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.
00:15:24 · Molly O'Shea
[00:15:24] S1: For those who don't understand where that revenue is coming from, could you break that down? And I know some of it is promised and future contracts and that kind of thing, but how do you break down that kind of revenue?
00:15:33 · Jaimin Rangwalla
[00:15:33] S0: Yeah, so I think that if you think about the pools of revenue for a company like Anthropic, it's going to be enterprises, it's going to be individuals and prosumers, and it's people that want to use their tools for efficiency. And so today, I don't think it's a straight one for one, like, oh, if you spend $100 on Anthropic, I have to reduce my labor by $100 because I think that there's productivity gains that are achieved, right? And so at the end of the day, everyone works X number of hours, but even if you listen to the top coders and the top developers, they say our increase in productivity is like 2X, 3X. And so most companies, at least in the technology world, you're not limited in terms of what you're able to do based on the lack of projects. It's because you have lack of people that can do all these projects you want to do. And so this is allowing them to work faster pace, increase productivity, run more projects. This also AI helps you reimagine how you're going to spend your IT budget broadly. Today, most IT budget is spent on hardware. It's spent on services, like you have consultants that are helping you manage certain parts of your stack, and then you have software. And so there's also an element where you're rethinking. Do you need all of that? Do you need it in the same size that you have it? Do you need to increase it, decrease it? And so I think that the budget comes broadly from growing productivity, which is just a natural return on investment for any company that wants to put another dollar in their business, and then it also comes from offsetting maybe some of the expenses that you have today, so I think it's a combination of both.
00:17:04 · Molly O'Shea, Jaimin Rangwalla
[00:17:04] S1: It's a lot about tokens. Token, token, tokens. I think one of the hot topics recently was around Mythos and getting access to those and the tokens around there. So for those who don't understand tokens and the token economy, can you explain that out? [00:17:18] S0: Yeah, I think that basically a token is the unit of thought by any AI model. So anytime you ask it a question, if it comes back with an answer, it has generated tokens, and that is what the answer is that you see. And so the way I think about it is any almost like a unit of intelligence, right? The same way that you would think about any you know, as a human, you think about, I wanna make this decision. I wanna do this action. If we were an agent, each one of those would be tokens. Right? And so the token economy, when people talk about it, is really the proliferation of agents and decisions made by artificial intelligence. And those decisions can actually be quantified in a number of tokens. And so it's like very different than human intelligence, which is hard to quantify a unit of human intelligence, but you're able to quantify a unit of AI intelligence. It's actually very interesting because it's almost causing us to rethink how we're framing the business opportunity for a lot of companies.