00:08:01 · Molly O'Shea, Jaimin Rangwalla
[00:08:01] S1: Yeah. Go Violets. [00:08:03] S0: Yeah, I might have graduated a little earlier than you. And after that, I did two years of investment banking at Merrill Lynch back when Merrill Lynch existed as an independent entity. And then I was thinking through what I wanted to do next. A lot of the folks that I would consider my peers were like, Okay, you're going to go to private equity and then go to business school, and then figure out what you want do after business school. I kind of went to undergraduate business school, wasn't that excited about that idea, and so I started thinking, okay, who are people I can talk to to tell me about what my opportunities would be? And someone mentioned, you should go be an investor, a public markets investor. It's different than private equity. You're not handholding a bunch of consultants and things like that. It's much more like action based. You only need to find a couple of really big ideas a year and you can be super successful. [00:08:51] S1: Is that true?
00:08:53 · Jaimin Rangwalla
[00:08:53] S0: At an early age, it is. If you can really find one big idea and really get the firm to get behind it, that's enough to, I think, change a it can change a funds year, and I think it can change an investment analyst career. And so I met with five places in 2007, in the 2007, which was a nice peak in the market as well. The market had a really strong run. Four of the places did not offer me a job, and Philippe offered me a job. So my decision was almost made for me because I knew I wanted to do this. I was like, okay, I'm going go join. We were $700,000,000 at the time, one fund, which is very different. You know, we had half a floor on 56th Street. And the first lesson I learned was that you never know what type of market you're stepping into because we all know what happened in 2008. And those other four funds all went out of business. So some of life is luck and some of life is what you make of that. And I think I had a little bit of both because even though the other four places didn't offer me a job and Philippe did, Philippe was the one who taught me the lesson that what goes up sometimes does come down, at least for a temporary period of time, in particular in technology. But macro factors do matter, and so I think that having learned that very early on in my career was a priceless lesson. And I started covering semiconductors over time, grew to taking on more sectors, having more and more responsibility. And then two years ago, I was given the opportunity to run all of our public business with Philippe, and it's been an awesome journey. And every day I tell people that working at Cotu is sprinting a marathon. Every day you have to sprint. There's no time to rest, but it is still thinking about the long run. And so it's been an amazing experience.
00:10:37 · Molly O'Shea, Jaimin Rangwalla
[00:10:37] S1: Wow, amazing. Congrats. [00:10:39] S0: Thank you. [00:10:40] S1: And thanks for taking the time out of the sprint to do this. I know it might sound redundant talking about the size of the market today, but it is astronomical. It is crazy. So, could you talk about the size of the MAG-seven when they IPO ed versus today the AI leaders in their size. It's a wild comparison. [00:11:03] S0: Yeah. So the biggest of the Mag-seven was Meta in 2012, IPO ed around $100,000,000,000 All of the predecessors had IPO ed in the 90s, in the $10 to $50,000,000,000 range, which for that time was very large. But today you look, OpenAI's most recent round was 800. SpaceX is most recent, when they did the transaction with XAI, $1,250,000,000,000. Anthropics last round was high 3 hundreds of billions. It's rumored to be significantly higher their next round. And so the fact that you now have private companies that are breaking into pretty much the top 25 in the world before they even go public, it's an unprecedented time. But I think that's what makes AI so exciting, is that you have these private companies that are capturing so much value and so much growth while staying private, and I think that that is like nothing we've ever seen before. [00:12:00] S1: And you've had to reshuffle some parts of the team to account for that too, like Frank, the claw genius.
00:12:00 · Molly O'Shea, Jaimin Rangwalla, S2
[00:12:00] S1: And you've had to reshuffle some parts of the team to account for that too, like Frank, the claw genius. [00:12:06] S0: Yeah. You know, we are constantly evaluating how do we make ourselves better, how do we stay in tune with the time, and yeah, we brought in Frank. Frank, you know, joined us at the beginning of this year, and he is our his official title might be, know, Chief AI or whatever. I think about him as our AI mad scientist. He's the one who is trying everything out there that is new, creative, on the cutting edge, figuring out how do we implement it, how do we as an organization utilize our twenty years of data that we've been collecting to give us another leg up against the competition and allow us to succeed. [00:12:39] S2: Another chart that I want to mention, and this time I'm doing the Bloomberg analysis. So first I want you to focus on the bold red line and this is the performance of the Nasdaq since the October 31 peak. So if you look at the bold red line, it's basically been fairly flat, but you could see at one point it was down 10% and today it's up 10%. And then what I want to do is I want to show you the performance of the Max seven and we actually plotted five of the companies. And what's interesting is that outside of Alphabet, so many of the MAX 7s have underperformed the NASDAQ by a lot. And in fact, three of them are down year to date. I just thought that's interesting because to me it's a further indication that even within the Mach seven, the reordering of the pecking order is happening quickly.
00:12:39 · S2, Molly O'Shea
[00:12:39] S2: Another chart that I want to mention, and this time I'm doing the Bloomberg analysis. So first I want you to focus on the bold red line and this is the performance of the Nasdaq since the October 31 peak. So if you look at the bold red line, it's basically been fairly flat, but you could see at one point it was down 10% and today it's up 10%. And then what I want to do is I want to show you the performance of the Max seven and we actually plotted five of the companies. And what's interesting is that outside of Alphabet, so many of the MAX 7s have underperformed the NASDAQ by a lot. And in fact, three of them are down year to date. I just thought that's interesting because to me it's a further indication that even within the Mach seven, the reordering of the pecking order is happening quickly. [00:13:37] S1: One of the slides was really wild to see the annualized revenue growth that these new players have, whether it's OpenAI or Anthropic. They're reaching $25,000,000,000 in annualized revenue at a third, if not half the speed of the hyperscalers and the mega mag-7s. So how do you take that?
00:14:00 · Jaimin Rangwalla
[00:14:00] S0: I think it comes back to the original comment about how big the market is, right? Like the fact that you have these two companies today that are north of $50 $60,000,000,000 of ARR growing, just using Anthropic and what they've talked about publicly, it was $9,000,000,000 at the December, and that number was $30,000,000,000 So they added $20,000,000,000 They're adding 10,000,000,000 plus or minus a month, almost $2,500,000,000 a week. Most of the companies in the SaaS universe don't even have $2,500,000,000 of ARR annually. They're adding that in a week. And so just the size of the opportunity is so significant. And then when you think about both of them combined, let's say they're close to $60,000,000,000 I mean, that is bigger than ServiceNow, that is bigger than Salesforce, that is bigger than any big name SaaS platform that you would think about. And these guys have done it in a matter of like handful of years versus the fifteen, twenty years that it took all these other companies. And so again, what gives us this excitement around AI is it is happening at a large scale with growth rates that are like nothing else in the market, and that gives us confidence that the market size itself is so much bigger. Because when I think about even what we're using it for, we are making new breakthroughs and we're finding new use cases, but it's nowhere near where I think the potential is. We're nowhere near where I see three years down the road or five years down the road. And so I still believe we're very early in this adoption.