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.
00:18:15 · Molly O'Shea
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00:19:52 · Jaimin Rangwalla
[00:19:52] S0: So if you think about your initial chat GPT interaction, you would ask it a question. It would think for some amount of time, depending on the complexity of the question, and then it would come back to you with an answer, or not even a lot of times an answer, maybe it comes back with an intermediate step of, okay, I searched all of X, Y, Z. I did this. Am I on the right track? Do you want me to go more in detail? Do you want me to do less? And it was a lot of human in the loop. An agent now, when you give it a problem, like the amazing part of Opus 4.5 when it launched at the end of last year was that an agent can now spawn its own agents and that can increase the depth of work, the time of work, and the quality of the work. And so all of those things, that was a huge model unlock. Agents launching agents, I think, is one of the most underappreciated unlocks that has happened because you can give an agent a project now and you can just go away. And you can come back and it has done, if not all of it, really, really far along. And the amazing thing is you can also, when you instruct it and prompt it initially, you can say, go do this, launch as many agents as you want, and then launch another 10 agents to check all of these agents' work. So not only like it's so collaborative and and iterative now where the human is almost out of the loop. And if you think about it, the human was almost a limiting factor in how much work the agents could do because before, one agent or one chatbot would interact with one human, but it would always constantly stop. A third of the way it stops to check-in and be like, did I do this right? Do want me to do this again? And then two thirds of the way it stops. So the human is still limiting how much work that can be done. Now the human just has to prompt it, and all of the work gets done on its own. And now what we're doing, I use Claude too, I don't just ask it one prompt or one problem. I go and say, Okay, I want this done. And I open a new one, and say, I want this done. And I open a new one, I want this done. And so they're all working concurrently, and so you're able to launch agents, and then you're able to launch multiple pools of agents that also launch multiple agents below. And so to me, it's just the sheer power of exponential growth of the number of agents and the amount of work that's being done.
00:21:51 · Molly O'Shea
[00:21:51] S1: Yeah, we definitely saw that in the Open Claw slide. That was crazy. So what was their growth rate?