Techin' Geek

A trip to Europe often means adjusting to a lot of walking, but don’t let it fool you: cars are still the default for urban mobility. However, entrepreneurs are seeking to challenge this status quo — and the first step is understanding why it is so ingrained.

For Belgian startup Any, the only way to boost the use of two-wheelers is to make sure they’re “designed for real life.” And for Any, that means they should be able to hold a lot of stuff, without sacrificing speed, range, or versatility.

The startup’s “designed for real life” belief inspired its first product, a modular electric motorcycle called the LUV1 that has the capacity of a cargo bike, but can reach a top speed of 100 km/h or 62 mph.

Any’s attempt to fill in the “missing link” left by car trunks is the reason the LUV1 can store up to 120 liters.

“It came from asking a simple question: what does a real week actually need? Groceries, weekend bags, suitcases… the stuff you don’t want to leave behind just because you’re on two wheels, not four,” the startup wrote on LinkedIn.

There’s even more that the LUV1 can carry. Customers can either pick an open frame or one with various types of panels — depending on whether they want to travel with a pet or carry work equipment or camping gear. The modularity extends to other parts as well, including an optional windshield, racks, rain poncho, and several other variations that could make it appealing to commuters and families, or outdoorsy riders.

This modular design is the brainchild of former Pininfarina design director Lowie Vermeersch, whose reputation helped the LUV1 immediately make headlines when its eye-catching prototype was unveiled in May during Milan Design Week.

Vermeersch’s collaboration with Any isn’t just a one-off; the Granstudio founder is in charge of creative direction at the startup and is also one of its investors. Together, they are hoping to pioneer a new hybrid category they call “life utility vehicles” — hence the LUV1’s name.

Starting at €7,000 (about $8,150), the LUV1 will be less expensive than cars and closer to the cost of higher-end electric cargo bikes. Compared to more powerful motorcycles, it will also have another differentiator — it should only require an A1 license, which is easy to obtain in most places.

The exact price tag will depend on the custom options each customer can pick through the configurator that Any launched alongside its preorder campaign. Boosted by the Milan buzz, Any secured more than 600 reservations in its first 100 days, its co-founder and CCO Erik de Winter said.

These reservations require a €49 fee ($57) that is credited toward the purchase price and, per Any’s terms, is fully refundable. Like de Winter’s former startup, Dutch e-cargo bike-sharing service Cargaroo, Any has also raised venture capital. The startup raised €1.5 million in November, followed by a €1.25 million top-up in May, for a total of €2.75 million ($3.2 million).

According to the startup, which enlisted former VC Pieter Van de Velde as its CEO one year ago, the round’s backers and its advisor roster include high-profile individuals from the automotive sector. Adding more expertise on that side, Any also hired a head of engineering, Alessandro Mangano, whose former employers include Aprilia, Ferrari, and Piaggio Group.

With two swappable batteries that give it a range of up to 87 miles per charge, the LUV1 should be able to hold its own against electric models launched in growing numbers by the likes of Piaggio and even Harley-Davidson. But it will take more than that to stand out in a two-wheeler market that the European Association of Motorcycle Manufacturers expects will be mostly electric by 2030.

Any is betting that all of that extra cargo space will be its main differentiator. LUV1 is meant to appeal to a customer base that goes beyond motorcycle riders, de Winter told TechCrunch. He said the startup has been approached by last-mile delivery players; and as it intends to develop not just the LUV1, but a “portfolio of vehicles,” that could include a third-wheeler.

Before spreading itself further, however, the startup needs to ramp up production of the LUV1 ahead of its first deliveries to customers. This will require more funding, and Any is now seeking to raise some €10 million ($11.65 million) or more in a Series A funding round that it hopes to close by the end of the year.

Pictured from left to right: Erik de Winter (CCO), Pieter Van de Velde (CEO), Alessandro Mangano (Head of Engineering).

Mark your calendars: on Thursday night, September 10, StrictlyVC — TechCrunch’s boutique evening series — is heading to New York’s West Village (the real one — brownstones, cobblestones, and all — not the Mission Bay stretch of San Francisco that’s earned the nickname “Vest Village” for all the Patagonia-clad VCs roaming around).

It’s our first New York event in two years, and after a run of great nights this year in San Francisco, L.A., and Athens, we couldn’t be more excited to be back — with a stacked lineup to match. What’s in store:

Keith Rabois. We’re kicking off the night with the inimitable Keith Rabois, who recently relocated East from Silicon Valley and, true to form, seems intent on shaking things up wherever he lands. Rabois has never been shy about what he thinks is working in venture and what isn’t — he’s backed Ramp four times and invested as often in State Affairs, a company using AI and local journalists to track statehouse-level news and policy data across all 50 states. He’ll share his strong opinions on founders who raise more capital than they actually need, just because they can. And we’ll get him talking about Khosla Ventures’ boldest AI bet yet: its early $50 million check into OpenAI back in 2019 when the outfit had no clear business model — plus what he makes of the narrative that OpenAI is facing more headwinds right now than its rivals.

Craig Shapiro and Jason Levien. After our sit-down with Keith, stick around for a conversation you won’t find anywhere else. Craig Shapiro’s venture firm, Collaborative Fund, is generously co-hosting the evening with us, and we’ll sit down with Shapiro and Jason Levien, CEO of D.C. United, to talk sports organizations as business investments — and the increasingly tangled intersection of sports, fandom, commerce, and community.

Tristan Walker. Staying on the theme of community, we’ll dig into a conversation with two founders building for a world where AI does more — but also takes something away. Tristan Walker, who sold his last company, Walker & Company Brands (maker of Bevel), to Procter & Gamble in 2018, is back with Heirloom Craft, a startup focused on reshoring American fine craftsmanship — training a new generation of artisans and rebuilding the supply chains behind them.

Brynn Putnam. Walker will be joined by Brynn Putnam, who sold her last company, connected-fitness startup Mirror, to Lululemon for $500 million, just three years after it was launched. Putnam’s newest venture is Board, a game company blending physical play with AI-powered creation tools — built as something of an antidote to the isolation tech has fueled, bringing people back together around, literally, a board.

Deven Parekh. Last but not least, we’re thrilled to catch up with Deven Parekh, who has co-run Insight Partners — the New York powerhouse investment firm — for more than 25 years. Insight doesn’t do a lot of press, but Parekh has agreed to pull back the curtain on how the firm is thinking about a landscape where it’s gotten harder to tell asset classes apart, as some of the biggest funds have grown exponentially larger. What we want to know: how does Insight compete in a world where capital itself is a commodity, but the potential returns on massive investments have also never been bigger?

Expect a fun night all around, with drinks, hors d’oeuvres, and plenty of networking before and after the fireside chats, including with Connie Loizos, Rebecca Bellan, and other TechCrunch writers (as well as our friends from other outlets).

Giant thanks again to the team at Collaborative Fund for making the night possible. More details and ticketing info right here. See you in the (real) West Village.

AI hasn’t just changed how startups build; it’s broken how they sell, secure their data and customers, and scale it more rapidly than ever before. At TechCrunch Disrupt 2026, the AI Stage is back to dig into the single hottest topic in the community for the past few years, presented by Google for Startups. This time around, we’re exploring the business models AI is rewriting, the wealth of unsolved security gaps, and the entirely new job categories AI has created from scratch.

From October 13–15 in San Francisco at Moscone Center, join leaders from across the AI industry as they get into the real questions founders are facing right now. We’re talking about the matter of how to price AI products when models become commoditized, why agent security has to be rebuilt from the infrastructure up, and what it actually means to have a go-to-market plan in an AI-native world.

We’re also closing in on the end of our current pricing window, so your chance to save up to $200 is ending soon, so grab your ticket here before it’s gone. Without further ado, let’s see what’s on deck for the AI Stage, with more announcements to come:

What Anthropic Sees When Enterprises Actually Deploy Claude

Most enterprise AI conversations happen before deployment. This one starts after. As Head of Applied AI at Anthropic, Cat de Jong works directly with the enterprises putting Claude to work across their most critical workflows — and sees patterns that never make it into press releases. Where deployments succeed immediately. Where they stall. What separates the organizations extracting real value from the ones still running pilots eighteen months in. This session pulls back the curtain on what applied AI actually looks like inside the world’s most closely watched AI company — and what it reveals about where enterprise AI is really headed.

With Cat de Jong, Head of Applied AI, Anthropic

What Building AI Native Actually Means. Join the Conversation with OpenAI’s Head of Productivity

Two years ago, GTM engineering did not exist. Today, it is one of the fastest growing roles in the industry, with independent practitioners building million-dollar businesses. This session traces how AI collapsed the traditional go-to-market stack and created an entirely new discipline in its place. Walk away knowing what AI-native GTM looks like in practice and how it’s changing the way companies grow.

With Tara Seshan, Head of Productivity, OpenAI

The Enterprise Isn’t Broken. Your Assumptions About It Are.

AI is now making autonomous decisions inside the most sensitive enterprise systems in the world, at a speed traditional security frameworks weren’t built for. This session breaks down what enterprise AI security actually requires in 2026 — from observability and governance to the architecture that separates deployments enterprises can trust from ones they can’t afford to touch.

With Arsalan Tavakoli, Co-founder and SVP of Field Engineering, Databricks

The Agent Security Problem Nobody Is Talking About

Agentic AI is powerful, but it was never built to be secure. Now, enterprises trying to harness that are learning to rebuild the basic elements of cybersecurity from scratch. This session is a candid technical conversation about what agent security actually requires at the infrastructure level, why application-level permission models are fundamentally flawed, and the architectural decisions that really matter when deploying agentic AI.

With Ric Smith, President of Product & Technology at Okta

The Video Intelligence Race: Real-Time, Reasoning, and What Comes Next

Visual AI has moved past attention-getting demos into real-time inference and physical reasoning. Founders building at the frontier discuss what happens when generation crosses into genuine intelligence.

With Dean Leitersdorf, Co-founder and CEO, Decart, and Amit Jain, Co-founder and CEO, Luma AI

Rewriting SaaS: Why AI Breaks the Old Business Model

Is the SaaS playook dead, or is it just evolving? This session brings together founders and platform leaders who are grappling with that question in real time – and coming away with real answers. Walk away with a sharper understanding of how to price AI products sustainably, how to build defensible moats when models are commoditizing, and how to make SaaS work in the AI era.

With Arvind Jain, Founder & CEO, Glean, Barr Moses, Co-founder & CEO, Monte Carlo, Cathy Gao, Partner at Sapphire Ventures, and Aaron Jacobson, Partner, NEA

The GTM Engineer: How AI Created Tech’s Next Big Job Category

GTM engineering didn’t exist two years ago — now it’s one of the fastest-growing roles in tech, with independent practitioners building million-dollar businesses. Walk away knowing what AI-native GTM looks like in practice and how it’s reshaping growth.

With Kareem Amin, Co-founder and CEO, Clay

Securing the AI Enterprise: Why the Cloud Just Got a Lot More Complicated

AI is running inside the most sensitive enterprise systems in the world, making autonomous decisions at a speed and scale that traditional security frameworks were never designed to handle. This session delivers the infrastructure-level view of what enterprise AI security actually requires in 2026, from observability and governance to the architectural principles that separate deployments enterprises can trust from ones they cannot afford to touch.

With Chet Kapoor, VP, Security Services & Observability, AWS, Katie Moussouris, Luta Security, Wendy Nather, 1Password

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Whether you’re rethinking your pricing model, closing the security gaps in your AI stack, or building the go-to-market playbook that doesn’t exist yet, the AI Stage is where the builders shaping this next wave get specific.

Plus, you’ll be doing all this alongside 10,000+ startup, tech, and VC leaders, with access to every other stage, Startup Battlefield, a wealth of networking opportunities, and the exhibition floor. Register today!

Learn more about Disrupt 2026 

Check out Disrupt’s headline speakers 

Everything Founders should know about Disrupt 

Get the best hotel deals ahead of Disrupt 

How to host your own Side Event at Disrupt 

Take part in Disrupt and learn how to exhibit your startup 

Rivian’s chief financial officer Claire McDonough is resigning her position at the end of October, the company announced in a regulatory filing Thursday.

The company said McDonough is stepping down to “pursue a new opportunity and relocate to the East Coast to be closer to her family.” McDonough said in post on her LinkedIn page that she is taking a CFO position at GE Vernova.

Rivian said her resignation is “not the result of any disagreement.” The company is already searching for a replacement, and vice president of finance Derek Mulvey will serve as interim CFO once McDonough leaves her post.

Her departure comes as Rivian takes on some of its biggest projects to date, including scaling up production and sales of its R2 SUV, which started shipping to customers this summer.

McDonough was hired to the CFO spot in January 2021, replacing Ryan Green, at a critical and tumultuous time for Rivian. The EV maker, which was still a private company, had raised billions of dollars in its bid to bring three compelling electric vehicles — its flagship R1T truck and R1S SUV and a commercial delivery van — to market.

Rivian was plagued with delays and added costs, which was compounded by supply chain constraints.

In her first year at Rivian, the company started production of its R1T truck and raised $12 billion in one of the biggest IPOs of the year. The company’s stock, which debuted at $78, has since fallen to $16.80, as of Thursday’s closing.

McDonough was a key figure in the company’s quest to improve its balance sheet, and specifically its cost of revenue. While she didn’t have any direct experience working in automotive — her previous job was at JP Morgan and Fairway Market — McDonough was known for working closely with the design and engineering teams as well as founder and CEO RJ Scaringe to ensure future vehicles were modern and compelling without losing money on every sale, according to insiders who have spoken to TechCrunch in the past.

As CFO, she also played a vital role in a technology joint venture with Volkswagen Group. Under that deal, which was finalized in November 2024, VW agreed to invest up to $5.8 billion into Rivian by 2027 in exchange for access to its electrical architecture and software known-how.

In addition to paying out up to $18 billion and adding child safety measures, Meta’s settlement agreement with attorneys general from 29 states includes an interesting provision: The states have agreed not to sue Meta under existing child safety laws over its retention and use of children’s data.

That permission is being granted for the limited purpose of training and testing Meta’s age-assurance model and includes guardrails, but it’s a curious policy decision to make in a case centered on child safety, and one that could be difficult to properly enforce.

As specified in the settlement agreement, Meta must develop, train, and begin testing a model designed to detect which users on Meta’s platforms are under the age of 13. This must be done within a year of the document’s effective date. (While the agreement doesn’t specify that the model has to be AI-based, Meta’s current age-detection tools are powered by AI technology.)

Under U.S. child safety law, COPPA (Children’s Online Privacy Protection Act) typically requires that websites and apps limit the collection and retention of children’s personal information. Meta’s settlement agreement says that Meta shouldn’t need to violate COPPA to train or implement its age-assurance models. However, the agreement also says that the state AGs have agreed “fully, finally, and forever” not to bring any past, present, or future COPPA claims — or claims under similar state laws — related to Meta’s use of children’s data.

The agreement makes clear that Meta can’t use data from users under age 13 for ad targeting, marketing, or algorithmic optimization.

Meta’s request for legal protection, and the state AGs’ willingness to grant it, isn’t unreasonable, says Philip N. Yannella, a partner at law firm Blank Rome and co-chair of its Privacy, Security & Data Protection practice. “These kinds of data minimization guardrails are pretty typical for privacy compliance: e.g., verifying compliance with deletion requests,” he said, though he noted a caveat: COPPA is a federal law primarily enforced by the FTC, not the states, so it’s unclear whether the FTC, which isn’t a party to this settlement, has separately agreed to the same compromise.

It can be difficult for companies to keep data technically and organizationally isolated from the rest of their systems. Yet Meta is being asked to do just that — to isolate its understanding of children’s behavior signals and other data and use it solely for detecting and removing under-13 users. Fortunately, an independent auditor will be involved in monitoring Meta’s compliance with the settlement so we don’t only have to rely on Meta’s word.

Policing this limitation could be complicated. The data could hypothetically feed into other Meta systems over time, or could raise questions over whether the data, signals, or insights derived from it are being used elsewhere within the company. What’s not clear from the agreement is what data Meta will retain for training the model, how much behavioral information that may include, or how long it will retain the data. We also don’t know how these models will change in the future as Meta meets the settlement’s terms.

Barring state AGs from raising COPPA or similar state-law claims over this use of children’s data in the future could complicate the legal avenues states can pursue if questions arise around how Meta is using the data.

That doesn’t prevent them from pursuing legal claims, notes Joshua Wurtzel, a partner at Schlam Stone & Dolan LLP. “If Meta uses the data outside those lines, the release and covenant not to sue don’t apply,” he said. But those legal disputes could still be complicated, since they’d hinge on whether Meta’s use of the data fell within the settlement’s terms.

Peter Jackson, a Data & IP attorney at Greenberg Glusker LLP, agrees, saying the carve-out here could “disincentivize future enforcement actions.”

“The Settlement Agreement’s age-assurance measures bear all the hallmarks of a heavy, and perhaps hasty, negotiation,” he says.

The decision also touches on a broader question that’s been coming up across the AI industry lately, especially as more AI agents are being developed to help consumers with various tasks. The systems often require significant access to users’ personal data to work well. Similarly, Meta may need deep insight into children’s use of social media in order to identify which accounts belong to young people.

Nvidia has agreed to buy Hugging Face for $12.9 billion, The Information reported Wednesday night, citing a source familiar with the matter. Business Insider, which first reported over the weekend that Hugging Face was fielding takeover interest, reported Wednesday night that the talks — which would value the company at more than $13 billion — had not yet produced a signed agreement and could still atomize.

TechCrunch reached out earlier to both Nvidia and Hugging Face for comment, and neither has yet responded. (Nvidia’s silence is particularly noteworthy here as the company has moved quickly in the past to address reports it considers inaccurate.)

Maybe it was destined from the start. Hugging Face, founded in 2016, is one of the most popular hubs where developers share and download open-source AI models. Buying it would give Nvidia a strong foothold in the world of open-source AI, right as open-source developers are doing their level best to catch up to closed AI systems from companies like Anthropic and OpenAI.

Why would Nvidia want that? Most obviously, it comes down to protecting its dominance in AI chips, which, from the outside at least, appears increasingly at risk, even with Nvidia’s aggressive chip-release schedule. Pretty much all of the biggest closed-source AI labs (OpenAI, Google, Amazon, and Anthropic) are now in the process of building their own AI chips to lessen their reliance on Nvidia. A thriving ecosystem of open-source AI models gives customers more alternatives to those closed labs, which in turn keeps more of the market dependent on Nvidia’s hardware. That’s also why Nvidia has already poured tens of billions of dollars into building its own open-source AI models.

Should we be surprised that Hugging Face’s days as an independent outfit appear numbered? Not really. Hugging Face CEO Clem Delangue has spent much of this year publicly aligned with Nvidia’s open-source push, amid a debate that has been building for months, as Washington officials reportedly weighed restrictions on open-weight models. (Chinese labs like Moonshot AI had released systems — like its Kimi K3 model — that matched leading U.S. models on benchmarks while costing a lot less to run, and talk of competitive and national-security concerns grew in Washington as a result. Some critics of closed labs, like White House advisor David Sacks, suggested those fears were being fanned by the “duopoly” of Anthropic and OpenAI.)

In an appearance on CBS’s “Face the Nation” earlier this month, for example, Delangue said Hugging Face used an Nvidia-modified version of a Chinese open-source model to defend itself after a cyberattack and pointed to a recent letter — signed by Nvidia CEO Jensen Huang and 24 other companies, including Hugging Face — urging the U.S. government to support open models rather than restrict them. In a separate CNBC interview in late July, Delangue made similar points, citing that same letter while warning that China is “clearly dominating” open-source AI.

The deal would also mark something of a comeback for Nvidia in cloud computing. Nvidia reportedly scaled back its own cloud business, called DGX Cloud, about a year ago. But according to The Information, owning Hugging Face — which already helps developers run their AI models using rented computing power — could give Nvidia a way back into that market without starting from scratch.

There’s also a financial safety net at play. Nvidia has promised to help cover the cost of tens of billions of dollars in cloud computing deals for its customers. If those customers end up not using all the computing power they signed up for, Nvidia could get stuck with it. Owning Hugging Face would give Nvidia the ability to sell that unused capacity to Hugging Face’s customers.

The price marks a huge jump from Hugging Face’s last known value. The company raised $235 million in 2023 in a funding round that valued it at $4.5 billion. That round was led by Salesforce Ventures, with money also coming from Alphabet’s GV, IBM Ventures, and Nvidia itself, among others.

This wouldn’t be Hugging Face’s first brush with an Nvidia offer, either. Hugging Face turned down a $500 million investment offer from Nvidia late last year that would have valued it at $7 billion, the Financial Times previously reported. Hugging Face said at the time it didn’t want a dominant investor that could sway its decisions.

As for why it would say yes now, one could argue that a buyout is different from taking on one giant backer — a scenario that often means ceding control while being pressured to continue growing.

Hugging Face is also still a comparatively small business by revenue in the world of AI. The Information reported it was recently generating about $150 million a year in revenue, up from roughly $100 million just two months earlier.

That growth has enabled the company to get “close to profitability,” as Delangue told TechCrunch last month. Still, a price near $13 billion would be a massive multiple for a company this size and hard to resist.

Not last, the deal would give Hugging Face access to Nvidia’s much deeper pockets just as other, AI infrastructure competitors start to get pulled into other outfits, as suggested by Stripe’s recent deal to acquire OpenRouter, a startup founded in early 2023 that helps customers select different AI models to perform different tasks depending on their needs and budget.

OpenRouter was valued at just $1.3 billion back in May during its Series B round. Stripe reportedly paid more than $7 billion to make it its own earlier this month.

Amazon and Nvidia just got a lot closer. The two companies announced Wednesday an expanded partnership that includes a deal to add another 2 million Nvidia GPU chips to Amazon’s data centers.

These GPUs, which are designed to handle the heavy compute demands of training and running AI models, include Nvidia Blackwell Ultra, Rubin, and Rubin Ultra GPUs. The chips will head to Amazon Web Services’ data centers in 2027 and 2028. 

The announcement, made during Nvidia’s quarterly earnings call, comes just five months after Amazon agreed to deploy more than 1 million Nvidia GPUs across AWS infrastructure starting this year. Nvidia said in a statement that since then, “demand has exceeded those expectations.”

Neither company shared financial terms. It’s unclear what the exact return will be for Nvidia. But considering GPU units costs, the deal is worth tens of billions of dollars.

The announcement is notable not just for its size and the speed in which it grew, but also because it extends beyond Amazon buying more Nvidia chips. And it’s happening even as Amazon invests in its own potentially competing AI chips. 

Nvidia said Wednesday that its technology, including the networking hardware that connects thousands of GPUs into one system, as well as its open models, CPUs, data processing software, and robotics platform, will also be integrated across AWS. 

The companies said “surging demand” from startups, enterprises, AI labs, and even governments influenced the decision to work more closely. 

The expanded partnership comes as Amazon ramps up its own AI chip efforts — particularly with CPUs, which are the general purpose processors at the heart of servers. 

Amazon has been building its own chips to lessen its dependence on Nvidia and even compete with the chip giant. Amazon’s AI chief Peter DeSantis has said that AWS is in talks to sell its Trainium chips — which are a direct alternative to Nvidia’s H100 or Blackwell chips for deep learning workloads — to other companies for use in data centers. Amazon’s Arm-built Graviton CPU is also seen as a challenger to traditional server chips from Intel and AMD. 

Amazon has said its custom chip business is growing, noting on its last earnings call that it crossed a $25 billion annualized revenue run rate, driven by $225 billion in total commitments from AI labs like Anthropic and OpenAI. 

But, it seems Nvidia is still the GOAT in the world of AI chips. 

With the 2 million GPU chips Amazon is adding to AWS starting in the third quarter, Nvidia also plans to send an unspecified number of Vera CPUs, “some integrated with Rubin, others standalone,” according to Nvidia CFO Colette Kress. 

Nvidia CEO Jensen Huang has big plans for the company’s Vera CPUs, boasting back in May that he had found a “brand new $200 billion TAM” for the company.

Aside from AWS, Kress said Wednesday that Nvidia expects Vera to be deployed by “every major hyperscaler, neocloud, AI lab, and system OEM, with shipments already underway to our lead partners,” which include Oracle and SpaceX AI.

The partnership is also extending to Amazon’s warehouse robots and enterprise offerings. 

Kress said Amazon plans to adopt Nvidia’s full physical AI stack to power its fleet of robots. The stack includes Omniverse (its simulation and digital twin platform); Cosmos (its world model platform); Isaac (its robotics development platform); and Jetson (computing hardware for robots and edge AI). This week, Nvidia also introduced a new version of Jetson designed as a more accessible robotics computer for “entry-level edge AI.”

On the enterprise side, AWS will serve Nvidia’s Nemotron family of open models on Amazon Bedrock, its managed foundation model platform, and SageMaker, its managed cloud service. 

Nvidia also reported Wednesday that it recorded sales of $96.2 billion for the second quarter, beating analyst estimates. Data center revenue made up the majority of Nvidia’s sales for the quarter at $89 billion, up 117% from a year ago. 

Nvidia said it expects revenue to reach $108 billion in the third quarter, some of which will come from its next-gen Rubin GPUs. Nvidia said it began production shipments this quarter. Investors have been looking out for Rubin’s initial Q3 sales for signs that demand will continue into Nvidia’s next generation of hardware. 

Nvidia has committed $279 billion to secure supply and manufacturing capacity for current and future data-center projects, up substantially from $119 billion last quarter, as the chipmaker looks to secure memory and manufacturing capacity to meet AI demand over the next few years. That commitment includes $92 billion in projected spending for the rest of the fiscal year and another $87 billion in fiscal year 2028. 

“The thing that matters for the industry is that AI is now doing productive and useful work,” Huang said during Wednesday’s call. “AI is generating profitable tokens…If we had more compute, we could generate more profitable tokens, which results in more profit for all of the services. This is the exact phase where we’re at, which is the reason why everybody’s leaning in.”

Investors will be watching to see if additional compute indeed translates so neatly into additional profits as AI companies pour hundreds of billions of dollars into infrastructure. 

Google gets something right in its Wednesday announcement about new Gemini Live voice features when it says, “You shouldn’t have to guess whether a task requires Spark, a Daily Brief, or a quick inbox search.” Google means that as a promise — that the updated Gemini app can handle a variety of tasks via voice commands. But there’s a ridiculousness here: Google has given every Gemini AI feature under the sun its own branding, which undercuts that very message.

In the Gemini app, users can switch between chat, Spark, and Daily Brief — three separate features, each with its own icon and place in the app’s navigation. This clutters up what could otherwise be a more straightforward consumer experience, and it suggests that Gemini is still struggling to find a killer feature.

Take Daily Brief, for example. The feature comes across as the kind of thing an AI engineer, not an everyday user, would think is clever. It’s essentially an AI-enabled agenda that offers “proactive, personalized updates” using data pulled from Google’s apps, like Gmail and Calendar. In practice, though, the Brief can’t tell the difference between information that’s urgent or actionable and unsolicited nudges to follow up on other things — like prompting you to continue research you started in the chatbot, or worse, resurfacing your prior Google searches.

That second part doesn’t feel useful; it feels creepy. So what if I had been researching college scholarships or animal rescues on Google? That doesn’t mean I want an AI tapping me on the shoulder about them later.

Spark has the opposite problem. It’s one of the more useful aspects of Gemini’s app — an AI agent that can take action on your behalf — but Google has packaged it as its own standalone brand, which it doesn’t need to be. Sure, internally, Google engineers may want to be on the Spark team, and that’s fine — but a mainstream AI app user definitely does not need to think about which “side” of the AI app they need to be in for a given task. They should just be able to type their request, and the AI figures out how to handle it, spinning up an agent if the task calls for one.

In fairness, the problem isn’t limited to Gemini. The AI industry at large seems to expose its internal architecture directly to consumers rather than hiding it behind a simpler interface.

Today, people have to think about whether they want to “chat” with Anthropic’s Claude or “Cowork” with its help. (Until this week, those two modes inside the Claude app didn’t even share a memory of past conversations.) ChatGPT is the same, requiring you to swap between “Chat” and “Work.” This is the kind of engineering-minded design that makes engaging with AI feel unnatural. Consumers are being asked to learn the brand names for what are essentially interaction modes or surfaces, powered by a company’s AI model.

This may be why Apple’s somewhat anticlimactic approach to Siri could ultimately win over consumers. iPhone and Apple device owners don’t have to change any of their existing behavior to take advantage of it. Apple simply makes the apps and features that people already use — like Spotlight Search, the Photos app, the iPhone’s Camera, and Siri voice requests — smarter without asking users to learn a new interface.

This same principle may explain the rise of text-based AI services, where users simply text a chatbot — like Poke, Ollie, Lindy, Orchid, Lucas, Folk, Tomo, Instinct, or others — and the assistant just does what’s asked.

Text messaging is a clean and simple, well-understood user interface, and it doesn’t require extra mental effort to figure out which feature or product inside a larger app you’re supposed to use.

As a16z investment partner Justine Moore recently wrote, “People don’t want to open an app every time they need help – they want a contact they can text like a friend. And the gold standard is iMessage.”