The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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?
00:44:55 · Harry Stebbings, Erik Bernhardsson
[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? [00:45:22] S0: Yeah, it's just gonna eat more. Mean, like, again, like we've seen this so many times, like you make something more efficient, you make something more powerful, it just unlocks like five new use cases, then and demand just goes up in the end. Like, that's that's just the history of like the last fifty years of computers and software. But like, I wouldn't be here if I wasn't such an optimist. That's why I started a company. Know, like, I'm just like the biggest, like, optimist that there is in the world about software and computers, and I just love this shit. And I think it's just gonna [00:45:46] S1: keep growing. The data market is an interesting one. We have so many players that have scaled so fast. [00:45:53] S0: Yeah, it's incredible. If I was 20, and I just wanted to make a lot of money, I would go out and start an environment company and just sell data to Frontier Labs. There is so much money to be made in that space, it's kind of incredible. [00:46:03] S1: Let's go a layer deeper. What would you do next? Do you specialize on a specific market?
00:46:03 · Harry Stebbings, Erik Bernhardsson
[00:46:03] S1: Let's go a layer deeper. What would you do next? Do you specialize on a specific market? [00:46:07] S0: I mean, the coding space is where you see the most demand right now. If you can sell code environments to the labs, you can make a lot of money. It's a big space in that. [00:46:15] S1: Still? Still, [00:46:16] S0: yeah. Yeah, I meet startups all the time. I angel invested in a bunch of that. I mean, they're incredible businesses. What I will say, my one piece of advice is make a lot of money today, but like, is this a venture fund? Do you wanna get on the venture treadmill? I'm not sure. Like, I think these are companies that are gonna make a lot of money today. I don't know if they're gonna make a lot of money in the very long run, but like who cares? Like, take the money. It's like great money. Like it's a fantastic business today, and that's all that matters. [00:46:38] S1: I think there's data marketplaces and then there's data and services. And when you look at the leading providers, they started as the data acquirers and then just sell you the data, and that's kind of it. And I think they've really gone full lifecycle now to deployment, implementation, evaluation, monitoring with a real enterprise handholding ability, similar to the FDEs, where they will really be that partner to ensure full implementation. That's how you expand margin and have some form of business in the long term. [00:47:07] S0: For sure. Yeah. And I think it's gonna be a tough business in the very long run, but today, you can have extremely good cash flow selling these data sets.