The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:00:00 · Naveen Rao
[00:00:00] S0: We're gonna be out of energy pretty soon in the next few years. This is a blip. It's not some kind of bubble being burst. We don't treat the model as sacrosanct, and we're energy first in our in our mindset. Intelligence, to me, it will have infinite demand. I do think we will be in a post transformer world. I don't think the transformers would be all end all. I actually think the hardware is the place to be over the next ten years. I would say if you're a pure software investor, you're kind of fucked, honestly. The inference market is gonna be a $10,000,000,000,000 market within ten years. I don't look at, oh my god. AI is gonna kill all humans. That's the stupidest worldview I've ever heard.
00:00:34 · Harry Stebbings
[00:00:34] S1: This is 20 VC with me, Harry Stebbings. Now what started as a very planned discussion, it evolved into one of the most phenomenal discussions that we've had across a wide range of topics from AI to data to robotics and everything in between with an incredible guest in the form of Naveen Rao. Naveen is a serial entrepreneur. I mean, he sold his last business to Databricks for $1,300,000,000, Mosaic ML. Now he's back with unconventional AI, which has raised hundreds of millions of dollars. Their last round, close to $5,000,000,000 in valuation. And I think Naveen is one of the most articulate and clear thinkers across such a wide range of very complicated topics, and he's got this unique skill of being able to really break down very complex ideas into very digestible lessons. He was fantastic, and this show was a joy to do. But before we dive into the show today, founders and investors sleep on Eight Sleep. It is the intelligent sleep system trusted by mister Mark Zuckerberg, doctor Andrew Huberman, Charles Leclerc, and the Aston Martin f one team. The pod is a smart mattress cover that fits on any bed. It cools each side to 55 degrees, heats to a 110 degrees. It even tracks your heart rate, HRV, and sleep stages without a wearable. Thank god. The new pod six has a hub 50% smaller than the last one and starts at just $1,999. Visit 8sleep.com/20vc. That's eightsleep.com/20vc. While Eight Sleep helps you sleep better, MongoDB helps you build better. MongoDB has always been the database developers love. Well, now it's the data platform AI agents need. Agents need accurate context, fast. MongoDB stores, searches, and reasons over your data in real time. JSON native database, vector search, and Voyage AI embeddings all in one place. One system instead of 10. No data pipelines to maintain. Ugh. This sounds too good to be true. Build and scale from your first user to billions of vectors. Run on any cloud, on prem, or your laptop even. That's why 75% of the Fortune one hundred run their most critical apps on MongoDB, moving trillions of dollars every single day. And it's why AI native companies like Eleven Labs run 40,000,000 agents on MongoDB. So if you're building an AI, MongoDB for startups helps you move faster with Atlas and Voyage AI credits and dedicated support. Don't build agents that answer once and forget.
00:03:11 · Harry Stebbings, Naveen Rao
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00:04:40 · Naveen Rao, Harry Stebbings
[00:04:40] S0: Absolutely. Yeah. I was looking forward to [00:04:41] S1: it. Yeah. I think it's gonna be a lot of fun because I I went through a ton of your shows. I wrote down what I thought were the most controversial quotes. There were a few zingers, by the way. But I actually wanted to start on, like, the foundational layer of this infrastructure, which is energy. You've spoken before about how it will be the bottleneck and the importance of it. How do you evaluate the state of energy supply today, and what are we not seeing that we should see?
00:05:09 · Naveen Rao, Harry Stebbings
[00:05:09] S0: Energy supply continues to be difficult to build. Right? I I don't think people appreciate it. I mean, I've seen numbers from general industries about $50,000,000,000 to build one gigawatt. So people are throwing around the word gigawatt like it's like it's easy. And it's like, guys, this is not easy. Just to put that in perspective, building a fab, like a semiconductor fab, is on the order of 20 or $30,000,000,000. So this is... One gigawatt is more than building a fab, like, you if you look at it in terms of dollars and complexity. So it's hard. I think what we've seen over the last several years has been soaking up the excess demand that's been around. So we overbuild generally, and we don't run infrastructure at a 100% output. So what we've been doing, where we've been getting energy for data centers has primarily been from this. Like, can we run things at higher utilization, you know, and get that next couple of gigawatts out of it? So if you look at The US power grid, we're on average something like 900 gigawatts of outputs, but I think it's capable of peaking up to, like, 1.2, 1.3 terawatts, so 1,300 gigawatts. So there is some excess capacity, and that's primarily what we've been going after. That's what you can get access to quickly. But building infrastructure is hard, and we need to build more. We're behind. [00:06:20] S1: How far behind are we?
00:06:20 · Harry Stebbings, Naveen Rao
[00:06:20] S1: How far behind are we? [00:06:21] S0: I think over the last twenty years, China surpassed us in terms of total amount of capacity that happened, I think, in 2023. But they were building six x faster than us for twenty years. They were coming back from far behind, but they really went fast and hard at this. And we criticized that a lot because we said, oh, they're using dirty methods to do this, coal burning, this and that. True. But they're also trying to grow as fast as possible, and I think now they're sort of going back and and cleaning up some of this. So meanwhile, we didn't really invest in energy infrastructure, and that's gonna hurt us because it's gonna take time to really get there. [00:06:53] S1: Dude, what can we learn from China being so much further ahead of us that we should be, like, way more loose on policy and regulation? What are the takeaways?