Uncapped with Jack Altman
· Bret Taylor
[00:00:00] S1: I’ve been surprised by how many large incumbent enterprise software companies’ first foray into AI was an agent-building tool. It just feels like an inevitable commodity in my mind. Maybe making a website was hard in 1995, but today there’s a million ways to make a website. Most of them are open source. You have cool companies like Vercel, which I love, but it’s not like there’s a huge market for this stuff. In practice, I think the same will happen with agent building. OpenAI will have a great tool. Probably all the foundation model companies will. There’ll be open source packages like LangChain and LangGraph. The idea that you have the right to win there… I don’t know if anyone has the right to win there because it’s just a horizontal technology and I believe in open source. It’s going to become a commodity. My belief is that value is really going to be in agents that do things, hiring those agents and purchasing those agents for what they do. I believe in companies like Sierra, I believe in companies like Harvey. I really admire what they do. They have an agent that will do an antitrust review. I think there’ll be a finance agent that audits your financials. There’ll be one that helps you onboard a supply chain vendor. Just think about onboarding a new vendor. There’s a procurement process, a legal process, a contract review process. Whether or not it’s completely autonomous or human-in-the-loop, all of that could be augmented by AI. That’s a product. Agent building’s not a product. Agent building is a technology.
· Jack Altman
[00:00:00] S0: Speaking of the platforms, aside from being the founder of Sierra, you’re also on the board of OpenAI. You’re the chairman there. I wanted to ask you specifically about Codex. Over the last couple weeks, it’s been unbelievable. It’s like a curtain just came down. Did you expect this? Did you think that what has happened here was going to happen? When did you start to have an inkling that code was going to go vertical like this?
· Bret Taylor
[00:00:00] S1: I’ll say yes, I expected it just because being on the board of OpenAI we talk a lot about it. All the labs—Anthropic and OpenAI in particular—talk a lot about using coding agents to help build AI. Certainly building an AI researcher is an important part of building an AGI lab. The weird part about it, for me as someone who is a software engineer, is I didn’t feel it until I used it. You can talk about it all the time, and then the first time you one-shot something and it turns out really good—not slop, but really good—it’s an emotional experience. For me it was. It was like, “Holy shit. This is real.” As you said, it’s really over the past three months that it has felt really materially different to me. I’ve been thinking about it a lot. I’ve been thinking about the past 20 years of software engineering. I remember the first time I worked on an engineering team that had real CI/CD, where you’d check in code and it would just automatically end up in production. I remember how it felt. If you’ve ever worked on an engineering team that did that versus one that did manual releases, it’s completely different. Because to have something that can safely go from commit to production, there are so many things that have to happen to make that work. You end up relying a lot on testing—unit testing, integration testing, and canary testing—because the last thing you want is someone clicking a button and taking down the service. It’s almost impossible for a team that is doing manual releases to convert into true continuous delivery because there are so many implied processes that are incompatible with that. It’s easy to start that way and very hard to convert. Clearly in three years, we could talk about what the best practices are to set up a software team that’s optimized for this technology and we’ll know what those best practices are. Right now we’re just figuring them out in real time. My hypothesis is that the companies that figure it out first will move the fastest. The other part of that is the companies that don’t will move much more slowly. Andrej Karpathy had a really interesting post about this too. A lot of folks are deep in here and have been thinking about it and it’s fun to see the industry you love flipped on its head in real time.
· Jack Altman, Bret Taylor
[00:00:00] S0: It’s interesting because you have software engineers on one end and then somebody who’s in some part of the country where AI has not yet gotten its tentacles fully extended into. There’s a wide gap in people’s current comprehension of what AI’s going to do. It’s a little bit unknown. There are a lot of blog posts going on right now that are breathlessly saying it’s all over. I’m probably more in the camp of, maybe software is solved? I don’t know if it’s that. But I’m curious if you have a view on whether Codex and Claude Code, the latest in coding, is going to change the way companies are built? Here’s one easy straw man question there. People, like my brother, have been claiming there’s going to be these 10-person billion-dollar companies. Are we at the precipice of that? Does that make sense? Are there other changes? What’s going to happen now? [00:00:00] S1: There probably will be a 10-person billion-dollar company, but I don’t necessarily think it’ll be the norm. The reason for that is competition. If you imagine the mobile phone market in the United States, there are three main competitors: Verizon, AT&T, and T-Mobile. They’re all competing for a fixed pie of mobile subscribers. It’s why it’s extremely competitive. There’s promotions, there’s ads. [00:00:00] S0: They can’t make more of us.
· Jack Altman, Bret Taylor
[00:00:00] S0: They can’t make more of us. [00:00:00] S1: They can’t make more of us. They can build up their network, they can do pricing and packaging. It’s a really complex business to run. All of them have access to AI, every single one. So the idea that you could deploy AI and not have to do things that you’re currently doing because of AI is probably true. But if any one of them figures out a way to use a person to gain market share against the other one, they’re going to do it. As a response, their competitors will do it too. We spoke about this earlier. It’s the reason why when automated teller machines were introduced to banks, the teller job went away, but there are no fewer bank branches and no fewer people in those bank branches. Because JPMC or someone figured out, “Hey, if we put financial advisors in there and other things, we can actually make more revenue per branch.” My personal take is that in a competitive market—and that’s the key, you need competition so people can’t just pass the cost savings onto shareholders or dividends—the second-order effect of the efficiencies of AI will be investment to compete, lower prices or customer acquisition or whatever it might be. [00:00:00] S0: We won’t have fewer engineers per company. They’ll just be way more productive, so you end up with way better software.
· Jack Altman, Bret Taylor
[00:00:00] S0: We won’t have fewer engineers per company. They’ll just be way more productive, so you end up with way better software. [00:00:00] S1: Or you might have fewer engineers and more of something else. Or you might have more engineers, I’m not sure. But the idea that it’ll be what it is today but just more efficient, I think that’s a lack of imagination, in my opinion. The interesting thing though is that software engineering does feel special. I think people extrapolating too much from software engineering are being a bit simplistic. [00:00:00] S0: You’re saying the same thing might not happen to every other function. [00:00:00] S1: I’ll be really simple about it. Finance and software engineering might be limited by intelligence, meaning they’re largely digital. They’re largely manipulating digital things. You could imagine AI automating that. Most of the economy isn’t exclusively digital. If you need to ship a t-shirt from Vietnam to here, you could automate some of that stuff, but at the end of the day, that cargo ship still needs to be in the water. Imagine you run a pharmaceutical company. You can think about how to make a therapy, but you probably need a wet lab. That intersects the real world. Maybe you could do robotics. But then you need a clinical trial. A lot of the economy is real. It definitely will change the way companies are built, but I think when people say everything will be 10 people, billion dollars— [00:00:00] S0: It’s maybe just the stuff that lives in bits.