Nvidia’s AI advantage is moving beyond the GPU
6:00 AM PDT · August 29, 2026
Before this week, the dominant story about Nvidia went something like this: For the first few years of the AI boom, Nvidia was the only source for state-of-the-art GPUs, which became immensely profitable as the industry scaled out. In the last few years, hyperscalers like Amazon and Google have started building their own chips, and Nvidia is no longer the only game in town, leading many investors to wonder how durable its advantage really is.
It’s a compelling story, and mostly true. After growing its market cap 10x between the start of 2023 and mid-2025, Nvidia shares have been on a more modest trajectory for the past year, driven by concerns about GPU competition.
A new narrative has taken shape since the company’s earnings on Wednesday and investors are starting to realize that Nvidia’s advantage goes far beyond GPUs. As AI’s compute grows into the gigawatt scale, orchestration has become an increasingly complex task. Not surprisingly, Nvidia has built much of the state-of-the-art hardware needed to handle it, giving the company a huge advantage in the systems that surround the GPU even as it sees increased competition on the GPUs themselves.
For all the talk of compute as a commodity, it’s still incredibly difficult to operate a megascale data center at peak efficiency — and as deployments get bigger and faster, that challenge is only growing.
Rack by Rack
You can see some of this just by looking at the details of what Nvidia is actually selling. The company is currently rolling out its Vera Rubin architecture, which pairs the Rubin GPU with a collection of other units, including the Vera CPU, the Groq 3 LPX inference accelerator and similar racks for storage and networking.
Over the past week, I’ve been talking to folks at Nvidia about what those systems actually do, and the results have been surprising. Like the Rubin GPU itself, they’re extremely specialized systems, but instead of churning through tokens, they’re making sure everything outside the GPU works as efficiently as possible. If the GPU is the engine, these are the rest of the car.
The Vera CPU in particular is focused on the problem of orchestrating data. “Vera is important because there’s only so much memory that you can put in a single server or any sort of compute platform,” Jason Hardy, Nvidia’s VP of storage technology, told me.
As data centers have scaled up computing power, memory capacity has scaled up too, which is why companies like Micron have gotten rich in the second wave of the infrastructure boom. But getting that data to the GPU at the right time isn’t straightforward — and as companies look to drive tokens-per-watt lower and lower, they’re realizing how important that kind of traffic direction is.
“We saw upwards of 3x improvement in these operations, where the Vera CPU is allowing for acceleration,” Hardy said. “So now we can use our flash to its fullest potential, because we can get all that performance out of it without bottlenecking.”
You can see versions of the same problem outside of Nvidia. When OpenAI developed its Jalapeño chip, a major focus was avoiding these challenges entirely by minimizing the amount of data that needs to be moved around.
“We designed Jalapeño to minimize data movement and communication delays,” the company said in a blog post earlier this month. “Its large domain allows the entire workload to remain within one connected system, minimizing data movement and helping the complete request stay fast and efficient from beginning to end.”
It’s a different approach, avoiding data movement entirely by conducting a workload within one integrated chip. But the overall logic is the same, increasing efficiency with smarter traffic control instead of just more processor cycles. That in turn opens up a whole new layer of infrastructure for companies to compete over.
This new focus on data orchestration isn’t automatically a win for Nvidia. The company will have to compete with rival chipmakers and hyperscalers just as it has with GPUs. But the competition has moved to a new layer, where building a rival GPU matters less than being able to make the entire system work efficiently.
And at least in the early stages, Nvidia looks to have a commanding lead.
Open-weight AI companies are the Valley’s hottest acquisition targets
11:19 AM PDT · August 28, 2026
Everyone’s waiting for Nvidia to confirm this week’s most interesting tech deal: A reported $13 billion acquisition of Hugging Face, a platform for sharing open-weight AI models and benchmarks.
Now best known as the target for a team of reward-hacking OpenAI agents, Hugging Face is at the center of the ecosystem of developers building and deploying LLMs that aren’t owned by frontier labs. Think of it as a kind of GitHub for the AI era.
Rumors of that deal come after Nvidia struck a $6 billion agreement with Poolside, an open-weight model builder, that will see most of its employees move to the chip-making giant. And two weeks ago, Stripe acquired OpenRouter, the top provider of open-weight models to businesses, for more than $7 billion.
That’s a lot of capital pouring into a sector based on giving stuff away, and it reflects the latest trends in the AI sector.
For Nvidia, there’s a need to avoid further dependence on its deals with the major hyperscalers and frontier labs. That’s particularly the case when major AI model builders like OpenAI and Google are also building their own inference chips, like OpenAI’s Jalapeño, whose capabilities were announced this week. If model builders are making chips, Nvidia wants a chunk of the model-making business.
Nvidia already builds its own Nemotron family of open-weight models, but their uptake hasn’t been huge. By taking control of the largest U.S. developer space for open models, the company will have access to a mass of users it can drive to its chips and standards.
There are also growing questions about the cost of AI inference, which has companies exploring cheaper models built by Chinese companies like Moonshot, DeepSeek, and Alibaba. Right now, adoption is relatively small but growing — just 6% of companies use open-weight models, according to a survey of spending data by Ramp, or just 2% of software engineers measured by Jellyfish, which makes tools for developers.
Nik Albarran, the AI product lead at Jellyfish, told TechCrunch that open-weight models are primarily used by companies whose products rely on repeated inference workloads, like those providing customer service chats. Because these are high-volume tasks with a lot of repetition, an open-weight model can be tuned to answer the questions cheaply.
That’s certainly how Stripe has framed its OpenRouter acquisition. “Tokens are the central currency for companies building with AI, and it’s clear that the real-world economic potential will depend on making good use of scarce compute resources,” Patrick Collison, Stripe’s co-founder and CEO, said in a statement.
For coding and agentic tasks, however, varying requests and more reasoning mean that frontier models often win out, in part because the proprietary labs provide easier access, and in some cases a token subsidy. Albarran says that as companies dial in AI workflows, it will be easier to turn to open models. Still, the main reason companies look to those models now is for control and configurability, not because of spending concerns.
“There are not many companies where that is the case yet … [but] if the prices continue to go up from the frontier labs, more and more companies will be forced to at least consider it,” Albarran told TechCrunch. “When your AI-driven workflows are much more mature, that’s when it makes sense to invest in self-hosting models.”
Lin Qiao is the CEO of Fireworks, a leading open-weight models router and host for corporate users that is often discussed as a potential acquisition for a tech giant. Qiao says her company processes 40 trillion tokens a day, more than either of Gemini’s or OpenAI’s APIs.
Fireworks’ bet is on model diversity: As LLMs proliferate and improve, it will be easier for companies to train them specifically for their needs. “Every single app company should consider hiring an in-house researcher,” she told TechCrunch last week. “They can use their product and product data to build their own model. The future is actually specialized intelligence. Literally, every single company should have their own model per use case, and that will happen automatically.”
It’s easy to forget how early we are in the development of AI as a tool and a business. The dominance of OpenAI and Anthropic, however, isn’t inevitable. As the tech giants look to hedge their bets on the biggest labs, the allure of open technology is proving tough to resist.
At TechBBQ, Europe’s AI conversations kept coming back to: Who’s actually in control?
10:51 AM PDT · August 29, 2026
At TechBBQ in Copenhagen, the conversation among investors, founders, and operators from across Europe wasn’t just about what to build with AI, but who should control it. No matter where you were during the annual Nordic conference, from onstage to cocktail hours and after-parties, the chatter kept coming back to how Europe can gain more control over the technology powering AI.
That question of sovereignty — which fittingly matched this year’s theme of “Emerging from Agency” — was especially timely after Anthropic’s AI models Mythos and Fable became unavailable to users outside Europe earlier this year.
The incident led many in the European ecosystem to think seriously about what it means to own the models and the infrastructure underpinning this AI wave, instead of renting that power from the U.S. and China.
One startup exec told me the Mythos and Fable incident seriously disrupted his software team, while another shrugged it off. Sure, the startup exec said, relying on two other geopolitical powers for AI might one day cause Europe severe headaches, but for now, everything is still pretty much OK.
“TechBBQ’s theme of agency felt particularly timely this year as the conversation shifted from what AI can do to what we’re actually willing to let it do,” Ellen de Brever, an angel investor and head of partnerships at the Novo Nordisk Foundation Cellerator, told me during the conference that wrapped up August 27.
There were panels about the future of women’s health, a summit on how the Nordic and African ecosystems can collaborate on innovation and investment, and talks about how to get more growth capital into Europe. AI was an underlying theme in many of the conversations, but as de Brever pointed out, “the debates weren’t about smarter machines, but about human judgment and who gets to stay in the driver’s seat.”
“The age of AI agents isn’t really a story about machines gaining agency,” she continued. “It’s a story about humans deciding what to give up.”
An attendee told me that one of the most refreshing sessions at the event was the panel with Signal President Meredith Whittaker, which I moderated and focused on whether privacy and AI can coexist. Whittaker didn’t mince words, and spoke out against AI assistants and agents being integrated into operating systems, like what ChatGPT is doing with iMessage.
She said this AI era is creating a “data collection apparatus” and that AI labs are using clever marketing to make people forget “about the collateral consequences” of acquiring so much data.
“There is still a huge market need for privacy,” she said. “And particularly with the sovereignty concerns, we will have customers here.”
Elsewhere, Emad Mostaque, co-founder of Stability AI and Intelligent Internet, sat on a panel with me about how an agentic workforce will impact the economy and what that means for personal sovereignty.
“Sovereignty is the ability to resist power being exerted over you,” Mostaque said. He spoke about the concentration of power in the hands of a few AI labs and said, “Inevitably, every country will be run by AI and that “the person that controls the AI controls the country.”
Mia Negru, the advocacy and engagement director for the nonprofit Life With Artificials, sent me a message after the panel about what crossed her mind as she listened.
“If intelligent agents perform cognitive work and robots increasingly perform physical work, we may be approaching something much bigger than another technological revolution,” she said in the message. “We may have to rethink ownership, work, democracy, economic participation and eventually even who gets to participate in society.”
But when the panels weren’t in session, people let loose at happy hours and rooftop soirées, where more lighter conversations eventually took over.
Lovable, Nvidia, OpenAI, AWS Startups, and HSBC came together to host a rooftop barbecue where I spoke to founders about Denmark’s growing tech hubs.
London’s Ada Ventures hosted a gathering at the cocktail bar Bird where I chatted with one health tech founder about increased innovation in the women’s brain health space. Then, there was the speakers’ dinner on an island in the middle of Copenhagen where the international press sat together and watched fire dancers. The after-party continued upstairs, where everyone was tempted to stay for one more drink until the bar tab closed.
“In a room full of conversations about machines, the most memorable moments still come from the simplest thing,” de Brever said. “Human connection, face-to-face, building relationships, sharing ideas, and reminding each other of what technology can never quite replace.”
