Principal Venture Partners

songyeeyoon Verified account @songyeeyoon · 4d

Most AI today lives in the cloud. The next wave is moving somewhere more useful: onto the device in your hand, the factory floor, or the car you’re driving.

@Principal_VC portfolio company @liquidai is making that shift real, in a new partnership with @MercedesBenz covered today by @WSJ.

Mercedes is building Liquid’s models directly into its cars, so drivers can talk to the vehicle with the AI running on the device itself, not the cloud. That means faster responses, more privacy, and intelligence that works even without a connection.

This is the future I’m watching: efficient, purpose-built intelligence running close to where it’s needed, in the things we already use.

Congratulations to the Liquid AI team. Proud to back them, and excited for where on-device AI goes next.

You can read the full piece here: wsj.com/cio-journal/me…

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songyeeyoon Verified account @songyeeyoon · 5d

This week’s midterm results have rattled some nerves about the future of AI infrastructure spending, but beyond politics, there's an interesting signal here. The buildout is finally being asked to justify its economics.

The worry itself isn’t new. The market has been uneasy for a while about how much is being spent on AI infrastructure, and how much of that spending is fueled by debt. The elections didn’t create that concern, only resurfaced it.

This isn’t a referendum on whether AI is here to stay. But it does mark the moment capital discipline enters the picture: the market is increasingly asking not how big the buildout can get, but whether the spending produces durable returns.

That pressure tends to reward the companies that do more with less, rather than the ones racing to spend the most. Constraint is often where real advantage gets built, and a slower, more scrutinized buildout favors efficiency over sheer scale.

The market is starting to favor the companies that spend well over the ones that simply spend big, and that’s the shift worth watching.

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songyeeyoon Verified account @songyeeyoon · Oct 28

Last month, @OpenAI published hundreds of math papers claiming an AI model had cracked over 300 hard problems.

The initial reaction to the news was mixed, but to me, the development raises a more fundamental question than how AI should be used in mathematics: why do we pursue these proofs in the first place?

The field’s most famous unsolved problems have resisted researchers for decades, and new progress is valuable. But solving them isn't about collecting trophies. Often the methods invented in pursuit of a proof matter as much as the conclusion.

Calculus did more than settle questions about motion; it gave scientists a language for change. Probability did more than answer puzzles about chance; it created a way to reason about uncertainty. Great mathematics doesn’t simply resolve problems. It changes which problems we are even capable of asking.

That is why counting AI-generated proofs is the wrong measure of progress. Proofs can establish that something is true without illuminating why. They only become useful knowledge once people verify them, connect them to what’s known, and build upon them.

The opportunity here isn’t to feed machines a longer list of famous conjectures. It’s to put mathematicians and scientists in a position to ask bigger questions.

I explored the evolving role of AI in mathematics in “Beyond Proof,” my latest piece for the @Principal_VC newsletter. Read it here: principalvc.substack.com/p/beyond-proof

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songyeeyoon Verified account @songyeeyoon · Oct 23

This week @Principal_VC celebrated a milestone: our move into a new Palo Alto office, with an Office Opening Reception that brought together leaders in AI and friends of the firm.

The evening centered on conversations with founders we’re proud to back, moderated by @WIRED Senior Writer Paresh Dave (@peard33).

We were lucky to hear from Daniela Rus, Director of MIT’s Computer Science and Artificial Intelligence Laboratory (@MIT_CSAIL) and co-founder of our portfolio companies Venti Technologies and @LiquidAI, and from @TimKentleyKlay, co-founder of @zoox and founder of our portfolio company HYPRLABS (@hypr).

Both spoke to the same shift: the developments in physical AI, intelligence that doesn’t just generate answers on a screen but operates in the unpredictable conditions of the real world with clarity and precision.

It’s one of the changes we’re watching closely as we look to what’s next in the AI trade, and these discussions were a fitting way to mark the start of a new chapter for PVP.

Thank you to Paresh, Daniela, and Tim, and to everyone who joined us. Glad to be in our new home, and looking forward to growing and building here.

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songyeeyoon Verified account @songyeeyoon · Oct 21

The next stage of AI isn’t a better chatbot. It’s intelligence that can operate reliably in the physical world, where conditions are messy and mistakes are costly.

We’re starting to see what that looks like. @Principal_VC portfolio company Venti Technologies recently announced its first fleet of driverless trucks in an American rail yard, moving shipping containers in live operations.

This isn’t a leap out of nowhere. It builds on the work Venti has already done globally: its driverless vehicles have moved more than 360,000 containers over 500,000 miles, positioning themselves within an inch of their targets.

The implications are massive. Supply-chain inefficiency is an estimated $600 billion problem every year, and Venti’s trucks can cut transportation costs 40 to 70 percent.

This is what it looks like when AI stops generating answers and starts moving goods in the real world.

Congratulations to the Venti team. We’re proud to back them, and excited for what’s next.

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