00:28:09 · Molly O'Shea, Brian Armstrong
[00:28:09] S1: Sure. That'll be fun. [00:28:10] S0: Yeah. Yeah. I think, honestly, I think there'd be support on both sides of the aisle for that. It it would just yeah. It'd be good for Americans. [00:28:18] S1: To go back to the everything exchange, it does seem like every fintech and financial service company is trying to be the financial super app. So why does Coinbase win? [00:28:32] S0: Yeah. Well, you're right. I think lots of people now are starting to integrate crypto trading, for instance. It's actually you know, even though this idea is something a lot of people are talking about, the execution on it is really not there from almost anybody. I think we're actually leading now on this, where you can get access to, you know, millions of crypto assets, including, like, DEX trading support that we added. You can trade every stock out there in the world pretty much. Pre IPO perps, you can trade commodities, you can do prediction markets. So I would say, like, this you know, even even, like, getting these approvals in The US, we were the first for, these, perpetual style futures and things like that, for for crypto and and options trading. We're the first company to do that. I don't think any other company in The US has gotten that yet. So there's a number of things where we've been first, and so doing it all in one product with HITRUST, where we're storing more crypto than any other company in the world, and we have this kind of deep crypto native expertise. Those are our strengths. Lots of people are gonna try this. Our job is just to be out there in the front.
00:29:40 · Molly O'Shea
[00:29:40] S1: We've gotten pretty far, and we haven't really talked about AI that much, so I wanna talk about it. You talked about your agents, but there was a tweet that you put out that got over 24,000,000 views. It went really viral. Mhmm. I'm gonna just share a little bit of it for the audience, and then we'll just link it. But you essentially said that Coinbase is rebuilding around intelligence. And so what does that mean, and why that path forward?
00:30:06 · Brian Armstrong, Molly O'Shea
[00:30:06] S0: Yeah, well, it's interesting. When I talk to our board members or even interns that have come into Coinbase, they all tell me this is one of the top few AI enabled companies in the world. Like, all companies right now are trying to figure out how to leverage AI, and and I think Coinbase is really on the frontier of this. So we've done that in a in a couple of different ways. One is we're using it to improve both the quantity and quality of code that we ship. The amount of code being shipped per developer is up about two x year over year. But believe it or not, you know, there's actually like, if the average developer is shipping maybe like eight pull requests a week, the top few engineers are doing like a 100 pull requests a week. So there's this actually like a true outlier effect of these like super builders that have become super AI enabled, and we're essentially taking those folks and using them to train the rest of the engineering team just to keep driving that number up. And then, yeah, I I I think, like, another thing we've been doing is we've actually been changing the traditional team size. Right? Traditionally, in tech companies, you'd have, like, a team of pod of 10. There'd be one product manager, one designer, eight engineers, something like that. And we're now seeing these teams that are two to four people. In some cases, we're seeing a one person team. [00:31:24] S1: Really?
00:31:24 · Molly O'Shea, Brian Armstrong
[00:31:24] S1: Really? [00:31:24] S0: Yeah. Because they're using 10 AI agents along with the one human. And so on many of these teams, we now have AI agents as collaborators. In the Slack channel, you can talk to them. They're creating pull requests. They're creating new designs. There's actually about 1,200 full time agents working at Coinbase now. Really? We try to basically estimate it because you can spin up an agent for five minutes and shut it down, but if you look at it as how much time AI agents are working compared to a typical forty to sixty hour work week, yeah, it's about 1,200 full time agents now work at Coinbase, and that's what's allowing us to get this productivity with higher quality, I should mention. We're actually seeing the rate of bugs and incidents go down per line of code shipped. And so it's driving up quality while driving up the quantity as well. [00:32:23] S1: I mean, there's mass hysteria for jobs right now. [00:32:26] S0: Yeah. [00:32:27] S1: How much of that do you think is rational versus, like, irrational? I mean, like, clearly speaking, organizations will be rebuilt around AI and technology and agents and token maxing for some, but what do you think the future organization would realistically take shape? Like if you were starting a company today?
00:32:54 · Brian Armstrong, Molly O'Shea
[00:32:54] S0: Yeah. So I think that the average team size will be smaller, but that just means companies can do more things. And so I don't actually, I'm in the camp of people who don't think AI is going to actually cause high unemployment. I I think that employment will be about the same. I think that work will be optional in some sense, in the longer term. I think most people will actually choose to work on something. It just might feel a little bit different. Right now, you're a podcaster, and I'm a CEO going and talking to people on stage. Like, a hundred years ago, this would not have been considered real work. Know? Like, real work is like digging a ditch in a field with like, most of the people in The US economy were working in agriculture with very manual labor, like a hundred years ago, and we don't have to do that anymore. So this is what we call work. I think that in the future, people are still gonna work on all kinds of things, but it may not be what we would consider work. AI is gonna eliminate tasks and toil. It's not gonna eliminate jobs, in my view. By the way, you reminded me of something else on your prior question about how we're shaping Coinbase. One of the next big things we're reaching for to get in place is is recursive self improvement. Right? [00:34:05] S1: So
00:34:05 · Molly O'Shea, Brian Armstrong
[00:34:05] S1: So [00:34:06] S0: we're actually using our apps to collect feedback from customers, whether that's bugs bugs they're encountering or even new feature requests. And we're using AI to aggregate all of this input from customers. And then the next set of agents actually take those priorities. They go plan it out. They draft the code and the pull requests. And then a human being can literally just sit there every day and say, okay, here's like the 100 things that we heard from customers. AI went and implemented them. You a can code review and say approve, approve, approve, approve. And like a 101 person is just reviewing a 100 changes that improvements that went out. And the next day the AI collects another set of 10,000 pieces of input from customers. So the closer we can get to this self recursive self improvement loop, I think we're gonna see really dramatic gains. [00:34:56] S1: Are you having fun with all of this?