GoKawiilAnother Y Combinator Demo Day took place on Thursday, but this batch looked a little different. While every cohort brings a fresh wave of founders, the startups presenting this week skewed far more toward deep tech than in past years.
As we do every quarter, TechCrunch asked early-stage VCs to name the hottest startups in this batch — both their top picks and the deals everyone was talking about. And as usual, we put together a list of startups flagged by at least two investors as the buzziest in the batch.
The tech in this batch felt “like science fiction,” as one investor put it. But beyond the wild ideas, the consensus was that valuations were far more grounded than in recent cohorts.
We’ve listed the top picks alphabetically below:
Automarine
What it’s building: Nuclear-powered data centers floating at sea
Why it’s a fav: Power is in short supply and local communities are increasingly opposing the buildout of new data centers. Atomarine — co-founded by an MIT computer science and naval engineer and an MIT PhD in nuclear engineering — wants to solve the compute shortage by putting data centers on barges at sea, where seawater could provide near-free cooling. The startup plans to launch a gas-powered pilot by 2028 and plans to transition to floating nuclear power ships in 2032. Atomarine claims it has already secured over $4 billion in customer interest through letters of intent. That revenue potential has helped make it one of the highest-valued startups in the batch, one VC told TechCrunch.
Dipole Labs
What it’s building: Effective and energy-efficient high-speed optical networking hardware for AI data centers
Why it’s a fav: The idea is that GPU clusters waste a lot of compute time simply waiting for data to move between each chip. Within the networking layer, data is converted from light to electricity and back again in a process that burns lots of power and yields lots of heat. But Dipole Labs says it built an optical switch that skips the conversion so the data can stay as light and go directly where it needs to go. It’s a timely problem to tackle as GPUs are crazy expensive and data centers want the most out of their compute hardware without leaving any time to spare.
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