The Truth About Building AI Startups Today

how would you differentiate between an idea that could be a great foundation for a billion doll company and an idea that is likely to get run over by GPT 5 something that's boring might actually be an incredible business but why is that yeah let's talk about GPT rappers are people worried about giving these data sets to open AI all these AI agents are passing the touring test I mean this is why I think the chat interface is wrong you want to do something in AI like this is a good place to like look into big generational companies are getting built as we speak great startup ideas just lying on the ground you'd like trip over them this might actually be like a once- in a lifetime opportunity and I I think I actually agree what a time to be alive welcome to the very first episode of the light cone I'm Gary this is Jared Harge and Diana and we're group Partners at Y combinator and we get to work with some of the best Founders in the world Jared why are we calling it The Light cone well in special relativity the light cone is the path that light takes from a flash of light you can imagine a flash of light and it spreads out in a cone shape and in special relativity you think about it spreading out in a cone both in the future but also in the past and in this podcast we are here in the present but we are going to talk about both the past and future of technology so that's how we came up with the name and one of the things that we're all seeing is the encroachment of AI into almost every piece of uh Society at this point you know every business transaction every uh thing that we sort of use with computers uh suddenly a new burst of technology is sort of entering everything we're doing and we're seeing it in the startups that we're funding which is why we're so excited about it I think you know what what's the percentage of companies you've backed right now that have large language models I think for summer 23 was close to 50% of the batch and it's pretty interesting like I think a lot of people like see that number and they think oh YC must have funded so many AI companies because we have this thesis about Ai and like it's just easier to get into YC if you're an AI company because we just like love funding AI companies and it's funny to us because we know how that's not true and yet that's probably what like 90 that's probably how 90 plus per of people actually think YC Works how does Howes how's it actually work can we tell people like how it actually works I actually think it's interesting the smart Founders apply to us with what they want to work on and we fund the smart Founders like irrespective of what they want to work on actually and exactly and so the fact that half the batch is working on AI says something much more interesting than just the YC Partners think AI is cool it's an emergent phenomenon of what the the smart Founders want to work on right now is like where do they think there's the high beta to build the largest company and I think the most ambitious and smartest Founders are going after this because it's definitely I think the exciting thing about right now with AI I think it's like real there's been a lot of waves for AI and multiple AI Winters but this one actually gbt 3.5 and then four blew out of the water a lot of task and it impressed a lot of smart people when a lot of smart people start paying attention and building in this current idea mace I think big generational companies are getting built as we speak one thing I'm seeing that's interesting is I feel like a lot um a lot more Founders are dropping out of college to start working on AI because they don't there's a f off yeah there's like an actual like and usually it's so funny my my interview question is almost always like what's the rush like why do you want to drop out of college like why don't you just like graduate because it makes a lot more sense to graduate and then do a startup um and the reply is usually like well like this might actually be like a once in A- lifetime opportunity and I I think I actually agree and and the other cool thing is that this is an opportunity where college students are particularly well like young Founders are particularly well positioned to work in it because nobody has like like there's no one walking around with like four years of LM experience so like everyone is starting from the same playing field and so if you can learn fast you're going to be at the same level as everybody else that's right and you know one an area I've seen that come to play is like developer tools for prompt engineering I've been seeing like these sorts of tools are getting uptick it's like ability to like chain together different prompts and test your prompts and see like the second order effects um and actually a lot of college students are the people who are just like playing around with like prompting models and seeing what comes out and it's a really easy startup idea for them to like just build the tools that they want and like the tools that they want are literally setting like the standard for what every developer should want like I know a lot of the headlines are all around like AGI and all of the fancy stuff and then the really cool demos of like multimodal AI like AI generated video and and this kind of stuff the stuff that I've seen in the batches actually taking off is a little bit more mundane like it's um I probably say a lot of it sort like workflow automation like um it's finding things where there was like a human doing some repetitive task usually involved like searching for things or filling out forms and then using like llms to replace that it feels very obvious to us the people who work at YC that this is an amazing opportunity there's so many jobs in the world that are basically very mundane information processing typically stuff that's hidden in some back office somewhere where there's somebody who's just like reading stuff and summarizing it re-entering it from one system into a different system and like a slightly different format and it's such a perfect fit for llms LMS are like perfect for this job and yet we actually don't get that many applications for people working on this and there's a lot of Founders out there who are searching for a great idea so if you're out there and you're looking for a great startup idea and you want to do something in AI like this is a good place to like look into I give you an example so last patch had a company I worked with called sweet spot and we funded them the idea was something about like food ordering from food trucks something like random and they pivoted immediately looking for a new idea and the idea they found was um using llms to automate searching for government contracts to bid on and God such a good idea yeah and submitting the proposals that sounds so boring what could be more boring than searching through like a list of all the government contracts you know how they found it is um exploring startup ideas and then they realized one of their friends his job was to work for one of these like government contractors and his whole day was just spent like refreshing this government website um to like find things and then submitted proposals and they're like what like that's like exactly that that's so boring like wouldn't you like a tool that did this for you yeah and they launched and like pretty much straight out of the gate got like um a pretty decent amount of traction because they're like opening up um the people who who would actually do it like it becomes easier to like find government contracts to bid on when it's all automated away and like software does it for you you know obviously we all know that you know something that's boring is actually kind of awesome but why is that that's like you know just off the bat you know we have a sense that something that's boring might actually be an incredible business there's an old PG essay where he talks about this and he says um he he quotes a phrase where there's muck there's brass it's like it's as it's almost like Old English you want to explain it har just means like you can find treasure in surprising places yeah and I think the cool thing is you have to go deep and vertical and solve a very concrete problem like some of the problems with let's maybe talk about AI tarpits what a tarpet idea is is it's an idea that from the outside looks really shiny and attractive it looks like a great startup idea and so lots of Founders go and they start working on it and then you realize once you're in it that it's actually not a good startup idea but but by the time you're there you're like stuck in it and so it just attracts founder after founder and they just get stuck in the tarpet idea and we see this a lot at YC because we see all these applications and so it's really obvious to us when like 500 people apply to a YC bat for the same idea but they don't know that 499 other Founders are also stuck in the same tarpet what's tricky I think about topet ideas for AI is like we know something's that top it idea in hindsight once like enough people have been stuck in it so with AI it's so new we don't know yet so I have a couple that I'm actually like Keen to get your's thoughts on um a very common one is AI co-pilot so it's like hey I'm going to make it easy for um people to like build an AI co-pilot for their product or or service it's it's really unusual type of phenomenon where there's so much interest from potential customers to like want a co-pilot that it's actually quite easy to start getting getting like inbound leads if you pitch this and if it's even easy to get people to pay you money up front but what's really hard is to get them to actually like use the co-pilot because they don't actually know what they want it for like they just heard that AI co-pilots might be changing the feature of software so we should have an AI co-pilot but they don't actually know what their customers will use it for I think for me and maybe I just have a uh a mental block around chat interfaces but I've never been that big a fan of chat because it puts so much of the emphasis on the user knowing how to speak to a computer and you know while in the next five or 10 years I think we will all get far more used to using it that way um I think the the lwh hanging fruit right now is just using the large language model to actually do the sort of knowledge work that a human being could do and then package it into the UI that you know whether it's a mobile app or a web app that is just familiar like sort of what people use to do their work right now and it's you know basically the llm is better used as sort of this like I I mean it's almost like you know this thing that's sprinkled in that you know the software suddenly does something really powerful but you don't have to change the way you would want to use the software as it is sort of like a an example of a phenomenon that like I I think we have seen in the past when like some technology gets really hot and all of a sudden like all these companies are like they're being asked by people like what's our AI strategy they're like oh well we better get an AI strategy or like with crypto there was like oh everybody needed a blockchain strategy and even before that it was like everybody needed a mobile strategy for a moment in time it's like easy to sell them something that like placates their desire to check some box but in the end you've got to actually make it successful for them like otherwise it's not going to stick I agree and so like perhaps with this AI co-pilot thing like maybe it's too early to call like perhaps they actually will find product Market fit maybe with something that's not a chap out UI like they'll like keep iterating on the UI until they find something that's an AI co-pilot people actually want or maybe it's just going to like fizzle it just like turns out most people don't need an AI co-pilot some of the advice I've been giving those those specific companies is the another old PG essay about if you if you're trying to sell technology to someone and they're not buying like see if you can just build a competitor and so it's like hey if you're trying to sell like um uh fintech company a co-pilot and they're not buying it well like if you are convinced they should have a co-pilot like why don't you just like build the company with the co-pilot as the main experience and see if you can out compete them or not I like that that I like that I think getting people to focus on the use case I think the problem is the whole thing with um kind of the Gold Rush people selling more the shovels and the tools and even then in this case it is a bit of that but a lot of people aren't digging gold yet like the reality is this is such a new technology and even the end applications that apply AI the reality is there so early they don't have product Market fits so it's sort of bit of a the blind leading the blind in here it's like what do I even know what the pattern is for copilot I mean it sounds cool just to join the cool kid Club of we're doing Ai and we're going to check mark So I think that's the danger for a lot of these uh startup it's like it seems that they're getting traction as you mentioned but then when you we poke them closer is anyone actually using you what are the actual use case and then the founders come back and they startare a blank at us oh but look at all the sign up look at the revenue but then they're not really using your product I mean we're seeing even the second order effects right so a bunch of us are funding uh Dev tools companies that sell to AI companies and they're selling tooling but then they might you know they might sell an Enterprise contract to someone who also Upstream has a Fortune 00 that said that they'd pay $100,000 a year for that contract and then 6 to n months later that you know Fortune 100 went back to the incumbent uh you know some other leading you know IBM Salesforce like something like that um because they ended up adding large language model technology to what they they were doing and people just switched back and suddenly the dev Tool Company suddenly realizes oh I had five contracts but three of them went away because my customer actually their customer so it's actually like sort of remarkable how fast this is evolving you know right now in 2024 a specific type of idea I'm curious to get thoughts on here as well is um offering like fine-tuning open source models sort of as a as a service broadly like that's a very popular idea I think over the course of 2023 here's what I've seen so like why do people want like why is there any demand for a fine-tuned like open source model at all um it tends to be initially I think the Big Driver was cost like open AI like chat GPT was expensive and people wanted a um cheaper version of it and so I think it was very easy to get customers with the pitch of hey like we can f tune an open source model and it's just going to be much cheaper what I think a bunch of the companies in space are seeing is that like that's not enough to keep the customers especially because like open a like the cost of all of the models just going down and that's going to keep happening with the open AI has a plan for all of those so there's something more that all these fine-tuning companies need to do yeah it has be better not just cheaper I think where is exactly that where I think is having more legs is when these companies need to customize it to private data sets so you have the open General big foundation model but then you have to tune it up to specific data sets that for example a healthcare or fintech can't give out can give out and they don't have the team of um experts to do it so I think the one company that I think Brad worked with was credle that kind of was doing that what are you seeing about like so the concern around data privacy is another big reason like are you seeing that as being enough like are people worried about giving these data sets to open AI it's really interesting I mean whenever you have something so new like this it's actually um sort of resets the clock on the competitive landscape again so you know you almost can expect all the same things will happen again um you know just as 10 15 years ago Cloud was brand new and then you had Cloud cyber security and Cloud strike and all these companies sort of come out um you know we're seeing the first wave of cyber security companies you're like prompt armor so they sort of wrap your API calls and uh what they actually have figured out is that for a lot of large language models if you do any sort of fine-tuning or training with private data you can actually just speak to the model and get it to spit out your private data again and they have a solution that stops IT so it's so interesting because you know it's entirely possible you know they're basically creating a new industry again um of cyber security for llms sort of in the same way that cloud opened up that space and created cyber security for the cloud yeah I definitely think that whole world of controlling within an Enterprise in particular like controlling who has access to like which llm has access to like what data and who has permission is like a really ripe space for building interesting software I think the other exciting area that a lot of the tools are getting built is getting more this is like a step further fine-tuning but more purpose trained models that are smaller so take a for instance a llama and getting those to run locally in machines for inference and when you customize some train on a specific domain and Target data is going to perform better than the general model The General model was kind of trained on all of the human language for all of the task but if you wanted to build like the best let's say um language model for parsing SQL queries you would then parse very specifically just a set for SQL quer and I think some of those that are interesting companies that we funded is like AMA that you funded that's trying to make the development process for running all of these locally a lot faster and I think we're also funding some of these that are custom for coding the thing that was surprised learning from some of the startups that are building um coder type of uh co- Pilots which I think is is a use case that's working out making a lot of the workflow for programming a lot faster it's kind of like autocomplete and co-pilot type of thing they're training on older models of a GPT they don't even need the newest one and then I asked like why is that and even for like one of the companies who funded last batch metalware for Hardware they're not using the stateof the AR model like the older GPT I forget which one was like the older 2.5 or three was sufficient and actually creating good enough results because the vocabulary for a specific domain for Hardware or software is a lot smaller than the human language so this is other world where the open model that's customized I think is going to win and compete versus the big one for specific domains so there lots of companies with this yeah that's what uh Toby loty from uh shop actually still dabbles with the stuff I think he actually built the uh internal co-pilot for Shopify and what he was saying is the best way to use whatever gp4 or the you know latest Clos Source models that are most expensive and have the most parameters uh just think of it as a prototyping tool anything you do with those prompts you can get your own model to do with a little bit more training it's kind of like uh when people build Hardware you have the analogy of uh prototyping with fpga which are very expensive right and then when you have the right architecture for Hardware then you do the circuit path and actually do the custom s so so right now for some of these tasks the large language model is sort of like your fpga whatever GPT 4 and then when you customize it you do like the super efficient one coding path for I don't know Shopify for coding assistance and Hardware software Etc that becomes your so that you train and customize which is cool I think that patterns emerging it's like as I hear you talk about that what's I just think it's just like so many different startups that could be built it just feels like we've never had this moment at least I didn't feel like I've never experienced a moment where there's just so many potential startup ideas to be built like all that ones yeah there there absolutely hasn't in we we definitely saw this in the last batch with all the pivoting companies oh yes people don't always realize this but like many of the companies get into YC within a month after we fund them they're looking for a new idea cuz the old thing didn't didn't work or they lost interest in it or something and it's normally like not actually that easy to find a great startup idea for a team to work on but man was it easy last summer God it was just just like great startup ideas just lying on the ground you'd like trip over them yeah that was a fast I think you actually had a tweet about it that was one pretty uh viral that talked about this is the batch the batch ever in your whole career working at YC where Founders got to good ideas the fastest ever and hard has been here even even longer yeah know it definitely feels unique I've never had so many successful pivots yeah and Gary to your point about the chat gbt rapper I think back like I feel like that Meme really came out like just about a year ago yeah let's talk about GPT rappers yeah like like I feel like the first sort of group of ideas I saw in the batch were all generative AI ideas built on Chop top of chat gbt so was stuff like hey like automate your marketing copy or automate like your creative content or something like that and that term got thrown out oh these things are all just like rappers on top of chat GPT and um open AI is going to like take all of like it's just going to build all of these things and they were going to release their App Store and like it's just going to take all the value and these things will die of the mem all of all of SAS software is just my sequel rappers exactly I think this is a great analogy you can think about any SAS product as basically a database rapper like you could imagine like negging any SAS product CU like the first version of a sass prod it's basically just a crud app and just like you took like my SQL then you like built like a website on top of it and I think people are going to look back on this term GPT GPT rapper like similarly how we think of like how we would look at the term database rapper which just seems like silly I mean this is why I think the chat interface is wrong like I actually think there is value acur to really great ux like good copy good um you know interaction design information hierarchy uh you know being able to approach a product and say like this is the job to be done and for for users to come in just sort of naturally understand what to do like there is a craft to building software that is timeless and that sort of transcends whether or not you're using a large language model and so you know that that I think is what I mean by you know these things are not you know SAS software is not uh a MySQL rapper well here'd be a question I'd be interested in in in everyone's thoughts on suppose you're a new founder and you really want to build a company and you want to do something on top of LMS how would you differentiate between an idea that could be a great foundation for a billion dollar company and an idea that is likely to get run over by gbt 5 and is probably like not a good starting point I think if a Founder is working on something too General and not solving a specific need for a user they can actually go talk to another use case so I I worry about the ones that are too generic generic and building going after some kind of abstract it will solve all the things yeah if it's like hey like throw your data in here and we'll do like automations on top of it like for everything that's probably hard to compete with whatever one of the foundation models might offer but if it's like hey we are give us like your sales log data and will like um spit back like suggested next actions like you can like for sales people to make them better at sales that's probably going to work better or give us all your compliance checklist to pass Hippa compliance and process that it's like that's very specific and lots of business logic or give us all of your data for processing government forms right yeah so a lot of custom business logic so the same thing with the SAS era a lot of the applications and how you build applications in there there's always the separation business logic and they crow in a lot of architectures for these app and a lot of the value of the company is accured on that business logic that is so custom per company and there's a whole pattern of uh programming patterns on how people separate those yeah gu as this all goes multimodal this is going to get really interesting so early days but yeah we've seen companies work on voice AI apps to be like a sales rep and I think um it's an interesting example of the kinds of ideas that might be possible now with AI is where you take something like a Salesforce and you try and reimagine like what would Salesforce do if it were started today with all the power of AI what it almost certainly do more than just be like a CRM right like it would make like it would find who your leads might be like maybe now it can make the calls for you it could like set them up like maybe it goes all the way to start like implementing like the first version of the product for them like I think it's just like the scope of software you can build with AI now is so big I think that's another good way to find ideas like look at software today and reimagine it with the power of AI today which you funded a number of companies that effectively are AI voice agents for small businesses because they receive I don't know if you're like a flower shop or a AC repair man in the middle of U the US there's a lot of calls for you to schedule and you don't have a lot of stuff automated and there's these YC companies that are using that building these AI voice agents to basically be the receptionist I know one of our partners Paul buight is quite worried about this actually he's worried about there's going to be a world of just s like all these AI agents that are out trying to do malicious things and that we're going to need like our own like good defensive AI agents out there making sure we don't get scammed out of all of our money I mean this is actually why I'm so uh an advocate for open source AI because these things are sort of real considerations um you know can you imagine there only being one hyperd dominant AGI and it's totally close Source it's owned by one company and uh you know it's only available to the highest bidder and uh you know imagine you being uh you know someone who just had to go to the doctor and uh on the other end of it is uh some health insurance company that uh you know bought the bought access and blocked it out from everyone else and you know you getting on the phone you're not able to sort of navigate or go against the sort of you know impenetrable AGI that is able to sort of get around anything that you know your side might throw at it like we actually want you know some form of actually Equity at the AI level like we actually want uh you know not merely the biggest companies to own the most capable AIS we want all consumers to be able to have from the bottom up uh the same access to that same technology and that's uh you know the best insurance against tyranny certain that's actually what a lot of uh also not just Founders but smartest researchers who are really at The Cutting Edge is I went to near IPS this past December which was incredible to see the energy in there the conference has grown so much I think it like over 10,000 attendees there were 3,000 papers more than 3,000 papers accepted and I think um back in 2017 there was only around 600 papers when I went back in 2010 it was was just in a ski lodge and maybe like a 100 papers it's crazy the kind of exponential growth and one of the big topics of Interest was a lot around AI ethics and Regulation and how do we measure that so that that was interesting um but the thing that's different about typically that was interesting in this conference is the amount of interest from researchers wanting to start companies too one interesting data point is um a lot of this era with GPT came about from from One Foundation paper is all attention you can need it was this paper that got released got launched in a New York IPS back in 2017 it was a team at Google who was trying to figure out how to make a machine translation between languages more cheap because the English translation to any language was actually pretty good but if you wanted to do I don't know German to Japanese there was not enough data so they figur out this way to compress data which became the Transformer models for GPT and it was like groundbreaking and this is the foundation for llms that paper came out in 2017 and the fun fact I was just looking this up out of all those author eight authors seven of them start at different companies and all of the companies in total their rate their worth valuation more than six billion and now people are seeing oh these like industry Pioneers did this and it's creating this new crop of I think Founders that I don't think would have started because I talked to a lot of AI researchers and I don't think they wanted to be Founders and I got a l this question how can I turn my paper into a company which I think is cool because this is like going back to the root of um why I F funding hardcore technical Founders and I think it's cool to see that energy there so when we went and host our event we uh I didn't plan and it was like 3x over subscribed nice standing room only huh yeah yeah it's that sounds like really the new Homebrew Computer Club so NPS in December yeah we got to mark it on the calendar we'll come back yep Diana I love your point about how this is sort of like returning YC to its roots it definitely felt that way last summer because when YC got started the internet was really new and the people who were building stuff on the internet were mostly technologist because actually like pretty hard to build websites back then and pretty hard to build like good software and like as building software and building websites got commoditized a lot more people came into the space and this is a cool reversion back to the like Origins where like the people who are building the most interesting stuff are like mostly really hardcore like researchers and technologists because there's actually real new technology being invented it's not just like innovating on business models with like commoditized technology and again just like every great technology it's being dismissed right so going back to like the chat gbt rapper meme I actually think that was great for YC because it meant we only got the people who are like tune who could tune that out and we just like hey like either I'm just so interested in this technology I don't care like what the memes are or I'm just too busy building it to pay attention to the meme on Twitter which is also great but like I feel like this has always been the case right like Homebrew Computer Club like PCS are like dismissed as like toys like the internet is dismissed as a toy like all all of these things so feels like that moment again yeah there is a a class essay that I love that I saw off Hacker News do you guys remember this it's Geeks mops and sociopaths in a subculture Evolution and you know I think that that actually is the one thing that's quite durable and like keeps returning right it's always the Geeks Who are going to be into the tech no matter what they're on The Cutting Edge you know uh I always think of Steve wnc talking about like you know we started Apple computer with no idea that it would ever be a company like we just wanted computers for ourselves and our friends and so you know at some point the you know sociopaths come along and they start sort of uh monetizing the people who you know come to the scene and then the cycle returns and repeats so that's why I like being at the beginning of a new cycle and clearly AI is exactly that so don't don't count it out don't write it off it's one of the most interesting things that are is happening out there um but you know there are clearly things to be careful of like don't be uh attracted to the new shiny thing uh instead look for the muck because where there's muuk there's brass so that might be a great place to call it for the very first episode of the light cone we'll see you next time