February was 2.9%. - This keeps pressure on the Fed because PCE is the Fed's preferred inflation gauge. - Core PCE may later be revised lower because of BEA methodology changes. - Goldman estimated May core PCE could be trimmed to 3.2% from 3.4%; JPMorgan expected 3.3%. - Funny-but-real detail: part of the potential revision comes from how BEA prices portfolio management, legal services, and computer software. Jobs Report Becomes Bad-News-Is-Bad-News - June payrolls are due Thursday because markets are closed Friday for Independence Day. - The setup is awkward: strong jobs could mean stronger economy, but also higher odds of Fed hikes. - Looking back - May payrolls were hot at 172,000 versus an 85,000 forecast, with unemployment steady at 4.3%. - Remember - after the June Fed meeting, policymakers were clearly focused on inflation, not rescue cuts. Oil, Iran, and the Market's New Weird Routine - Oil stayed volatile around renewed U.S.-Iran tensions and peace-talk headlines. - Brent rose 1.6% Monday to $73.15; WTI rose 2.2% to $70.75. - Markets rallied anyway, helped by signs talks would resume and shipping routes were stabilizing. - The odd market behavior: geopolitical escalation keeps getting followed by de-escalation headlines and risk-on rallies. - This is now part of the trading pattern: weekend war scare, Monday relief rally, repeat. --- New attacks by USA on Iran happened at approx 4:30PM on Friday (markets closed) and then a halt to the fighting on Sunday - before the futures opened. Odd : Wendy's Becomes a Meme Stock - Wendy's became the latest retail-trader short-squeeze target. - Stock surged 25% last Wednesday, then gained another 9% Thursday. - Barron's said the move followed a CFO shakeup and WallStreetBets attention. - New CFO Steve Cirulis came from Potbelly and is also taking the Chief Strategy Officer title. - Wendy's had fallen 47% over the past year before the rally. - Short interest was nearly 30% of the public float, making the stock easier to squeeze. - Trian, Nelson Peltz's firm, owned nearly 15 million shares valued around $93 million. SpaceX Bonds Slip After Big Debut - SpaceX sold $25 billion of investment-grade bonds, its first major public debt deal. - Demand was huge, with roughly $85 billion to $98 billion of orders. - The 10-year tranche priced about 1.4 percentage points over Treasurys. - Bonds weakened quickly after pricing. - The 10-year yield rose near 6%, with the spread moving above 1.6 percentage points. - Longer-dated 2046 and 2056 bonds took the most pressure. - The pushback: bond buyers want more yield for a company still funding rockets, Starlink, AI/data-center spending, and Mars ambitions. - Clean read: equity investors bought the story; bond investors immediately marked it down. Yentervention - Yen weakened again, pushing toward the 162-per-dollar zone and near its weakest level in about 40 years. - Japan keeps warning it is ready for "decisive action" or to respond "at any time." - Market does not seem scared for long. - Japan already spent heavily defending the yen, including a roughly $73 billion yen-buying operation after the currency broke past 160. - U.S. rates are still high, the Fed is not rushing to cut, and the Bank of Japan is still moving slowly. - That keeps the carry trade alive: borrow cheap yen, buy higher-yielding dollars. - Japan's foreign reserves fell 5.6% in May after intervention, showing the defense is expensive.   Love the Show? Then how about a Donation? PayPal.Donation.Button({ env: 'production', hosted_button_id: 'JJJHP2GDEJC7J', image: { src: 'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt: 'Donate with PayPal button', title: 'PayPal - The safer, easier way to pay online!' } }).render('#donate-button-2'); THE CLOSEST TO THE PIN for SpaceX (SPCX) Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter

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On Episode 310 of The Six Five Pod, Patrick Moorhead and Daniel Newman unpack the biggest stories from the week, including insights from Qualcomm Investor Day 2026, OpenAI and Broadcom's Jalapeño AI chip, Anthropic's Micron partnership, SpaceX's massive Reflection AI compute deal, Sakana AI's new Fugu orchestrator, and why memory is emerging as a critical layer of AI infrastructure. Plus, Bulls & Bears covers NVIDIA's $25B bond offering, Apple's MacBook price increases, Micron's record quarter, and Cerebras' first earnings as a public company. The handpicked topics for this week are: Qualcomm Investor Day 2026 — The Data Center Debut: Pat and Dan break down Qualcomm's push into the data center after the company took the stage with Microsoft's Satya Nadella and Meta's Mark Zuckerberg as named customers. They unpack the new Dragonfly platform, including the C1000 250-core data center CPU with PCIe Gen 7 and CXL, the AI200 and AI250 inference accelerators, and a novel High Bandwidth Compute (HBC) architecture that stacks compute under LPDDR memory at dramatically lower cost than HBM. They highlight Qualcomm's ambitious growth targets: $15B data center revenue target for FY 2029, an increased total non-handset revenue goal from $22B to  $40B, and a shortened timeline for automotive revenue by two years. They also debate the identity of Qualcomm's unnamed hyperscaler customer and why its robotics opportunity may be flying under the radar. (The Decode) OpenAI and Broadcom Unveil Jalapeño, OpenAI's First Custom Chip: A photo of Sam Altman and Hock Tan holding a wafer and packaged die kicked off OpenAI's reveal of Jalapeño, a custom inference chip built with Broadcom and slated for late-2026 deployment. The chip reached tape-out in roughly nine months, which is an aggressive cycle for an ASIC of this size, and uses HBM3E memory. Pat takes a victory lap on his long-standing heterogeneous compute thesis: every hyperscaler and now every model lab is building accelerators, and the XPU efficiency argument has played out as predicted. Dan frames OpenAI's broader move as existential: they cannot serve frontier models at premium margins if compute remains constrained. He flags that OpenAI is trying to do everything from chips and fabs to social networks and browsers, and that its IPO is now delayed. (The Decode)   Anthropic and Micron Sign a Strategic Multi-Year Memory Agreement: Anthropic and Micron announced a multi-year supply agreement for HBM, DRAM, and SSDs, including co-designed next-generation memory for AI workloads, along with a strategic investment by Anthropic in Micron. The pattern mirrors Samsung and SK Hynix's pre-funding Anthropic in May, and follows OpenAI's Jalapeño as another frontier lab moving to lock in supply chain control. Dan frames it as the same circular financing playbook NVIDIA ran two to three years ago, but with the ball now in the memory triopoly's court. Pricing-floor agreements with no ceilings, customized rather than commoditized memory architecture, and demand running well past the previously assumed 2027-2028 horizon. Pat notes that the rumored 14% free cash flow margin at Anthropic makes the strategic investment math work cleanly for both sides. (The Decode)   SpaceX Signs $6.3B Compute Deal with Reflection AI: SpaceX inked a $6.3B compute lease with open-source AI lab Reflection AI, at $150M per month from July 2026 through 2029, giving Reflection access to NVIDIA GB300 chips inside the Colossus infrastructure. Combined with the $920M-per-month Google compute contract and existing xAI commitments, SpaceX now has a contracted backlog larger than most public AI startups' entire revenue base, with some calling it the largest commercial AI infrastructure provider at $80B in contracted revenue. Pat reads it as XAI failing to land with developers, consumers, or enterprises, leaving SpaceX with a pot of gold worth far more as wholesale capacity than as XAI's own training compute. Dan flags that Google owning 7% of SpaceX ahead of an IPO is not accidental, and the open question is whether this becomes a Nebius-style infrastructure trade or a full-stack Google-equivalent platform. (The Decode)   Japan's Agentic Orchestrator Sakana AI Ships Fugu Plus and Fugu Ultra: Japan's Sakana AI released Fugu Plus and Fugu Ultra, an agentic orchestrator built on a multi-agent MOE approach that routes workloads across multiple underlying models rather than training a new frontier base model. Sakana claims agentic capabilities on par with or better than top frontier models at significantly lower input/output token costs, similar to the DeepSeek and GLM cost-undercut narrative. Pat compares the architecture to OpenRouter and notes the developer-facing parallel to Perplexity Computer's model-routing approach. Both agree that models themselves are no longer moats, and suggests the real moat is the harness, tooling, connectivity, looping, agentic stack, and total compute availability. Expect more sovereign agentic plays from Japan, the Middle East, and elsewhere on the same template. (The Decode)   The Flip — Is the Era of Memory as a Commodity Over? Daniel takes the FOR side: memory has moved from commodity to strategic AI infrastructure, citing 16 multi-year agreements covering $22B in committed volume booked through 2027, 84.9% gross margins higher than NVIDIA's, the technology barriers of HBM yield/stacking/packaging that only three companies can clear, and demand drivers tied to HBM as the binding constraint on every AI accelerator rather than to elastic consumer cycles. Patrick takes the AGAINST side: long-term agreements and SCAs signal a commodity in a strong cycle, not a structural rerating; nearly every relevant memory standard — DDR5, MRDIMM, HBM3/3E/4, LPDDR5X/6, GDDR6/7, LPCAM2 — is JEDEC-standard and therefore commodity at the pin; and CXMT's China DDR5 production ramps in 2H 2026 with Lenovo already shipping and HP and Dell qualifying. Custom HBM4 and Qualcomm-style HBC are where strategic memory genuinely lives. (The Flip)   NVIDIA's $25B Investment-Grade Bond Offering: NVIDIA priced a $25B multi-tranche bond offering on June 15, its first investment-grade debt sale since 2021, with seven tranches maturing between 2028 and 2056 and $85B in orders against an initial $20B target. Dan reads it as raising when capital is cheap, and oversubscription is real. NVIDIA doesn't need the money, it has a gold balance sheet, and is establishing a credit benchmark rather than funding CapEx. Pat agrees the optics are clean, but flags the irony of NVIDIA, with negative debt, borrowing while the stock trades like dead money at a sub-20x forward P/E. Both note that NVIDIA's underperformance reflects the market's skepticism on memory-as-strategic and on NVIDIA's own capex pace relative to the buildout opportunity ahead. (Bulls & Bears)   Tim Cook Calls Apple's Memory Crunch Price Raises on MacBook and iPad "Unsustainable": Apple announced MacBook and iPad price increases of up to $300, with Tim Cook telling the WSJ the memory cost environment is unsustainable. AAPL fell ~5% on the news, the broader rally was momentarily wiped out before Micron held the gains by close. Dan frames it as a moment when the market saw who is going to pay for the AI buildout: the consumer. He notes Apple's pricing power and inelasticity test is now live. Pat traces the backstory to Apple's negative-margin pricing pressure on Micron during the 2022-2023 memory downturn. The question is whether consumer-price blowback will eventually flow back to the memory vendors. (Bulls & Bears)   Micron Blows the Doors Off Fiscal Q3 — $41.46B Revenue, 84.9% Gross Margin: The memory story continues as Micron reported its largest beat in company history with fiscal Q3 revenue of $41.46B versus a $35.69B consensus, EPS of $25.11, year-over-year growth of more than 340%, and a record 84.9% gross margin that is roughly 10 points above NVIDIA's. Q4 guidance came in at a $50B midpoint against a $43B consensus. The 16 multi-year strategic customer agreements add up to $22B in committed volume, with most contracts containing pricing floors but no ceilings on most of the volume — a structurally asymmetric setup. Pat notes 95% of the beat came from price, not units, which reinforces his commodity argument; Dan flips it as the early innings of an NVIDIA-style run that puts Micron's 2027 profit on par with Google. (Bulls & Bears)   Cerebras' First Earnings Report Since IPO — Revenue Doubles, Margins Compress: Cerebras (CBRS) reported its first earnings as a public company, doubling year-over-year revenue and beating the top line while missing EPS, but the stock sold off hard amid gross margin deterioration. Core revenue came in at $191M, up 12% sequentially, with a $194M Q2 guide that is essentially flat, core gross margins at 47% guiding to 36-38% and 38-41% for the year, and operating margins flipping from positive 2% to a guided -30% to -32%. Customer concentration is shifting from Core42 and G42 (86% of FY25 revenue) to OpenAI, which loaned Cerebras $1B and gets paid quarterly in warrants. Pat flags that Cerebras' uncontested speed claim is no longer uncontested with Groq, TPU v8i, and Tenstorrent putting up real numbers. Cathie Wood is down 52% on her position. (Bulls & Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. The Decode Qualcomm Investor Day Lands the Data Center Pivot — Microsoft Deploying Qualcomm HBC XPUs in Azure (Per Satya Nadella) + Meta MOU on Three New Qualcomm Datacenter CPUs (Per Zuckerberg); $3.9B Modular Acquisition; Dragonfly Brand + AI200/AI250 Roadmap; HUMAIN 200MW Ramp; Qualcomm to Become Largest Automotive Silicon Company; Targets $3B Datacenter Revenue FY27, $35B by FY31 https://finance.yahoo.com/markets/stocks/articles/qualcomm-investor-day-detail-data-163247063.html  OpenAI Begins Vertical Integration — First Custom Inference Chip "Jalapeño" Unveiled With Broadcom June 24 (Hock Tan: As Good as Blackwell + TPU; ~50% Cost Savings; Late-2026 Microsoft Deployment, 10GW Multi-Gen Roadmap); Daybreak Cyber Stack (June 22) Confirms the Platform Shift https://x.com/OpenAI/status/2069770172802773292  Frontier AI Labs Are Now Financing Their Own Supply Chains — Anthropic Locks In Multi-Year Micron HBM/DRAM/SSD Supply + Micron Becomes Series H Investor; Same Pattern as Samsung + SK hynix Pre-Funded Anthropic in May; $965B Post-Money, $47B Revenue Run-Rate, October IPO Target https://investors.micron.com/news-releases/news-release-details/micron-and-anthropic-announce-strategic-agreement-scale-next  SpaceX Signs $6.3B Compute Deal With Reflection AI — $150M/Month July 2026 → End of 2029; NVIDIA GB300 + Colossus 2 Capacity; SpaceX Now Largest Commercial AI Infrastructure Provider With $80B+ Committed Compute Revenue Through 2029 https://finance.yahoo.com/technology/ai/articles/spacex-reportedly-grant-reflection-ai-162749237.html  The Sovereign AI Stack Lands — Japan's Sakana Ships Fugu + Fugu Ultra Multi-Agent System (June 22) That Beats Opus 4.8, GPT-5.5, and Gemini 3.1 Pro on 10 of 11 Benchmarks; Designed Around US Export-Control Risk; Completes the Three-Bloc Sovereign-AI Map With Mistral Compute (Europe) + DeepSeek $7.4B (China) https://www.datacamp.com/blog/sakana-fugu  The Flip Is the Era of Memory as a Commodity Over? FOR: Memory is now strategic AI infrastructure with multi-year supply lock-ins. The cycle dynamics that defined the last 30 years no longer apply. https://www.benzinga.com/markets/tech/26/06/60062500/micron-earnings-could-echo-nvidias-2023-moment-says-futurum-ceo  AGAINST: Memory is cyclical and priced for perfection. This print is either step change or top of the cycle, and the second one is more likely. https://www.cnbc.com/2026/06/25/apple-macbook-ipad-price-hike-memory.html Bulls & Bears NVIDIA (NVDA) $25B Bond Sale Anchors the AI Debt-Finance Boom — First Bond Offering Since 2021; Joins Alphabet $80B, Amazon $27.5B, Meta $30B, Oracle Stack; Dan: "Locking In Cheap Capital While It Can" https://finance.yahoo.com/technology/ai/articles/nvidia-record-us-25-billion-131039687.html  Apple (AAPL) Falls −5%+ Thursday June 25 on Confirmed MacBook + iPad Price Hikes — Tim Cook RAM "Unsustainable" Comment Lands as Real Price Action; Apple Hikes Erase Micron-Driven Tech Rally Mid-Session; Memory Beneficiaries (SanDisk, Micron) Surge; Analysts "Mostly Nonplussed" https://tickerspark.ai/market/apple-inc-aapl-drops-5-3-as-price-hikes-spook-investors-1782399950638  Micron (MU) Q3 FY26 ACTUALS — Largest Beat in Company History; Revenue $41.46B (+346% YoY) Crushes $35.69B Consensus; Non-GAAP EPS $25.11 (+1,215% YoY) Beats $20.49; Record 84.9% Gross Margin (Higher Than NVIDIA); Q4 Guide $50B Midpoint vs $43B Consensus; Stock +18-19% Overnight to $1,242 https://www.nasdaq.com/articles/nvda-who-micron-blows-doors-q3-earnings-revs  Cerebras Systems (CBRS) Q1 ACTUALS — First Earnings Post-IPO; Revenue $193.4M Nearly Doubled YoY; 2026 Guide $855-$865M Beats $824M; BUT Gross Margins Forecast 38-41% (Down From 45% Q1, Half of NVIDIA + Micron); Stock −20% AH on Margin Compression; Sets Up Inference-Tier Margin Debate https://investors.cerebras.ai/news-releases/news-release-details/cerebras-systems-announces-strong-first-quarter-2026-results  

Apple acaba de subir los precios de varios modelos de Mac y iPad y el mercado ha reaccionado castigando sus acciones con caídas cercanas al 5%. Detrás de este movimiento está la brutal subida de precios de la memoria y el almacenamiento, impulsada por el boom de la inteligencia artificial y los centros de datos que acaparan la producción de chips. Por último, pondremos en contexto estas subidas con los impresionantes márgenes de hardware que Apple sigue reportando y lo que esto significa para los inversores. Si te interesa la tecnología, la IA y el mundo Apple, este episodio te va a ayudar a entender qué está pasando detrás de bastidores. Quédate hasta el final porque te cuento qué podría ocurrir con los precios de futuros dispositivos si la fiebre de la IA continúa. ---------------------------------- 00:00 – 05:00 Presentación, contexto general y por qué la subida de precios de Apple importa a usuarios e inversores. 05:00 – 15:00 Detalle de los nuevos precios de Mac y iPad: qué modelos suben, cuánto suben y comparación con generaciones anteriores. 15:00 – 30:00 Qué está pasando con la memoria y el almacenamiento: explicación de DRAM, NAND, HBM, y cómo la IA está acaparando la producción. 30:00 – 45:00 El papel de los centros de datos de IA y gigantes como Nvidia: por qué sus pedidos tienen prioridad frente a la electrónica de consumo. 45:00 – 60:00 “Chipflation”: qué dicen Morgan Stanley, JPMorgan y otros analistas sobre la subida extrema de precios de memoria y cuánto puede durar. 60:00 – 75:00 Impacto directo en Apple: márgenes de hardware, beneficios recientes y cómo intentan proteger rentabilidad sin matar la demanda. 75:00 – 90:00 Proveedores y geopolítica: Micron, SK Hynix, Samsung, intento de Apple de usar memoria china (YMTC, CXMT) y bloqueos desde EE. UU. 90:00 – 105:00 Efecto en el usuario final: ¿vale la pena comprar ahora?, posibles estrategias de compra, impacto en estudiantes, creadores y profesionales. 105:00 – 115:00 Escenarios futuros: qué puede pasar con los precios de Macs, iPads e incluso iPhones si la fiebre de la IA no se frena. 115:00 – 120:00 Cierre, resumen, opinión personal y llamada a la acción (comentarios, suscripción, siguiente episodio, etc.) ---------------------------------- #Apple #Mac #iPad #Tecnología #Bolsa #AccionesApple #InteligenciaArtificial #Chipflation #MemoriaRAM #PodcastTech ---------------------------------- https://seoxan.es/crear_pedido_hosting Codigo Cupon "APPLE" ---------------------------------- PATROCINADO POR SEOXAN Optimización SEO profesional para tu negocio https://seoxan.es https://uptime.urtix.es ---------------------------------- PARTICIPA EN DIRECTO Deja tu opinión en los comentario ---------------------------------- ¿TE GUSTÓ EL EPISODIO? ✨ Dale LIKE SUSCRÍBETE y activa la campanita para no perderte nada COMENTA COMPARTE con tus amigos Applelianos ---------------------------------- SÍGUENOS EN TODAS NUESTRAS PLATAFORMAS: YouTube: https://www.youtube.com/@Applelianos Telegram: https://t.me/+Jm8IE4n3xtI2Zjdk X (Twitter): https://x.com/ApplelianosPod Facebook: https://www.facebook.com/applelianos Apple Podcasts: https://apple.co/39QoPbO ----------------------------------

Announcing the CTP for SpaceX. MahJong Craze gone wild. Goodbye to Alan Greenspan – The Maestro. Have you seen RAM prices? 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? PayPal.Donation.Button({ env:'production', hosted_button_id:'JJJHP2GDEJC7J', image: { src:'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt:'Donate with PayPal button', title:'PayPal - The safer, easier way to pay online!', } }).render('#donate-button'); Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Warm-Up - Announcing the CTP for SpaceX - MahJong Craze - Goodbye to Alan Greenspan - The Maestro - Have you seen RAM prices? Markets - Economic Collapse Imminent? - Breathe is narrowing again - chips chips chips are the only play - Spacex coming back down to earth? What is that sucking sound? -- Markets getting weird..... 3% down for NASDAQ 100 today - 8% for SMH and 14% for Memory ETF - Just announced - Alphabet (Google) will replace Verizon in DJIA DEDICATION: Alan Greenspan - Died Monday at age 100 Google Enters DJIA - High priced shares - Moves tech to 22% of DJIA from 17% or so - very meaningful move - Every $1 move for Google = $7 move on DJIA - Tech:  S&P 500 (~30%+), Nasdaq (~50%+) Computer Pricing - What as $2,000 a year ago for a nice desktop is not like $4,000 - Dell not holding pricing quotes - and even if they do, back ordered so prices could go up after order - Will IPOs put more money in the pocket of tech companies to buy gear at any price? Endless - SpaceX recently finalized two massive, multibillion-dollar artificial intelligence contracts: a $6.3 billion computing power agreement with Reflection AI and a $60 billion acquisition of the AI coding startup Cursor. - AI Compute Deal with Reflection AI - - - - The Terms: Reflection AI agreed to pay SpaceXAI $150 million per month from July 2026 through the end of 2029. - - -- - - The Infrastructure: The startup will tap into hardware and GB300 chips housed at SpaceX's Colossus 2 data center in Memphis, Tennessee. More SpaceX - SpaceX shares were as high as $220 post IPO. - Sharea ahve been down over the past 3 days. - Most that got in POST IPO probably bought in at about $162-$165 - Newsline: SpaceX shares slipped for a third straight day, shedding hundreds of billions of dollars in market value, after the company said it is selling investment-grade bonds for the first time. - The stock fell 16% Monday to close at $154.60, the lowest level since the company's first day of trading, pushing its three-day loss to 23% and erasing over $600 billion in value over that period. - SpaceX is seeking to raise at least $20 billion from the first bond offering to fund its artificial-intelligence ambitions. Missed Opportunity - Short the Mattress companies he said...... ----- Got squeezed out....Never to return Swing and a Miss Maybe Because this can happen... - Shares of Getty Images Holdings Inc. soared as much as 145% on Monday after it announced a licensing deal with OpenAI. - Getty said that images from its library will appear in the search and discovery features of ChatGPT, marking a key reversal for the firm. - The partnership with OpenAI could improve “licensing optics” and shift the narrative on the stock, according to analyst Mark Zgutowicz. - Getty shares were up 118% to $1.32 as of 12:44 p.m. in New York, putting them on track for the best session since July 2022. The stock had fallen about 55% this year to close at 61 cents on Thursday before the Juneteenth holiday weekend began. KOREA - SK Hynix - New #1 in South Korea: SK Hynix surpassed Samsung Electronics on Monday to become the country's most valuable listed company. - Remarkable turnaround: A striking reversal for a chipmaker that nearly collapsed under heavy debt roughly two decades ago. (CYCLES) - AI memory leader: Now the dominant supplier of high-bandwidth memory (HBM) chips powering AI systems. - Marquee customers: Key buyers include Nvidia (NVDA) and Alphabet's Google (GOOGL). - Massive 2026 rally: Shares are up more than 340% year-to-date, fueled by the global AI boom. - Market cap milestone: Valuation now exceeds both Samsung and Micron (MU). Markets Get Chopped - Questions being asked about if AI spend boom producing fast enough return - Back to earth on valuation scare - (all of a sudden?) - KOSPI down 11% - Chips getting hit - 12% for Memory ETF - MU down 9%, Intel 4%, ASML 7% RAM Prices... - Looking at some additional RAM today for some office computers .... --- ARE THEY KIDDING? RAM Prices Imminent Collapse???? - President Donald Trump said the prospect of global economic collapse was a big reason he signed an interim peace deal with Iran. - According to sources, the deal reopened the Strait of Hormuz and set in motion waivers for sanctions on Iran's oil sales to the international market, with the effect being an immediate drop in oil prices and a rise in US stocks. - The agreement has been seen as skewed in Iran's favor, giving the country broad gains before the next round of talks, and has prompted pushback and anger from Republican lawmakers. - MOU signed lat Wednesday - also now more waivers of sanctions on sale of Iranian oil - 60 day reprieve. China - Weak economic conditions - H Shares about to enter bear market - Hong Kong - Close to a technical bear market, dragged down by weak domestic consumption, a struggling property sector, and an exodus of funds fleeing "old tech" for AI plays elsewhere in Asia. - A-shares are listed in mainland China (Shanghai/Shenzhen) and primarily target domestic investors. H-shares are listed in Hong Kong and are freely available to international investors More China - Retail sales declined for the first time since December 2022, dropping 0.6% from a year earlier. - China's urban fixed-asset investment contracted 4.1% as of end-May, dragged by real estate and manufacturing. - Manufacturing fixed-asset investment contracted for the first time since December 2020. - Industrial output was the lone bright spot, rebounding from April's near three-year low. - The national unemployment rate fell to 5.1% in May, compared with 5.2% in April. Marrrr Jonggg - Mahjong can be highly addictive due to its rewarding blend of strategy, luck, and social interaction. The rapid tile-drawing, need for pattern recognition, and "just one more round" mentality trigger dopamine releases. If compulsive play disrupts your finances or daily life, it can become a behavioral addiction requiring intervention. - Tactile and Auditory Appeal: Many users on community forums like Reddit agree that the physical weight, texture, and distinct clinking sound of shuffling tiles provide soothing, sensory satisfaction. - There has been a 70% surge in mahjong content on TikTok in the past year - Yelp recently named the Chinese tile game a top trend of 2026, noting that searches for mahjong clubs surged 4,467% year over year for the period from September 2024 to August 2025 and that searches for mahjong lessons rose 819%. Alphabet - WHAT>????*&*^ - Alphabet shares slid 7%, on track for the search giant's worst day in a year. - Alphabet's Google has seen consecutive high-profile researchers leave in the last several days. - The company also has exposure to the market's concerns around commoditized AI and ballooning capital expenditures. - The share slide also came on the heels of a Sunday Wall Street Journal interview with Microsoft CEO Satya Nadella, who called for less dependence on “AI Giants” and said the AI market was commoditized. Back to Oracle - Oracle reduced workforce by 21,000 employees over past twelve months. - Cuts broader than previously disclosed, driven by artificial intelligence adoption. - Global headcount fell from 162,000 to 141,000 full-time employees year-over-year. - Workforce reductions generated $1.8 billion in restructuring costs, company reported. - Company warned AI deployment may continue resulting in workforce reductions. NVDA - Underperforming - Nvidia shares slipping recently despite remaining up about 12% in 2026. - Stock down roughly 3% past month, underperforming semiconductor peers. - SMH ETF surged 84% year-to-date, gaining 15% last month. - Traders predict Nvidia chip pricing power is beginning to decline. - Wall Street focus shifting toward memory and infrastructure AI buildout. - Micron and Sandisk shares jumped nearly 60% over past month. Gloom and Doom - JCD sent interesting take from Chris Bloomstran - Traditionally asset light companies with all sorts of revenue, high margins now.... ---- Converting into asset heavy with no real understanding of what the profitability or even revue will be in the future ----- Here are the highlights of his commentary we can explre: ------------AI buildout shifting markets from asset-light toward capital-intensive infrastructure cycle - Hyperscaler capex surge reflects move into heavy, long-duration asset base - Massive capital requirements challenge economics versus prior asset-light models - Depreciation burden rising sharply as infrastructure scales across AI ecosystem - Returns depend on utilization of expensive, long-lived physical compute assets - Asset-heavy cycles historically lead to overbuild, weak returns, eventual consolidation - Infrastructure spending absorbing nearly all operating cash flow for hyperscalers - Off-balance-sheet financing masking true scale of capital intensity shift - AI economics hinge more on physical capacity than software-driven scalability - Echoes of past asset-heavy booms with eventual oversupply and value destruction Amazon Day - Today - June 26th - US consumers will spend $26.3 billion online at Amazon and other retailers during the four-day sale, up 9% from last year's event in July, according to Adobe Inc. - About 201 million Amazon shoppers in the US were Prime subscribers as of March, up about 3% from a year earlier - Amazon will capture about 60% of all US online spending during Prime Day, its highest market share since 2019, according to estimates from EMarketer Inc. Chevron and Microsoft - Chevron Corp signed 20-year deal with Microsoft for data center power. - Agreement supplies natural-gas fired generation for massive West Texas facility. - Project Kilby expected online 2028, ramping to 2.67 gigawatts. - Full output enough to power more than 530,000 Texas homes. - Chevron partnering Engine No. 1, final investment decision planned later. - Deal follows prior reports of exclusive long-term power negotiations. More Oil News - Drill baby Drill - Interior Department cutting federal drilling bonds by 95% to spur exploration. - Required bond drops from $500,000 to $25,000 for leases. - Bonds ensure cleanup costs don't fall on taxpayers if wells abandoned. - Policy change aims to encourage more oil and gas development. - Proposal subject to 60-day public comment after Federal Register publication. FedEx Earnings - FedEx posted strong fiscal fourth-quarter earnings on Tuesday in the company's last quarter that included the freight business before its spin off. - FedEx Freight spun off into a separate publicly traded company on June 1. - The company said it saw a 3% year-over-year increase in domestic volume. - Stock down 6% A/H   Love the Show? Then how about a Donation? PayPal.Donation.Button({ env:'production', hosted_button_id:'JJJHP2GDEJC7J', image: { src:'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt:'Donate with PayPal button', title:'PayPal - The safer, easier way to pay online!', } }).render('#donate-button'); ANNOUNCING the THE CLOSEST TO THE PIN for SpaceX (SPCX) Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter

Uno de los efectos colaterales de la fiebre por la inteligencia artificial es la carestía de la memoria RAM, un componente imprescindible en cualquier dispositivo electrónico de consumo, desde los ordenadores personales hasta las consolas de videojuegos pasando, naturalmente, por los teléfonos móviles. En los últimos dos años las grandes empresas tecnológicas se han lanzado a construir inmensos centros de datos para poder mover y entrenar gigantescos modelos de lenguaje. Todos esos servidores necesitan grandes cantidades de memoria, especialmente de un tipo muy avanzado conocido como HBM o memoria de alto ancho de banda, además de los módulos DDR5 más rápidos del mercado. El problema radica en que fabricar chips no es algo que se pueda acelerar de la noche a la mañana, la capacidad de la industria es limitada. Los tres principales fabricantes a nivel mundial, que son Samsung, SK Hynix y Micron, han visto que vender memoria para los servidores de inteligencia artificial es un negocio extremadamente rentable, mucho más que destinarlo a la electrónica de consumo. Por ello, han decidido desviar gran parte de sus líneas de producción hacia ese segmento tan lucrativo, lo que irremediablemente significa que están fabricando mucha menos memoria RAM tradicional para el mercado de consumo. Al haber mucha menos oferta de la memoria estándar en las tiendas y mantenerse la demanda, los precios se han disparado, han llegado a duplicarse o triplicarse desde finales del año pasado. Además del boom de los centros de datos, venimos arrastrando una situación creada por los propios fabricantes. Hace un par de años los precios de la memoria cayeron a mínimos históricos y estas empresas empezaron a perder dinero. Su reacción fue recortar la producción de forma intencionada para secar el mercado, eliminar el exceso de stock y recuperar sus márgenes de beneficio. Cuando quisieron darse cuenta, ese recorte premeditado se chocó de frente con la sed insaciable de chips de los gigantes de la inteligencia artificial. Todo junto ha creado gran escasez y la escalada de precios actual. No parece que los precios vayan a normalizarse a corto plazo. Montar una nueva fábrica de semiconductores para producir más chips cuesta miles de millones de euros y requiere años de planificación y construcción. Aunque la industria ya está invirtiendo en nuevas instalaciones, la mayor parte de esa capacidad de producción adicional no estará lista y operativa hasta el año 2027 o 2028, por lo que nos toca vivir una temporada con los precios bastante inflados. En medio de la tormenta está Apple, acostumbrada a exprimir su cadena de suministro, pero que ahora tendrá que subir los precios. Su problema es estructural, ya que contabiliza la memoria en el coste de los productos vendidos mientras los gigantes de la nube reparten ese gasto como inversión amortizable. La presión recae sobre unos márgenes que Wall Street espera que sigan subiendo. El daño va más allá, alcanza a todo el mercado del PC. Los analistas advierten que la escasez podría prolongarse como mínimo un par de años más. Mientras tanto, el usuario que renueva su móvil o su portátil estará financiando sin saberlo los servidores de la IA. En La ContraRéplica: 0:00 Introducción 3:40 Armagedón de la RAM 31:21 El pasaporte de Begoña 36:59 Las hijas de Zapatero 40:19 El voto CERA · Canal de Telegram: https://t.me/lacontracronica · “Contra el pesimismo”… https://amzn.to/4m1RX2R · “Hispanos. Breve historia de los pueblos de habla hispana”… https://amzn.to/428js1G · “La ContraHistoria del comunismo”… https://amzn.to/39QP2KE · “La ContraHistoria de España. Auge, caída y vuelta a empezar de un país en 28 episodios”… https://amzn.to/3kXcZ6i · “Contra la Revolución Francesa”… https://amzn.to/4aF0LpZ · “Lutero, Calvino y Trento, la Reforma que no fue”… https://amzn.to/3shKOlK Apoya La Contra en: · Patreon... https://www.patreon.com/diazvillanueva · iVoox... https://www.ivoox.com/podcast-contracronica_sq_f1267769_1.html · Paypal... https://www.paypal.me/diazvillanueva Sígueme en: · Web... https://diazvillanueva.com · Twitter... https://twitter.com/diazvillanueva · Facebook... https://www.facebook.com/fernandodiazvillanueva1/ · Instagram... https://www.instagram.com/diazvillanueva · Linkedin… https://www.linkedin.com/in/fernando-d%C3%ADaz-villanueva-7303865/ · Flickr... https://www.flickr.com/photos/147276463@N05/?/ · Pinterest... https://www.pinterest.com/fernandodiazvillanueva Encuentra mis libros en: · Amazon... https://www.amazon.es/Fernando-Diaz-Villanueva/e/B00J2ASBXM #FernandoDiazVillanueva #ram #ia Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Semiconductors have moved from the background of the technology stack to the center of the AI economy. What used to be a specialized industry discussed mostly by engineers and investors is now shaping the speed, cost, and strategic direction of modern computing.In this episode of TechSurge, host Michael Marks speaks with Stacy Rasgon, Managing Director and Senior Analyst covering U.S. semiconductors and semiconductor capital equipment at Bernstein Research. Stacy has spent years analyzing the chip industry across cycles, but argues that the current moment feels different in scale: AI demand has created an unprecedented scramble for compute, memory pricing has surged, and companies across the stack are being forced to rethink capacity, architecture, and capital allocation.The conversation explains the 4 different kinds of semiconductor cycles—supply, inventory, product, and demand — and why Stacy believes the industry is currently in a demand cycle of unusual magnitude. The discussion also unpacks the distinction between DRAM and NAND, why high-bandwidth memory is becoming strategically central to AI systems, and how the physical realities of wafer capacity and silicon area are constraining supply in ways the broader market often misses.Stacy and Michael also discuss the hardware economics behind the current boom, with Michael pressing Stacy on why compute remains so scarce and how companies are improving performance through packaging and system design. Michael then moves the conversation beyond market headlines to the core business questions: who is actually paying for this compute, which use cases are generating real revenue, and whether AI spending is creating durable economic value or simply shifting costs elsewhere. Together, these questions highlight two of the episode's clearest insights: coding may be one of the earliest AI applications with meaningful willingness to pay, and inference, not training, is the real test of whether the current buildout becomes a lasting business or just another expensive wave of infrastructure.Stacy explains the concentration of power among the major wafer fabrication equipment players, the rise of ASICs as a meaningful share of AI silicon, Broadcom's rapidly expanding AI opportunity, and the growing role of Chinese companies as new entrants, especially in memory and semiconductor equipment. Along the way, the conversation asks the defining question facing the sector: is this just another semiconductor upswing, or the first true supercycle the industry has seen? Stacy believes that this might be the biggest supercycle he has seen in his career.Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future episodes.Links:Stacy Rasgon on LinkedIn: https://www.linkedin.com/in/stacy-rasgon-6924963Bernstein: https://www.alliancebernstein.com/corporate/en/home.htmlReferences Mentioned During the DiscussionNVIDIA Blackwell Platform: https://www.nvidia.com/en-us/data-center/blackwell-platform/High Bandwidth Memory (HBM) overview from Micron: https://www.micron.com/products/memory/hbmDRAM overview from IBM: https://www.ibm.com/think/topics/dramNAND flash overview from IBM: https://www.ibm.com/think/topics/nand-flash-memoryFurther ReadingMcKinsey on the semiconductor industry outlook: https://www.mckinsey.com/industries/semiconductors/our-insights/the-semiconductor-industry-in-2025Semiconductor Industry Association: 2025 State of the U.S. Semiconductor Industry: https://www.semiconductors.orgNVIDIA on the Blackwell architecture and AI infrastructure roadmap: https://www.nvidia.com/en-us/data-center/blackwell-platform/Broadcom AI investor materials and infrastructure commentary: https://investors.broadcom.comASML on lithography and advanced chip manufacturing: https://www.asml.com/en/technologyMicron on HBM and AI memory demand: https://www.micron.com/products/memory/hbmChapters[00:00:00] — Highlights[00:00:26] — Welcome to  the Episode[00:01:29] — Meet Stacy Rasgon[00:02:01] — Is This the First Real Semiconductor Supercycle?[00:05:33] — Inside the Strongest Memory Cycle in History [00:09:14] — Can Innovation Keep Up With AI Demand?[00:11:33] — Chiplets, Blackwell, and the New Economics of Compute [00:12:37] — What Could Signal the Cycle Is Slowing[00:14:26] — Vertical Integration at the Hyperscales [00:16:36] — The Difference between Apple and Meta[00:17:15] — What is Vertical Integration Being Done For?[00:18:15] — Will other bottlenecks develop as This Progresses? [00:21:13] — Oligopoly Pricing in the Market[00:22:22] — Any New Entrants into Memory?[00:23:46] — Why the Industry Must Pivot From Training to Inference[00:25:10] — Agentic Coding and the First Real AI Revenues[00:26:57] — Groq, Low-Latency Inference, and What GPUs Cannot Do Alone[00:29:28] —-Could The Smaller Companies All be Bought Up ?[00:30:19] — Why Semiconductor Equipment Matters More Than Ever [00:31:00] — How Semiconductor Equipment is Affected by the Cycle[00:32:55] — A Long Upcycle for Semiconductor Equipment Guys?[00:33:13] — The Big Five and the Rise of Chinese Equipment Players[00:34:24] — The Effects of Geopolitics[00:35:02] — Broadcom's Quiet AI Breakout[00:40:46] — ASICs vs GPUs and the Next Wave of Custom Chips[00:41:06] — Intel, Foundry Strategy, and the Long Turnaround[00:46:46] —-The Risks the Market May Still Be Underestimating[00:49:32] — Where Startups Still Have Room to Win[00:50:39] — What the Semiconductor Industry Could Look Like Next Year

La flambée actuelle des prix de la mémoire vive ne tombe pas du ciel. Elle est directement liée à l'explosion de l'intelligence artificielle. Les accélérateurs dédiés à l'IA, notamment les GPU utilisés dans les centres de données, consomment des quantités considérables de mémoire très rapide. Résultat : la demande dépasse l'offre, les prix montent, et une partie de l'industrie technologique se retrouve prise de court.Mais dans ce paysage sous tension, un acteur affirme avoir vu venir la crise : NVIDIA. Selon Collette Kress, directrice financière du groupe, l'entreprise avait anticipé la pénurie. Dans un entretien relayé par Wccftech, elle explique que NVIDIA « savait que cela allait arriver », contrairement à d'autres entreprises surprises par l'ampleur du phénomène. Pour elle, cette tension était prévisible, à condition de regarder suffisamment loin dans la chaîne d'approvisionnement.Pour comprendre l'enjeu, il faut revenir à la mémoire HBM, pour High Bandwidth Memory. Il s'agit d'une mémoire à très haute bande passante, conçue pour transférer énormément de données très rapidement entre les puces et les modèles d'IA. Elle est indispensable pour entraîner et faire fonctionner les grands modèles modernes. Chaque accélérateur peut embarquer des dizaines, voire des centaines de gigaoctets de cette mémoire ultra-rapide.Le problème, c'est que la production de HBM mobilise des ressources industrielles proches de celles utilisées pour fabriquer d'autres mémoires, comme la DDR présente dans les ordinateurs grand public. Quand l'IA absorbe une part croissante de ces capacités, le reste du marché se tend mécaniquement. Smartphones, PC, consoles ou composants grand public peuvent alors subir des hausses de prix. NVIDIA affirme avoir limité ce risque en passant commande très tôt. Mais le groupe ne s'est pas contenté d'acheter ce qui existait déjà. Collette Kress explique que l'entreprise travaille directement avec les trois grands fournisseurs de mémoire, en leur présentant ses futurs besoins et ses prochaines architectures. Autrement dit, NVIDIA ne subit pas seulement la chaîne d'approvisionnement : elle tente de la façonner en amont. Une stratégie qui illustre sa puissance actuelle. Dans la course à l'IA, le vainqueur n'est pas seulement celui qui conçoit les meilleures puces, mais aussi celui qui sécurise la mémoire nécessaire pour les faire tourner. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

The Head of our Europe and Asia Technology Team, Shawn Kim, explains how AI's appetite for memory chips is boosting the cost of everything from data centers to smartphones, with consequences that may reach far beyond the tech industry.Read more insights from Morgan Stanley.----- Transcript -----Shawn Kim: Welcome to Thoughts on the Market. I'm Shawn Kim, Head of Morgan Stanley's Europe and Asia Technology Team. Today, we're talking about chipflation – when memory chips stop getting cheaper over time, and become more expensive and even harder to find. It's Monday, June 8th, at 3pm in London.Memory chips are easy to ignore, until your laptop slows down, your phone costs more, or your cloud bill jumps. Memory is the computer's workspace. It holds whatever the machine needs at that moment, whether that is a web search, a video, a spreadsheet, or an AI model answering a question. DRAM is the fast memory inside servers, PCs and phones. NAND is what stores files in solid-state drives. And HBM, or high bandwidth memory, is the high-performance version sitting right next to the AI chip, helping them move huge amounts of data quickly. That last one – HBM – is key because AI has become intensely memory hungry. Memory prices have risen more than six-fold over the last year, a sharp break from decades when the cost of DRAM generally kept falling. The pressure is coming from AI infrastructure buildouts. We see servers accounting for 59 percent of DRAM demand by 2028, up from 37 percent in 2023. We also see enterprise solid-state drives reaching 65 percent of NAND demand, up from 18 percent. And simply put, data centers are taking a much bigger share of the memory pie. AI memory use is climbing fast, and at every scale. A newer AI chip uses 7.2 times more HBM than earlier generations. A full system uses about 65 times more. Across an entire AI data center buildout, the jump gets even bigger. HBM has gone from roughly 10 terabytes in 2020 to about 18 petabytes in 2026, orders of magnitude more. This demand is running into a supply chain that cannot respond quickly. New memory capacity takes years to build, qualify and ramp up. Supply relief is a process, not a switch. And that creates a two-tier market. Large AI and cloud buyers can sign long-term agreements, prepay and secure priority access. Traditional buyers, including PC makers, smartphone makers and industrial hardware companies, must compete for what remains. This impacts everyday products. In 2027, we see PC memory demand potentially facing a 15 percent shortfall, equivalent to about 58 million PCs. Smartphones could face a 12 percent shortfall, equivalent to about 134 million units. Companies may have to raise prices, cut specifications, delay launches, and accept lower profits. The dollar numbers are striking. We see the memory market growing from about $220 USD billion in 2025 to about $890 billion in 2026. Expectations for 2026 memory revenue rose 71 percent in just three months. That implies roughly $600 USD billion of incremental memory revenue in 2026, more than the annual market for smartphones, PCs, or servers, each taken on its own. The broader economy may not see a significant direct inflation shock. We estimate the direct impact on headline CPI at about 0.1 percent in 2026. But pressure is showing up in producer prices, in corporate margins, cloud costs, capital spending plans and delayed technology upgrades. AI has turned memory from the cheapest part of the digital economy into one of its most contested resources. These tiny chips most people never think of may now decide what gets built or delayed, and how much we all end up paying. Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Nvidia unveiled the RTX Spark, an Arm-based consumer chip family built with MediaTek on TSMC 3, plus a DGX Station desktop that runs 1T-parameter models. Intel detailed its Crescent Island GPUs, MiniMax launched a coding model rivaling Opus 4.7 at 1/40th the price, and Anthropic bans AI in interviews. Nvidia announces the RTX Spark, an Arm-based consumer chip family it calls "the most efficient PC chip ever built", made on TSMC 3 in partnership with MediaTek (The Verge) Intel details its Crescent Island data center GPUs, built on its Xe3P architecture and using LPDDR5X memory instead of HBM, calling them "built for agentic AI" (Tom's Hardware) Nvidia unveils DGX Station for Windows, a desktop PC powered by a GB300 Grace Blackwell chip with up to 748 GB of memory, capable of running 1T-parameter models (SiliconAngle) Chinese AI developer MiniMax debuts M3, a new coding model that it says rivals Claude Opus 4.7, costing $0.12 per 1M input tokens, compared with $5 for Opus 4.7 (The Information) A look at Anthropic's hiring process, which prohibits AI use in interviews and features a culture interview that candidates describe as highly intense (Bloomberg) Learn more about your ad choices. Visit megaphone.fm/adchoices

Patrick Moorhead and Daniel Newman cover Daniel's acquisition of Enterprise Technology Research, IBM's historic $15 billion single-day commitment spanning quantum and open-source security, Anthropic's Claude Opus 4.8, and the heaviest single earnings night of the season featuring Dell, Marvell, Salesforce, Synopsys, Snowflake, HP, and Micron crossing $1 trillion in market cap. The handpicked topics for this week are: Anthropic Releases Claude Opus 4.8: Six Weeks After 4.7 Anthropic dropped Opus 4.8 just six weeks after 4.7, claiming it surpasses GPT-5.5 and Gemini 3.1 Pro on agentic coding, knowledge work, and computer use. Benchmark improvements across the board: agentic coding up from 64.3% to 69.2%, knowledge work from 1753 to 1890, agentic computer use from 82.8% to 83.4%. Three new features ship alongside it: Dynamic Workflows for multi-subagent orchestration inside Claude Code, Effort Control for managing token spend, and mid-task system messages via the API. Fast mode is now 2.5x faster and 3x cheaper. Pat's honest take: what it says on paper is good, particularly on tool triggering and citation precision, but he has lost significant trust in the company and is watching closely. (The Decode)   IBM Commits $10 Billion to Quantum: The Largest Single Quantum Bet in History IBM announced a $10 billion commitment over five years targeting a large-scale fault-tolerant quantum computer by 2029, landing the same day as the $5 billion Project Lightwell announcement for a single-day IBM strategic commitment of $15 billion. Pat has been calling 2029 to 2031 as the realistic commercial quantum window and calls this the strongest single corporate financial signal yet that the timeline is real. Daniel's framing: IBM wants to be the NVIDIA of quantum, and with a $10 billion commitment, it's sending a flare to the entire industry that pure-play quantum companies cannot compete at this balance sheet level. (The Decode)   IBM and Red Hat Launch Project Lightwell: $5B to Secure Open-Source Software IBM and Red Hat committed $5 billion and a global force of 20,000 engineers to secure open-source software for enterprises through frontier agentic AI, anchored by 11 of the largest US and Canadian banks including Bank of America, Goldman Sachs, JPMorgan Chase, Mastercard, and Visa. Pat's read: this is the productization answer to Anthropic Mythos. Mythos found the vulnerabilities. Lightwell is the industrial-scale patching and validation layer enterprises can actually buy on a subscription. Daniel adds that IBM is flexing its engineering talent base as a premium strategic asset, a direct counter to the narrative that AI replaces engineers. (The Decode)   Anthropic Project Glasswing: 23,000 Vulnerabilities Found Across 1,000 OSS Projects Anthropic's Claude Mythos scanned more than 1,000 widely deployed open-source projects and surfaced approximately 23,000 candidate vulnerabilities, with 1,094 confirmed as critical severity. The Cyber Verification Program now gates the strongest cyber-capable Claude variant behind vetted defenders only. While the tool creates real value, the surface of attack will likely grow as fast as any tool built to defend it. (The Decode)   Anthropic in Talks to Run Claude on Microsoft Maia 200 CNBC and The Information reported Microsoft is in active negotiations to supply Anthropic with its custom Maia 200 inference chip, which would make Anthropic the only frontier lab simultaneously running production workloads on four distinct silicon stacks: NVIDIA, AWS Trainium, Google TPU, and Microsoft Maia. Pat's context: Maia 200 delivers 30% better tokens per dollar than the latest Azure fleet per Satya Nadella, and this deal would be Maia's first major external deployment. Daniel's read: what can be built will be sold right now, and Anthropic chasing every available compute source is simply the structural reality of growing at 80x when you planned for 10x. (The Decode)   The Flip: Is AI CapEx Too Expensive to Earn Its Return? Pat takes the affirmative. With $725 billion in hyperscaler CapEx tracking for 2026, likely $1 trillion next year, memory has become the choke point making it even more expensive, and open-source models have closed enough of the quality gap for most enterprise tasks that the premium of frontier APIs is increasingly hard to justify. A recent Signal65 white paper shows on-prem payback at 18 months. Daniel's counter: Dell just booked $24 billion in AI orders in a single quarter. Agentforce crossed $1 billion ARR at 169% growth. NVIDIA guided to $91 billion. Only 20% of enterprises are using AI and only 2% of consumers. Both hosts admitted off the flip their notes looked nearly identical. (The Flip)   Micron Crosses $1 Trillion Market Cap Micron became the 12th US company ever to cross $1 trillion in market cap, surging 19% on May 26th as UBS raised its price target to $1,625, implying a $1.8 trillion market cap. Samsung's Q1 memory ASP jumped 146% year over year. DRAM spot prices spiked 55 to 60% quarter over quarter. Daniel has been pounding this call since sub-$100 and calls it a cycle elongated beyond anything seen in the 27 prior memory cycles, driven by HBM capacity reallocation away from consumer DRAM creating structural shortage. (Bulls and Bears)   Dell Technologies Q1 FY27: The Biggest Enterprise AI Infrastructure Print of 2026 Record $43.8 billion revenue, up 88% year over year, crushing the $35.7 billion consensus by $8 billion. AI-optimized servers at $16.1 billion, up 757% year over year. $24.4 billion in AI orders booked in a single quarter. FY27 AI server revenue guide raised from $50 billion to $60 billion. Non-GAAP EPS of $4.86 beat the $2.96 consensus by 64%. Stock up 18% after hours. Pat's framing: Dell was very clear about what they were going to do. Rack engineering, sales, and service. The basics. And they executed the basics at an extraordinary level while building a special relationship with NVIDIA who views Dell as a market maker for both enterprise and NeoCloud. Daniel's add: play nice and win. Michael Dell navigated the political landscape brilliantly and pulled the entire Dell brand along with him. (Bulls and Bears)   Marvell Technology Q1 FY27: Record Revenue, Data Center at 76% of Mix Record $2.418 billion revenue, up 28% year over year. Data center at $1.833 billion, up 27% year over year, now 76% of total revenue. Q2 guide of $2.7 billion at midpoint accelerates growth to 35% year over year. Operating cash flow a record $638.8 million. Daniel went on TV and said it's "written in the stars," arguing the market had misunderstood this one for too long by conflating its custom AI ASIC story with the full breadth of its connectivity and networking portfolio. Pat's closing: the shorts are eating it now and the custom AI ASIC versus merchant GPU debate is finally settling into the right answer, which is both in lockstep. (Bulls and Bears)   Salesforce Q1 FY27: Agentforce Crosses $1 Billion ARR Revenue $11.13 billion, up 13% year over year. Non-GAAP EPS of $3.88 crushed the $3.12 consensus by 24%. Agentforce ARR crossed $1 billion, up 169% year over year, with 28.6 trillion tokens processed, up 152% quarter over quarter. 50% of Agentforce bookings came from existing customers expanding. Daniel flagged the $25 billion accelerated buyback funded by new debt as an interesting signal worth watching. Pat's bottom line: it's not perfect, but certainly no "SaaSpocalypse" in those numbers. (Bulls and Bears)   Synopsys Q2 FY26: First Full Quarter With Ansys Integrated Revenue $2.276 billion, up 42% year over year, beating consensus. Non-GAAP EPS of $3.35 beat $3.15. FY26 guide raised to $9.665 billion midpoint. Daniel's framing: every chip runs through Synopsys tools, and the Ansys addition makes it the full-stack co-design platform Jensen Huang keeps talking about. Synopsys is not just the pick and shovel of current AI silicon. It is the pick and shovel of quantum, robotics, and space as well. (Bulls and Bears)   Snowflake Q1 FY27: Strongest Sequential Dollar Growth in Company History Product revenue $1.33 billion, up 34% year over year, the strongest sequential dollar growth in Snowflake history. Net revenue retention 126%. FY27 product revenue guide raised to $5.84 billion. Natoma acquisition announced for secure agentic enterprise connectivity. New $6 billion multi-year AWS commitment. Daniel's closing: proprietary unique data is the real moat of the agentic era, and that data has to live somewhere. It is going to go to platforms like Snowflake. (Bulls and Bears)   HP Inc. Q2 FY26: Eight Straight Quarters of Growth With AI PCs at 44% of Shipments Revenue $14.4 billion, up 9% year over year, the company marks its eighth consecutive quarter of top-line growth. Non-GAAP EPS of $0.86 beat the prior guide. Personal Systems at $10.2 billion, up 13%, with 30% operating profit growth. AI PCs jumped from 35% to 44% of shipments quarter over quarter, with HP guiding to 60 to 70% next fiscal year. FY26 EPS guide raised. Pat's note: they still need a permanent CEO, which would help investors sleep better at night. Daniel's add: the real explosive moment for device companies comes when AI moves to the edge and enterprises shift from expensive frontier model consumption to on-device inference. (Bulls and Bears)   Everpure Q1 FY27: Record Revenue, Rebrand Complete Record revenue of $1.1 billion, up 35% year over year. Product revenue $577 million, up 55%. Subscription ARR at $2 billion. FY27 guide raised to $4.41 to $4.51 billion. Pure Storage officially completed its rebrand to Everpure. Daniel's emerging thesis: the agentic era has focused enormous attention on memory and compute, but after the inference runs, the data has to sit somewhere. Storage has not seen its full inflection yet and Everpure is well positioned when that wave arrives. (Bulls and Bears)   The Decode Anthropic Releases Claude Opus 4.8 May 28  https://techcrunch.com/2026/05/28/anthropic-releases-opus-4-8-with-new-dynamic-workflow-tool/ IBM Commits $10B Over Five Years to Quantum Computing the Same Day as $5B Project Lightwell, Bringing IBM's One-Day AI https://www.barrons.com/articles/ibm-stock-quantum-computing-aafbb1eb IBM + Red Hat Announce Project Lightwell  https://newsroom.ibm.com/2026-05-28-ibm-and-red-hat-commit-5-billion-to-redefine-the-future-of-open-source-in-the-ai-era Anthropic Project Glasswing / Claude Mythos Finds 23,000 Potential Vulnerabilities Across 1,000+ Open-Source Projects https://www.securityweek.com/anthropic-mythos-detected-23000-potential-vulnerabilities-across-1000-oss-projects/ Anthropic Negotiating to Run Claude on Microsoft's Maia 200 AI Chips  https://www.cnbc.com/2026/05/21/anthropic-microsoft-maia-200-ai-chip.html OpenAI + Anthropic Walk Back the AI Jobs Apocalypse Ahead of IPOs https://finance.yahoo.com/sectors/technology/articles/ai-chiefs-walk-back-job-193605798.html https://x.com/RiskCentre/status/2059397756016611668 The Flip Is AI Capex Becoming Too Expensive to Earn Its Return — and Will the Result Be a Forced Shift to Open-Source and Smaller Use-Case-Specific Models, or a Continued $725B+ Hyperscaler Buildout That Vindicates the Capex on Productivity Gains? FOR:  The shift is to open-source + smaller use-case-specific models with better token economics, not away from AI https://x.com/danielnewmanUV/status/2059822712122400975 DeepSeek 75% permanent price cut + Anthropic Claude Code restriction reversal https://www.buildfastwithai.com/blogs/ai-news-today-may-26-2026 $190B Microsoft capex + $725B+ aggregate hyperscaler capex with no analog ROI yet  https://www.buildfastwithai.com/blogs/ai-news-today-may-26-2026   AGAINST:  Salesforce Agentforce ARR crossed $1B this quarter on 28.6T tokens processed  https://www.stocktitan.net/sec-filings/CRM/8-k-salesforce-inc-reports-material-event-3b8ead2852bb.html Lenovo +105% AI revenue, +84% Q4; Dell $43B AI backlog: the AI infrastructure flywheel is converting capex to revenue today https://investor.marvell.com/news-events/press-releases/detail/1023/marvell-technology-inc-reports-first-quarter-of-fiscal-year-2027-financial-results NVIDIA $91B Q2 guide + $1T Blackwell+Vera Rubin CY25-CY27 reaffirmed  https://www.cnbc.com/2026/05/20/were-raising-our-price-target-on-nvidia-after-another-knockout-quarter-and-guide-.html DeepSeek + Chinese price war is a Chinese export-controls story, not a US economic ceiling story https://www.cnbc.com/2026/05/21/anthropic-microsoft-maia-200-ai-chip.html   Bulls & Bears Micron (NASDAQ: MU) Crosses $1 TRILLION Market Cap for the First Time https://www.cnbc.com/2026/05/26/micron-stock-trillion-market-cap.html Dell Technologies Q1 FY27 ACTUALS  https://www.cnbc.com/2026/05/28/dell-q1-earnings-report-2027.html Marvell Technology Q1 FY27 ACTUALS https://investor.marvell.com/news-events/press-releases/detail/1023/marvell-technology-inc-reports-first-quarter-of-fiscal-year-2027-financial-results Salesforce CRM Q1 FY27 ACTUALS  https://investor.salesforce.com/financials/quarterly-results/ Synopsys SNPS Q2 FY26 ACTUALS https://investor.synopsys.com/events-and-presentations/events/event-details/2026/Q2-Fiscal-Year-2026-Earnings/default.aspx Snowflake SNOW Q1 FY27 ACTUALS  https://www.businesswire.com/news/home/20260527027931/en/Snowflake-Reports-Financial-Results-for-the-First-Quarter-of-Fiscal-2027 HP Inc. HPQ Q2 FY26 ACTUALS https://finance.yahoo.com/markets/stocks/articles/hp-q2-earnings-call-highlights-230459161.html Everpure (NYSE: P, formerly Pure Storage) Q1 FY27 ACTUALS  https://investor.salesforce.com/financials/quarterly-results/ Synopsys SNPS Q2 FY26 ACTUALS https://investor.synopsys.com/events-and-presentations/events/event-details/2026/Q2-Fiscal-Year-2026-Earnings/default.aspx Snowflake SNOW Q1 FY27 ACTUALS  https://www.businesswire.com/news/home/20260527027931/en/Snowflake-Reports-Financial-Results-for-the-First-Quarter-of-Fiscal-2027 HP Inc. HPQ Q2 FY26 ACTUALS  https://finance.yahoo.com/markets/stocks/articles/hp-q2-earnings-call-highlights-230459161.html Everpure (NYSE: P, formerly Pure Storage) Q1 FY27 ACTUALS https://www.prnewswire.com/news-releases/everpure-announces-first-quarter-fiscal-2027-financial-results-302783502.html

Nvidia entra en la guerra del portátil con chips Arm propios, Intel promete hardware para IA agéntica sin HBM, Vast se convierte en unicornio 3D, un gran sindicato docente pide frenar la IA en primaria y los astrónomos detectan una posible fábrica de planetas más allá de Júpiter.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

Dan Nathan hosts Dan Niles of Niles Investment Management on the Risk Reversal podcast to discuss macro conditions, AI-driven market leadership, and lessons from prior tech cycles. Niles compares the current AI build-out to 1997–1998's internet infrastructure boom, arguing recent macro scares (tariffs, Iran/oil) created buying opportunities and that a bubble can persist, with further gains likely before a potential 30–50% drawdown next year. He cites a January 30 “agentic AI” step-change increasing token/compute demand, supporting strong CapEx and earnings growth, and notes Nvidia's growth versus valuation relative to past leaders like Cisco. They debate rising yields, inflation measures, and expectations for a rate-cutting Fed chair (Kevin Warsh). The conversation covers Intel's potential benefit from agentic shifts, corporate AI cost pressures, likely disruption to software/IT services and knowledge work, Micron's HBM-driven surge and cyclicality risks, and how major IPOs like SpaceX, OpenAI, and Anthropic could reshape flows and create new short opportunities. —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media

就在本周，全球三大存储芯片巨头——SK 海力士、三星、美光——市值同时突破万亿美元。过去一年，SK 海力士股价累计涨幅超过 900%。财报显示，SK 海力士 2026 年第一季度营收接近三倍于去年同期，三星存储部门同期利润同比暴增 8 倍。存储，正在成为这一轮 AI 浪潮中最炙手可热的硬件赛道。 这背后是一场史无前例的供需错配。SK 海力士 2026 年全年 HBM 产能已经售罄，短缺预计将持续至 2027 年。与此同时，AI 服务器对内存的疯狂抢占，已经传导到了普通消费者：同等配置的手机更贵了，想要的机型根本没货——内存，被 AI 抢走了。但存储行业是出了名的「涨得多猛、跌得多惨」——过去 40 年，一个周期内暴涨 10 倍、暴跌 90% 的情况屡见不鲜。这一轮，究竟是又一场超级周期，还是 AI 真的重写了游戏规则？ 这期节目我们请来前半导体行业从业者、公众号「傅立叶的猫」主理人张海军，我们与他聊了聊：为什么存储行业现在如此火爆、这一轮周期在哪些维度上真的不一样；从 HBM 缺货到 NAND 角色转变，存储的五层架构如何被 AI 重塑；以及，国内长鑫、长江存储的进展，和周期拐点究竟何时到来。 本期人物 Yaxian，「科技早知道」主播 张海军，前新思科技高级工程师、半导体公众号「傅里叶的猫」主理人 时间轴 [02:46] 这轮周期是什么时候开始的？ 去年 Q1 现货价格已现端倪，三星海力士宣布停产 DDR4 引发囤货恐慌 三重触发因素叠加：AI 需求拉动、DDR4 停产、关税预期 [04:05] 存储的五层架构：离处理器越近，速度越快、价格越贵 从 HBM、DRAM 到 NAND、机械硬盘，带宽与价格的权衡逻辑 "内存墙"：芯片算力已不是瓶颈，存储带宽才是 [07:33] HBM 为什么缺货缺到 2027 年？ 英伟达每代芯片 HBM 需求翻倍 台积电 CoWoS 先进封装扩产周期长，供给端是硬约束 [09:26] 三家原厂格局：海力士领跑，三星和美光追赶 HBM 市场从 350 亿美元预计增至 2027 年近 800 亿美元 第四代 HBM 格局重洗 [13:00] 这轮为什么不一样：结构性转变的五个维度 需求端：从周期性补库存转向 AI 结构性爆发，三重需求（DRAM、HBM、NAND）同时拉升 供给端：原厂从抢份额转向利润优先，扩产极为克制； 长协模式（LTA）：锁量不锁价，甚至要求客户绑定原厂资本开支 [22:37] 看多 vs 看空：分歧在哪里？ 短期超买、股价涨幅过大是看空方主要依据 需求缺口太大、算法效率提升反而会扩大需求 [25:48] 绕过 HBM 瓶颈的各路方案：英伟达 CMX、谷歌 CXL 内存池、Cerebras 晶圆级芯片 英伟达 CMX 方案用 DPU 预取 NAND 数据降低延迟 谷歌用 CXL 协议构建 TB 级 DRAM 内存池 Cerebras 晶圆芯片面临散热、扩展性硬伤 [32:00] NAND 的角色转变：从"温数据仓库"到 AI 推理参与者 AI agent 推理链路长、中间结果需要落盘，NAND 直接参与推理过程 闪迪 HBF（高带宽闪存）：容量是 HBM 的 8~16 倍、成本更低，但受限于擦写寿命和工作温度 [38:06] 长鑫、长江存储即将上市，国内存储进展如何？ 长江存储 NAND 国内已大规模销售，长鑫 HBM 尚未量产商用 小米等手机厂商参股长鑫，本质是提前锁定产能 阶跃星辰 Step 3.7 Flash 模型 阶跃星辰 Step 3.7 Flash 是专门面向生产级 Agent 的高效率 Flash 模型，一开始就为 Agent、Coding、Search 和多模态工作流设计，让你的生产工作流又快又稳。 感兴趣的小伙伴欢迎点击链接（ https://sourl.co/ZUaCXf ）试试，新注册用户有代金券，可以直接上手跑一跑，看看它在你的工作流里表现怎么样。 幕后制作 监制：Yaxian 后期：迪卡 运营：George 设计：饭团 商业合作 声动活泼商业化小队，点击链接直达声动商务会客厅（https://sourl.cn/9h28kj ），也可发送邮件至 business@shengfm.cn 联系我们。 加入声动活泼 声动活泼正在招聘全职商务运营经理、早咖啡内容实习生和社群实习生，如果你也对播客行业的内容制作感兴趣，欢迎点击招聘入口 关于声动活泼 「用声音碰撞世界」，声动活泼致力于为人们提供源源不断的思考养料。 我们还有这些播客：声动早咖啡、声东击西、吃喝玩乐了不起、反潮流俱乐部、泡腾 VC、商业WHY酱、跳进兔子洞 、不止金钱 欢迎在即刻、微博等社交媒体上与我们互动，搜索 声动活泼 即可找到我们。 期待你给我们写邮件，邮箱地址是：ting@sheng.fm 欢迎扫码添加声小音，在节目之外和我们保持联系。Special Guest: 张海军.

Nesse vídeo analisamos o desempenho de gigantes como Nvidia, Intel e Micron, e como o boom de semicondutores e chips HBM impacta o mercado financeiro global. Entenda a correlação entre o investimento em IA e o crescimento econômico de Taiwan e Coreia do Sul, comparando o cenário atual com a crise das empresas "ponto com" nos anos 2000. Avaliamos riscos, projeções de lucratividade e o impacto macroeconômico real desse avanço tecnológico. Descubra se estamos vivendo uma nova bolha da inteligência artificial.00:00:00 – Analisando a atual bolha da IA00:00:59 – Desempenho dos índices de mercado financeiro00:01:58 – Nvidia e empresas de hardware beneficiadas00:03:24 – O papel estratégico dos chips HBM00:04:33 – Concentração excessiva no índice S&P 50000:05:42 – Boom da IA na Ásia impactante00:09:24 – Impactos na economia global e PIB00:10:30 – Exportações de Taiwan e Coreia disparando00:15:10 – Comparação com bolhas históricas do mercado00:22:00 – Conclusão sobre riscos e investimentos futuros

00:00 - Issues with Singh's wearing patke03:15 - Technique for tying a patka 04:45 - Joora and receding hairlines08:40 - Canadian content creators 10:50 - Why did HBM change their content and move away from brown topics?14:40 - Indy's not a fan of Shai Gilgeous-Alexander (SGA)15:50 - Growing with your audience 17:20 - What type of content does best?20:30 - Knowing comes from doing22:10 - People need to understand there's enough to go around 24:30 - Being at the Nagar Kirtan in Surrey28:20 - Apologising for lack of awareness30:30 - Condemning Parmvsthewrld's actions31:50 - Rules were given to N3ON; he just didn't follow it35:33 - Sikh policing in Canada is more extreme than in the UK 40:10 - Everyone's journey with Sikhi is different 43:40 - What's going on with the Indian hate movement in Canada?45:30 - Do immigrants need to integrate?48:50 - Going into drive-thrus on a tractor52:10 - Celebrity houses being robbed in Canada 53:30 - Crossbow Singh - the best meme55:26 - Why is the Khalistan movement so strong in Canada? 57:45 - What would actually happen if Khalistan existed?01:02:55 - Quick-fire questions01:05:03 - HBM  dropping final thoughtsFollow Hours Before Midnight:https://www.youtube.com/@UCwQd_2VrNwysI0il5dmdnXg Follow Ekdeep: https://www.instagram.com/ekdeep21k/Follow Us On:TikTok - https://bit.ly/indy-and-dr-tik-tokInstagram - http://bit.ly/indy-and-dr-instaFacebook - http://bit.ly/indy-and-dr-facebookSpotify - http://bit.ly/indy-and-drAlso available at all podcasting outlets.#hoursbeforemidnight #canadianpolitics #sikh #indianculture #desiculture

En el Radar Empresarial de hoy ponemos la atención en SK Hynix, que, igual que Micron, ha logrado entrar en el grupo de compañías valoradas en más de un billón de dólares. La empresa surcoreana se ha beneficiado del impulso de la inteligencia artificial y de las mejores previsiones de firmas financieras. Ese contexto explica el avance en el Kospi, donde protagonizó una de las subidas de 2026. Este año, sus acciones acumulan un crecimiento cercano al 250% aproximadamente. La compañía se ha convertido en una pieza esencial dentro del sector de memorias, considerado de los próximos meses. Una de las razones principales es su estrecha relación con Nvidia, ya que es único proveedor de memorias HBM para la tecnológica estadounidense. Estos componentes permiten gestionar volúmenes de datos con velocidad, algo imprescindible para los desarrollos vinculados a la inteligencia artificial. Con ese liderazgo, SK Hynix controla el 57% del mercado de HBM y el 32% del segmento DRAM. El aumento de la demanda de semiconductores y la escasez de suministros han elevado la importancia estratégica de fabricantes como SK Hynix En apenas dieciséis meses, la empresa pasó de 100.000 millones de dólares a superar el billón de capitalización Sin embargo, la compañía reconoce los riesgos en la industria Su presidente, Chey Tae-won, advirtió hace meses que la falta de obleas de silicio podría provocar un desequilibrio hasta 2030 y generar una diferencia entre oferta y demanda 20%. Las advertencias de SK Hynix coinciden con las de otros directivos del sector tecnológico Michael Dell aseguró que la demanda de memoria inteligencia artificial podría multiplicarse por 625 durante años el director ejecutivo de Micron, Sanjay Mehrotra, explicó en CNBC que la memoria se ha convertido en un recurso imprescindible Según el ejecutivo, los sistemas de inteligencia artificial necesitan más capacidad y un rendimiento superior para desarrollar todo su potencial y sostener el crecimiento esperado del sector tecnológico

Le patron de Mistral.ai dresse un tableau sans concession de l'IA en Europe • Google muscle Android avec Gemini et réinvente la souris • TikTok attaqué en France • Un conflit social chez Samsung menace la production mondiale de puces • Les rédactions bousculées par l'IA • Publier un livre avec l'intelligence artificielle • Un futur moteur de recherche français boosté à l'IA • Gare à l'IA documentaire mal maîtrisée en entreprise. ⭐️ Découvrez Frogans, l'innovation française qui réinvente le Web [PARTENARIAT]===============================Plaidoyer sans concession pour la souveraineté européenne de l'IAAuditionné à l'Assemblée nationale, Arthur Mensch, cofondateur et directeur général de Mistral AI, a livré une analyse offensive sur l'état de l'intelligence artificielle en Europe. Pour lui, l'IA est devenue une ressource stratégique comparable à l'énergie : elle conditionne la souveraineté économique, militaire et culturelle du continent. Il appelle à investir massivement dans les modèles, les infrastructures et l'électricité bas carbone, faute de quoi l'Europe risque de dépendre durablement des États-Unis et de la Chine. Reste à savoir si Mistral aura les moyens financiers et politiques de rivaliser avec les hyperscalers américains.Vidéo de l'audition d'Arthur Mensch : https://www.youtube.com/watch?v=kKWOkWv6pJMLa cybersécurité nouveau champ de bataille IALa course à l'IA se déplace sur le terrain stratégique de la cybersécurité. OpenAI aurait présenté un modèle spécialisé capable d'anticiper des vulnérabilités inédites, avec un accès encadré mais ouvert à certaines organisations européennes, tandis que Mistral AI travaillerait également sur un modèle dédié. Ces outils, capables de détecter des failles avant qu'elles ne soient exploitées, deviennent des instruments de souveraineté numérique. Leur contrôle et leur accès sont désormais des enjeux diplomatiques autant que technologiques.Android passe à l'IA agentiqueLors de sa conférence Android, Google a présenté une version agentique de Gemini appelée à s'intégrer au cœur d'Android. Capable d'agir à la place de l'utilisateur, l'assistant pourra naviguer dans les applications, extraire des informations ou remplir des formulaires, avec validation humaine pour les actions sensibles. Une évolution majeure vers un smartphone proactif, qui transforme l'IA en véritable copilote numérique. Le déploiement est attendu progressivement sur les futurs appareils Android.Google réinvente la souris avec l'IALa filiale DeepMind de Google a dévoilé un prototype baptisé “Magic Pointer”, combinant capture d'écran locale et intelligence artificielle. En survolant un contenu, l'utilisateur peut demander instantanément un graphique, un résumé ou un recalcul contextuel. Cette interaction homme-machine repensée pourrait intégrer Chrome et les Chromebooks à terme. Une démonstration spectaculaire qui illustre l'intégration toujours plus fine de l'IA dans les gestes informatiques du quotidien.TikTok dans le viseur de familles françaises endeuilléesSeize familles réunies au sein du collectif Algos Victima ont déposé plainte contre TikTok pour abus de faiblesse, après plusieurs suicides d'adolescentes. Elles accusent l'algorithme de la plateforme d'avoir favorisé l'exposition répétée à des contenus anxiogènes ou dangereux. Au-delà du volet judiciaire, l'affaire relance le débat sur la régulation des réseaux sociaux et sur une possible interdiction avant 15 ans.La CNIL alerte sur les lunettes connectéesLa CNIL (Commission Nationale Informatique et Libertés) met en garde contre les risques de surveillance diffuse liés aux lunettes équipées de caméras et de micros. L'autorité française appelle à un usage responsable et propose plusieurs recommandations pour préserver la vie privée. Si ces dispositifs peuvent rendre des services, notamment pour les personnes malvoyantes, ils posent une question sociétale majeure : comment éviter une banalisation de la captation d'images dans l'espace public ?Samsung sous tension : menace sur la production mondiale de puces IAEn Corée du Sud, 50 000 salariés de Samsung menacent de se mettre en grève pour réclamer une meilleure redistribution des bénéfices liés à l'IA. Le groupe est l'un des rares fabricants mondiaux de mémoire HBM, composant clé des puces d'intelligence artificielle. Un arrêt prolongé de la production pourrait perturber toute la chaîne mondiale des semi-conducteurs. La planète tech observe avec inquiétude ce bras de fer social.IA et journalisme : vers la fin des tâches répétitives ?En direct de Gaspésie, Bruno Guglielminetti, animateur du podcast Mon Carnet, analyse l'impact de l'IA dans les rédactions. Entre menaces de grève et suppression de postes, la réécriture automatisée des dépêches cristallise les tensions. Pour lui, l'IA peut devenir un assistant éditorial précieux, libérant du temps pour l'enquête et le terrain. Un basculement culturel qui oblige les écoles de journalisme à revoir leurs formations.Mon nouveau livre sur le podcasting... publié grâce à l'IAJe vous présente mon livre Lancez votre podcast à l'ère de l'IA, consacré à la création de podcasts avec les outils d'intelligence artificielle. Autoédité avec l'appui d'outils IA pour la structuration, la correction et la mise en forme, le livre explore les nouvelles pratiques du média audio. Une démonstration concrète des mutations en cours dans l'édition et la création de contenus.Ibou, le pari d'un moteur de recherche françaisSylvain Peyronnet, cofondateur et CEO d'Ibou, présente ce moteur de recherche conversationnel français, conçu pour proposer une information plus transparente et pluraliste. L'objectif : éviter les bulles de filtrage et valoriser les sources en exposant les différents points de vue. Grâce aux modèles de langage récents, l'équipe affirme pouvoir bâtir un moteur performant sans dépendre d'une collecte massive de données utilisateurs. Un défi ambitieux face aux géants américains.IA et gestion documentaire : les risques invisibles en entreprise [PARTENARIAT]Guillaume Brault, directeur technique Europe du Sud chez Box, alerte sur les dangers d'une IA branchée sur des bases documentaires mal gouvernées. Sans classification préalable, un agent conversationnel peut faire remonter des informations sensibles à des collaborateurs non habilités. La clé réside dans l'étiquetage et la gouvernance des données afin de contrôler précisément ce que les modèles peuvent consulter et restituer. L'IA devient ainsi un révélateur des failles organisationnelles internes.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Many investors were not aware of Advantest. This Japanese company quietly controls roughly 70% of the global semiconductor test equipment market — the quality assurance layer that every AI chip, every HBM memory module, and every packaged GPU must pass through before it ships to a customer. As AI chips have gotten more complex and more expensive, the cost of shipping a faulty one has risen dramatically, and the demand for Advantest's equipment has followed.The stock reflects that. Up 450% in the past year. Up roughly 250% since CSI first wrote about Advantest eleven months ago. Analyst earnings per share expectations have roughly doubled in twelve months — that is what drove the run. Advantest just reported FY2025 results of $7.1 billion in revenue and guided FY2026 to approximately $9 billion, a 26% year-over-year increase. In a test equipment market the company itself sizes at $12.5 to $13 billion for 2026, that would put Advantest's global market share approaching 70% — a level of dominance that is genuinely rare in any industry.Nick and Kasey cover the full picture in CSI's first public video on Advantest: how it became the undisputed leader, why the test equipment slice of the $140 billion wafer fab equipment market is small but critical, what the 47x forward earnings and 56x forward free cash flow multiples actually imply, and why some analysts are already flagging a potential cycle downturn starting in 2027 even as the bulls hold firm.The close is pure CSI. Radical moderation. Patience is a strategy. Stay in the game and survive.What we cover:— Advantest FY2025: $7.1B revenue and dominant market share vs. Teradyne and Aehr— FY2026 guidance: ~$9B — approaching 70% of a $12.5–13B global TAM— Why AI chips and packaged modules require testing — and why it is getting more expensive— Test equipment as a slice of the $140B WFE pie — small, critical, and cyclical— Valuation: 47x forward P/E and 56x forward FCF — what the re-rate means now— Analyst EPS doubled in twelve months — the mechanics of why the stock ran— The 2027 cycle risk: bear vs. bull analyst expectations laid out clearly— Radical moderation: the CSI framework for parabolic stocks and surviving the cycleSponsored by fiscal.ai — the platform behind CSI's research charts. Get 15% off at fiscal.ai/csiDisclosure: This content is for general information only and is not individual investment advice. All investing involves risk.chipstockinvestor.com

Stocks for Beginners and Tykr proudly present "Weekend Watchlist". We dissect a company using Tykr's risk rating and fair value analysis process. Learn how to avoid emotional mistakes, choose investments with a rationale, and build wealth with confidence. Get your free trial and special discount offer. Join Tykr today and take advantage of this special offer of 30% off with coupon code SAVE30. See for yourself why Tykr is the essential tool for every serious DIY share investor. 14-day free trial included, then a no-quibble 30-day money back guarantee: Get your free trial and special discount offer.Micron Technology has become a critical player in the AI revolution - not because it makes GPUs, but because it makes the high‑bandwidth memory that keeps those GPUs fed with data. In this episode, we explore Micron's business model, why HBM is so essential, and how Micron has become responsible for more than half of all earnings‑estimate upgrades across the S&P 500.Weekend Watchlist is about helping beginners sharpen their investing process through real companies and real stories.Disclosure: The links provided are affiliate links. I will be paid a commission if you use this link to make a purchase. You will receive a discount by using these links/coupon codes. I only recommend products and services that I use and trust myself or where I have interviewed and/or met the founders and have assured myself that they're offering something of value. Stocks for Beginners is a production of Finpods Pty Ltd. The advice shared on Stocks for Beginners is general in nature and does not consider your individual circumstances. Opinions expressed by guests are theirs alone and may not represent the views of Finpods, Money Sherpa, or Phil Muscatello. Stocks for Beginners exists purely for educational and entertainment purposes and should not be relied upon to make an investment or financial decision. If you do choose to buy a financial product, read the PDS, TMD, and obtain appropriate financial advice tailored towards your needs. Philip Muscatello and Finpods Pty Ltd are authorised representatives of Money Sherpa PTY LTD ABN - 321649 27708, AFSL - 451289. Hosted on Acast. See acast.com/privacy for more information.

Our 239th episode with a summary and discussion of last week's big AI news!FYI: this one has pretty out of date news, I was traveling last week and failed to upload... apologies. Recorded on 03/25/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:OpenAI is discontinuing the Sora iPhone app and seemingly shutting down its video generation API, while retaining internal video world-modeling work; the move is framed as a compute- and focus-driven pivot toward coding and productivity agents, alongside a collapsed Disney Sora deal. Anthropic's Claude Code/Cowork gains full computer control via keyboard/mouse/display, tied to the recent Cept acquisition, and Google's Gemini rolls out background “task automation” on select phones for limited delivery/ride-share use. Cursor releases the cheaper, benchmark-strong Composer 2 coding model amid controversy over its Kimi-based origins and licensing attribution. Other items include Adobe Firefly custom model training, Luma's Uni 1 image model, US contracting and legislative proposals affecting AI safeguards and state preemption, major chip/memory developments (Meta ASICs with Broadcom, Micron's HBM-driven surge, Musk's “Terra Fab”), robotaxi scaling, and research on monitoring agent misalignment, shutdown resistance, “consciousness cluster” preferences, and self-improving “hyper agents.”Timestamps:(00:00:10) Intro / BanterTools & Apps(00:01:48) OpenAI Discontinues Sora App, Shuts Down Video Generation Service and API - Bloomberg(00:07:12) Anthropic's Claude Code and Cowork can control your computer | The Verge(00:13:15) Gemini task automation is slow, clunky, and super impressive | The Verge(00:19:44) Cursor Launches Composer 2 AI Model to Challenge OpenAI & Anthropic(00:28:28) Adobe's AI image generator can now be trained on your own art | The Verge(00:29:40) Luma AI launches Uni-1, a model that outscores Google and OpenAI while costing up to 30 percent less | VentureBeatApplications & Business(00:32:41) Trump Contracting Clause Would Override AI Safeguards(00:40:00) Meta accelerates AI ASIC roll-out as Broadcom secures four-generation chip design deal(00:47:07) Micron revenue almost triples, tops estimates as demand for memory soars(00:50:54) Elon Musk Unwraps $25 Billion Terafab Chip-Building Project - CNET(00:56:40) Zoox to widen US robotaxi footprint with San Francisco, Vegas expansion(00:57:39) Waymo hits 170 million miles while avoiding serious mayhem | The VergePolicy & Safety(00:58:43) The White House just laid out how it wants to regulate AI | CNN Business(01:06:54) How we monitor internal coding agents for misalignment(01:12:30) Incomplete Tasks Induce Shutdown Resistance in Some Frontier LLMs(01:18:15) Summary: Mechanisms to Verify International Agreements about AI Development(01:23:09) Scoop: Anthropic meets with House Homeland Security behind closed doorsResearch & Advancements(01:24:24) Consciousness Cluster: Preferences of Models that Claim they are Conscious(01:30:22) HyperAgentsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Simon Erickson of 7investing breaks down two high-potential watchlist stocks: Micron Technology (NASDAQ:MU) — a memory giant riding the AI boom with explosive margin expansion — and Infleqtion (NYSE:INFQ), a newly public quantum computing company using groundbreaking neutral atom technology. Micron's high bandwidth memory (HBM) is completely sold out through 2026, with gross margins expanding 16 percentage points year-over-year on $13.6B in quarterly revenue. This is one of the most compelling AI infrastructure plays in the semiconductor space right now.Infleqtion just hit public markets via SPAC in February 2026 and is already partnered with NVIDIA (NASDAQ:NVDA) through a CUDA integration called Qlink. With quantum computing threatening RSA encryption and unlocking solutions classical computers can't touch, government contracts, defense spending, and research grants are flooding the space — justifying premium valuations for early-stage leaders.

삼테성즈 2026년 3월호. 아이들 감시용 지피티를 직접 만들다? 그리고 HBM 칩의 신묘한 냉각 시스템!-오프닝비둘기 뇌를 해킹해 만드는 러시아 생체 트론한국 유튜버, 일론 머스크의 뉴럴링크 실험 지원- 이용의 디벼보기아이들을 감시하기 위해 지피티를 직접 만들어 보다!?- K2의 공학썰HBM칩은 도대체 어떻게 냉각하는거냐-자료https://www.slideshare.net/slideshow/2626-3-k2-hbm-pdf/286682771과학과사람들 제공