How To Get AI Startup Ideas

I have news for you guys YC is throwing our first ever AI startup School in San Francisco on June 16th and 17th Elon Musk Sacha nadela Sam Alman Andre karpathy Andrew Ang and F Fe Lee are just a few of those confirmed the world's top AI experts and Founders who will teach you how to build the future it's a free conference just for computer science grad students undergrads and new grads in Ai and AI research and will even cover your travel to SF but you have to apply and space is limited Link in the description

to apply for a spot now on to the video right now if you're building a startup working on like Cutting Edge AI even if you haven't find the right idea yet why give up and go back to Google or college or something there's a high probability that your lucky break is just around the corner yeah and this is kind of the most exciting thing you could be doing with your time something's happening I'm not happy with that let me go all the way to the edge let me go into you know this outside world and

uh from first principles understand the root cause of this and then uh you're going to discover all kinds of things that software and especially AI the current form of AI can actually solve there is um a very important question that all founders have to ask when they commit to an idea and that is uh if not us then who [Music] welcome back to another episode of the light cone I'm Gary this is Jared Diana and Harge and collectively we have funded companies worth hundreds of billions of dollars often with just an idea and that's what

we're going to talk about today there are a lot of smart technical people out there right now who are following along with this AI stuff they see incredible potential in the technology they totally buy that this is a special time to start a company and thing that is holding them back from doing so is they just don't have an idea that they're really excited to go and work on I think we should just basically open- Source all the tricks that we've learned that we've so far only discussed in office hours but we should just like

tell everyone and hopefully this actually helps some people come up with great startup ideas well one of the blueprints that seems to be emerging is that you can't just stay too close to where you're at like basically the default uh bad startup idea is lazy and that it's almost like a hackathon idea idea like I read about it on X and you know a bunch of people are doing it and you know why don't I do that um another version of it is just figuring out what is hot out there and jumping on a bandwagon

Jared you know one of the things you caution Founders uh against is actually just maybe running with their hackathon idea yeah often when people are thinking about startup ideas they tend to gravitate towards things that seem very easy to build the first version of um but most of the best startup ideas are actually like at least somewhat hard to actually ship the first version of and so that would sort of be my metal lesson from doing this individually with Founders is I'm always trying to push them in the direction of like harder more ambitious ideas

and their subconscious is trying to push them back in the direction of things that they can build in like a weekend I guess the interesting thing about it is uh you need to get out of the house so rather than just do what is right in front of you uh you have to either aggressively introspect and look within into your history and what you are uniquely great at or you need to aggressively get out of the house into other places uh on the outside being industry being uh government being other places that serve Humanity in

some way that and that's not in your house either actually so aggressively you know internal or aggressively outside um so why don't we make that real by diving as deeply as we can into some specific examples what is within us already there are lots of examples of people who already spent years and years to get all the way to the edge of understanding whether it's AI or some other field uh in the world you know and they did it either in their studies in their research or where they worked Diana I feel like you have

a few really good examples Yeah so basically the examples here are of Founders who had this very unique experience in Prior jobs they were working at and one of them is this company that I mentioned before called Salient they're basically building a AI voice agent that does uh loan processing for auto Deb collection that is a bit of an esoteric idea and when I was working with the founders it took a bit of time to land on a good one and this turned out to be a good one when I heard that AR the founder

learned about it because he used to work at Tesla and as part of being in the Tesla Finance Ops Team part of the problem with leasing Teslas was this whole process of getting all the payments back and it was all done very manually with all these business operation units that were outsourced and he thought oh why not build a AI agent for that so that was really good and that ended up working really well and now they're servicing a bunch of large Banks that's a good one another one is uh which I also mentioned previously

in another episode is the founders of diode computer they're basically building the AI circuit board co-pilot and their Insight is that both of the founders were electrical engineers but also software Engineers so that turned out to be a very unique Gap intersection of skills that has not been is not that the world of software and Hardware don't tend to talk to each other so they had built circuits at Apple at startups and even custom processors so they had a lot of exper experience building very highend electronics and they saw the Gap and frustration of working

with Hardware Engineers that why didn't they do things like software why didn't the world of git exist why in the world of I I have to power through all these data sheet to verify all the components manually that's the electrical engineer job why do I have to do that manually why not just get a lm2 parts and do all that verification like in code with like QA right so that was the insight and that was so unique to them because they were the only ones that had the unique experience of one of them being a

super strong software engineer and the other one being super strong in Hardware I think this is one of those cases where um starting a startup that actually turns out to be successful requires you to uh be similar to a uh PhD or postdoc researcher in some sense where you have to go all the way to the edge of what human beings know and understand and then instead of like you know publishing research that you know sort of pokes that edge out a little bit instead it's like you're creating a product or service that people really

want to that point of being the PHD level of expert in that world when founders get into this they have such a unique fit with founder market fit they're the best in the world literally there's no one like like them that had that work experience and that happened to want to do a startup that happened to now be really interested in Ai and there's this moment in time that is only n equals one that's only them that can do it which is cool yeah Ari being in a place like Tesla is always very interesting to

think about because you know there is um a very important question that all founders have to ask uh when they commit to an idea and that is uh if not us than who do you want to do you want to talk about spur Jared sure so spur is building an AI QA agent so the way testing works now is if you have a large company you probably have QA Engineers who like write tests to test your software and they're just building an AI agent that writes the test for you and the way they came up

with the idea is one of the founders worked at figma which has like a notoriously complex front end that's very hard to test and she realized that the engineers were spending a ton of time testing the front end and writing and maintaining tests for it and that AI just like obviously would enable you to automate oh how do that work I guess figma is a really good place to sort of come out of and that you know if you're already at the edge of design and collaboration you know plus this AI thing sort of happens

you already have exposure to the right customers and know what the people at the edge are doing cuz you're at the edge I have a kind of crazier one which um might be heartening to some of the people in the audience and that this is probably my youngest team I've ever funded how old are they they were 19 they dropped out of freshman year at University of waterl uh this company called Data curve and actually uh one of a few pivots they actually came in as a company called Uncle GPT and it was sort of

this uh Toy hackathon idea really like I think they literally won a hackathon with the idea your sort of standard chat GPT rapper back when rapper was the pejorative that everyone was saying um but you know the deeper problem was people didn't really want it it was a really cool demo but there weren't customers that were willing to pay for it and use it all the time um and then during the batch they actually became um AI for product managers to go back to what we were saying earlier it's sort of that again is a

not leaving the house enough sort of idea for them because uh neither of them as 19-year-olds had been product managers so it's actually very very hard to make software or products for PE you know people and uh teams where you actually don't have direct experience or knowledge of uh of it and so luckily you know and this is maybe a really good example of looking back within uh the founder actually you know she actually was an intern for coh here which was all the way out on the edge in terms of llms and Coen and

so she had already been working on uh data tools and you producing synthetic and real data for uh large language models for coh here and she went back to her old boss and uh they said hey this is what we need and she said oh well I could build that and so now she's basically off off and running I mean she had a great demo day and then she's making mid to high seven figures for a company that just started uh June of last year I have noticed this pattern with a lot of startup Founders

whenever I have a team in the batch that's pivoting and they do the office hours where they're like you know lost confidence in my old idea what should I go work on now like the first note in my decision tree when I'm trying to like help them find a new idea is like are the founders experts in anything because the founders are experts in anything then like often that's the place to look for for ideas first and the thing that I've noticed is that it's often surprisingly hard for the founders to actually know what they

themselves are experts in and sometimes you kind of have to pull out of them the their their actual areas of expertise this is why it's extra hard for uh 19-year olds but at the same time that's part of the reason why I really love this example it's you know that founder uh just had to reach back into her internship from the prior summer and there was something lying in plain sight there I think uh what you're saying about that I I've seen it a lot I think a lot of times when founders come in into

these office hours with us it's almost a bit of a allergic reaction to what they were doing by definicious because they were experts and worked and grinded out years and years they're like oh I don't want to do another decade on this thing that I put all this time it's like this is so so boring boring and they want to chase some shiny object that they don't know anything about sort of the grass is greener but then they sound so much smarter when they talk about that particular domain and then when you kind of reflect

it back to them and they're like oh yeah you're right it's like I never heard anyone go so deep into this as what you eloquently have said versus going over the shiny Ada which is very surface level of insight and internships is another interesting meta Point here I mean some huge percentage of y's billion dollar companies can be traced directly back to not just a job but specifically an internship that one of the founders had and so maybe like a meta point is like if you're like in college and you want to be in a

place to have good startup ideas like do internships at like really cool companies that are on the bleeding edge of something because that's like a really like tried intrude path to get you a great startup idea I think the other meta point is also being picky at where you end up working I mean the example with uh the founder of data curve working at coher coher is at the bleeding edge the founder of coher was one of the authors of the all attention you can need paper which is the seminal paper that pretty much created

this whole AI boom now she was working there another good example I have is this other company called David AI the founders were working at scale and scale is at the bleeding edge of providing all data sets for right now the AI boom as well and david. a found this Niche where scale wasn't going into which is the scarcity with data sets are multimodal data with speaker separated audio and going deeper into that because scale got very deep into more the llm world so that turned out to be good kind of to your same point

kind of coher and here in this case with having worked at coher slash now scale working on the bleeding edge you get to find high quality problems that are going to be the future so that's not the only way to look within uh maybe the one that people really look to and is a little bit more obvious is what are things that you want to see in the world that you could see yourself just working on for the rest of your life you know there be dragons for this but on the other hand we have

some really noteworthy examples of companies that uh have really found something and have made something people want I have um one story in particular that i' share this story really like stuck with me it's about a company called can of soup we funded can of soup and the founder Gabriel had been an early engineer at substack which is a company we funded ye years earlier and very early on he like lost confidence in the idea that we had funded him for and then he kind of wandered in the wilderness in sort of a pivot hell

period where he was like trying to come up with a new idea in sort of sort of an artificial way as often happens when founders are pivoting and he was looking at these various like B2B SAS ideas that were all totally plausible but he just wasn't really excited about any of them and he went on a walk with his old boss uh Chris the the CEO of substack and um Chris gave him a piece of advice that's really stuck with me Gabriel pitched him one of these BW SAS ideas and Chris was like who cares

work on something that captures the human imagination um and that was the prompt got Gabriel to just start thinking about like a much bigger idea that he would actually be excited to work on for for like a really long time and that's what sort of led him down the path of coming up with can of soup which is this like AI Instagram like thing that's like a totally new kind of social network and it's a really big crazy ambitious idea we don't know if it's going to work yet but it's like super interesting and like

so much cooler than the sort of like manufactured BB SAS ideas I mean social networks seemed like it's uh pretty ripe for things that people really want to work on um you know one of my favorite AI companies right now is called happen stance the founder was a apple AI researcher sold his last startup and then he started realizing especially once like word DEC and Vector databases started coming out that you know when you use things like LinkedIn how often is it that you're like typing something that you're looking for and it just you know

I think it's just still using plain old plain text search like I think it's just using indices from my sequel for all we know it's literally not smart and um the thing about llms and especially you know llms plus Vector search now mean that the the search engine itself can be so much more intelligent and so you know I'm always trying to connect people in the batch to people who could buy their thing or who could help them with access or all of that and then happen stance now is just this wild thing where I

can type almost anything more or less in a fuzzy way I can even describe the people I'm trying to help I can even describe sort of the level or area inside the company I think I want to connect this founder to and it'll just figure all of that stuff out it'll write the SQL queries and then uh you know use its own you know a mix of uh Vector search llms and SQL to find those people in a way that like LinkedIn search just fails 10 times out of 10 for some of these really complex

queries part of our job is kind of helping Founders to think bigger because the whole process of starting a startup is already scary and sometimes Founders start with a very small idea that could be inconsequential but if you 10x it then how could the world change and I think Jared you had some really good example for this one I do and incidentally if you're looking for a startup idea what one thing you should definitely do is you should go and read or reread Paul Graham's essay called how to get startup ideas which is really kind

of how definitive work on the on the topic and he talks about this concept called blinders where if you're looking for a startup idea you tend to have blinders on where your subconscious doesn't even allow you to see certain ideas because they seem too ambitious and too scary and so you don't even they don't even consciously bubble to the surface for you to be able to like decide if you want to work on them or not and a great example I have this a company called easy dubs easy dubs is building the universal translator like

from Star Trek so imagine um you go to Japan but you don't speak Japanese and you want to have a conversation with someone who only speaks Japanese you can use Easy dubs and it'll translate like simultaneously and in real time so you can have a real-time conversation with somebody who speaks a different language so one of the um common things people run into when they go through this path of idea is just what we said before like I I really don't have any expertise I've mined everything I can I've mind all my experiences and I

can't generate a good startup idea that way and um that then takes you to like Gary's Point around like you have to get outside of the house and you have to start putting in the work to like build the expertise and so I feel like actually Our advice to startups during the batch when they're going through this path will often change it's like stop thinking about kind of what your like twoe Revenue goals are and start treating yourself as researchers and just try and like build expertise in um in something in the hope that you

find startup ideas I kind I have a story on on that type of way of generating idea it's um company called ESS health and they spent a while pivoting trying to find an idea weren't kind of landing on anything that worked really well and so I think one of their parents I think it was um one of the founders mothers like was a dentist who ran her own small dentist office uh and he just went to work with her for a day just to kind of see like how does a dentist office work and like

is there anything that software could do better and he realized a lot of the admin work involved in sort of um Insurance um processing someone's insurance and pre- authorizing them and all of this work was just like routine that could really be um processed Away by an llm and so they started working on that they started building an llm powered back office for dentists uh and it's working like really really well so cool yeah I I I I love it when founders end up in this branch of the decision Trea to find a startup idea

because through them I get to learn about all these corners of the world and like think about where there might be interesting problems there there's a couple parts about that story that I want to kind of pull out Harge one is um like using family connections like a lot of her best startup ideas like a lot of YC's like billion dooll startups literally it was like the founders parent or uncle or cousin or brother or some like old college roommate or just some like random connection that was like just enough of an opening to like

lead them to an interesting place it's surprising how important that is is you know basically you could cold email a thousand people sometimes and get literally zero responses but if you have someone who you're going to see every Thanksgiving uh I think they're going to give you some access and access is all you need actually like that's sometimes like right at the beginning moment you just need access into an some underserved Place some place where no good software engineer or AI engineer has ever seen or has seen yet like it works now better than ever

because what we said in a previous episode about just how AI agents are going to be so much bigger than SAS I think take the Salan example the eess health example probably 5 years ago building software for just like car loan lenders or just for like a small dentist practice was probably not a big enough Opportunity by itself and so people probably felt oh like my connections or my expertise are not that valuable but now it's like any one of these things with AI you it's so much more valuable than just building like a CRM

for a dentist office you like replacing a human the human's probably being paid like 60 to 80,000 bucks minimum like per year and so just the value of your software just went way way way up and I also love that they actually went on site and spent a day in the dentist office just being like a fly on the wall like anytime you can find anyone working in any industry who will let you do that is freaking gold like you're you're going to discover something cool I think it's this concept of uh going undercover as

an undercover secret agent to to learn all the Deep secrets about a industry which is all kept kind of behind close s for good reasons outside of The Outsiders but because you have this special connection with a family member or someone like that or or sometimes Founders are very charming and they get in through that as well I met one example like that and you you can kind of learn a lot about these esoteric Industries one example is this company called happy robot they're basically building AI agents for uh coordinat logistic for truckers they don't

come from Trucking such an esoteric World from them the founders are Spanish which is like very far removed from this and PhD students and the way they landed onto that is just the founders are very personable they they're very friendly and when you talk to them you want to be friends with them well the good news is uh even if you aren't connected by family or friends you might also not friendly and extroverted enough to make it work the way happy robot did uh there is still another way and I'm going to uh not put

this company on blast because they are an AI billing company that is doing well um but the way they came to the idea was not through connections per se one of the co-founders actually got a job doing medical billing as a biller as a remote person for a new york-based uh automatc office and he did not actually disclose that he was using software or building software but that's what he did he got a job it was like an undercover job like it wasn't like he happened to be working as a medical biller he's like I

want to automate medical billing but in order to do that I need to understand how it works so I'm going to get a job as a medical biller in order to understand how it works from the inside am I understand right exactly he actually got a real job and was paid as a medical biller I have a Founder who did the same thing in a different industry it's wild right it's totally wild uh but it works if you have if you don't have connections and you can't you know walk and sweet talk people to get

access you know there are just jobs that are knowledge work jobs that you can do and then this is actually uh my pitch to you know sometimes to Regulators that open source is actually a very important piece of this because the reason why this person was able to do it legally was he was building his own software to automate the work all locally on his own computer so you know he was like building his own AI agent robot using llama 3 to replace himself on two MacBook Pros at the time and you know there was

no violation you know no laws were broken it's you know it's legal to use your own computer and use zoom and use those things to actually go and work with an external parties thing because you know it's a it was a remote laptop job and you could do that really easily with it so I think that was one of the crazier examples and it sounds like that's something founder should totally do this they should totally just go get random jobs like working in random Industries and learn about them from the inside it doesn't take that

long it's not like you have to you know get like an MD or something in order to to to become a medical biller I think it's like a it's like a like a two to four- we training program or something exactly and those are actually sort of the ideal things to get automated right now sort of uh you know laptop remote laptop jobs that you can get very easily uh it turns out llms are very good at doing those jobs these days and uh laptops are very powerful and uh you know synthetic data down to

lower uh lower you know smaller parameter models is also very good so this is kind of the golden age of uh truly truly going undercover I think one of the themes here is uh how do you go all the way to the edge of uh you know what people know uh especially if you yourself are an engineer or AI engineer and uh this one was uh kind of an intense one so able police actually does work with uh police department departments and the reason why that found the founder is actually uh growing Daniel on Twitter

so the way he found out about this problem was actually you know pretty serious like one of his friends was the uh victim of a crime he started doing research uh and discovered as I discovered in San Francisco that many police officers are actually just drowned in paperwork you know you might do an you know 8 10 hour shift and then you're spending two or three hours of those 8 hours or 10 hours just filling out paperwork at the end of it San Francisco has this crazy law where if you stop anyone to even talk

to them you have to fill out as much paperwork as if you had arrested them oh my God so how can you do police work when you have a police commission that drowns you in paperwork like that and this is not merely a San Francisco thing this is like almost all over of the United States and so you know this is a very good example of literally going you know undercover in that he went on ride alongs he uh investigated this thing that was very upsetting to him in society discovered the root cuse and then

llms were happening he's like why why would anyone sit in front of a web browser filling out uh multiple hours of like click click click like enter data literally like you know transcribe people's driver's licenses like this is you know why are we turning police officers who are supposed to look out for Public Safety and a lot of the job is clerical work and you know of course you can use uh llms plus computer vision to take that two hours or 3 hours down to five or 10 minutes especially because you already have uh camera

data all day from the person doing the work can I leak uh Alpha trick about the the previous topic uh finding jobs this is something that I suggested their founder do you can literally go to indeed.com and search for jobs that have keywords like this like remote analyst remote like clerk things like that and just look at all of the jobs that people are hiring for like some of them are probably weird jobs that like most people haven't even heard of there's all of these jobs like this out there you just go get one of

those jobs there's another trick um that TR is if you don't want to work the boring job yourself is think of if you have any friends who have very boring jobs um and go follow them and Shadow them at work for a day so I have a company you have an example like this where this actually happened right yeah it's a company called sweet spot and when we accepted them into YC I think the idea was something about payments for taco trucks just something totally random and then they started searching for ideas uh and they

had a friend who just worked his full-time job was to sit in front of a computer on the government website looking the government post like contracts are available for bidding and so his whole job was just to keep refreshing that page and every time there was like a bid um that was relevant for the company he worked at he just like pasted the link somewhere um and they like well that seems like something that could definitely be like ultimated with AI and this was about a year and a half coming up to almost 2 years

ago now and and that inside actually turned into a really exciting idea where now it's a AI platform for just all of your Government Contracting in procurement that they can they both like find the thing for you they generate like all of your Bard they give you advice on how to like sharpen it to um uh have the best chance of Landing it and price it optimally all of these things like a single package and it's growing like incredibly fast I think that category of uh Indeed jobs it's also this jobs that AI is very

good at automating that are temporary I think Jared you work with one of those companies any category of jobs that is being outsourc to a low-wage country is like uh a strong signal that there is a startup to be built right now in the in the current ERA like that's just like like an amazing place to look um one company like that that I worked with last year is a company called lilac labs and what lilac Labs is doing is they are automating the person at the drive-thru who takes orders they were snooping around other

startup ideas that didn't end up panning out but in the process of looking at the startup ideas they realized that for a lot of the drive-throughs in America when you drive up to the drive-thru and you like place an order the person on the other end who's like listening to your order and like transcribing it into the point of sales system lives on the other side of the world because that job which you was always done by like the person sitting at the drivethru has now been outsourced to like bpos in like low low wage

cont countries and that was a clue to them that like this was an amazing like Target to go after you have one too di right uh it's a little bit different flavor so this is more for looking into spaces where there's a product that is not 100% working and there's a bunch of Consultants that are making a bunch of money to just get that product to work and one example is uipath is this giant company that went IPO that does robotic process automation maybe a lot of the audience it's macros a lot of macros to

automate workflows on desktops that Enterprise buy it but in order to get UI path live it's just so much work it takes a consultants and Consultants that you need to be certified what these Founders found is like hey what if we actually build a way better product that actually works and actually does RPA without the need of expensive Consultants so this is a company uh that fund that call automat that basically is this solution but a lot better that actually works and is only possible now because of uh AI was this in part inspired by

like recent advances in getting LMS to browse the web and use desktop applications uh totally I mean they actually went through YC before computer use was uh launched but the interesting thing about this team they have always been living at the edge of Technology they actually were ex googlers that got access to B when now it used to be called B now it's called Gemini so they actually prototyped bunch of apps on AI before it was even cool so this is during the pandemic and before it so it had a lot of experience building with

AI before everyone did and they saw this is a place that's going to go I think that's a cool M up lesson like PG has this line in this essay uh live at the edge and notice What's missing like you're you're in a way better position to have a great AI startup idea if you're constantly trying the latest stuff and like actually personally consuming and developing on the the very L stuff because then you're you're one of the first people to realize that something new is possible and also if your friends are doing the same

like actually I think it's an example if you have friends who are um working at interesting companies um or companies themselves that are pushing things on the edge it can be really valuable I have another story of a company that searched and pivoted an IGA during the batch called pre DB and they they had friends who had sold their startup to another startup uh that was just this is just during the era where pine cone and Vector databases were becoming more and more popular say I was just taking off this is around like late 2022

early 2023 their friend told them hey like the big problem we have is like we want like a good quality real time sync between like our postgress data base and pine cone and no one's really built that and we're going to have to build a all internally in house so Deb founds okay well we could just build that for you but then as they actually got deeper into the idea they realized oh well like PG Vector is relatively new extension within postgress that you can actually just do a lot of the pine cone the stuff

that you want to do on Pine Cone you can actually probably just do in postgress and so they just started pushing the limit of PG Vector to see how much of the functionality they could replicate and they got like surprisingly far and now they have sort of Enterprise customers um that are using using them you know not just as sort of a replacement for a separate Vector DB but also just as a replacement for elastic um and various things for search and semantic search across all of the stuff in their database and I think that's

they got that idea just because they were in the world of technical Founders doing things and Building Things and telling them oh hey like this is our our issue you can just do things yeah you can just do things good advice and to that point I think the thing that makes this category of looking outside in is is to hang out with very smart people like like your example I have another one is this company called reducto and because they were in YC they got to be friends with a lot of uh other Founders that

were building at the edge of AI and they found an interesting problem for a lot of the rag applications in order to get them to work well you need to be able to stract the chunks on it very well but this kind of problem you would only find out if you end up working with the top percentile of Builders are really at the edge of really building the next generation of applications so that's how they found and the idea of a redactor which extracts perfect chunks and data from PDFs another way I've seen people build

the expertise to get an idea it's I wouldn't even call it quite expertise but this is something um uh just getting going and building any product um uh like makes you like an expert and whoever the users of it are and puts you in a good spot to find ideas uh and an expert in building products like that so you build expertise in like multiple areas yeah wait I can't just become an expert from being you know an ex influencer you're saying I have to go talk to users you actually have to build something ship

code um a company that went through that I worked with YC in um yeah 2022 again so almost three years ago uh searching for ideas the idea they landed on was the freelancer marketplace and in honesty they didn't have the company's called juice boox they didn't really have much different differentiation for that idea but they were just excited about it they done some freelance work themselves they just wanted to start building it go ahead and they worked on that idea for a decent chunk of time um and it didn't really take off but in the

course of sort of working on freelancer Marketplace and working with companies who are hiring Freelancers and hiring other people as the llms developed they realized oh there's actually like a a real need for just like llm powered people search for recruiters specifically and they started building that um and that has really taken off it's called people gbt and it's just like the really effective search especially for recruiting teams to just find exactly who they want sort of a fuzzy prompt and it would just give them a list of all the dream candidates to go and

ping and you can see how like they wouldn't they're not they they've never run they've never been recruiters themselves they never actually really hired people um but the expertise they built was because they just launched something there's this funny thing during the batch especially around fundraising uh which is a very interesting phenomenon I'm sure you guys have seen this too where we have Founders who are out there they ship a product they're talking to real users those users turn around and give them their credit card number or you know sign on the dotted line like

big Enterprise contracts for 10 or $100,000 a year and then fundraising rolls around they start getting the first nose and then they get it's like just getting gut punched by you know gut punches after gut punches and they come back in office hours and they're like investors don't get it and uh the thing that I find myself saying over and over again is like yeah investors don't get it because they're trying to do it the way like a Founder would trying to be an ex- influencer trying to just reading feeds from like literally their toilet

like and posting right like literally that's not how you figure out what's going on why would you the person who's outside of the house not on the toilet outside of the house out there talking to people shipping software and doing things like why would you be taking any cues at all from the person who's still sitting on the toilet like scrolling an X feed like it doesn't make any sense right you have direct knowledge of the world out there and you're coming back into Plato's Cave and this person is like saying well I don't see

the Shadows yet on the back wall and it's like let me tell you it's out there right like you literally have seen it with your own eyes here's another instance that I've seen where Founders like psych themselves out car that's related to that which is Founders who to psych themselves out because spaces seem too competitive and they end up like shying away from going after ideas that are actually really good because like two competitors launched on Tech Crunch and like raise seed funding or something like that H you you have a good example of a

of a company yeah it's a company both Gary and I worked with actually we mentioned before like gigl they originally implied with a edtech idea it was an idea to help Indian college students apply to us colleges Indian high school students apply to us colleges then they pivoted into uh fine-tuning as a service or around the time where that was just like the open source models had just been released uh they couldn't build a sustainable business so they didn't quite crack that nut and they were looking for applications of the fact they had become experts

in fine-tuning like models for specific purposes and they were trying to find a vertical application and the one that they were most excited about was customer support um but they felt it was very crowded like there were lots of people doing customer support but they went for it anywhere and specifically they're really focusing on one company zepto which has really been willing to be like a real early Cutting Edge adopter I mean another meta Point here is like zepto themselves really want to be like the most like operationally efficient delivery company in the world so

they were looking for these really like high quality pieces of tech rumors of iping later this year rumors who knows who knows we don't know what I will say about the gig ml Founders is they are incredibly smart engineers and not natural sales people at all and just one thing I'm used to pre AI I'm curious if your opinions on this is it often feels especially with B2B SAS things like if you're entering a crowded space it's it's often as much about how can you differentiate on just sales versus like necessarily like your first product

and so you gravitate towards like I need to feel that this team can really like sell in order to get anywhere if they're going to launch you know a a new payroll product for example um but with gig ml what I noticed is that so many of the things just actually don't work very well doing like AI that can really replace your customer support team of humans is a hard technical problem and so although lots of people are pitching that they have it very very few people can actually deliver to the level that customers want

and it just turned out that gigl were like their technical strength meant that they could actually deliver what no one else could and that got them this deal and now it's s snowballing from there it's like a huge Enterprise deal did they close up to during the batch no this I mean for quite a while actually this this is one of those stories of where um I think it took them about a year if I don't recall to find the right idea and that's quite normal actually it's quite normal actually yeah a lot of our

best companies in recent years have been that yeah yeah which is flies in the face of I think what everyone believed you know ancient history 5 to 10 years ago like you know there were uh you know it's hard to believe but you 10 years ago there were entire seed funds that would say like we never do seed extensions either you're going to be great and you're great immediately or you know seed extensions are a sucker bet and uh these days I'm pretty glad that we're in the days where that's just not true anymore like

you can see that people are getting product Market fit my speculation on it is AI moves so quickly that every few months there's just a new set of possible ideas that I generated I also this is like a more anecdotal thing I also feel just like because it's so exciting to work on a startup and work on AI right now that the teams just have um morale Reserves for longer like it's like why would like if you're building if you're right now if you're building a startup working on like Cutting Edge AI even if you

haven't found the right idea yet like why give up and go back to Google or college or something like there's there's a high probability that your lucky break is just around the corner yeah and this is kind of the most exciting thing you could be doing with your time like these product releases that keep changing the the whole Space the whole time yeah which is crazy well that's all the time we have for now but I think that that's a pretty great thing for everyone out there to keep in their mind you know you can't

stay in your house or sit on your toilet scroll you know Doom scrolling a you have to either look very deeply within you to find that you're already on the edge because of something you've done or you need to radically get out of the house go into uh you know other people's real businesses and the real problems that Humanity faces and then get first principal's understanding of what's going on out there and then you can build a billion-dollar business using AI we'll see you guys next time [Music]