The tech industry has spent the last decade asking whether self-driving cars need lidar sensors, cameras, or all of the above. Lidar company Ouster says it has a new answer: put them both in the same sensor.

On Monday, the San Francisco-based company announced a new lineup of lidar sensors it calls “Rev8,” all of which offer so-called “native color lidar.” These sensors are capable of capturing color imagery and three-dimensional depth information at the same time, doing the work of two sensors in one.

Ouster CEO Angus Pacala said the development has been a decade in the making at his company, and he wasn’t shy about his ambitions for the new product lineup in an exclusive interview with TechCrunch, calling it the “holy grail of what a roboticist has always wanted.”

“For all of human history, it’s been: you buy a lidar sensor, you buy a camera, and you try to make sense of the combination with some higher level reasoning, and waste an enormous amount of time doing this,” he told TechCrunch. “And companies only get really halfway there in terms of calibrating and fusing the data streams.”

Ouster’s new sensors, he said, change this equation.

“The goal is to obviate cameras. There’s no reason that one sensor can’t do both,” he said.

The Rev8 lineup arrives at a dynamic moment for lidar companies. There has been a years-long wave of consolidation happening, with Ouster buying Velodyne, and Luminar’s assets recently getting acquired in bankruptcy.

At the same time, the market for sensors is exploding. Waymo and others have finally deployed working robotaxis and are scaling quickly. Robotics companies — humanoid and industrial — are hoovering up investment dollars and need sensors to perceive the world. There’s so much interest in the space that new companies like Boston-based Teradar are popping up and testing the waters with entirely new modalities. (In Teradar’s case, it’s using terahertz imaging.)

A color lidar that combines pinpoint depth information with camera-quality image data could be especially valuable to the robotics players, Pacala said. And he said Ouster worked with Fujifilm and image science company DXOMARK to understand “what it means to build a great camera.”

In fact, Pacala claims Ouster’s color lidar is “improving in many ways on a modern camera” thanks to the way the company already designs and builds its sensors.

Ouster uses so-called “digital lidar” architecture. Instead of the analog approach, which involves many moving parts, Ouster captures the lidar info directly on its custom chip using what’s known as single photon avalanche diode (SPAD) detectors.

The company is using this same SPAD technology to capture the color image data in the Rev8 sensors. Pacala said this novel technique allows its image capture to be more sensitive than a normal camera.

“It’s 48-bit color, 116 dB of dynamic range, like mega pixel resolution. These are top line numbers that make it pound for pound good camera. But it just so happens it’s coming as a pre-fused data stream as a 3D colorized point cloud,” he said. “You can actually use the data as a camera stream as well, but it’s that’s one of the powers of this system, is you can use just the lidar data stream, you can use just the camera data stream, or you can use the pre-fused data stream, depending on how kind of forward-thinking your perception team is.”

Pacala said his company has already shipped samples to existing customers and that it’s now taking orders. He said he’s particularly proud of the OS1 Max sensor, which he said he considers to be “the industry’s best long range lidar.” It can see 500 meters in all directions and is smaller than other long range lidar “by a big margin.”

“We’ve had a long range LiDAR, but it hasn’t been just like clearly a cut above everything else,” he said. “That’s a big leap for Ouster. I think it means that we’ll start to see it much more on high-speed robo-trucking, robotaxi applications, I think a lot of drone stuff will transition to the OS1 Max.”

Other new lidars built on the Rev8 platform will include the OS0, OS1, and OSDome, according to a press release.

Ouster isn’t the only company that has started talking about color lidar. Last month, Chinese company Hesai announced its own color lidar platform that it says will enter mass production by the end of this year. Other companies, like Innoviz, have previously pitched their own takes on “color lidar.”

Pacala says most other players trying to “fuse” cameras and lidar sensors are basically packaging them together in a box, though. The approach Ouster (and, to be fair, Hesai) is taking is putting the lidar and imaging tech on the same chip.

This dramatically cuts down on the amount of work Ouster’s customers have to do to make sense of the competing sensor streams, Pacala said, and it also sets those customers up to eventually eschew cameras altogether — all while being cheaper and smaller than Ouster’s previous technology.

“This is kind of fundamentally changing the value proposition of what we’re selling to a customer from this stage forward,” he told TechCrunch.

Nicolas Sauvage believes it takes four years for the best bets to look obvious — thinking that he shared on stage last week at StrictlyVC’s San Francisco event, which TDK Ventures co-hosted.

It’s a theory he’s been working to prove since 2019, when he founded the corporate venture arm of the Japanese electronics giant, which is now managing $500 million across four funds. The AI chip startup Groq, valued at $6.9 billion during its most recent funding round last fall, is the highest-profile example of this thinking.

In 2020, well before the generative AI boom made infrastructure bets look obvious, Sauvage wrote a check into the company, which was founded by Jonathan Ross — one of the engineers who built Google’s Tensor Processing Units. Groq was focused from the start on inference: the computational heavy lifting that happens every time a model responds to a query. Ross had designed his chip by building the compiler first, stripping the architecture down until, as Sauvage describes it, “you can’t remove one part and have it still work.”

It might have looked niche to some, but knowing what he did about his parent company’s constraints, Sauvage saw asymmetry. Unlike consumer hardware, which has a natural ceiling, demand for inference keeps compounding with every new application and every new model. Sauvage couldn’t know then that demand for inference would explode this year, thanks to every AI agent that plans and acts across dozens of calls (where a single query used to suffice).

But in some ways, Ross got lucky, too. After all, a Japanese electronics conglomerate best known for magnetic tape is not, on its face, the most obvious investing partner. In fact, Sauvage describes TDK Ventures’ own existence as very unlikely. But after two back-to-back Stanford lectures — one making the case for corporate VC, one cataloguing every reason it fails — Sauvage, who is French and joined TDK in Silicon Valley through an acquisition, pitched the idea to higher-ups at TDK headquarters despite having no obvious standing to do so. (“I’m not Japanese. I don’t speak Japanese; I don’t live in Tokyo,” he told this editor.)

After refusing to take no for an answer, he finally received the green light in to build a fund whose mandate was to answer one question: What’s the next big thing for TDK, and what might kill it?

The portfolio he has since assembled is dotted with technologies that have become more widely interesting to VCs over the last year: solid-state grid transformers, sodium-ion batteries for data centers, alternative battery chemistries that sidestep the geopolitical fragility of lithium and cobalt.

The discipline behind all of it is the same: identify the bottleneck four years out, then find the founders already working on it.

The question, of course, is what’s next. For his part, Sauvage is watching physical AI closely — not all of robotics but robots with a highly specific job to be done. Agility Robotics, for example, in his portfolio, focuses on the single, mundane task of moving things from one place to another in warehouses facing workforce shortages. Another portfolio company, Swiss portfolio ANYbotics, builds ruggedized robots for environments too hazardous for human workers — places where the job definition is essentially to go where people can’t. The through-line is clarity of purpose. The robots Sauvage is betting on don’t try to do everything; instead, they do one hard thing reliably.

Sauvage says he’s also watching the compute stack shift again. GPUs dominated training — the massive, parallel computation of teaching a model. Inference chips like Groq’s are reshaping what happens when that model speaks: faster, cheaper, at scale. Now, Sauvage argues, CPUs are due for a renaissance. They’re not the most powerful chips or the fastest. But they’re the most flexible and best suited to the branching, decision-making logic of orchestration. When an AI agent delegates a task, checks on its progress, and loops back across dozens of steps, something has to manage the whole choreography. That something, increasingly, looks like a CPU.

And then there’s China. A recent report from Eclipse — a venture firm he follows closely — documented what Sauvage describes as “vibe manufacturing” — the rapid, AI-assisted iteration of physical hardware prototyping, mirroring what vibe coding did for software. Chinese manufacturers, the report found, are compressing the design-build-test cycle for physical products in ways Western supply chains aren’t yet equipped to match.

For Sauvage, it’s a bottleneck signal — and one he’s already moving on with TDK Ventures’ various investments. One remaining unsolved problem, he says, is dexterity. Models are improving fast enough that physical AI feels inevitable; what’s still missing is the physical fluency to match. The countries and companies that figure out how to iterate on atoms as fast as others iterate on code will have a manufacturing advantage. That’s the wave for which he’s positioning TDK Ventures today.