The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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.
00:51:54 · Harry Stebbings, Naveen Rao
[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. [00:52:05] S0: It's unbounded. It's multi trillions. Like, if you asked me ten years ago, you know, what were trillion dollar markets, It's basic needs. It's housing, food, transportation. Right? You would never have thought something in tech would be a trillion dollar market. And I think just AI models themselves will be a trillion dollar market in the next few years. And the inference market is going to be a $10,000,000,000,000 market within ten years. So this is insane to think about, right? And to your point, we're moving some of that labor market and part of the value generation stack to technology. So that's where we're getting these giant TAMs. So the way I look at unconventional, the reason that I think it's a fantastic market to be in is that if we knock off a zero, one order of magnitude of efficiency on delivery of a token, that effectively 10 x's the market in some ways. So something was a trillion dollar market, now all of a sudden, have way more applicability. The base of potential uses just increases more than 10 x. This is Jevan's paradox. So I I think we're in this place where we can build these exponentials and just unlock massive value in market. So I I actually think robotics is gonna be a 100 times larger than agentic coating.
00:52:05 · Naveen Rao, Harry Stebbings
[00:52:05] S0: It's unbounded. It's multi trillions. Like, if you asked me ten years ago, you know, what were trillion dollar markets, It's basic needs. It's housing, food, transportation. Right? You would never have thought something in tech would be a trillion dollar market. And I think just AI models themselves will be a trillion dollar market in the next few years. And the inference market is going to be a $10,000,000,000,000 market within ten years. So this is insane to think about, right? And to your point, we're moving some of that labor market and part of the value generation stack to technology. So that's where we're getting these giant TAMs. So the way I look at unconventional, the reason that I think it's a fantastic market to be in is that if we knock off a zero, one order of magnitude of efficiency on delivery of a token, that effectively 10 x's the market in some ways. So something was a trillion dollar market, now all of a sudden, have way more applicability. The base of potential uses just increases more than 10 x. This is Jevan's paradox. So I I think we're in this place where we can build these exponentials and just unlock massive value in market. So I I actually think robotics is gonna be a 100 times larger than agentic coating. [00:53:11] S1: Woah. Woah. Woah. Well, that is slightly disagreeing with me, Navin. Can we just, like... Before we do a quick fire, can we just unpack that? Because I I don't wanna be completely moronic. Why do you say that?