StreamSparkAI

speaker 1: There's all this like tooling and infrastructure still to build. There's clearly still a bunch of startups yet to be built in just the infrastructure space around deploying AI and using agents that .
speaker 2: if you're living at the edge of the future and you're exploring the latest technology, like there's so .
speaker 3: many great startup ideas, you're very likely to just bump into one. You apply the right prompts and the right data set and a little bit of ingenuity, the right evals, a little bit of taste, and you can get like just magical output. Welcome back to another episode of the light cone. Every other week, we're certainly realizing there's a new capability, a million token context window in Gemini 25 pro. It's just really insane right now. And the thing to take away from that though, is that we have an incredible number of new startup ideas, some of which are actually very old, and they can only happen right now. Harwhat are some of the things you're seeing.
speaker 1: Well, one thing I've be thinking a lot about recently is what a types of startup ideas that couldn't work before AI or didn't work particularly well that are now able to work really, really well. And one idea that is very personal to me would be recruiting startup, since I ran a recruiting startup triple bite for almost five years. And I think something that I've clearly seen is that there was a period of time when we started triple bytes around 2015, where recruiting startups were kind of like a really popular type of startup. And I think a lot of the exciton around those ideas back then was this idea of applying marketplace models to recruiting, because there were marketplaces of everything except how to hire great people and specifically, great engineers. And we started trouwith the thesis of, you don't just want to let anyone on your marketplace. You want to build a really curated marketplace that evaluates engineers and gives people lots of data about who the best engineers are. And this was all pre llm. So we had to spend years essentially building our own software to do thousands of technical interviews to squeeze out every little data point we could from a technical interview so that weeffectively build up this label data set that we could run machine learning models on. But we didn't even get to do that until like years three or four.
speaker 3: And initially it was actually a three sided marketplace and that you needed to hire an interviewer in between to get that human. Settwe had companies .
speaker 1: hiring engineers. We had the engineers looking for jobs, and then we had engineers we contracted to interview the engineers. So there's was like lots of things going on right now. And all of the evaluation piece of at least now with AI is very, very possible. I mean, we specifically with the AI code gen models, you can do code evaluation. And I think probably one of the hot AI startups at the moment is this coming called mercore, which is essentially similar to the triple eight idea, and it's a marketplace for hiring software engineers. But I think what AI has unlocked for them is the evaluation piece of it they could just do on day one using llms that they need to build up this big label data set. And they've been able to expand into other types of knowledge work quite easily. For us to have gone from like engineers to analysts to all these other things would have taken years because, again, we had to rebuild the label data set. But with llms, you can just do that on you day one effectively. And so I think this whole this whole class of recruiting startups that are trying to evaluate humans at being good at specific tasks or not is a really interesting space that's much more exciting to find good startup plain now than it was five years ago.
speaker 3: So that's a very powerful prompt for anyone listening. What are marketplaces that are three sided or four sided marketplaces that suddenly become two or three sided? Or now there are two sided marketplaces like Duolingo that are a little bit under fire because they're sort of starting to say, actually, maybe we're just going to use AI for the person that you're gonna to talk to in another language that is totally a coherent thing, that you could go to almost any marketplace in the world and say, what if what what will llms do in that marketplace?
speaker 1: I think the other thing I really respect the Merkel founders for is is also just a psychological element as a founder to when you enter into a space where there's been lots of smart teams and lots of capital that's flown into it, there's was definitely with recruiting startups, I mean, triple bite raise something like $50 million. Our main competitor hired raised over 100 million. I think in aggregate, hundreds of millions of dollars went into funding recruiting marketplace companies. And overall as a category did not do particularly well. And so I think going in, you face a lot of skepticism if you're gonna to go out and pitch investors for an idea, even when you have the, like, well, llms change everything, that pitch two years ago was still not as compelling as it is today. And so you have to be willing to sort of push through a lot of sort of cynicism. And people who are burnt out have lost lots of money on an idea to even kind of keep going to test it out and make it work. That's something .
speaker 3: that repeats actually all the time. I mean, instacart was that story exactly like web van was sort of this ritting corpse of a startup just hanging in that doorway. And most people looked at that and said, Oh, man, I don't want to walk near that. Like there's there's going to be more. But you know simultaneously, the iphone and android phones were everywhere and you could have a mobile marketplace for the first time. And I guess that's why we're pretty excited about this moment, because suddenly all you know the idea maze just moved like all of the walls to the idea ma's have shifted around. And the only way to find out is you've got to actually be in the maze.
speaker 2: Is very similar to instacart and Webvan if we go back in history, right? Because like the big technology unlocked for instacart was the fact that everyone had a phone. Now it enabled like the Webvan model to actually work for the first time. And like it's the same thing with lms and recruiting companies now and a .
speaker 4: whole bunch of other ideas. I think it makes focusing even on specific parts of the marketplace to be great ideas to start with, even with this recruiting idea space, this is company called apririora, the Niko, the other gp here at yc, funded back in winter 24. And their whole premise is to build AI agents that run the screening for technical interviews, where a lot of engineers spend a lot of time just doing a bunch of interviews. And the PaaS rate is so tiny. When I used to run engineering teams at Niantic, all that pre screening was just so much work.
speaker 2: The engineers .
speaker 4: hate doing it. Yeah and even that one piece, not exactly, let's say, marketplace or what is the hardest part of it. And if you solve it right now, it works out. So apriiars actually does a pretty good job, is being used by large companies and it's been taking off.
speaker 1: It's not example we can actually expand the market because I think the there are plenty of technical screening products pre a priora, but you could only use them to do fairly simple evaluations to like weed out people who weren't engineers at all effectively or very, very junior. But ap's product now with llms, you can do more sophisticated evaluations to kind of get more nuanced levels of screening. And so suddenly now companies will be like, Oh, actually I could actually give this to not just like my international applicants or my college students. I'll just give it to senior engineers who are applying, which is opens up the opportunity.
speaker 4: So you were talking a bit about education as well, Gary, about Duolingo. I think that's aspect of doing hyper personalization is one of the holy grails where has been difficult for at tech companies to crack, right? Because every student as they go through their learning journey, everyone is very unique and knows different things. And it sounds really cool to build like the awesome personal AI tutor with that harch did an rfs for right.
speaker 1: Yeah the thing I'm excited about for as long as I can remember the interhas been around like one of the like dreams of it was that everyone now have access to like personalized learning and knowledge. And weall just you know have these like great intellectual tools to learn anything. And clearly, the Internet's made it easier to learn, but we've never had really truly personalized learning or a personalized tutor in your pocket idea, which is possible now for the first time. And I think we're definitely seeing smart teams applying to yc who are interested in .
speaker 4: building that type of product. A couple of companies that we funded that are kind of working out is there's other company that also Nico funded called revision dojo that helps students do exam prep and is sort of the version of a flashcards, but not like the jankie, just like boring going through content, but the version that actually students like and gets tailored for their journey. And that one has like a lot of daus and a lot of power users, which is super interesting. And I think, Jared, you had worked with this other company called .
speaker 2: eda as well. Yeah edxia does tools for teachers to grade their assignments, which is another example of work that like is like not people's main job, but it's this other thing that they have to do, like engineers doing like recruiting that they generally hate doing. There's like a lot of studies that show that like the biggest reason that teachers churn out of the workforce is that they hate grading assignments. It's just like no fun at all. And so addxia like is an agent that's like very good at helping teachers to great assignments.
speaker 3: Yeah one of the interesting trends for some of this stuff is that it's private schools who are actually much more nimble. And you know I'd be curious what policy changes we need to make to actually support this in public schools because the public schools need it the most.
speaker 1: Actually, I guess Christian for you. Actually, Gary, I'm curious about this stuff is it's clearly possible to be able to much better products with llms. So we take the the learning apps, for example. They can go far beyond anything you could do for personalized learning pre llms, but it doesn't necessarily mean that you instantly get more distribution, especially if you're going after the consumer market. You how do you think that plays out? Do better products automatically get more distribution? Or will these startups have to work equally as hard to get distribution .
speaker 3: to be big companies as before? I guess one of the more awkward things that's still is that you know intelligence is much cheaper. It's quite a bit cheaper than it was last year, but it's still enough that you have to charge for it. Probably. But that's something I would probably track. I mean, it seems clear that know distillation from bigger models to smaller models is working. It seems clear that the mega giant models are teaching even the production model size of today to be smarter. The cost of intelligence is coming down quite significantly. So you I know that we tease this sort of almost every other episode, but like consumer AI. It finally might be here soon. And I think the thing to track is, well, how smart is it such that like any given user, incrementally only costs, I don't know, pennies or like ten or $0.15? Like then it becomes so cheap that you will just have intelligence for free. Maybe itbe a return to the freemium model that we got used to during web 2.0, this idea that you could basically give away your product and then for five or 10% of those users, there are things that they so want that you know you're going to sell them a five or $10 or $20a month subscription.
speaker 1: That's basically what OpenAI is doing right now.
speaker 3: Like that is like perplexity, does it OpenAI? You know, going back to education, study with 2D's, they're doing it and they're seeing a lot of success. I mean, on average, the kids who use that actually get on grade level or you know kind of go up even a couple grade levels. Those are real outcomes for students. So you know right now, you still got to pay for it, but maybe not for a while. And that's actually a really big unlock. You know that that's the moment where you could have 100 million or a billion people using it. OpenAI might be furthest ahead with it, but the hope is that you know really thousands of apps like this start start coming out across all the different things you'll need. And that's something that I know we'll keep saying it like it's gonna to happen.
speaker 4: I mean, it's kind of happening already for a tech speak. It's this company that got started couple of years ago before llms were a thing at all. It was team of researchers that really believed that you could personalized language learning, which might have been a big contrarian back then because Duolingo seemed to be the game in town that was winning. And they really focus on really personalizing that whole language learning. And they got, they started taking off in Korea for a lot of learners. They were trying to learn English. And when GPT -3 and 3.5, they were early adopters of it started coming out. They saw that, wow, this is going na be the moment they double down. And they've been in on this trajectory now with lots of Mau zus that's really working out. I think one thing going out to the consumer .
speaker 1: thing that we haven't talked as much about, we've seen a lot with the startups that are selling to enterprises or companies about how the budgets become so much bigger when companies are willing, when companies stop thinking about you as software, as a service, but they start thinking about you as replacing their customer support team or their analytics team or something like that, they will just pay way, way more. So the same thing will apply in consumer, right? Like if you think about a personalized learning app, often ed tech companies struggle with who's actually the buyer who's going to pay. And if you go for like younger children, for example, it's like you've got to get the parents to pay, but the parents aren't going to pay that much for an app that their kids like don't retain or complete like some sort of online course that they're disengaged with. But we know that parents will definitely pay for like human tutors and like you know that's like actually probably quite a big market. And so if your app goes from being like a self study course that doesn't get any completion to actually being on par with the best human math tutor for your twelve year old, parents will pay a lot more for that. And so those like it's possible that like the product now just become has a business model that you didn't have before. And that alone means you don't necessarily need millions of parents using it, but even 100000 parents using it, paying you a sniffan amount means you now have like a much bigger business .
speaker 3: than was possible before. Yeah, I feel like we have to talk about modes a little bit. I mean, it's pretty clear a company like speak or almost any of these other companies that could have durable revenue streams, like what you need is brand, you need switching costs. Sometimes it's integration with other technologies that are sort of surrounding that experience, like in a school itwould probably be being connected to clever, for instance, like login is authentication is pretty obvious. So Yeah, I feel like Sam Altman has talked about this a bunch. You know it's it's not enough to drop AI in it. You know you still have to actually build a business. I don't think OpenAI is necessarily you know out to get all the startups. Like I actually think that on the api side, they very much hope that a lot of them do really, really well. And certainly.
speaker 1: we want that too. They did just hire like teams to call CEO as their CEO application does kind of seem like they are definitely paying more attention to the application layer.
speaker 3: That's right. Yeah. I mean, yoube crazy not to, right? Like by all accounts, OpenAI is highly likely to be a trillion dollar company at some point and you know as powerful as a Google or an apple or any of them. The interesting thing right now is like they're still on the come up. And then if anything, the big tech platforms are actually still holding back a lot of the AI labs. And the most profound example of this is, why is Siri still so dumb? It makes no sense, right? Totally. I mean, I think that points to something that we actually really need in tech today. We actually really need platform neutrality. So in the same way, you know, 20, 30 years ago, there are all these fights about net neutrality, this idea that there should be one Internet that isps or big companies should not self preference their own content or the content of their partners. You know, that's what sort of unleash this giant wave of really a free market on the Internet. The other profound example of that is actually windows. You know, if you open up windows, you actually have to choose your browser, and then you also need to be able to choose which search engine you use. And these are things that you know the government did get involved in and said, Hey, you know, you cannot self preference in this way. And you know, if you remember the moment where Internet Explorer had a majority of web users, like that could have been a moment where Google couldn't have become what it became. So we actually have a history of the government coming in and saying, this should be a free market. And for that free market to create choice and then therefore, prosperity and abundance. And so I would argue like you know why doesn't this exist for voice on phones? Like you should be able to pick. You shouldn't be forced to use Google Assistant, you shouldn't be forced to use Siri. You should be allowed to pick. And you know it's been many, many years of having .
speaker 1: to use a very, very dumb Siri. On the note topic, something I just find fascinating, I saw some numbers recently about how Gemini pro models like just their usage, particularly from consumers, is just an intive contraction of chat GPTs. I at yc, we've been doing our own internal work, building agents and actually being at a cutting edge of a lot of the AI tools. And we found that Gemini 2.5 pro is like as good and in some cases, a better model than zero three for various tasks. That hasn't trickled down into public awareness yet.
speaker 2: right? And is fascinating. Google already has all the users with their phones.
speaker 1: and I don't think anyone would say OpenAI is not a startup anymore. But relative to Google, it it actually is. So there is clearly some sort of intangible mode around being the first in a space and sort of staking your claim as like the best product for a specific use case.
speaker 3: And I feel like actually making it good.
speaker 1: Yep, Yep, Yep, Yep. But at some point, maybe it doesn't even necessarily need to be like objectively the best. It just needs to be good enough.
speaker 3: I mean, that's the bet that I think a lot of the big tech companies are trying and failing at. I mean, there's Microsoft has a Copilot built into windows now that it is still quite inferior to anything OpenAI puts out. Gemini itself is very, very good, and I use it quite a lot. It's probably, I don't know, 40% of my agent, you know sort of if I need to especially summarize YouTube videos, it's very, very good at that. For multimodal is really good. Yeah a lot of the Gemini integrations into gmail or you know Google Drive or not, they're totally used. Yeah. It's like, is there someone at the wheel over there?
speaker 4: I don't get it. You know I mean, I think that's even confusing for us is even using it as a developer. There's actually two different products. There's Gemini where you can consume, Gemini and vertex Gemini. And I think they're like different. Ori think it's suffering a little bit from being too big of a company and essentially shipping the orthere's, like these two apis you can consume to use Gemini. And we're like, why two? One is from deep mine and the other one is from gcp.
speaker 3: I think that comes from the culture of Google, though. I mean, there's definitely the sense that if two orgs are competing and fighting normally in a normal org, you go up and in a functioning startup, for instance, you know it goes up to some level and then ultimately the CEO or founders and then they just say, okay, well, I see the points over here. I see the points over there. We're going this way. You know having lots of friends from Google, it doesn't seem like that's the culture there. Like there's a layer of vp and sort of management that is actually like.
speaker 4: you guys just fight it out, and so then you ship the org. I think the crazy thing about Google, they probably should have wa lot of the experience of the best model. There's almost like, I don't know where all this game of throne analogy could be used. It might be a little bit like Dennis Targaryen, because they secretly have dragons. The dragons are the tpu's. And this is one of the reasons why I think they could be the one company that could get a lot of the cost of intelligence to be very low. And they also have the engineering to be able to do a cost effective large context windows. I think one of the reasons why the other labs haven't quite shipped as big of a context window is cost actually.
speaker 2: Is it actually the hardware? Like it's just like you can't actually do it without.
speaker 4: I think you can do it, but I think it's just very expensive and not cost effective. But I think they done it so well and they got tpu's, which I think is smart for Sam. If you saw his little announcement, he's still the CEO of compute, quote unquote. So I'm sure they're probably working on something .
speaker 1: around there too. Those are just classic innovatives dilemma. It's like if Google replaced Google com with Gemini pro. It would instantly presumably be like the number one chatbot lllm service in the world.
speaker 2: but that it would give up 80% of its revenue.
speaker 1: You would probably need a pretty strong founder CEO to do that. The kind of thing I can imagine zc doing right, like being no, Yeah you just you can't imagine a hired CEO is gonna to do that.
speaker 4: He's done that. He rename ed the .
speaker 3: company to meta. Yeah Yeah meta has its own issues too. Like I'm so surprised, you know, I mean, you have meas AI and WhatsApp. It's in the blue app. It's everywhere. But I mean, who actually uses it?
speaker 1: I don't think any of us. I started using the meta AI in WhatsApp. It's very classic. It makes me feel like Zach is clearly still in charge of product because I don't think anyone else would launch it that way. You just, you now you have an AI assistant that's just in all of your chats and you sort of, it comes with a, you can just add it and they will just start talking in a group chat .
speaker 3: and it feels quite invasive actually. Well, it's not that smart and then it can't do anything. Yeah. And then you have, I mean, most people are surprised .
speaker 1: that it's in there. It's just it's just like it feels like having someone from Facebook just in your chats and it's just like it's I know, reminding me of like the original newfeed launch or something. It's just like the classic meadow style of like this is sort of you know objectively optimal. Like I'm sure people love it. You need to add .
speaker 3: a little bit of design taste into these things. I mean, it blows my mind that I can go to the blue app, which I still can't. You know it's probably people watching this sort of like what the heck of the blue app this is like Facebook com, which maybe nobody uses anymore. Very millennial. Yeah but you know you have this meta AI and you ask it, Hey, who are my friends? I'm going to Barcelona next week. Who are my friends in Barcelona? And it's like, sorry, as an AI, I actually don't have access to that.
speaker 2: It's like, what you know, what is the point of this? Our partner, Pete cuman wrote this really great essay where he talked about the Gemini integration with gmail. And he really like broke down in great detail why Google built this integration all wrong and how they should have built it. It's almost like he was a piamo Google. Oh wait, he he was a piamo Google gle.
speaker 3: It was very profound in that. One of the things he pointed out was that you know you have a system prompt and a user prompt. And if you are actually going to empower your users, you actually allow your user to change the system prompt, which is the part that normally is like above you know to use the vencatesh rouse idea of like sort of the api line. It's sort of like the system prompt is actually what is exerted. It applies. It's like sort of imposed upon the user. And so you know Gemini follows this very specific thing. I think the example is actually an email saying that Pete's going to be sick to me. He's like, sorry, I'm not going to be able to come in. And he asks the agent to write this letter. And it's very formal. And of course, it is because there's no way to change the tone. It's actually one of the best blog posts in that. I think he had to vibe code the blog post itself because you can actually try the prompts yourself .
speaker 2: on that web page. Yeah, it's just super cool. It's like a, it's in this like interactive Yeah temthink language.
speaker 3: which made me think it's time to start an AI first vibe coding a blog platform. Oh.
speaker 2: like an AI, like like an AI poststerous.
speaker 3: Yeah, basically that might be might be Yeah with all my extra time, that's what I'm going to work on. But that's a free idea for anyone's watching. We'll fund it.
speaker 2: There's another class of startup ideas that I'm particularly excited about that I think are perhaps the time is now, which is do you guys remember the tech enabled services wave? Yeah so for folks who didn't follow this the in the 2010s there is this huge boom in companies called tech enabled services. Triple bite was one actually Yeah does like tech enabled services for recruiting Yep, right. We also had atrium which was tech unenabled .
speaker 3: services for law firms.
speaker 1: It's not with ballogies blopriabout full stack startups. You remember like the concept was just that software eats the world means software just kind of goes into the real world. And so this is not like the success example, but an example of it was, Hey, like instead of just having an app to deliver food, you should also like have a kitchen that cooks the food and software to optimize the kitchen and you just do everything. And that like the four stack startups, in theory, would be more valuable than just the software startups because they would do everything. Yeah.
speaker 2: because instead of just selling like software to like the restaurants and capturing like 10%, you could just own the restaurant, you could capture 100%.
speaker 1: This is exactly what triboy was because we're like we're gonna to be a recruiting agency. Effectively, we're not telling software to recruiting agency. We're actually just doing the whole thing. Like we're gonna we also had recruiters on staff that were just there to help people negotiate salaries and match them to the right companies. And Yeah, it was very much in that wave of do everything.
speaker 3: but that wave of startups generally forgot that you need gross margins. Yeah.
speaker 2: what happened? Like I mean, like fast forward, basically the short version is like it didn't really work and the full stack startups actually were not more valuable than the sas companies. And the sas companies sort of like won that round of the like darwindian competition of different business models.
speaker 1: I think fundamentally is just what Gary eat it says. It's just they were actually not great gross margin businesses, but it was actually, I think what it was just hard to scale them, at least introvising situation. We actually got to like $20 million annual run rate, $24 million run rate within a few years. Or like if you compare us to like a regular recruiting agency, it was like super fast. But if you compare us to like the top software startups, not that like impressive.
speaker 2: And it became harder and harder to scale.
speaker 1: but you hit more and more people. Yeah. Yeah. Basically like the margins work out particularly on so then you need to keep raising more capital. And so if you were like a fearsiingly good fundraiser, you could sort of do it and kind of push yourself. But even in those cases, I think most of those businesses, at some point, it just caught up with them. Like at some point, like actually, we have to figure out a way to scale the business and have good margins and make this like profitable and not just rely on the next fundraising round, is what I felt hurt. A lot of the.
speaker 3: you could argue Xenopus was one of those for insurance and a bunch of different hr related things. It was actually they basically relied too much on hiring more salespeople and more customer success people instead of actually building software that then would create gross margin. And so Parker Conrad said, well, I'm not going na do that again. And I'm also going to force all the engineers to do the customer support so that they go on to build software that doesn't require so much support. And thus, there is gross margin. And that, you know was a whole lesson that I feel like the whole tech community learned collectively through the 2010s. If we learned one thing, it's gross margin matters a lot. Like you, you cannot and should not sell $20 bills for $10 because you're going to .
speaker 1: lose everything, I think, to non financial reason why the gross margins matter is low gross margin businesses usually mean you have some ops component and then you have to like run the ops component. So if I think of my like triby experience, there was like a lot of brain power spent on like how do we like manage this team of like contracted engineers, a team of like humans looking after the and like essentially the human recruiting team, like lots of pieces of the business where actually the exstential issue we had is how do we get to like millions of engineers across the world like all like on like on our platform and all locked day e, like how do you just get lots of distribution? And I think something that's nice about a high gross margin business, just another way of saying it's just a simpler product or a simpler company to run, and you can actually just spend all of your time focused in on how do I make the product better and how do I get more users and get more distribution so that you can keep that like exponential growth for a decade. And I think a lot of four stack startups partly plateaued out because he's just the complex businesses to run.
speaker 4: Maybe a very famous example of that was like we worked, right? Yeah, Yeah, which is very took it to the limit. The margins were not there. It was not didn't have the tech margins right.
speaker 3: It had community adjusted a bit, which very creative. What I've been .
speaker 2: excited about recently is like I think you can make a bull case that like now is the time to build these full stack companies because like you know like you're were saying like the triple by ₩2.0't have to hire this huge ops team and have bad gromargins theyjust have agents that do all the work. And so like now actually like full stock companies can look like software companies under the hood .
speaker 1: for the first time. And you gave a great example. So atrium fstarted by Justin ton, full stack law firm, didn't work out for, I think, a lot of these same reasons.
speaker 3: But I heard him say that before. It's like, look, we went in trying to use AI to automate large parts of it. And it wasn't the AI was not good enough at that moment, but it's good enough now. If you look at within yc.
speaker 1: we have legora, which is like this, like one of the fastest growing companies we've ever funded and is not building a law firm, but they're essentially you know building AI tools for lawyers. But you can see where that's gonna to externout to you, right? Like eventually their agents are just gonna to do all of the legal work and theybe the biggest law firm on the planet. And Yeah, I think that's a kind of full stack stard that just .
speaker 3: wasn't possible pllm. I think this started right when Uber and lift and instacart and all of these companies were happening. And the thing is now, I mean, you can actually have llms do a lot of the knowledge work and then I mean, increasingly, it could actually have memory. I mean, this is one of the rfs. It's literally you can have virtual assistants. But they become less and less virtual if they can also hire real people to do things for you.
speaker 2: Virtual assistant marketplaces was definitely like a whole category of companies for like 15 years, including exactly c, where you build like a marketplace of like people in the Philippines and like other other countries, and then you like exposed to sort of like AirBNB ui. I don't think any of them ever like really got really became like amazing businesses though .
speaker 1: going back to Pete post, I think the other thing that's interesting about the points he made around sort of the system prompt and the user prompt, and maybe we want to expose the system prompt to users a little bit more. It's an example of just how we're still so early in just using aiand building agencies, all this like tooling and infrastructure. Like still to build, you have to do evos. You have to run the models like a whole bunch of stuff to build still. And so there's clearly still a bunch of startups yet to be built in just the infrastructure space around you know deploying AI and using agents. And Jared, you know it's interesting. Something that struck me about when I first came back to ic in 2020 is I remember a class of idea we weren't interested in funding was anything in the world of like ml machine learning operations or ml tools. And I remember reading some applications and like like another ml ops like team like these sort of never go anywhere. Clearly, if you were working on ml ops in 2020 and you just stuck it out for a few years, are you in the right spot? Any content you .
speaker 2: can share from that? Yeah. I remember I got so frustrated after years and years of funding these ml ops companies with really smart, really like optimistic founders that just like didn't go anywhere that I ran a query to count. And I remember finding that I think this was around 2019. We had more applications in 2019 for companies building ml tooling than we had applications for like the customers of those companies, like like anyone who's like applying ml to like any sort of product at all. And like I think that was the core problem is that like these people were building ml tooling, but there was no one to sell it to because like the ml didn't actually work. So there just wasn't anything useful that you could build with with all this ml tooling.
speaker 3: People didn't want it yet. I mean, directionally, it was absolutely correct. Like from a Sci fi level on a ten year basis, it was beyond correct.
speaker 2: Yes, it just wrong .
speaker 3: for that moment.
speaker 4: You actually have a team that stuck it out. I mean, part of the lesson is sometimes it will take a bit of time for technology to catch up. And this company called replicate that you worked with stuck it out.
speaker 2: It was from that era. Yeah replicate was from winter 20. And they started the company right before Covid and during the pandemic, it was going so poorly that they actually stopped working on it for several months and just like didn't work on it because like it wasn't clear that the thing like had a future at all. And then they picked it back up and just started like working on it quietly. But it basically was just like they were just building this thing in obscurity for two years until the image diffusion models came out.
speaker 4: and then it just like exploded .
speaker 2: like overnight. Ollama is another good example.
speaker 4: Yeah, you werabout Olama. So the Olama folks were also from that pandemic era, similar story to replicate. They were kind of trying to do different things around here too, and they were trying to work it out to make open source models deploy a lot better. And they were also quietly working on it. For a while, things weren't really taking off. And then suddenly, I think the moment for them was when lama got released, that was like the easiest way for any developer to run open source models locally. And you took off because suddenly the interest to run models locally just took off when thing started to work, but not before that because there were all these other open source models that were in hugging face and especially the ones from like birmodels. Those were like the more used deep learning models. They were like just okay, but not many people were using them because they .
speaker 3: weren't quite working. What's the moral of the story? I mean, some of it is like be on top of the oil well before the oil starts shooting out of the ground. But is that actionable? It's kind of the classic .
speaker 1: staradvice of follow your own curiosity. Like most of these teams or almost all these teams were working on it because they were just interested in ml. They wanted to deploy models. They were frustrated with the tooling, probably weren't necessarily commercially minded and trying to pick the best startup idea they could possibly work on. But I know if sometimes you get lucky.
speaker 3: sometimes there's so many ways to do it. I mean, we were just sitting with veroon from windsurf and he pivoted out of ml ops into cogen. Deep gram is another one.
speaker 2: Deep gram was one of the first companies I worked with back in 2:16. And it was these two physics PhDs. They had done string theory, so they weren't even computer scientists. And they got an interest in deep learning because they saw like parallels with string theory. And they, it was exactly what you said. Arthey found the mathematics to be elegant and interesting, like that's really the origin. And so they started working on deep learning before anybody really. And they built this speech to text stuff, and it just like didn't really work that well for like a long time. And so like nobody really like paid much attention to this company. Wasn't famous. The founders, to their credit, just like kept working on it. And then when the voice agents took off, they all needed speech to text and text to speech. And most of them are actually using deep gram under the hood. And so they've just like exploded in the last couple of years. I mean.
speaker 1: I guess essentially the whole AI revolution is built on Elias would give a following his own curiosity for like a long .
speaker 3: period of time.
speaker 2: And more of that actually, this is maybe a met a point on this whole conversation. So we were at colleges, Diana and I went on this college tour, and we spent several weeks speaking to college students. And I realized that there's this piece of startup advice that became canon that I think is outdated. Back in the pre AI era, it was really hard to come up with good new startup ideas because like the idea space had been picked over for like 20 years. And so a lot of the startup advice that people would hear would be like, you you really need to like sell before you build. You have to do like detailed customer discovery and make sure that you've like found a real like new customer need .
speaker 3: was like the lean startup.
speaker 2: Is that lean startup?
speaker 3: Yeah salfast scale fast.
speaker 2: all this stuff. And that is still the advice that college students I think, are receiving for the most part because it became so dominant. But I would argue that in this new AI era, the right mental model is closer to what hard dset, which is just like use interesting technology, follow your own curiosity, figure out what's possible and like if you're if you're doing that, if you're living at the edge of the future, like pg said, and you're exploring the latest technology, like there's so many great startup ideas, you're very likely .
speaker 3: to just bump into one. I guess the reason why it could work extra well today is that you apply the right prompts and the right datset and a little bit of ingenuity, the right evals, a little bit of taste, and you can get like just magical output. And then that's still a secret, I think Yeah. I mean, you can tell it's still a secret because you could look at there are like hundreds of unicorns out there that still exist and that are doing great, you know, like growing year on year, have plenty of cash, all of that. But the number of them that are actually doing any sort of like transformation internally, it's not that many. Like a shocking few number of companies that are you know, 100 to thousand person startups that you know they're going na be great businesses, but that class of startup, like by and large, they are not entirely aware. Like there isn't a skunk works project in those things yet. Like you know the extent of it is maybe the CEO is playing around with it. Like maybe some of the engineers who are really forward thinking are doing things in their spare time with it. Maybe they're using windsurf reccursor for the first time. And it's like you look down and you're like, what year is it? Like it's a little bit like, Hey, you know, get on this. Like I think Bob McGrew came on our channel and he was just shocked. Like he was one of the guys as chief research officer, like building you know, building what became zero one and zero three and all these things. And then he releases it and like, who's using it? Like he expected this you know, crazy you know outpouring of like intelligence is too cheap to meor. This is amazing. And it's like actually like people are mainly just we're just still on our quarterly road map, unchanged from you know even a year ago. Yep, pretty wild. Hey, cool. I think that's all .
speaker 1: we have time for today. My main takeaway from this has been there's never a better time to build so many ideas possible today that one even possible a year ago. And the best way to find them is just follow your own kirosteine. Keep building. Thanks for watching. See you on the next show.