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. If you're living at the edge of the future and you're exploring the latest technology, like there's so 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 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 2.5 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. Harj, what are some of the things you're seeing? Well, one thing I've been 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 startups since i ran a recruiting startup triple byte 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... excitement around those ideas back then was this idea of applying marketplace models to recruiting because there were marketplaces for everything except how to hire great people and specifically great engineers and we started triple byte with 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 we'd effectively 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.
Initially, it was actually a three-sided marketplace in that you needed to hire an interviewer in between to get that human signal. We had companies hiring engineers. We had the engineers looking for jobs. And then we had engineers we contracted to... interview the engineers um so there's like lots of things going on right now um and all of the evaluation piece of at least now with ai is very very possible i mean we specifically with the ai cogen models you can do code evaluation um and i think
probably one of the hot ai startups at the moment is a company called mercore which is essentially similar to the triple white idea it's a marketplace for hiring software engineers um but i think what ai has unlocked for them is the evaluation piece of it they could just do on day one using llms they didn't need to build up this big
label data set and they've been able to expand into other types of knowledge work um 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 know 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 um is a really interesting space that's much more exciting to find good startup ideas in now
than it was five years ago. 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 going 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 will LLMs do in that marketplace? I think the other thing I really respect the Merkel founders for is there's 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. This was definitely with recruiting startups. I mean, TripleByte raised 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 going to go out and pitch investors for an idea even when you have that 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 who have lost lots of money on an idea to even kind of keep going to test it out and and make it work that's something that repeats actually all the time i mean instacart was that story exactly like web van was sort of this
rotting 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. There's going to be more. But simultaneously, the iPhone and Android phones were everywhere. and you could have a mobile marketplace for the first time.
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 maze have shifted around and uh the only way to find out is you've got to actually be in the
It 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 enabled like the web van model to actually work for the first time and like it's the same thing with lms and recruiting companies now and a whole bunch of other ideas i think it makes uh focusing even on
parts of the marketplace to be great ideas to start with. Even with this recruiting idea space, this is a company called Apriora that Nico, 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 pass rate is so tiny. When I used to...
run engineering teams at Niantic, all that pre-screening was just so much work. The engineers hate doing it. Yeah. And even that one piece, not exactly, let's say, marketplace of what is the hardest part of it. And if you solve it right now, it works out. So Aprior actually does a pretty good job. It's being used by large companies.
and it's been taking off it's another example we can actually expand the market because i think the there are plenty of technical screening products pre a priori 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 a priori 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 like senior engineers who are applying which is opens up the opportunity. So you were talking a bit about education as well, Gary, about Duolingo. I think that aspect of doing hyper-personalization is one of the holy grails.
where it has been difficult for ed 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 that we did uh that harj did an rfs for right yeah the thing i'm excited about is for as long as i can remember the internet's been around like one of the like
um dreams of it was that everyone now have access to like personalized learning and knowledge and um we'd all just um 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 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 building that.
type of product. A couple of companies that we funded that are kind of working out is this other company that also Nico funded called Revision Dojo. that helps students do exam prep and is sort of the version of flashcards, but not like the janky, just like boring going through content, but the version that actually students like and gets tailored for their journey.
And that one has 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 Adexia as well? Yeah, Adexia. does tools for teachers to grade their assignments which is another example of work that like is like not people's main job but is 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 Adaxia is an agent that's very good at helping teachers to grade assignments. 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, actually. I guess a question for you, actually, Gary, I'm curious about this stuff, is it's...
clearly possible to build much better products with llms if we take the the learning apps for example they can go far beyond anything you could do for personalized learning pre-llms um but it doesn't necessarily mean that
you instantly get more distribution, especially if you're going after the consumer market. 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 to be... big companies as before i guess one of the more awkward things that's still true is that um 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 distillation from bigger models to smaller models is working. It seems clear that the mega giant models are teaching even the production model size. today to be smarter. The cost of intelligence is coming down quite significantly. So 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 10 or 15 cents like then it becomes so cheap that you will just have intelligence for free
Maybe it'll be 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, you know, five or 10% of those users, there are things that they so want that, you know. you're going to sell them a $5 or $10 or $20 a month subscription. That's basically what OpenAI is doing, right? Perplexity does it, OpenAI.
You know, going back to education study with 2Ds, 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. can 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 coming out across all the different things you'll need. And that's something that I know we keep saying it, like it's going to happen. I mean, it's kind of happening already for... edtech speak it's this company that got started a couple of years ago before llms were a thing at all it was a team of researchers that really believed that you could personalize
personalized language learning, which might have been a bit contrarian back then because Duolingo seemed to be the game in town that was winning. And they really focused on really personalizing that whole language learning. And they... they started taking off in Korea for a lot of learners that were trying to learn English.
And when GPT-3 and 3.5, the early adopters of it, started coming out, they saw that, wow, this is going to be the moment. They doubled down and they've been on this trajectory now with lots of...
MAUs, EAUs, that's really working out. I think one thing to go back to the consumer 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.
something like that they'll just pay way way more so the same thing will apply in consumer right like if you think about a personalized learning app uh often ed tech companies struggle with who's actually the buyer and who's going to pay and
if you go for like younger children for example so 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 12 year old parents will pay a lot more for that and so those like
It's possible that the product now 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 100,000 parents using it, paying you a significant amount. means you now have like a much bigger business than was possible before. Yeah. I feel like we have to talk about moats 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.
What you need is brand. You need switching costs. Sometimes it's integration with other technologies that are sort of surrounding that experience. A school, it would probably be being connected to Clever, for instance, like login. Authentication is pretty obvious. So, yeah, I feel like Sam Altman has talked about this a bunch. You know, 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 we want that too. they did just hire like
the Instacart CEO as their CEO application. So it does kind of seem like they are definitely paying more attention to the application layer. That's right. Yeah, I mean, you'd be crazy not to, right? Like, by all accounts, OpenAI is highly likely to be a trillion-dollar company. at some point.
you know as powerful as a google or an apple or um any of them the interesting thing right now is like they're still on the come up and then if anything um 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? 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, 20, 30 years ago, there were 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. 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. 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 the government did get involved in and said, hey, you cannot self-preference in this way. If you remember the moment where Internet Explorer had a majority of web users, 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 to use a very, very dumb Siri. On the moat topic, something I just find fascinating is I saw some numbers recently about how Gemini Pro models, like just they... usage particularly from consumers it's just incident infraction of chat gpt's and i think at yc we've been doing our own internal work building agents and um 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 o3
for various tasks that hasn't trickled down into public awareness yet right which is fascinating since google already has all the users right with their with their phones and i don't think anyone would say open ai is not a startup anymore but relative to google it essentially 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. And I feel like- And actually making it good. Yeah, yeah, yeah. But at some point, maybe it doesn't even necessarily need to be like- objectively the best it just needs to be good enough
I mean, that's the bet that I think a lot of the big tech companies are trying and failing at. I mean, Microsoft has a co-pilot built into Windows now that is still quite inferior to anything OpenAI puts out. 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 are not. They're totally useless. Yeah. It's like, is there someone at the wheel over there? 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 orgs. I think it's suffering a little bit from being too big of a company and essentially shipping the org. There's like these two APIs you can consume to use Gemini. And we're like, why two? One is from DeepMind and the other one is from GCP.
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 you guys just fight it out and so then you ship the org i think the crazy thing about google they probably should have won a lot of the experience of the best model. There's almost like...
I don't know where all this Game of Thrones analogy could be used. They might be a little bit like Daenerys Targaryen because they secretly have dragons. The dragons are the TPUs. 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 is it actually the hardware like it's just like you can't actually do it without i think you can do it but i think it's just very expensive and not cost effective but i think they've done it so well and they got tpus
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 around there too. Those are just classic innovators dilemma. It's like if Google replaced Google.com. With Gemini Pro, it would instantly presumably be the number one chatbot LLM service in the world. But it would give up 80% of its revenue. Yeah. You would probably need a pretty.
strong founder CEO to do that. It's the kind of thing I can imagine Zuck doing. You can't imagine a hired CEO is going to do that. He's done that. He renamed the company to Meta. Yeah, Meta has its own issues too. Like, I'm so surprised, you know, I mean, you have Meta's AI and WhatsApp, it's in the blue app, it's everywhere, but... I mean, who actually uses it? I don't think any of us. I started using the meta AI in WhatsApp. It's very classic.
It makes me feel like Zuck is clearly still in charge of product because I don't think anyone else would launch it that way. Now you have an AI system that's just in all of your chats and you can just at it.
And they all just start talking in a group chat. And it feels quite invasive, actually. Well, it's not that smart. And then it can't do anything. Yeah. And then, I mean, most people are surprised that it's in there. It feels like having someone from Facebook just in your... and uh it's just like it's i don't know it reminded me of like the original newsfeed launch or something it's just like the classic meta style of like this is sort of
I don't know, objectively optimal, like I'm sure people will love it. You need to add 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 are like, what the heck's the blue app? This is like Facebook.com, which maybe nobody uses anymore. It's 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. What is the point of this? Our partner, Pete Koeman, 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.
um it's almost like he was a pm at google oh wait he yeah he was a pm at google it was very profound um 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 Venkatesh Rao's idea of like sort of the API line. It's sort of like the system prompt is actually what is exerted.
it applies 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 and that i think he had to vibe code the blog post itself because you can actually try the prompts yourself on that web page yeah it's super cool it's like a it's in this like interactive yeah template language. Which made me think it's time to start an AI first vibe coding blog platform. Oh, like an AI posturist? Yeah, basically. Is it time for posturist 2.0? 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. There's another class of startup ideas that I'm particularly excited about that I think are like perhaps the time is now, which is. Do you guys remember the tech-enabled services wave? Yep. Yeah. So for folks who...
didn't follow this, in the 2010s, there was this huge boom in companies called tech-enabled services. TripleByte was one, actually. Yeah, very much. That was like tech-enabled services for recruiting. We also had Atrium, which was tech-enabled services for... law firms it started with balaji's blog post about full stack startups you remember like the concept was just that um
software eats the world means software just kind of goes into the real world and so this is not 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 um and that like the full stack startups in theory would be more valuable than just the software startups because they would do everything yeah 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 this is exactly what triple i was because we were like we're going to be a recruiting agency effectively we're not selling 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. But that wave of startups generally forgot that you need gross margin. What happened? 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 SaaS companies. And the SaaS companies sort of like won that round of the like Darwinian competition of different business models. I think fundamentally it's just what Gary.
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 i at least in tripwise situation we actually got to like 20 million dollar annual run rate 24 million dollar and run rate within a few years so like if you compare
to like a regular recruiting agency it was like super fast but um if you compare this to like the top software startups not that like um impressive and it became harder and harder to scale because you had more and more people yeah yeah basically like the margins
work out particularly well and so they need to keep raising more capital and so if you were like a fearsomely 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 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
You could argue Zenefits 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 to do that again.
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. You cannot and should not sell $20 bills for $10 because you're going to lose everything. I think a non-financial reason why the...
gross margins matter is um 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 triple i experience there's like a lot of brain power spent on like how do we like manage this team of like contracted engineers
the team of like humans looking after the like essentially the human recruiting team like lots of pieces of the business where actually the existential 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 in i.e like how do you just get lots of distribution and i think something that's nice about a high gross margin business is 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 you can keep that like exponential growth for a decade and i think a lot of full-stack startups partly plateaued out because it's just the complex businesses to run Maybe a very famous example of that was like WeWork, 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?
It had community adjusted EBITDA, which was very creative. What I've been 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 saying like the triple by 2.0s won't have to hire this huge ops team and have bad gross margins they'll just have agents that do all the work and so like now actually like full stack companies can
look like software companies under the hood for the first time. And you gave a great example. So Atrium, started by Justin Kahn, full-stack law firm, didn't work out for, I think, a lot of these same reasons. 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 we have lagora which is like this like
one of the fastest growing companies we've ever funded um and it's not building a law firm but they're essentially um you know building ai tools for lawyers speaking see where that's going to extend out to you right like eventually the agents are just going to do all of the legal work and they'll be the biggest law firm on the planet um and yeah i think that's a kind of full stack
startup that just wasn't possible pre-LLM. I think this started right when Uber and Lyft 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 RFSs. 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.
virtual assistant marketplaces was definitely like a whole category of companies for like 15 years including exec where you build like a marketplace of like people in the philippines and like other other countries and then you like can expose to sort of like airbnb i don't think any of them ever like really got really became like amazing businesses though
Going back to Pete's post, I think the other thing that's interesting about the points he made around sort of the system prompt and 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 and just... using ai's and building agents there's all this like tooling and infrastructure like still to build you have to do evals 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 yc 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 people like like another
MLOps team, they sort of never go anywhere. Clearly, if you were working on MLOps in 2020 and you just stuck it out for a few years, you're in the right spot. Any context you 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 I think that was the core problem is that these people were building ML tooling, but there was no one to sell it to because the ML didn't actually work. So there just wasn't anything useful that you could build with all this ML tooling. People didn't want it yet.
I mean, directionally, it was absolutely correct. Like from a sci-fi level on a 10-year basis, it was beyond correct. Yes. It was just wrong for that moment. 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. 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
And then it just exploded overnight. Olam is another good example. Oh, yeah. Do you want to talk about Olamah? So the Olamah folks were also from that pandemic era. and 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 Llama got released. That was like the easiest way for any developer to run open source models locally. And it took off because suddenly the interest to run models locally.
just took off when things 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 BERT models, those were like the... more used deep learning models they were like just okay but not many people were using them because they 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 startup advice of follow your own curiosity. Like most of these teams, 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 in trying to pick the best startup idea they could.
possibly work on, but I know sometimes you get lucky. There's so many ways to do it. I mean, we were just sitting with Varun from Windsurf, and he pivoted out of MLOps into CodeGen. Deepgram is another one. Deepgram was one of the first companies I worked with back in...
2016. And it was these two physics PhDs. They had done string theory, so they weren't even computer scientists. And they got interested in deep learning because they saw parallels with string theory. And it was exactly what you said, Harge. They found the math.
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 to this company it wasn't famous the founders to their credit just like kept working on it and then when the voice agents
off they all needed speech to text and text to speech and um most of them are actually using deepgram under the hood and so they've just like exploded in the last couple of years i mean i guess essentially the whole ai revolution is built on following his own curiosity for like a long period of time more of that actually this is maybe a meta point on this whole conversation so um we were at colleges
Diane 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 the bat like the idea space had been picked over for like 20 years and so a lot of this
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. It was like the lean startup. It was a lean startup. Yeah, exactly. Fail fast. Fail fast, 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 that the right mental model is closer to what hard set, 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 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 data set and a little bit of ingenuity, the right evals, a little bit of taste.
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, the 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 hundred to thousand person startups that you know they're going to be great businesses but that class of startup like by and large they are not entirely aware like there isn't a skunkworks project in those things yet yeah like you know the extent of it is um
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 Windsor for cursor for the first time. And it's like, you look down and you're like, what year is it? It's a little bit like, hey, get on this. I think Bob McGrew came on our channel and he was just shocked. He was one of the guys as chief research officer.
building you know building what became 01 and 03 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 meter this is amazing and it's like actually like people are mainly just we're just still on our quarterly roadmap unchanged from you know even a year ago
yep pretty wild okay cool i think that's all we have time for today my main takeaway from this has been there's never a better time to build so many ideas are possible today that weren't even possible a year ago and the best way to find them is just follow your own curiosity keep building See you on the next show.