The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:45:34 · Naveen Rao
[00:45:34] S0: I think it's a bit techno optimist because I I think you have to have a certain amount of scale in order to build your own intelligence and own it. Keep going with law firms, for instance. If you're Cooley or one of these big firms, yeah, you can invest in these things and you can make it happen. If you're a mom and pop shop in the middle of Nebraska, you can't do that. So what you're going to use then is a service of some sort. Right? You may augment your attorneys with that service, and that's how you'll you'll provide service to your clients. So I don't see this as every company will use AI in a way of, leveraging their past data to make their own models better. They're not gonna do that. It's just that it's too hard for them. They want an out of the box solution. So I think there is a market for selling those out of the box solutions. But it's it's gonna be this long tail. The big players will start to customize because they have the scale to justify it. It makes sense. And it goes back to this idea that, you know, every part of our world is adversarial. Like, it depends on who you're fighting. Right? If you're fighting another big law firm and that big law firm goes and uses AI to supercharge their margins, well, you kinda have to do the same. The mom and pop shop probably isn't doing that, and the the people they're, you know, fighting against are not doing that. So they're gonna stay in this other other realm for some time.
00:46:45 · Harry Stebbings
[00:46:45] S1: I think one thing that underpins a lot of law coding is the percent of salary that we will then transition to technology spend. Yeah. When we look at lawyers today, you know, I think it's about a $300,000,000,000 legal market in The US, and you're looking at that going, well, how much of that will be eaten by these providers? I think you see that same question there with coding. You saw Benioff say that they'll spend $300,000,000 of a $6,000,000,000 developer salary budget on core code and coding tools significantly. My question to you is, will that stay at 5% for developers? Will that go up to 30%, 50%, or will it come down to 1%? Because that is the mother lode question.
00:47:27 · Naveen Rao
[00:47:27] S0: I actually think the percentages will remain maybe sort of stable because we end up with this verification problem. It's hard because training humans to know what's good is actually the hard part. Developers, you know, it's actually not about what your direct output is on this day. It's not like lines of code. It's actually like quality of code that matters. So very experienced, very top notch developers, those 10 x developers, whatever, you know, they develop the sense of what is good, what looks good, what's aesthetically right, and they can they can make things work fast. In this new world, you know, those coders, those people who know what's good can actually verify the output of models and become more efficient. So I think what happens is we just produce more. And the market size, let's say, that we're paying for humans plus AI doesn't change drastically, just their output greatly changes. That's the way I look at it because play it out a little bit. So I'm spending, in a company, let's say, $10,000,000 a year to develop my software. Now all of a sudden, my customers of that software start having different demands. Ten years ago, they're, like, happy with one year release cycles. Now they're saying, hey. I want my bug fixed in a week. And so the only way to do that is actually to use AI with the humans. I'm not doing less. I'm actually doing way more. I'm fixing those bugs in a week and eventually fixing them within an hour, but I'm still paying $10,000,000 to keep developing this whole thing. And I need the humans that can verify those outputs and actually move to a a goal that's that's useful, fixing the bug, adding features. So I think the the pace of delivery goes up because the demand side starts to demand it. And and I think that is the the key thing here that's being left out is that it's not that everyone's job goes away because the same task could be done for less with AI plus humans. It's that now... Yes. That is true, but I will I will now do way more of those tasks because the demand side has increased. You you don't get one without the other. When the supply gets cheaper, it changes the demand side as well. So I think that's what's gonna happen is that we just end up doing more. We have more release cycles, more changes. Like, here's a a physical example I think that people can get their heads around.
00:49:24 · Naveen Rao
You know, let's say today, we have a rate of building a building. If I wanna build a skyscraper, it takes two years of construction, say. And that's based upon x number of humans with, you know, y sets of skills. So let's say all of a sudden I have robots that can do 80% of that. What's gonna happen? Well, okay. There might be a disruption in physical labor for a moment. But then you'll say, you know what? All of a sudden, my cost and time to build that same skyscraper, it was two years, but now 80% of those tasks are done by robots. I can bring the whole thing down to eight months. So it's not that you're just gonna build the same number of skyscrapers and just do it in eight months. You're gonna build twice as many skyscrapers. Right? There's gonna be more things churning, more buildings being torn down, more labor going into that market. So the total number of projects will go up. The amount of dollars going into all of those projects may be the same, but it'll be a combination of humans and and robots to build them.
00:50:16 · Harry Stebbings, Naveen Rao
[00:50:16] S1: Naveen, you said that you obviously angel invest. You angel invest very successfully. You can downplay it. I think where I invest today, a contrarian thought for me is I do not like robotics investments. I think we are so far from where we need to be, and I do think that timing is important. Now I'm not saying the infrastructure to power robotics. I think the datasets around them are very interesting. We see a lot of people like Mecca or micro AGI that are actually in the infrastructure build out for it. But, like, the actual, like, application layer where we see, like, robot cleaners, robot builders, We are so far away from where we need to be. I don't wanna be there, and I don't wanna take the timing risk. Am I wrong? Am I naive? You're much smarter than me. [00:50:59] S0: Well, again, I'm pretty pilled on this one, so I am gonna disagree strongly on it. Okay. When you say we're so far away, put that in put that in some of the time. What do you what do you think is the timeline? [00:51:10] S1: Five plus years. [00:51:11] S0: Okay. We're not so far apart on this. I think we'll have working prototypes for some of these tasks in the next two to three years, and the market will emerge in five to seven years. I think that's right. [00:51:21] S1: I think there's also a clear line of delineation between, like, humanoids and nonhumanoids. Nonhumanoids absolutely see that clearer pathway. The humanoids, I'm I'm I'm really just... Whenever I think something's really obvious, I'm always like, I must be very wrong. Because the humanoids is so obviously so far off.
00:51:21 · Harry Stebbings, Naveen Rao
[00:51:21] S1: I think there's also a clear line of delineation between, like, humanoids and nonhumanoids. Nonhumanoids absolutely see that clearer pathway. The humanoids, I'm I'm I'm really just... Whenever I think something's really obvious, I'm always like, I must be very wrong. Because the humanoids is so obviously so far off. [00:51:40] S0: You know, I I think we're gonna be in this weird world where we live in exponential development cycles. It's really hard to apply our old models. Something that looks like so far away today with an exponential curve ends up being much faster than you thought. [00:51:54] S1: And and like TAMs, what... You know, a market size is... Like, I think the inference market, what do I know now that I wish I'd known twenty four months ago? That the inference market was just, like, unbounded.