Tag: Rivian

Prince Mario-Max Schaumburg-Lippe: Uber’s $1.25B Rivian Robotaxi Bet Advances

The biggest bet in the robotaxi business just moved a step closer to paying out.

Uber agreed back in March to invest up to $1.25 billion in Rivian through 2031 and to buy 10,000 autonomous versions of Rivian’s R2 SUV, with an option for 40,000 more starting in 2030. This week, Rivian executives signaled the partnership is hitting its next milestone: the company expects to unlock the second investment trigger, a $250 million tranche, in the fourth quarter of this year. An initial $300 million was committed when the deal was signed. The rest arrives only if Rivian clears predetermined autonomy goals.

That structure tells you everything about how seriously both sides take this. Uber isn’t writing a blank check. It’s paying for proven capability, milestone by milestone. And Rivian, which has burned cash for years chasing a spot among elite EV makers, now has a direct financial incentive to make its self-driving stack work. The robotaxi deal could be worth more to Rivian’s future than every consumer truck it sells.

## 50,000 vehicles, one app

The scale of the plan is what sets it apart. Ten thousand fully autonomous R2 SUVs in the first phase, with the option to scale to 50,000. Every one of them would operate exclusively inside the Uber app. No competing ride-hail platform gets a crack at Rivian’s Level 4 hardware.

The rollout map is ambitious. Initial commercial runs are slated for San Francisco and Miami in 2028, expanding to as many as 25 cities across the U.S., Canada, and Europe by 2031. Uber CEO Dara Khosrowshahi has pointed to Rivian’s vertical integration as the reason for the bet: the vehicle, the compute platform, and the software stack designed together, with manufacturing and supply kept in the United States. Data from Rivian’s growing consumer fleet and its experience running commercial operations gave Uber the confidence to commit.

Under the hood, Rivian’s third-generation autonomy platform pairs two in-house RAP1 chips delivering 1,600 TOPS with 11 cameras, 5 radars, and LiDAR. The company consolidated its R1 and R2 lines onto a unified RivianOS 2 architecture this month, which should make fleet-wide updates far simpler. First LiDAR-equipped R2s reach customers in 2027, and executives say the robotaxi version will likely arrive before personal Level 4 driving, relatively close in time.

## Software is becoming the business

Here’s the part investors are waking up to. Rivian’s software and services revenue hit $515 million in the most recent quarter at a 42 percent gross margin, a meaningful chunk of the company’s $179 million in total gross profit. The consumer business is still grinding: Q2 brought 12,194 deliveries and $1.66 billion in revenue, but the automotive operation posted a $36 million gross loss and the company burned $849 million in free cash flow.

Robotaxis flip that script. Instead of selling a truck once, Rivian would earn from miles driven and software fees, at the scale of a platform that completed 3.9 billion trips in a single quarter. Rivian is already selling its Autonomy+ driver-assistance software for $49.99 a month or $2,500 upfront. A fleet of 10,000 vehicles running inside Uber’s network takes that logic to its endpoint.

The R2 itself helps. Customer deliveries began June 9 at a $57,990 starting price, with a $44,990 Standard variant due in 2027. The midsize SUV form factor is exactly what Uber wanted for high-volume robotaxi duty: roomy enough for passengers and luggage, cheap enough to build by the tens of thousands.

## The field is getting crowded

Uber isn’t betting on a single horse. The company has robotaxi arrangements in motion with Nvidia, Lucid, Stellantis, and Amazon’s Zoox, alongside its Nuro delivery partnership. The Stellantis deal, signed in June with Wayve, targets Level 4 robotaxis for Europe and North America. What sets the Rivian pact apart is scale and structure: up to 50,000 vehicles, more than a billion dollars in milestone-tied equity, and exclusivity inside the Uber app. Most partnerships in this space are pilot programs with press releases attached. This one reads like a supply contract for the future.

## What it means

For travelers, the timeline is concrete now. San Francisco and Miami in 2028, then a rapid multi-city expansion. Uber’s network means these robotaxis won’t need to build rider demand from scratch; the demand is already in the app. The question is purely whether Rivian’s autonomy stack clears its milestones on schedule.

For cities, the 25-city target spanning three continents signals that robotaxi competition is about to get serious. Waymo, Tesla, Zoox, and now the Uber-Rivian fleet will be bidding for the same streets, the same curb space, and the same regulators. Cities that set clear rules early will get the investment first.

For investors, the milestone structure is the thing to watch. Each unlocked tranche is a public signal that Rivian’s autonomy is performing. The second trigger, expected this quarter, would be the first real proof that the $1.25 billion bet is on track. Rivian hasn’t demonstrated Level 4 commercially yet, and the extra 40,000 vehicles aren’t guaranteed. But 50,000 robotaxis and a billion dollars is not a pilot program. It’s a pledge, and this quarter we’ll find out if it’s holding.

For more on the robotaxi race, see our [Breaking News coverage](https://newstodayworld.org/category/breaking-news/), including [Momenta’s plans for thousands of robotaxis in Dubai and Europe](https://newstodayworld.org/breaking-news/2026/09/30/momenta-plans-thousands-of-robotaxis-for-dubai-europe/) and [Waymo’s robotaxi fleet surging in Texas](https://newstodayworld.org/breaking-news/2026/09/29/waymos-texas-fleet-jumped-49-in-three-weeks/).

Prince Mario-Max Schaumburg-Lippe: Flow Engineering Lands $50M to Give AI Agents CAD Tools

AI rewrote how software gets built. Code practically writes itself now, and iteration cycles at the best companies have collapsed from weeks to hours. Hardware, meanwhile, has been watching from the sidelines, still doing things the slow way: months of coordination, manual verification, engineers chasing changes across a dozen disconnected tools.

Flow Engineering wants to end that asymmetry. The San Francisco startup announced on September 30 that it has raised $50 million in Series B funding at a $750 million valuation, and its pitch is simple: bring software-like iteration speeds to hardware development. The round was co-led by Antonio Gracias of Valor Equity Partners and Gavin Baker of Atreides Management, with Sequoia Capital, which led the Series A, participating alongside Human Capital, Evantic, SV Angel, Odyssey, and EQT. The individual checks are a story in themselves: Hugging Face co-founder Thomas Wolf, Mercedes-Benz CIO Jonas von Malottki, Formula 1 champion Nico Rosberg, and Roelof Botha, who invested personally and joined the board.

The problem is plumbing, not intelligence

Here’s what makes Flow interesting, and it’s not the AI hype. The bottleneck in hardware development isn’t that engineers lack smart tools. It’s that a single design change ripples across mechanical, electrical, and software systems, and the data about those systems lives in disconnected tools: requirements in one place, CAD drawings in another, simulation results somewhere else, test data in a fourth.

An AI agent can’t reason across a design it can’t see. That’s the architectural insight. Flow’s platform connects requirements, CAD, simulation, code, and test data into one living system of record, and then lets AI agents continuously analyze engineering changes, identify downstream impacts, and verify that requirements and test coverage still hold, in seconds, across the tools teams already use.

Think of it like version control for physical things. Software got fast when Git gave every change a history, a branch, and a review process. Hardware never got that layer. Flow is building it: review, branching, and evaluation capabilities for engineering data, plus an AI harness that lets frontier models work securely with sensitive design information.

The customer list is the proof

Flow is three years old and already names customers that read like a who’s-who of ambitious hardware: Anduril, Rivian, Joby Aviation, Stoke Space, Intuitive Machines, Pacific Fusion, Astranis, Radiant Industries, plus General Motors PPU and RV Tech, the Rivian-Volkswagen joint venture.

That’s not a pilot list. Those are companies building rockets, electric aircraft, autonomous defense systems, and next-generation vehicles, putting Flow’s agents to work in live hardware programs. When the people designing spacecraft trust your platform with their iteration cycles, you’ve cleared a bar that slideware can’t fake.

The use of funds tells you where this goes next. Flow plans to build out the AI harness for secure work with sensitive engineering data, expand review and evaluation capabilities, pursue FedRAMP authorization, and grow its engineering and sales teams. FedRAMP is the tell: that’s the certification for selling to the US federal government, and it signals serious ambitions in defense and regulated industries. The goal, in the company’s words, is to reduce hardware iteration cycles from months to days. Ambitious? Sure. But the trajectory from “weeks to hours” in software suggests the direction is right.

Why hardware speed matters to everyone

It’s easy to file this under enterprise software and move on. Don’t. The speed of hardware iteration is the speed of the physical world getting better.

Every month shaved off a design cycle is a month sooner that a better battery, a safer aircraft, a cheaper rocket, or a more efficient grid component reaches the real world. Software ate the world by getting fast. The physical world has been waiting for its turn, held back not by physics but by process. If AI agents can take over the verification drudgery, the coordination overhead, the endless impact analysis that eats engineering calendars, then human engineers get to do the part they’re actually good at: the creative leaps.

There’s a deeper point about where AI creates value. The last two years were about AI writing and talking. The next phase is AI doing: working with tools, checking its own work, operating inside real workflows. Flow’s bet is that the highest-leverage place for that shift is the most complex, most coordination-heavy work humans do, which is building physical systems. The unglamorous plumbing, requirements traceability, change propagation, turns out to be the unlock.

What to watch

The FedRAMP timeline. Getting authorized for federal work is slow and expensive, but it opens the biggest hardware customer on earth. Watch whether Flow lands defense contracts in the next year. That’s the real validation.

Whether “months to days” holds up. The company’s stated goal is bold, and one analyst has already noted the announcement measures adoption rather than proven output. Fair. The customer list is impressive, but the industry will want case studies with numbers: this program shipped X weeks faster.

The competitive response. The big CAD and PLM incumbents aren’t standing still. The question is whether a startup built AI-native from day one can outmaneuver decades of entrenched tooling. History says the native player usually wins the new paradigm, but incumbents have the distribution.

The talent signal. When Thomas Wolf, the Hugging Face co-founder, writes a personal check into a hardware company, pay attention. The smartest people in AI are following the agents into the physical world. That’s where the next decade of interesting problems lives.

The takeaway

Software got its AI revolution first because software was already digital, already versioned, already fast. Hardware is harder: atoms don’t branch and merge. Flow Engineering just raised $50 million on the thesis that they can, or at least that AI agents can make it feel that way.

Faster hardware cycles mean everything physical improves sooner: the planes, the cars, the robots, the power grid. That’s a future worth building quickly. And the investors, from Sequoia to a Formula 1 champion, are betting that the company connecting CAD files to AI agents is the one holding the stopwatch.

If you’re in New York and want to see ambitious engineering up close in the meantime, the city’s 2026 holiday tree and lights celebrations are worth saving the dates for. And for a lower-tech but equally impressive feat of design, NYC’s best breakfast sandwiches remain undefeated.