Nicolas Sauvage's Theory on Boring AI Investments

**The TDK Ventures founder shares his four-year investing strategy, which has already reaped a $6.9 billion return, and his bets on physical AI and compute stack shifts. **

Nicolas Sauvage's investing strategy may seem counterintuitive, but it has already paid off in a big way. The founder of TDK Ventures, the corporate venture arm of the Japanese electronics giant, has been working to prove his theory since 2019. He believes that it takes four years for the best bets to look obvious, and his investment in the AI chip startup Groq is a prime example of this strategy in action.

In 2020, Sauvage wrote a check into Groq, which was founded by Jonathan Ross, one of the engineers who built Google's Tensor Processing Units. The company was focused 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 it was as efficient as possible.

Sauvage saw asymmetry in Groq's approach, knowing what he did about his parent company's constraints. Unlike consumer hardware, which has a natural ceiling, demand for inference keeps compounding with every new application and every new model. The investment paid off in a big way, as Groq was valued at $6.9 billion during its most recent funding round last fall.

But Sauvage's strategy goes beyond just investing in AI chip startups. He's also watching physical AI closely, not all of robotics but robots with a highly specific job to be done. The discipline behind his investments is the same: identify the bottleneck four years out, then find the founders already working on it.

For example, Sauvage is watching the compute stack shift again. GPUs dominated training, but inference chips like Groq's are reshaping what happens when a model speaks. 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.

And then there's China. A recent report from Eclipse documented what Sauvage describes as 'vibe manufacturing' – the rapid, AI-assisted iteration of physical hardware prototyping. Chinese manufacturers 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.

When it comes to investing, Sauvage's strategy is to identify the bottleneck four years out and find the founders already working on it. It's a approach that has already paid off in a big way, and it's one that he believes will continue to drive success in the years to come.

"I'm not Japanese. I don't speak Japanese; I don't live in Tokyo," Sauvage told this editor. "But after two back-to-back Stanford lectures – one making the case for corporate VC, one cataloguing every reason it fails – I pitched the idea to higher-ups at TDK headquarters despite having no obvious standing to do so."

• Sauvage's investing strategy has already paid off in a big way, with a $6.9 billion return on his investment in Groq
• The TDK Ventures founder is watching physical AI closely, not all of robotics but robots with a highly specific job to be done
• Sauvage is also watching the compute stack shift again, with a renaissance of CPUs on the horizon
• The bottleneck signal for Sauvage is China's rapid, AI-assisted iteration of physical hardware prototyping
• One remaining unsolved problem, he says, is dexterity – the physical fluency to match the improving models