Transcription
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 Nadella, Sam Altman, Andre Karpathy, Andrew Ng, and F. Fei-Fei 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 we 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 cutting-edge AI, even if you haven't found 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, from first principles, understand the root cause of this. Then, you're going to discover all kinds of things that software, and especially AI, in its current form, can actually solve.
There is a very important question that all founders have to ask when they commit to an idea, and that is, "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. 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 the 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. We should just 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. Basically, the default bad startup idea is lazy, and it's almost like a hackathon idea. It's like, "I read about it on X, and you know a bunch of people are doing it, so why don't I do that?"
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 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. But most of the best startup ideas are actually at least somewhat hard to ship the first version of.
That would sort of be my metal lesson from doing this individually with founders. I'm always trying to push them in the direction of 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 you need to get out of the house. So rather than just do what is right in front of you, 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—on the outside, being in industry, being in government, being in other places that serve humanity in some way.
That's not in your house either, actually. So aggressively, you know, internal or aggressively outside.
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 in the world.
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. One of them is this company that I mentioned before called Salient. They're basically building an AI voice agent that does loan processing for auto debt 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. 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.
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. He thought, "Oh, why not build an AI agent for that?" So that was really good, and that ended up working really well. Now they're servicing a bunch of large banks.
That's a good one. Another one is, which I also mentioned previously in another episode, is the founders of Diode Computer. They're basically building the AI circuit board co-pilot.
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—an intersection of skills that has not been, is not that the world of software and hardware don't tend to talk to each other.
They had built circuits at Apple, at startups, and even custom processors. So they had a lot of experience building very high-end electronics, and they saw the gap and frustration of working with hardware engineers.
"Why didn't they do things like software? Why didn't the world of Git exist? Why in the world do I have to power through all these data sheets to verify all the components manually? That's the electrical engineer's job. Why do I have to do that manually? Why not just get an LLM to do all that verification like in code with QA?"
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 starting a startup that actually turns out to be successful requires you to be similar to a 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.
Then instead of, 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 them that had that work experience and that happened to want to do a startup that happened to now be really interested in AI.
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 a very important question that all founders have to ask when they commit to an idea, and that is, "If not us, then who?"
Do you want to talk about Spur, Jared?
Sure! 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 write tests to test your software. They're just building an AI agent that writes the test for you.
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. 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 obviously would enable you to automate.
Oh, how does 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 because you're at the edge.
I have a kind of crazier one, which 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 the University of Waterloo. This company is called Data Curve, and actually, one of a few pivots, they actually came in as a company called Uncle GPT.
It was sort of this toy hackathon idea, really. I think they literally won a hackathon with the idea—your sort of standard ChatGPT wrapper back when "wrapper" was the pejorative that everyone was saying.
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.
During the batch, they actually became 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 neither of them, as 19-year-olds, had been product managers.
So it's actually very, very hard to make software or products for people and teams where you actually don't have direct experience or knowledge of it.
Luckily, you know, and this is maybe a really good example of looking back within, the founder actually, you know, she was an intern for Cohere, which was all the way out on the edge in terms of LLMs and Cohere.
She had already been working on data tools and producing synthetic and real data for large language models for Cohere, and she went back to her old boss and they said, "Hey, this is what we need," and she said, "Oh, well, I could build that."
Now she's basically 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 in 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, I lost confidence in my old idea. What should I go work on now?"
The first note in my decision tree when I'm trying to help them find a new idea is, "Are the founders experts in anything?" Because if the founders are experts in anything, then often that's the place to look for ideas first.
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 their actual areas of expertise.
This is why it's extra hard for 19-year-olds, but at the same time, that's part of the reason why I really love this example. That founder just had to reach back into her internship from the prior summer, and there was something lying in plain sight there.
I think what you're saying about that, I've seen it a lot. I think a lot of times when founders come into these office hours with us, it's almost a bit of an allergic reaction to what they were doing by definition 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 into. This is so 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. When you kind of reflect it back to them, 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 idea, which is very surface-level insight.
Internships are another interesting meta point here. I mean, some huge percentage of YC's billion-dollar companies can be traced directly back to not just a job but specifically an internship that one of the founders had.
So maybe like a meta point is, if you're in college and you want to be in a place to have good startup ideas, like do internships at really cool companies that are on the bleeding edge of something because that's a really tried-and-true path to get you a great startup idea.
I think the other meta point is also being picky about where you end up working. I mean, the example with the founder of Data Curve working at Cohere—Cohere is at the bleeding edge.
The founder of Cohere was one of the authors of the "Attention is All You 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.
David AI found this niche where Scale wasn't going into, which is the scarcity with data sets—multimodal data with speaker-separated audio—and going deeper into that because Scale got very deep into more of the LLM world.
So that turned out to be good. Kind of to your same point, Cohere and here in this case with having worked at Cohere/Scale, working on the bleeding edge, you get to find high-quality problems that are going to be the future.
That's not the only way to look within. 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 have really found something and have made something people want.
I have one story in particular that I’d share. This story really 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 years earlier.
Very early on, he 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 trying to come up with a new idea in sort of an artificial way, as often happens when founders are pivoting.
He was looking at these various B2B SaaS ideas that were all totally plausible, but he just wasn't really excited about any of them. He went on a walk with his old boss, Chris, the CEO of Substack, and Chris gave him a piece of advice that's really stuck with me.
Gabriel pitched him one of these B2B SaaS ideas, and Chris was like, "Who cares? Work on something that captures the human imagination."
That was the prompt that got Gabriel to just start thinking about a much bigger idea that he would actually be excited to work on for a really long time. That's what sort of led him down the path of coming up with Can of Soup, which is this AI Instagram-like thing that's like a totally new kind of social network.
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 so much cooler than the sort of manufactured B2B SaaS ideas.
I mean, social networks seem like it's pretty ripe for things that people really want to work on.
You know, one of my favorite AI companies right now is called Happenstance. The founder was an Apple AI researcher, sold his last startup, and then he started realizing, especially once like Word2Vec and vector databases started coming out, that, you know, when you use things like LinkedIn, how often is it that you're typing something that you're looking for, and it just, you know, I think it's just still using plain old plain text search?
I think it's just using indices from MySQL for all we know. It's literally not smart. The thing about LLMs, and especially, you know, LLMs plus vector search now, means that the search engine itself can be so much more intelligent.
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. Happenstance 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, you know, use its own mix of vector search, LLMs, and SQL to find those people in a way that 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? I think, Jared, you had some really good examples for this one.
I do! Incidentally, if you're looking for a startup idea, one thing you should definitely do is go and read or reread Paul Graham's essay called "How to Get Startup Ideas," which is really kind of the definitive work on the topic.
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.
So you don't even—they don't even consciously bubble to the surface for you to be able to decide if you want to work on them or not. A great example I have is a company called Easy Dubs. Easy Dubs is building the universal translator like from Star Trek.
So imagine 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.
One of the common things people run into when they go through this path of idea is just what we said before: "I really don't have any expertise. I've mined everything I can. I've mined all my experiences, and I can't generate a good startup idea that way."
That then takes you to Gary's point around like you have to get outside of the house, and you have to start putting in the work to build the expertise.
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 two revenue goals are and start treating yourself as researchers and just try to build expertise in something in the hope that you find startup ideas.
I kind of have a story on that type of way of generating ideas. It's a company called ESS Health, and they spent a while pivoting, trying to find an idea, weren't landing on anything that worked really well.
I think one of their parents, I think it was one of the founder's mothers, was a dentist who ran her own small dentist office. He just went to work with her for a day just to kind of see, "How does a dentist office work, and is there anything that software could do better?"
He realized a lot of the admin work involved in sort of insurance processing—someone's insurance and pre-authorizing them—and all of this work was just like routine that could really be processed away by an LLM.
So they started working on that. They started building an LLM-powered back office for dentists, and it's working like really, really well.
So cool! Yeah, I love it when founders end up in this branch of the decision tree to find a startup idea because through them, I get to learn about all these corners of the world and think about where there might be interesting problems.
There are a couple of parts about that story that I want to kind of pull out, Harge. One is using family connections. A lot of our best startup ideas, a lot of YC's billion-dollar startups, literally, it was like the founders' parent or uncle or cousin or brother or some old college roommate or just some random connection that was just enough of an opening to lead them to an interesting place.
It's surprising how important that 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, I think they're going to give you some access.
Access is all you need, actually. Sometimes, like right at the beginning moment, you just need access into some underserved place—some place where no good software engineer or AI engineer has ever seen or has seen yet.
It works now better than ever because of what we said in a previous episode about just how AI agents are going to be so much bigger than SaaS.
I think take the Salient example, the ESS Health example. Probably five 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.
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, it's so much more valuable than just building like a CRM for a dentist office.
You're replacing a human. The human's probably being paid like $60,000 to $80,000 bucks minimum per year, and so just the value of your software just went way, way, way up.
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. Anytime you can find anyone working in any industry who will let you do that is freaking gold.
You're going to discover something cool. I think it's this concept of going undercover as an undercover secret agent to learn all the deep secrets about an industry, which is all kept kind of behind closed doors for good reasons outside of the outsiders.
But because you have this special connection with a family member or someone like that, or sometimes founders are very charming and they get in through that as well.
I met one example like that, and 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 coordinating logistics for truckers.
They don't come from trucking—such an esoteric world for them. The founders are Spanish, which is like very far removed from this, and PhD students.
The way they landed onto that is just the founders are very personable. They're very friendly, and when you talk to them, you want to be friends with them.
Well, the good news is even if you aren't connected by family or friends, you might also not be friendly and extroverted enough to make it work the way Happy Robot did.
There is still another way, and I'm going to not put this company on blast because they are an AI billing company that is doing well. 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 automatic office. 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. 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 understanding 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, but it works. 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.
This is actually 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.
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. No laws were broken.
It's legal to use your own computer and use Zoom and use those things to actually go and work with external parties.
Because, you know, 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 founders should totally do.
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 become a medical biller.
I think it's like a two to four-week training program or something.
Exactly! And those are actually sort of the ideal things to get automated right now—sort of, you know, laptop remote jobs that you can get very easily.
It turns out LLMs are very good at doing those jobs these days, and laptops are very powerful. You know, synthetic data down to lower, you know, smaller parameter models is also very good.
So this is kind of the golden age of truly, truly going undercover. I think one of the themes here is how do you go all the way to the edge of, you know, what people know?
Especially if you yourself are an engineer or AI engineer. This one was kind of an intense one. Able Police actually does work with police departments, and the reason why that founder is actually growing is Daniel on Twitter.
The way he found out about this problem was actually, you know, pretty serious. One of his friends was the victim of a crime. He started doing research and discovered, as I discovered in San Francisco, that many police officers are actually just drowned in paperwork.
You know, you might do an 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? This is not merely a San Francisco thing. This is like almost all over the United States.
You know, this is a very good example of literally going undercover in that he went on ride-alongs. He investigated this thing that was very upsetting to him in society, discovered the root cause, and then LLMs were happening.
He's like, "Why would anyone sit in front of a web browser filling out multiple hours of like click, click, click, like enter data, literally like you know, transcribe people's driver's licenses?"
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?
Of course, you can use LLMs plus computer vision to take that two hours or three hours down to five or ten minutes, especially because you already have camera data all day from the person doing the work.
Can I leak an alpha trick about the previous topic—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: remote analyst, remote clerk, things like that.
Just look at all of the jobs that people are hiring for. Some of them are probably weird jobs that most people haven't even heard of. There are all of these jobs like this out there.
Just go get one of those jobs. There's another trick, TR, is if you don't want to work the boring job yourself, think of if you have any friends who have very boring jobs 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. When we accepted them into YC, I think the idea was something about payments for taco trucks—just something totally random.
Then they started searching for ideas, and they had a friend whose full-time job was to sit in front of a computer on the government website looking for government posts like contracts that are available for bidding.
His whole job was just to keep refreshing that page, and every time there was a bid that was relevant for the company he worked at, he just pasted the link somewhere.
They were like, "Well, that seems like something that could definitely be automated with AI." This was about a year and a half, coming up to almost two years ago now.
That insight actually turned into a really exciting idea where now it's an AI platform for just all of your government contracting and procurement. They can find the thing for you, generate all of your bids, give you advice on how to sharpen it to have the best chance of landing it, and price it optimally—all of these things in a single package.
It's growing like incredibly fast. I think that category of Indeed jobs is also this—jobs that AI is very good at automating that are temporary.
I think Jared, you worked with one of those companies.
Any category of jobs that is being outsourced to a low-wage country is a strong signal that there is a startup to be built right now in the current era.
That's just like an amazing place to look. One company like that that I worked with last year is a company called Lilac Labs. 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 place an order, the person on the other end who's listening to your order and transcribing it into the point of sales system lives on the other side of the world.
That job, which was always done by the person sitting at the drive-thru, has now been outsourced to BPOs in low-wage countries. That was a clue to them that this was an amazing target to go after.
You have one too, Di, right?
It's a little bit different flavor. 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.
One example is UiPath. It's this giant company that went IPO that does robotic process automation. Maybe a lot of the audience knows it. It's macros—lots of macros to automate workflows on desktops that enterprises buy.
But in order to get UiPath live, it's just so much work. It takes 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 for expensive consultants?"
So this is a company that I funded called Automat. That basically is this solution, but a lot better—that actually works and is only possible now because of AI.
Was this in part inspired by recent advances in getting LLMs to browse the web and use desktop applications?
Totally! I mean, they actually went through YC before computer use was launched, but the interesting thing about this team is 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. They actually prototyped a bunch of apps on AI before it was even cool.
This was during the pandemic and before it, so they 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 meta lesson. PG has this line in his essay: "Live at the edge and notice what's missing." You're in a way better position to have a great AI startup idea if you're constantly trying the latest stuff and actually personally consuming and developing on the very latest stuff.
Because then you're one of the first people to realize that something new is possible. Also, if your friends are doing the same, like actually, I think it's an example.
If you have friends who are working at interesting companies 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 idea during the batch called PreDB. They had friends who had sold their startup to another startup.
This was just during the era where Pinecone and vector databases were becoming more and more popular. They said, "Hey, the big problem we have is we want a good quality real-time sync between our Postgres database and Pinecone, and no one's really built that."
We're going to have to build it all internally in-house. So the founders thought, "Okay, well, we could just build that for you."
But then as they actually got deeper into the idea, they realized, "Oh, well, PG Vector is a relatively new extension within Postgres that you can actually just do a lot of the Pinecone stuff that you want to do on Pinecone. You can actually probably just do in Postgres."
They just started pushing the limit of PG Vector to see how much of the functionality they could replicate, and they got surprisingly far.
Now they have enterprise customers that are using them, you know, not just as a replacement for a separate vector DB but also just as a replacement for Elastic and various things for search and semantic search across all of the stuff in their database.
I think 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 issue."
You can just do things!
Good advice! To that point, I think the thing that makes this category of looking outside in is to hang out with very smart people.
Like your example, I have another one. It's a company called Reducto, and because they were in YC, they got to be friends with a lot of other founders that were building at the edge of AI.
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 extract 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 who are really at the edge of building the next generation of applications.
That's how they found the idea of Reducto, which extracts perfect chunks of 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 just getting going and building any product makes you like an expert in whoever the users of it are and puts you in a good spot to find ideas.
An expert in building products like that.
So you build expertise in 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. A company that went through that I worked with YC in 2022, again, so almost three years ago, searching for ideas, the idea they landed on was the freelancer marketplace.
In honesty, they didn't have much differentiation for that idea, but they were just excited about it. They had done some freelance work themselves. They just wanted to start building it.
Go ahead! They worked on that idea for a decent chunk of time, and it didn't really take off.
But in the course of sort of working on the 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 real need for just like LLM-powered people search for recruiters specifically."
They started building that, and that has really taken off. It's called PeopleGBT, 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.
You can see how they wouldn't—they've never run—they've never been recruiters themselves. They never actually really hired people.
But the expertise they built was because they just launched something.
There's this funny thing during the batch, especially around fundraising. It's 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 $10,000 or $100,000 a year.
Then fundraising rolls around, and they start getting the first no's, and then they get—it’s like just getting gut-punched by, you know, gut punches after gut punches.
They come back in office hours, and they're like, "Investors don't get it." The thing that I find myself saying over and over again is, "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—why would you be taking any cues at all from the person who's still sitting on the toilet, like scrolling an X feed?
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."
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 psych themselves out, and that's related to that, which is founders who psych themselves out because spaces seem too competitive.
They end up shying away from going after ideas that are actually really good because like two competitors launched on TechCrunch and raised seed funding or something like that.
H, you have a good example of a company?
Yeah, it's a company both Gary and I worked with, actually. We mentioned before, like Gigl. They originally applied with an 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 fine-tuning as a service around the time where that was just like the open-source models had just been released.
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 models for specific purposes.
They were trying to find a vertical application, and the one that they were most excited about was customer support.
But they felt it was very crowded—like there were lots of people doing customer support. But they went for it anyway, and specifically, they were 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 operationally efficient delivery company in the world, so they were looking for these really high-quality pieces of tech.
Rumors of IPOing later this year—rumors! Who knows? Who knows? We don't know!
What I will say about the Gigl founders is they are incredibly smart engineers and not natural salespeople at all.
Just one thing I'm used to pre-AI—I'm curious if your opinions on this is it often feels, especially with B2B SaaS things, like if you're entering a crowded space, it's often as much about how can you differentiate on just sales versus like necessarily like your first product.
So you gravitate towards like, "I need to feel that this team can really sell in order to get anywhere if they're going to launch, you know, a new payroll product for example."
But with Gigl, what I noticed is that so many of the things just actually don't work very well. Doing AI that can really replace your customer support team of humans is a hard technical problem.
So although lots of people are pitching that they have it, very, very few people can actually deliver to the level that customers want.
It just turned out that Gigl, their technical strength meant that they could actually deliver what no one else could, and that got them this deal, and now it'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 is one of those stories of where 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.
A lot of our best companies in recent years have been that.
Yeah, which flies in the face of I think what everyone believed, you know, ancient history 5 to 10 years ago.
Like, you know, it's hard to believe, but you know, 10 years ago, there were entire seed funds that would say, "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."
These days, I'm pretty glad that we're in the days where that's just not true anymore. 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 are 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 morale reserves for longer.
It's like, why would—like if you're building, if you're right now, if you're building a startup working on 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?
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 whole space the whole time.
Yeah, which is crazy!
Well, that's all the time we have for now, but I think 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. 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, you know, other people's real businesses and the real problems that humanity faces.
Then get first principles 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!
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