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00:00And Raj, I do want to talk to you about the sentiment right now out there about this big investment that we've seen in the ecosystem writ large, not just in chips. So really the overall infrastructure out there. You've been at the forefront of this with a lot of early stage investment as well as growth stage investments like perplexity and rider and a few others. What is the state of that right now from where you sit? You know, I think the state of I is that is just getting started is the new electricity. But there's a lot of bottlenecks. The grid is made out of CPUs made by companies like in video and by data centers. And there's a lot of investment that needs to go on there. And I remain bullish on India and remain bullish on the fact that we're going to need a lot more in GPUs to power the next wave of the AI boom. When we talk about the mismatch, if there is one at all between what we're seeing in terms of expectations and what we're seeing in terms of returns, and I know there was a lot of hay made about that in my paper last week. Obviously a lot of criticism that there was really nothing new in there in terms of information. But I am curious as to how aligned right now investor expectations are with at least the current reality. You know, I, I think the paper from MIT is it's spot on. But to your point, it's nothing new. The startup world has always been about taking a lot of risk in trying new things. I think it has made it such that it's the best time ever that I've seen to be a founder, to build a company. You can be a sole founder now. You don't necessarily even need a technical co-founder because people who have never learned coding or vibe coding and building great products, I think you're going to see a plethora and a boom in terms of the number of startups. And what that means is that a lot of them won't work, a lot of them will fail. And it's probably one of the hardest times that I've seen to be a venture investor because we know that there is the next generation of companies that are going to be built. The last generation of the hyperscalers, the Max seven that have really driven the market. I think you're going to see public markets shift to a much broader, necessary 90, a broader set of companies that are durable and that have are really built for the next generation of A.I. native apps. But you can't just invest in the models. You've got to invest in the power, you've got to invest in the GPUs. You've got to generate also invest in the pipelines that are really going to drive the next wave of AI. Well, I'm sure a lot of people are looking forward to that next wave because there's a lot of focus on those seven critical companies right now. Speaking of the Mag seven, I did want to get your take on Nvidia's results and its forecasts, which disappointed a lot of people, even though we're talking about pretty robust growth rates here. We've seen the stock and I know that you don't follow the stock market as carefully as those who track public markets, but it opened lower and it has steadily paired its losses. What what does this say about the level of expectations tied to a company like in video and how that may translate into how people view tech startups overall? Yeah, it's a it's a great question. I, I think, you know, the boom is not going to be linear. I think you'll see starts and stops to it. You saw this in e commerce. For those of us who've been doing this since the late nineties, we saw this with e commerce. It took 20 years for e commerce to really get to much more of of where it is today. I think A.I. is going to be faster than 20 years, but I think we're in the first we're in, you know, the first two or three years of a 20 year cycle. This is going to take time. And I think Nvidia is suffering from just astronomical expectations. It's still pretty impressive growth. And I do think that we need other companies building these kind of GPUs. It can't just be NVIDIA, it can't be one company. And I think for the US to be able to continue to be a leader in AI, we've got to see a broadening of the MAG seven. I think we've got to see a broadening of the number of companies that are building the fundamental infrastructure of A.I. NVIDIA. At least there is some concern that the pace of investment in AI systems is perhaps not sustainable. How much do you see that reflected in the private markets? I think you're you're definitely seeing it in the private markets. There was a lot of focus on kind of the fundamental models. That focus is now shifting to the air app layer. You're seeing the big hyperscale scalar focus on the air player. We're seeing a lot of startups in that air app layer. It's a really tough layer because there are so many companies that are being built in it. And I think that there is a real stark divide between companies that are legacy companies, which when I started in tech, the legacy company was a 40, 50 year old company like Chewy. But now legacy companies, a company like Google, a legacy company is a company that was started just you know, even a ten year old company is a legacy company. And you're seeing a whole new wave of A.I. native companies. I think the legacy companies have an advantage in the data that they have, and I think if they can figure out how to leverage that data, I think you're going to see some legacy companies really pull ahead. But you're going to see a lot of new AI native app companies challenge companies that were started five, ten, 20 years ago. Well, that's an interesting comparison because someone pointed out we were talking a couple of weeks ago with someone about the dot com boom and the bubble burst. And it was a kind of 2002, 2001, 2002. And the idea of who the winners were in that sort of Internet race, the idea that alphabet, then Google for the most part, let's say, came out on top. Certainly when it comes to the search and advertising side of that space, This was a company that effectively didn't exist during that dot com bubble. It was still a private company, didn't go public in 2004. So where you sit right now, where you are investing in a lot of companies that are in the private space and maybe could one day enter the public markets and maybe potentially be that next big thing, is that a fair parallel maybe for investors in both public and private markets to be mindful of? I think it's it is absolutely a fair parallel we should all learn from, you know, not only from 1999, but we should learn from 2021. There's been frothy periods in venture capital and in tech investing before. You know, I was a skeptic in 1999. A lot of the companies that were being built that had no real kind of profitability. I think today what you're seeing is that a lot of these AI companies, they are fundamentally profitable. They are profitable because they have very low costs. They're able to have their developers use AI, so they have less development costs. They're able to have a high driven customer service. And so there is fundamentally better profitability. But I think what a lot of venture capitalists are looking at is that companies are getting very quickly to 10 million, 20 million of revenue, but that doesn't imply durability. It takes you know, I think it takes the track record of 3 to 5 years to really prove that you have product market fit. And we can't get fooled again by companies who, you know, in a very short period of time gain a bunch of traction. But they're not solving a fundamental problem that their customers have. They're much more about experimentation.

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AI Startup Boom Coming, But Many Will Fail: B Capital

Raj Ganguly, B Capital Co-Founder and Co-CEO, says AI is in its early days, calling it the “new electricity.” He tells Romaine Bostick and Scarlet Fu on “The Close” that he remains bullish on Nvidia and sees strong U.S. demand for GPUs to fuel the next AI boom. (Source: Bloomberg)

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