Open Models & American Diffusion; Nvidia & Poolside

Nvidia is paying $6 billion to license AI startup Poolside's models, aiming to build one of the world's most powerful open-weight AI models to compete with Chinese rivals like DeepSeek and Kimi K3, according to people familiar with the matter. The deal challenges U.S. frontier labs OpenAI and Anthropic, as open-weight models are cheaper and customizable, and aligns with Nvidia's Nemotron strategy to diffuse AI broadly, a key factor in economic competition per Jeff Ding's book.

Nvidia is paying $6 billion for Poolside, a model lab. From the WSJ’s Nvidia Is Spending $6 Billion to Build a Powerful U.S. Alternative to Chinese AI https://www.wsj.com/tech/ai/nvidia-is-spending-6-billion-to-build-a-powerful-u-s-alternative-to-chinese-ai-c51c38cc Nvidia is planning to use a $6 billion deal it struck this week to build one of the world’s most powerful open-weight AI models, one that would compete with Chinese heavyweights like DeepSeek and Kimi K3, according to people familiar with the matter. The chip giant’s licensing deal with the AI startup Poolside is also set to present a direct challenge to frontier U.S. AI companies including OpenAI and Anthropic, since open-weight models are generally far cheaper to operate and allow easy customization. Nvidia has long been shipping the Nemotron family of open models, with the stated goal https://www.youtube.com/watch?v= y9SEtn1lU8 of giving every company a trusted base model to specialize for their bespoke needs: “Our goal with Nemotron is to raise the bar for general intelligence, but provide that intelligence in a way that allows businesses around the world to specialize it for their own problems”. —Bryan Catanzaro, Nvidia VP Nemotron is open and customizable. Artificial Analysis https://artificialanalysis.ai/articles/nvidia-nemotron-3-ultra-released called Ultra “the leading US open weights model”, while noting it sits behind “the Chinese-led open weights frontier”. For now. Nvidia is working on it. Back in March, it launched the Nemotron Coalition https://nvidianews.nvidia.com/news/nvidia-launches-nemotron-coalition-of-leading-global-ai-labs-to-advance-open-frontier-models with partners like Mistral, Cursor, Perplexity, and Thinking Machines Lab to build competitive frontier open models. Can a coalition move at the speed of light though? As we’ll see below, Poolside can help with iteration speed… But first and more importantly, why do competitive frontier open models even matter? Diffusion matters as much as innovation A technology that can transform every sector is, by definition, a general-purpose technology. General-purpose technology is also abbreviated GPT, but not that GPT... I know, slightly confusing. Check out Jeff Ding’s book: Technology and the Rise of Great Powers: How Diffusion Shapes Economic Competition https://press.princeton.edu/books/paperback/9780691260341/technology-and-the-rise-of-great-powers?srsltid=AfmBOopm L8xGZxUaftNpp6S0E -gVTN8UDi3GFl6X432PXZMdsQXPaG . The important takeaway is that the countries that win with GPTs aren’t the countries that invent the GPT, but the countries that diffuse the GPT. We idolize invention; diffusion sounds boring. But it’s super important Actually, let’s back up a bit. Marc Andreessen’s 15-year-old " Software Is Eating The World https://a16z.com/why-software-is-eating-the-world/ " saw the diffusion of another general-purpose technology software and predicted it would upend every industry. Even the software industry Every company would become a software company. Of course software wasn’t new , but thanks to various innovations like cloud computing and higher-level programming languages & frameworks, the rate at which it could diffuse was increasing. Notably, Andreesen said the constraint preventing companies and industries from software-enabled productivity gains was people . “Many people in the U.S. and around the world lack the education and skills required to participate in the great new companies coming out of the software revolution... There’s no way through this problem other than education, and we have a long way to go.” —Marc Andreesen This is a diffusion argument. For software to eat the world, it needs to diffuse across every industry, and that’s predicated on having enough software-engineering-literate people in every industry. Crazy enough, fifteen years after Andreesen’s article, we see traditional software development diffusing rapidly across every industry thanks to generative AI Now, suddenly, domain experts can be literate software engineers. Hey Marc, let me update it 15 years later: … There’s no way through this problem other than~~education~~vibe coding. Jeff Ding’s interview with Microsoft’s Brad Smith https://www.youtube.com/watch?v=wXucwFa 7do makes the same skilling argument for Generative AI. Professor Ding argues that countries that see expanded GDP and increased productivity are those that can properly diffuse the general-purpose technology to the “average engineer”. Should that occur, industries will get rebuilt not just around software, but around generative AI. After all, agentic AI is a general-purpose technology that has already transformed software engineering and will eventually transform every industry. The question that follows, then, is what will it take for agentic AI to diffuse broadly? Not just the diffusion of software engineering thanks to agentic AI, but the actual application of agentic AI across every industry. Jensen argues that one important ingredient is the existence of frontier-class, competitive open-weight models. And I wholeheartedly agree. From Jensen’s “Open Weights and American AI Leadership” https://images.nvidia.com/pdf/Open-Weights-and-American-AI-Leadership.pdf published on July 24, 2026: “Our AI leadership will be judged not by one frontier AI model, but by whether the United States builds a strong, open ecosystem that diffuses into every sector” Yes. Diffusion. The fastest diffusion is predicated on the existence of competitive frontier opens weights models. Again from Jensen: “Startups, established businesses, universities, and public institutions can build on advanced models without training one from scratch or paying frontier-model prices for every task. Open weights let every organization match the right model to the right job at the right cost, reserving frontier-scale capability for genuine frontier problems and running efficient, specialized models everywhere else.” Preach it Brother Huang And ideally fully open source, i.e. open model, data, and weights — see OpenMDW. Also a nice explainer table from opensource.org: Why are open models what diffusion needs? First, open weights means the average engineer across any industry can tinker with it . Quantize it to fit the hardware you use. Prune it. Fine-tune it on your proprietary data. Want more control over costs and performance? Host it yourself, choose batch size vs interactivity tradeoffs, tier the KV cache to improve the hit rate, and so on. All the while keeping your data private, with full transparency into the methods powering your models. Don’t want to host it? Then don’t. The weights are portable, so you rent one host this quarter and a cheaper one next quarter. Free market. Second, open models spur competition on price. A handful of closed vendors who all monetize tokens have little reason to race each other toward pennies-per-task. But diffusion needs lower prices. Jevon’s paradox, right? Open models mean many hosts can serve the same model, pressuring token prices down toward the cost of compute. One more. Open models help companies retain and make use of their proprietary data. There’s the obvious sovereignty-and-regulated-industries angle, but that’s well-trodden. What’s also interesting here is having more control over the feedback loop: fine-tune a model, use it, generate more labeled data as you use it, feed that back into the post-training, and so on. Easiest and fastest when you’re in full control. Diffusion already happening, but can be sped up with competitive frontier open model Diffusion of GenAI across American industries is already happening, thanks in part to open models. It’s there in Nvidia’s numbers, although a bit fuzzy. From the Q2 FY27 CFO commentary https://www.sec.gov/Archives/edgar/data/1045810/000104581026000073/q2fy27cfocommentary.htm earlier this week, ACIE AI clouds, industrial, and enterprise was at about 45% of data center revenue, growing roughly twice as fast as hyperscale. Of course, ACIE includes AI clouds neoclouds , which mostly serve the model labs. So ACIE alone isn’t a direct measure of pure enterprise diffusion, but it’s in there. I turn to Dell for enterprise read-through. On the Q4 FY26 call, Dell said “theenterpriseportion of our five-quarter pipeline grew and actuallywas the fastest-growing portion of the five-quarter pipeline.” Yes, it’s easier to grow small numbers. But the Q1 FY27 call said the AI customer count “surpassed five thousand” and up over 50% in six months. And note what those customers buy. Dell’s mix is shifting to “x86 + Blackwell... driven primarily by enterprise deployment, air cooling being the number one consideration”. Dell is referring to products like the air-cooled PowerEdge server on HGX B200/B300 baseboards with x86 CPUs, not the Arm-based GB200/GB300 NVL72 racks hyperscalers buy. One can argue this is a “diffusion SKU”. Yet to accelerate the diffusion of GenAI across healthcare, insurance, agriculture, retail, manufacturing, defense, and so on, we need to close the gap between proprietary, frontier-leading models and American open-source models. I previously thought that a frontier-capable open model was simply not possible given the amount of recurring investment needed to stay at the frontier. Nvidia is one of the few companies with that kind of money, and I assumed Nvidia was incentivized to train only “big enough” Nemotron models to test their hardware end-to-end. i.e. eating your own dogfood. Catanzaro said as much https://www.youtube.com/watch?v= y9SEtn1lU8 : “Nemotron is not just a model, it’s all of our AI technology that helps us build the GPUs and systems for the future. There’s a lot of questions about how networking or the GPU should work in order to make AI progress, and we are able to answer those questions better because of building Nemotron”. Moreover, on the business strategy side, wouldn’t a competitive open frontier model compete with the closed labs, the biggest consumers of Nvidia compute? Well, Nvidia is buying Poolside, and I’ve changed my mind. I think Jensen wants to build competitive frontier open models. And it’s a good thing. Sure, Jensen told us analysts at GTC in March something to the effect of, “we don’t have to be absolutely the world’s best model, but we have to be near it”. But with Poolside and the rumored Hugging Face acquisition https://www.theinformation.com/articles/nvidia-agrees-buy-open-source-model-repository-hugging-face-12-9-billion?rc=ij60ts , Nvidia is spending ~$20B allegedly on the talent and distribution needed to succeed. And that $20B will pale in the long run compared to what Nvidia will spend on compute to train models. You don’t spend that kind of money if you aren’t trying to compete. Jensen Huang is a winner. I think he now wants to be as close to the frontier as possible, especially on the jagged edges that matter, like coding. My read on the acquisition of Poolside is that it gives Nvidia the iteration speed needed to catch up. Yes, via talent, but also via a bunch of internal tools and IP. According to Poolside’s blogs https://poolside.ai/blog/introducing-the-model-factory , training experiments that “used to take weeks to organize and schedule are now handled by the orchestrator in under an hour, often as few as ten minutes”. Batch size and learning rate sweeps run in “fewer than ten minutes, as opposed to days”. Deduplication went from “over a week’s worth of engineering time to schedule and run” to “the click of a button”. Jensen is adding speed to his team through tools and people. So this is somewhat like Meta buying rockstars for Meta Superintelligence Lab, except Poolside is 100+ people that already work well together and have already built a bunch of tooling. This makes sense for Nvidia and for the diffusion of agentic AI across America and the world. Why this is a good thing Competitive frontier open models mean GenAI diffuses more quickly into every industry. Open models enable fungibility, which unlocks competition. Competition and portability give the end customer control. Control unlocks innovation and cost efficiency. American and global productivity and GDP benefit accordingly. Yes, this aligns with Nvidia’s business model. As it should A lower token price means more tokens. Cheaper tokens mean more demand. More demand means more compute. Open models pulling forward diffusion also pulls forward Nvidia revenue. Give the model away, monetize the hardware. This can be done. Existence proof one is autonomous driving with Alpamayo https://www.nvidia.com/en-us/solutions/autonomous-vehicles/alpamayo/ , a family of open VLA models. Nvidia monetizes via three computers https://blogs.nvidia.com/blog/three-computers-robotics/ : “training NVIDIA DGX , simulation and validation NVIDIA Omniverse and NVIDIA Cosmos on NVIDIA RTX , and in-vehicle computing NVIDIA DRIVE AGX ”. Existence proof two, robotics. Cosmos https://www.nvidia.com/en-us/ai/cosmos/ world foundation model weights are open too. Monetize via same three computers. Nemotron https://developer.nvidia.com/blog/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task-execution-for-long-running-agents/ + enterprise AI looks similar. Nvidia trains the model, releases the weights, and sells the hardware needed for fine-tuning and deployment. DGX Station and DGX Spark on the desk if you want, too. An important part of enterprise GenAI tinkering will be on local compute. Zooming out, competitive open frontier models also lead to a healthier market structure. Dylan Patel on the Dwarkesh Podcast https://www.dwarkesh.com/p/dylan-patel-3 recently said “Anthropic and OpenAI are taking as much as 40% to 50% of compute next year”. Is a world where two labs buy half the world’s compute the healthiest outcome? Or do we want the long tail of enterprises buying compute or renting from neoclouds and CSPs, so one lab missing on a quarter doesn’t punish the entire AI infrastructure ecosystem? Like a local or state government putting the brakes on a datacenter buildout... Put differently, more compute buyers is good for anyone selling optics, copper, power, cooling, and memory. And everyone investing in it. Now the big question: is this in direct competition with Nvidia’s biggest end-customers, OpenAI and Anthropic? Yes and no. It’s nuanced. We have to ask on what plane of competition are we talking about frontier? It’s jagged after all, right? Having open models catch up at different points of different jagged spikes doesn’t mean death to Anthropic and OpenAI, and in fact it may help them focus on which spikes matter. Whatever happened to Sora? Oh yeah, Codex. Competition pushed OAI to be better, and that’s a good thing. More competition is good. This is technology expansion. It’s not zero-sum. We can have a healthy OAI and Anthropic, and competitive frontier open models too.