The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:40:07 · Harry Stebbings, Erik Bernhardsson
[00:40:07] S1: What revenue line do you not have today, or is tiny, that you think will be very significant in three years' time, five years' time? [00:40:15] S0: That is a good question. I think training is something we just launched. I think that could be very large. I hope that could get to 100,000,000 run rate in the next six to twelve months. Post training, similarly, working on batch jobs. I'm very excited also about a lot of like sort of traditional, like data warehousing stuff, databases, workflow orchestration, feature stores, like vector database. And there's just so many parts of this. And they all kind of fit together very nicely. And that to me is like what I'm so excited about. It's like once you start building in this space, there's so many natural adjacencies. Like this is like a problem with me. It's like I get excited about, I have like no lack of ideas. Like at Modal, like we have so many ideas of things we want to go. I would love to get into continuous integration. That's like a very interesting space. Agent harnesses. Maybe we should train models. Maybe, you know, there's just 5,000 directions, you know, and that's like my ADD brain, like wants to run-in all of those directions. You have to be obviously ruthless with prioritization and only take on, like, one or two things at at the right time, but but there's so many different directions we can go on. [00:41:09] S1: It's amazing that your wife calls you for decisions. She must get a very incoherent answer. [00:41:15] S0: I know. I know. What is this? Is
00:41:15 · Erik Bernhardsson, Harry Stebbings
[00:41:15] S0: I know. I know. What is this? Is [00:41:17] S1: this? Is this? I just wanted some curtains done. [00:41:19] S0: Yeah. You know, with the curtains, I just like you pick one of them. I've seen Simpsons and, like, Rainier Wolfcastle becomes the president, and they're like, which one do you wanna pick? You know, one to seven. He's like, I was born to lead, not to read. Number four. [00:41:33] S1: Yeah. Can I ask, we've seen this real shift, I think, in emphasis, but also in resource allocation from pre training to post training? How do you feel about that, and do you think that is the new normal?
00:41:45 · Erik Bernhardsson
[00:41:45] S0: I don't know. I think post training works because there's a lot of tasks that people do in real life that have fairly finite sort of feedback mechanisms. So coding is like a great example, right? Like you can do coding with reinforcement learning pretty well because, you know, you can run code in sandboxes. And a lot of labs are running modal sandboxes for that exact purpose. Start to look at cyber security is another area. Math, obviously, with Lean. I mean, seen some fantastic product. I think it's kind of incredible, all these like OpenAI proofs the other day. So law, maybe to some extent, you can, you know, do like eval. So I I think I think post training right now, like, there's, like, such incredible focus on it because you have all these tasks that traditionally only humans can do, but are actually well within reach for for agents and and for LMs. But like, I don't know if it extends beyond that. Like, I don't know. Like there's a lot of tasks out there that are like have way longer feedback loops that I don't know if like post training quite yet are is within reach. A lot of people ask me like, what's going happen with software engineers in the long term? And I think, you know, we're probably gonna automate everything that an agent can do within twenty four hours or a week or something like that. But I kind of feel like, you know, engineers, like they work on a longer time scale and they have feedback loops that are longer. So I have a feeling like learning, we're gonna start to run into some limitations around that. But I've been wrong for so long, so maybe I'll be wrong and just keeps going. And eventually we can do reinforcement learning on things that take six months for a human. I don't know.
00:43:04 · Harry Stebbings, Erik Bernhardsson
[00:43:04] S1: What does the day look like in three years for an engineer? [00:43:07] S0: I mean, I'm extremely bullish on software engineering. I've been coding for thirty years, and I've seen so many different changes with like programming languages and the cloud, and so many different frameworks. And every single time we make programmers more efficient, it just increases demand for software. There's so many parts of the world that desperately need software. And so I'm very, very bullish on software and the need for software engineering. And I think one of the biggest misconceptions from the outside is that developer is about like translating from English to code. Like that has never been in, you know, the job of software engineers. It's like to understand the requirements in the first place and be kind of an interface between the humans. You're building for humans at the end of the day and like trying to figure out like, what does that even mean in terms of processes and workflows? That continuously moves up in the stack, you know. Back in the 60s, we used to write assembly and punch cards, and then we moved to COBOL and then to C plus plus and then to Python, and now we're using agents. And that's just like one it's definitely like the biggest thing that I've seen in the last thirty years. But I do think we have many, many analogs and many parallels throughout the history of software and computers. To me, that makes me feel like, you know, software engineers are gonna be fine for the next ten, twenty years.
00:43:07 · Erik Bernhardsson, Harry Stebbings
[00:43:07] S0: I mean, I'm extremely bullish on software engineering. I've been coding for thirty years, and I've seen so many different changes with like programming languages and the cloud, and so many different frameworks. And every single time we make programmers more efficient, it just increases demand for software. There's so many parts of the world that desperately need software. And so I'm very, very bullish on software and the need for software engineering. And I think one of the biggest misconceptions from the outside is that developer is about like translating from English to code. Like that has never been in, you know, the job of software engineers. It's like to understand the requirements in the first place and be kind of an interface between the humans. You're building for humans at the end of the day and like trying to figure out like, what does that even mean in terms of processes and workflows? That continuously moves up in the stack, you know. Back in the 60s, we used to write assembly and punch cards, and then we moved to COBOL and then to C plus plus and then to Python, and now we're using agents. And that's just like one it's definitely like the biggest thing that I've seen in the last thirty years. But I do think we have many, many analogs and many parallels throughout the history of software and computers. To me, that makes me feel like, you know, software engineers are gonna be fine for the next ten, twenty years. [00:44:11] S1: How much money do you spend on coding agents a month at Modal? [00:44:15] S0: I think a 500,000 or something like that.
00:44:15 · Erik Bernhardsson, Harry Stebbings
[00:44:15] S0: I think a 500,000 or something like that. [00:44:17] S1: How many engineers do have? A 150? Dude, that's so worrying, you know. [00:44:21] S0: You wanna... Should be token maxing more? [00:44:23] S1: They should be 10x that. Yeah, maybe. I don't know. Brandon McCall, that they spend 3x their human salaries on inference. [00:44:32] S0: Yeah, we're not quite there yet. But I will say, our software engineers are extremely agent plus, including myself. I mean, I write code with agents all [00:44:38] S1: the time. I But think the big question, if you're looking at an anthropic IPO, and don't worry, I'm not doing a Bloomberg and pulling you into be like, what do you think of the anthropic IPO? I hate those Great. Yeah, you know where it's like, yeah. It's great. No one in AI is like, not a fan. Is [00:44:53] S0: against Go. [00:44:55] S1: Go. But I do think a big... The mother load, so to speak, is claw code. It's $60,000,000,000 of revenue. It largely kind of dictates the $2,000,000,000,000 price. And you look at Marc Benioff who says that they spend 5% of engineers' salaries, 300,000,000, on Claude Code today. Now if that comes way down as efficiencies improve, you name it, that's a challenge. Or will that go up as it just eats more and more of engineers' workflows?