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Technology's daily show (formerly the Technology Brothers Podcast). Streaming live on X and YouTube from 11 - 2 PM PST Monday - Friday. Available on X, Apple, Spotify, and YouTube.
You're watching TVPN. Today is Wednesday, 06/11/2025. We are live from the Palace Of Party Rounds. Oh my god. It's YC demo day.
Speaker 1:Welcome to our YC demo day stream.
Speaker 2:That is
Speaker 1:insane. Gary Tan joining us. Wow. You absolutely destroyed my laptop. That is insane.
Speaker 1:We brought lots of party favors. We brought lots of activities for when we, talk to founders who are doing extremely amazing things. If that's going off every time a founder hits a million dollars in ARR or signs term sheet, I think we're gonna be up to our next in confetti.
Speaker 3:Lot of action.
Speaker 1:We got some gifts we're gonna be giving out. We got some surprise gifts, some hats. We're gonna take you all through it. Gary Tan, welcome to the stream.
Speaker 2:There he is. What's going on?
Speaker 1:President of Y Combinator, Gary Tan Gary Tan, good to see Great
Speaker 2:to see Thank you
Speaker 4:so Great to see y'all.
Speaker 2:Alright. The there's a line around the block still.
Speaker 4:Yeah. Is that right? Yeah. Yeah. Oh, my God.
Speaker 1:Yeah. It it's it's absolutely slammed. Take us through what are you seeing, how's this one going, what's actually changed because we're in a different location.
Speaker 4:Yeah. We're literally
Speaker 1:Break it all down.
Speaker 4:In the middle of this is like our HQ,
Speaker 1:you know?
Speaker 4:Yeah. This is you know, we we got the spring batch together. This is the spring batch that batch. We've ever done.
Speaker 1:So it's x 25 now? Yeah yeah yeah.
Speaker 4:Paul Graham doesn't like x. Okay. So we're we're slowly replacing it with spring.
Speaker 3:Spring? Okay. Spring.
Speaker 1:We go.
Speaker 4:You know, when you create a place like this, I think you get to dictate little things like Sure. Sure. Sure. You know, not liking x.
Speaker 2:You know?
Speaker 1:Who's the likes
Speaker 4:of So, yeah. So we gotta write out spring.
Speaker 1:Okay.
Speaker 4:But, you know, I don't know. What do you think it should be? It could be p. Maybe if it's not x, it's pronto.
Speaker 1:Do we have do that in a mathematical notation for this stuff?
Speaker 4:Maybe. Yeah.
Speaker 1:Some, like, different you know, we have the y combinator algorithm.
Speaker 4:That's We need
Speaker 1:a different algorithm to define the different seasons.
Speaker 2:I think that's right. Think just forward, you know, winter, spring, summer, fall.
Speaker 3:Yeah.
Speaker 5:We have summer, fall.
Speaker 4:Well, it's clean. The energy from the founders is really electric. Honestly, it's insane.
Speaker 1:It's insane.
Speaker 4:Fantastic. Like, downstairs, we got a well, the fun thing about hosting all of the investors in our house Yep. Is that, I got a whole whose house, our house channel downstairs. So we yeah. We got
Speaker 2:the Give an overview of the we we heard a little bit of your talk earlier, but give us kind of a breakdown of of how you introduced today.
Speaker 4:Yeah. Absolutely. I mean, dude, it's like 90% AI. It's about 10% Uh-huh. 11% hard tech
Speaker 1:Okay.
Speaker 4:Which is awesome. Yeah. Yeah. And then the really crazy stat is, you know, how the last four batches about the last year, you know, the batch itself as a whole has been growing revenue by 10%. This time it's 12.
Speaker 1:Wow. There we go. Here we go.
Speaker 4:So we're actually inflecting up and Amazing. You know, that's what you would expect.
Speaker 6:We're in
Speaker 4:the middle of the age of intelligence. Yep. Yep. You know, six months ago, nine months ago, you know, the models were 90 IQ. Yeah.
Speaker 4:Know, then they were a 110. Now, they're about a 130. Yeah. You know, we're sort of entering super intelligence zone.
Speaker 1:Yeah.
Speaker 2:One digit IQ incoming.
Speaker 7:Yeah. That's right.
Speaker 1:It's a thousand. I don't even
Speaker 8:wanna talk
Speaker 1:to that. But Yeah. But, yeah. Probably very valuable.
Speaker 4:I thought Sam's essay yesterday was Very very prescient.
Speaker 1:I like
Speaker 4:that one. Like, that's that's sort of the silver lining. Like, everyone's sort of worried, you know, what's gonna happen to the jobs. Like, to me, what's gonna happen is the exact I mean, it's actually an an an opening. Yep.
Speaker 4:Right? Like, the wild stat that we're seeing is actually the number of 18 22 year olds applying to YC and getting it is up 110% Wow. Year on year. Right? So, you know
Speaker 1:Is that driven by people liking to drop out of college or skipping college entirely? Is that because they're viewing college as kind of like I mean, there was always a meme about like, oh, what well, you put YC in the education section of your LinkedIn.
Speaker 9:Yeah.
Speaker 1:Yeah. Now it's like, oh, wait. No. YC could actually be the replacement. Is that intentional?
Speaker 4:The extreme meme here that is interesting to think about, like, don't believe this, but, you know, the memes among, you know, the 18 to 22 year olds in particular is Mhmm. This might be the last time you can start businesses.
Speaker 1:Oh, interesting.
Speaker 4:Because, you know, once super intelligence hits, then, you know, the moats that, know, the businesses that will exist will sort of ensconce like the seven powers of the moats out there.
Speaker 1:Sure. Sure. This is maximally efficient.
Speaker 9:Yeah. Exactly. So
Speaker 1:just reach their terminal value immediately and maintain it forever.
Speaker 4:So you're trying to get network effects, you're trying to build
Speaker 10:build Yeah.
Speaker 4:We're trying to we're to get brand. Yeah. Yeah. You're trying to get a cornered resource.
Speaker 1:Sure. Sure. Sure.
Speaker 4:And then the wildest thing here is that, you know, this perfect Someone was telling me yesterday, right now, if you look at college grads, the rate of unemployment for CS grads is actually two x that of art history majors.
Speaker 1:That is crazy.
Speaker 4:I have not looked this up yet.
Speaker 2:Yeah. Everybody everybody's been quoting that. We've heard that too. We've talked
Speaker 1:about We've just be something crazy going on with art history It's sad be hallucinating. The community found some hack and they're all getting employed. It's full employment for them. Very interesting. Question on the AI side.
Speaker 1:How are the companies feeling about the current battle between little tech and big tech? What we saw with WWDC, it feels like Apple's kind of retreating from some territory. We could be seeing more opportunities
Speaker 2:for developers.
Speaker 1:For, like, mobile apps. We could see another mobile app boom because Apple's saying, hey, look, maybe we're not the company to build every single AI experience on the iPhone. We're seeing new hardware maybe come from OpenAI.
Speaker 3:There there's
Speaker 1:different stuff going on with open source. How how what's the interplay between the average YC company and and the Mag seven right now?
Speaker 4:Oh, I mean, the good thing is we still have, you know, we have net neutrality. Yeah. So and, you know, that thing we've you know, we have seven companies in the, you know, megatech world.
Speaker 11:Yep.
Speaker 4:But, you know what? Like, anyone can just put something on the Internet and they get distribution.
Speaker 1:Yep.
Speaker 4:Right? So, I think that, you know, the most important thing right now that I think all of us should really be considering is we need platform neutrality.
Speaker 1:Mhmm.
Speaker 4:Right? Yeah. You know, the that you know, today, you open your Siri and you don't get to choose, you know, do I want Perplexity?
Speaker 1:Do I
Speaker 4:want Anthropy? Do I want, you know, ChatGPT on there? Yeah. You know, no. Like, that this is actually the next sort of unfolding that needs to happen.
Speaker 4:Sure. We actually need the platforms to allow other people to
Speaker 1:enter.
Speaker 4:But if we do that, we can actually have tens of thousands or hundreds of thousands of companies, each of which can get to a billion dollars net revenue e you know? Cool. And like, you could do it probably with 10 people. Yeah. Right?
Speaker 4:Yeah. So if you link up these two super mega trends, like, that's the future that we wanna live. Yeah. Right? Like, you can be 18 to 22.
Speaker 4:You don't you don't necessarily have to go in, like, the credential matters a lot less. You know what matters now? It matters, you know, your agency and your taste.
Speaker 1:Yes.
Speaker 4:Right? And so and you don't have to like go Yeah. And get anyone's
Speaker 2:permission can get bet a better tutor from an LLM than you could from at times working under somebody at some big company who would maybe explain, here's how you make a financial model for a software company. But now you could just talk enough with ChadGPT that you can probably figure it out even better than having like a mentor in some cases.
Speaker 1:I wanna ask about applying to YC. Have you noticed that the apps are becoming more GPT driven? Are people using AI to write their apps? Do you recommend against that? What do you think?
Speaker 4:I mean, if anything, like, I think the best apps you know, in the past we might say, oh, this is AI slopped. I think we're the models are now smart enough, like, my writing process for instance has totally changed Yeah. To this point. Like, I am, you know, you're actually able to come up with better ideas, I think.
Speaker 12:Yeah. Totally.
Speaker 4:Know, I I would rather I would rather people actually prompt to get like all of the ideas out there. You know, one of the things I've been using in Chat lately is like, give me 10 options for different concepts or ideas that might fit here.
Speaker 1:Yeah.
Speaker 4:Yeah. And then, what I'm doing is I'm using my like, I'm using my prompting to do that Yep. And then, on the back end, it'll give me like 10 things. Five of them make no sense. Yeah.
Speaker 4:Two or three of them, in my brain, I'm like That's I didn't think about That actually is a useful thing that I can put in with these other concepts that are in my head. Yeah. And so, I think it's actually you know, the computer is a bicycle for the mind. Like, if you can prompt really, really well, you're using just I mean, you have to treat it like a vehicle. Yeah.
Speaker 4:Right? Yeah. It is not a destination Yeah. Of its own. It's actually know, it's this is like the self driving car for the mind.
Speaker 1:Yeah. I mean, just
Speaker 2:working towards Yeah. All that matters is the end output. Yeah. And if you're working on any type of project, not working with a smart collaborator is gonna lead to a worse output.
Speaker 1:Yeah. O3 Pro it. Condensing down these ideas because I've I've read a lot of apps where it's like, wow, there's actually like some really incredible KPIs in here, but you buried it in paragraphs of exposition that were completely unnecessary. And I know that the people that are actually reviewing the YC apps are not gonna read this far. And so just having it as a copilot seems like a really,
Speaker 2:really How how have you and the other group partners pushed to guide, the batch around reporting on revenue, right? There's a lot of conversation around what is ARR? It's sort of this flexible definition. Yeah. There's obviously a lot of pressure for every founder coming into demo day to like show results.
Speaker 2:How how do you guys kind of guide
Speaker 4:Yeah. Absolutely. I mean, what we'd like people to do is put a contracted ARR if it is. Mhmm. And then if it has an opt out clause where, you know, people can sort of get out, they should just have a little star on it.
Speaker 4:Okay. You know, opt out at thirty days or whatever this Sure. I think it is very very important that founders are just don't engage in securities fraud. Yeah. That's very That's like kind of a basic thing.
Speaker 4:Right? So, you know, I think that it is a you know, amazing thing about the tech today is like the demos are so impressive that people are willing to take a big chance Big companies are willing to
Speaker 1:sign up for big deals. Yeah. And, yeah, they obviously want some flexibility Yeah. But but being really clear about that. I mean, are non GAAP metrics
Speaker 13:That's right.
Speaker 1:You know, there is some gray area. We need to define new metrics as we move forward.
Speaker 2:Are you seeing companies able to pivot more times within a single batch and just iterate faster? Because historically, it wasn't unreasonable for a company to pivot a few times to get to something great and maybe they only have two weeks at the end to really sprint and show progress. Sure. Now I could imagine certain companies like really really really pushing it. Have you seen that at all?
Speaker 4:Yeah. I mean, I have something like 30% of the companies That's amazing. Changed their idea during the batch. Wow. And that's actually great.
Speaker 4:Mean, as
Speaker 1:you're researching and building. Yeah.
Speaker 3:Yeah. And I I
Speaker 4:think that that's always been true. I mean, some of the biggest companies end up being, you know, literally pivots from like two, three weeks ago. Yeah. And then I think that, you know, people might say, oh, that, you know, that kinda sucks or, know, why is that? I mean, I think it's good new the good news is like, you know, you can just do things.
Speaker 4:Yeah. The good news is
Speaker 1:like So when I think of the YC mantras, I think build something people want, talk to your customers, is you can just do things. The that we're adding Absolutely. Is that important to have that volition, that that that a a agency. Yeah. It's the distillation of agency.
Speaker 1:Right?
Speaker 4:I think so. Yeah. I mean, a lot of it actually I think the most important thing that I feel like everyone has to learn the hard way Yeah. Is to learn, you know, the sound of your own voice. Sure.
Speaker 4:To say, you know, not to get all bicameral mind about it. I love it.
Speaker 1:Know, like
Speaker 4:that's sort
Speaker 2:of where we're
Speaker 1:at actually. You can't do it.
Speaker 4:Well, no. I mean, if you read, you know, p p d's old essays, like, one of the things that really jumps out at me is, like, to what degree, you know, your childhood, your schooling, like, he has so many essays about, you know, what height is all like being a nerd, like, all these things are, like, you know, really spoke to me and that the process of becoming actually a founder Mhmm. Is actually a journey inward to actually learn the sound of your own voice that like, you can make your own way, you can have agency, you know, just because I mean, actually by definition, the startups that create new categories require a level of courage that like people have to find within themselves. Mhmm. Actually, you have to say, actually, you know what, like I know the Wall Street Journal says that Yep.
Speaker 4:And you know, the hater decel journalists say this and all this stuff. You know, you have to actually separate yourself
Speaker 1:Yeah.
Speaker 4:From the default view of how the world works. Yeah. And then you're actually trying to divine secret knowledge by going into the markets, going to to talk to people that have never, you know, even touched ChatGPT yet. Yeah. Right?
Speaker 4:Like, they don't know the rev it's like that meme where
Speaker 1:Yeah.
Speaker 4:You're in, you know, you're the tech guy like in the corner. Yeah. And then, like, everyone's dancing and they just don't know. Yeah. They don't like, that's where we're at still.
Speaker 4:Like, it won't be like that for another year or But, like, for now, that's still true. You go into any business in the world Yep. They don't know this revolution is about to happen to them.
Speaker 1:It's crazy.
Speaker 4:Yeah. We get to create it.
Speaker 1:Yeah. Yeah. Yeah. Last demo day, we talked to a company that was doing AI voice interactions to help the elderly process Medicare receipts essentially, and they discovered that the the elderly were talking to their chatbots for like three hours.
Speaker 14:Yeah. And that's just something
Speaker 1:that like you might you might be able to predict intuitively, but I thought it was just a very a very funny like discovery that only happens from actually trying to build in that in that market with the latest and greatest technology. Yeah. And that's what I love about YC demo day.
Speaker 2:What's your latest thinking on batch sizes?
Speaker 1:Mhmm.
Speaker 2:Or is that is that are we are we staying, you know, where at?
Speaker 4:The high level is like we want as much prosperity in the world as possible. Know, Brian Chesky is on my board. Oh, good. You know, Paul Graham and Jessica Livingston, Karen Levy. The board has given me my directives.
Speaker 4:It's, you know what, we need to, this is a tree of prosperity. Mhmm. Let's have the tree of prosperity grow. But, know, I think that YC fundamentally is the managed marketplace. You look at an Airbnb, I still
Speaker 12:super Interesting.
Speaker 4:I'm still, you know, bullish on Airbnb and that you look at the amount of space in the world. Yeah. Yeah. Like, the amount of space that has been listed on Airbnb is still a tiny fraction of what it could be.
Speaker 1:Yeah.
Speaker 4:And what does that unlock? And it unlocks new experiences, travel like a human, all these things, you know, it's a $100,000,000,000 company
Speaker 1:Yeah.
Speaker 4:From nothing. And I think that the same thing is about to happen to innovation and venture. Mhmm. But we have to do it thoughtfully. Right?
Speaker 4:Yeah. Like, you know, we're at demo day here. We have more than a thousand of the top investors in the world all congregated here. Right? We have a thousand people in this building.
Speaker 1:That's amazing.
Speaker 4:And Incredible. You know, we need to grow them thoughtfully. Like, we need you know, what I need is I need YC to continue to provide returns. Right? Yep.
Speaker 4:And, you know, can't comment on, you know, the rumors about scale this week, but like, we need a lot more things like that and we're getting it. Yeah. Yeah. This is sort of uniquely the community where that happens.
Speaker 1:Talk to us about the two new partners that were added to the YC partnership.
Speaker 4:Oh, There's three, actually.
Speaker 1:Oh, three?
Speaker 4:Yeah. Sorry. Andrew Nicholas and John Hsu, actually.
Speaker 1:Okay. Cool.
Speaker 4:Sorry. Almost announced. No. Yeah. Tyler two.
Speaker 1:Okay.
Speaker 4:Yeah. That's Tyler two.
Speaker 12:I got it.
Speaker 4:I mean, all of them created companies that exited for, you know, hundreds of millions to, you know, PagerDuty as
Speaker 1:Yeah.
Speaker 4:You know, a public company
Speaker 1:Wow.
Speaker 4:You know, north of a billion is
Speaker 1:That's remarkable.
Speaker 4:That those are exactly the kind of partners that we want at YC. It's they've been there and they've done that.
Speaker 1:So roughly how big is the partnership now?
Speaker 4:It's 15 partners total. 15
Speaker 1:partners. Yeah. That's great. Yeah. So you can still keep the actual, like, group sizes fairly small within the batch.
Speaker 4:I mean, that's the main you can just basically guess at what the batch size will be because each partner can do 10 to 25 companies depending on how hard they wanna work. But, you know, I I don't think it's a numbers game. I think it's you know, we wanna fund all the really really good founders and I feel a little bit embarrassed about, you know, we're accepting companies at a 0.8% rate right now. Wow. Right?
Speaker 4:And so Yeah. You know, I think that we're behind the eight ball, actually. Sure. Like, you know, I I feel really good about this. Yeah.
Speaker 4:Like, we're gonna keep growing what we got Yeah. But I also need to grow the partnership and then we need to fund better and better companies. Yeah. And then, you know, this is a ten, twenty, fifty, a hundred year thing, like we're trying to build Y Combinator into a multi hundred year institution. We
Speaker 1:we need it.
Speaker 2:Have you had any companies go through this batch and decide not to raise additional capital at this point because they're just making so much revenue that they they
Speaker 4:famously, Tom funded Acxiom last last batch.
Speaker 2:Yeah. Yeah.
Speaker 4:Yeah. And they're
Speaker 2:doing hundreds of
Speaker 4:millions of dollars in pure profit. So
Speaker 2:saw that and I was, like, trying to look back through our stream and, like, did we did
Speaker 4:we have wild, man.
Speaker 15:Yeah. It's a wild west.
Speaker 2:It's a wild west.
Speaker 1:That is insane. Yeah. Well, we'll let you get back to it. Thank you so much for coming on the
Speaker 12:stream there.
Speaker 4:For having me.
Speaker 1:Yeah. It's always fantastic. An
Speaker 4:honor. Yeah.
Speaker 1:It's great.
Speaker 4:Fun out there. Thanks.
Speaker 1:Great. Well, we will be moving into interviews with founders and VCs who are coming on the the show, who are here at YC demo day.
Speaker 2:This Stay
Speaker 1:maugen.
Speaker 2:Stay maugen.
Speaker 1:While we bring in the oh, we're ready. Let's do it. We got it. Let's do Fantastic. I thought we were gonna be on news.
Speaker 1:What up?
Speaker 2:What up, man?
Speaker 1:What up? Welcome to the show. What's going on? I'm John. What's going on?
Speaker 1:To meet to meet you. How you doing?
Speaker 2:What's going on?
Speaker 1:Break it down for us.
Speaker 2:How we doing?
Speaker 1:Break it down for us. How's how's demo day going? Grab a water. Introduce yourselves. Introduce your company, please.
Speaker 16:Alright. Ken.
Speaker 1:Ken, hold
Speaker 2:up the microphone a little bit. Both of you guys. Alright. There you go.
Speaker 1:Good.
Speaker 17:Ken. Cool. CEO of Kaizen.
Speaker 1:Nice.
Speaker 6:Michael, CTO. Okay. How guys doing?
Speaker 2:Pitch, like, five minutes ago or ten minutes ago? Just five minutes ago.
Speaker 17:We came right down here
Speaker 1:You did well?
Speaker 17:To spread the word. Yeah.
Speaker 1:How are
Speaker 2:you doing? How how the nerves? You guys was that, like, a walk in the park?
Speaker 17:Yeah. It was fun. It was fun. It was You've
Speaker 1:done it in alumni? And then and then probably, like, three to five full partnership pitches before. Right? Yes. Okay.
Speaker 17:So, I mean, I probably
Speaker 1:Yeah.
Speaker 17:Yeah. Said this a 100 times Yeah.
Speaker 18:A thousand times, almost a
Speaker 1:field say it one more time. Give us the high level. You don't have to give us the whole page, but break it down. Alright.
Speaker 17:So Kaizen helps developers instantly integrate into websites without APIs. Mhmm. You know, we work with companies across logistics, health care, and financial services to integrate to a wide variety of legacy portals.
Speaker 1:Very cool. Talk to me about how you're doing that. Is there MCP involved? Are you kind of using AI and LLMs to read the HTML, kind of reverse engineer an API from the front end? What's going on?
Speaker 17:A 100%. The latter of what you said. Cool. Computer use has changed the game a lot of this stuff. You know, you think about it, hey, There's a so such a small subset of software that has APIs that people can integrate with, build products on top of.
Speaker 17:Mhmm. But now with computer use, computers can do anything. Cool. Anything available on the Internet,
Speaker 19:and that's
Speaker 17:what we help companies with.
Speaker 1:Reactions to o three Pro? Have you tested it yet? Is it, is it improving things, or do you build on open source? Like, what do you like? What are you excited about in the in the AI race at the foundation model levels?
Speaker 17:Yeah. I mean, we love using all of them. We're friends.
Speaker 20:We're we're we're, Switzerland. Right?
Speaker 2:Google. Google.
Speaker 1:We we're hosting. Right. We have them all on the show.
Speaker 2:We're selling. We're like, please.
Speaker 1:Let the
Speaker 21:fox in the henhouse.
Speaker 1:We're transgender. Yeah.
Speaker 17:No. I mean, truly, like, models are different for different things. Yeah. You know, clicking on an item
Speaker 1:on the page. Okay.
Speaker 22:It's like we get the
Speaker 17:computer use model for Anthropic for that.
Speaker 1:Oh, okay. Cool.
Speaker 17:Like, pulling data from a very large table. Gemini, Oh, a little bit.
Speaker 1:The biggest bigger context window?
Speaker 17:Exactly. A 100%.
Speaker 1:So Cool.
Speaker 17:I mean, like, our approach, we abstract all this away from the end users. They don't think about it.
Speaker 1:Yeah. Yeah.
Speaker 17:Yeah. It just
Speaker 1:happens. So walk me through some of those use cases. Like, what's the customer that you've had that has been like, this solved my problem. This is amazing. Walk me through, like, a very concrete demo.
Speaker 17:One of our most one of our favorite customers to talk about is, you know, they're a voice agent for hotels.
Speaker 1:Okay.
Speaker 17:So in the Rio, you know, hotel in Vegas, you know, they call down. You say, hey. I want a burger up to my room or I don't know what kind of fancy stuff you all order. But, yeah, you'll talk to a voice agent. And the voice agent will take your request.
Speaker 17:Sure. Right? And then they'll use
Speaker 2:like calling to to get like tooth last night Rosewood of course.
Speaker 1:Of course.
Speaker 2:And we called it a toothpaste and we're like like it's like it's, you know, you gotta I don't even wanna wait like, you know, I don't know, twenty seconds while it rings and oh, let me transfer you here and all that stuff. You should just be able to pick up Yeah. Toothpaste, please.
Speaker 1:And it just And it just comes. Exactly. So how how does your service integrate with that experience? Because I know I pick up the phone, I'm talking to a voice agent. Yeah.
Speaker 1:I imagine you're using voice APIs to actually mediate that, but then you're designing that in that interaction. Right?
Speaker 17:Or rather our customer is traversing maybe I wanna shout them out. Are a voice agent for hotels. Okay. Great. Then they use us to write the data back into the property management system Oh,
Speaker 17:amazing. That service order. So they a toothpaste goes right up to your room.
Speaker 1:Yeah.
Speaker 2:Yeah. Yeah. It's out
Speaker 17:of talking to a person, and they're to understand you, all this stuff, it just happens instantly.
Speaker 1:What did
Speaker 2:you guys do before this?
Speaker 17:We were I was head of engineering at a company called TruckSmarter. I've worked with small trucking companies my whole career.
Speaker 1:Okay. Love them. Great guy.
Speaker 2:So, a big truck guy.
Speaker 17:But, with
Speaker 1:lots of experience working with legacy systems forms,
Speaker 17:and That's that's where it comes from. Like, every shipper in The United States has a completely different website. I must have written dozens of these. I manage teams. I wrote hundreds.
Speaker 17:Yep.
Speaker 15:And Probably a
Speaker 1:lot of web scraping. Exactly. You know, now you're the next And we're building
Speaker 17:Kaizen so that no one has
Speaker 15:to do
Speaker 1:that every day. I love it.
Speaker 23:There we go.
Speaker 1:Tell us about the metrics. Did you share a headline number of users, ARR, something to get the investors excited today?
Speaker 17:Yeah. I mean, we're in the hundreds of thousands of dollars of ARR. We started working on this. Yeah. Let's blow.
Speaker 17:We got the we got all the acronyms, all this about, you know, what the a 16 z Yeah. Like, Did you get
Speaker 1:those around your new track record?
Speaker 17:Did you guys
Speaker 2:you guys got term sheets yet?
Speaker 24:Yes. We're
Speaker 17:we're closed. We
Speaker 1:have a term sheet of the TV behind Waxii demo. Congratulations. I love this. Love
Speaker 2:They're spreading so much debris.
Speaker 1:Such terrible Anyway, thanks for playing along with us.
Speaker 16:Guys, this is awesome.
Speaker 1:We're super excited for you. That's awesome. What's next on the build out? How big is the team? I imagine you're raising money.
Speaker 1:You're gonna scale that, or are you building, like, a much smaller team? How are you thinking about that?
Speaker 17:Yeah. And this is a massive opportunity, and we're excited to sprint after it.
Speaker 3:Cool.
Speaker 17:We actually have an
Speaker 11:employee right now.
Speaker 1:One? Yeah. Nice.
Speaker 17:Crazy enough to to join us during the batch Cool. With the 500 k in
Speaker 1:the bank account. That's great.
Speaker 20:And we're we're we're doing a work trial
Speaker 1:I love it.
Speaker 17:Tomorrow. Tomorrow.
Speaker 1:So here we go.
Speaker 17:We have the money. We gotta go spend it. Yeah. And and we're gonna build a big business.
Speaker 1:Fantastic. Well, we're rooting for you.
Speaker 2:Amazing, guys. Congratulations. Thank you so much for coming on. Yeah.
Speaker 1:Good luck. Thank you. Good luck, buddy. Yeah.
Speaker 2:My man.
Speaker 18:Good to you.
Speaker 2:Fantastic. These party poppers are a big problem. They they like, it basically was spraying, like, you know, little shards of of of paper everywhere. But
Speaker 1:I think he was doing it. We're not gonna stop.
Speaker 3:I think
Speaker 1:it as long as it didn't go in my Red Bull, I think we're good. Let's let's ideally bring in the next person. You wanna come on? Let's let's let's on. Okay.
Speaker 1:We got some people coming into the stream. The YC demo day stream twenty twenty five. Welcome to the stream. Come on. Come on and sit down.
Speaker 1:You're gonna have to rush off all the confetti.
Speaker 16:What's up?
Speaker 14:What's up?
Speaker 1:What's up? Hey. How you doing? Man.
Speaker 2:What's going on? Alright. Hey. Congrats.
Speaker 1:What's happening? Thank you. Real pleasant. So so we've emailed or or or chatted before. Right?
Speaker 2:Yes. Yes.
Speaker 1:Back in
Speaker 14:the stack share days. So now People don't know your history, by the way.
Speaker 1:They don't.
Speaker 14:I I was talking to a bunch of founders. They were like, what?
Speaker 1:Yeah. Yeah.
Speaker 17:He did what? Crazy.
Speaker 2:Crazy. I've
Speaker 1:been around, around Silicon Valley for, over a decade. YC twenty twelve, my batch. Wow. That's good. Good times.
Speaker 1:But what this we're here to talk about your batch.
Speaker 25:Yes.
Speaker 1:What are you building? Introduce yourselves.
Speaker 14:Alright. So we are building Cursor for DevOps.
Speaker 1:Okay. Right.
Speaker 14:Basically, the challenge right now is you're using Cursor, and it's like this futuristic, agentic experience.
Speaker 1:Right?
Speaker 16:And then as soon
Speaker 26:as you leave
Speaker 21:the up a little bit.
Speaker 14:You're back in the past. Okay. And so you're dealing with broken CI builds. You're dealing with exceptions. Dealing with outages, like service outages Mhmm.
Speaker 14:From PagerDuty. Yep. All of those things are manual. Yep. So what we're doing is we're bringing AI agents to all of your developer tools outside of the code editor.
Speaker 1:Okay. Yeah. Walk me through how how that actually works because in cursor and in the IDE, am I am I are you puppeteering the AWS dev, you know, like, Or are you operating at a lower level? What's the actual interface? Is this are you rune pilled?
Speaker 1:Is this like a text is the universal interface play?
Speaker 14:Yeah. So, basically, you land on a developer homepage Okay. And you see all of your tasks from across those tools. So, like, exceptions from Sentry. Okay.
Speaker 14:And then you see an auto fix button
Speaker 2:Okay.
Speaker 14:For each of those Oh, okay. Oh, So it's like a priority inbox
Speaker 1:Got it.
Speaker 14:Right, that prioritizes everything across those developer tools and then gives you one click actions
Speaker 1:Got it. How much of what you're doing requires just building on top of an API for something like PagerDuty or actually doing a deal with them to integrate at a deeper level or just puppeteering and computer use and not even they they they they're never none the wiser.
Speaker 20:Yeah.
Speaker 27:Yeah. So so we're actually doing both. Right? So some of the dev tools haven't built out a lot of agentic features. Sure.
Speaker 27:So we're building on top of their API.
Speaker 3:Sure. Sure. Sure. And then
Speaker 27:other partners, like one of our integrations is with Sentry. Okay. And we integrate with Sentry Yeah.
Speaker 1:Okay. I remember Sentry. Yeah. Co founder
Speaker 11:is actually here.
Speaker 1:Oh, cool. So Did
Speaker 2:you guys come in with this idea? Did you have to iterate to it? How how what did that look Yeah.
Speaker 1:Yeah. We did.
Speaker 27:Yeah. We came in with it. So so before this, Daniel here, at Netflix, I I was on the team that built the internal developer portal, which was the most used engineering tool at Netflix. Yeah. That helped accelerate the company.
Speaker 27:You probably heard like, you know, they shipped like ads and live streaming and all
Speaker 1:these things Yeah.
Speaker 27:And it really helped organization move faster.
Speaker 1:Thought that they were gonna have to partner with Microsoft and I think they wound up doing it internally because they're Yeah. So fast. Right? Yeah. That's Exactly.
Speaker 1:About Netflix. Exactly. Right?
Speaker 27:So it it really helped accelerate the team and that had no AI. Yeah. No AI in
Speaker 1:it. Yeah. Yeah. And you can what is the go to market motion? Cursor obviously just goes bottoms up directly to the dev.
Speaker 1:This something that, like a DevOps engineer can bring in, or do you need to go through a CTO, get approval before you do
Speaker 14:an enterprise deal? Any developer can actually just sign up your account and start using it. Wow. The beautiful part is this isn't for DevOps engineers.
Speaker 18:This is
Speaker 14:actually for all the engineers
Speaker 1:Okay.
Speaker 14:That have to touch those tools. Sure. And so it's like pretty much every engineer at Yeah. Modernsoftwareengineeringorg can just sign up on their own without talking to anyone else.
Speaker 1:Very the product. How's the progress? Are you are you live? Are you growing? What metrics did you share with the Demo Day crew today?
Speaker 14:Yes. So, less than a month ago, we launched. Yes. And we have over 400 companies that have signed up for the private beta, including DoorDash. Snowflake.
Speaker 14:Congratulations. Goldman Sachs.
Speaker 1:Goldman Sachs?
Speaker 2:Yeah. Yeah.
Speaker 1:Let's do it
Speaker 2:up for big clients.
Speaker 27:So everyone wants agents. Yeah.
Speaker 14:Yeah.
Speaker 2:That's crazy. Where are you guys going from here? Did you you close the round already?
Speaker 21:Yes. We have closed it where we are.
Speaker 1:There we go. We Yes.
Speaker 2:Yes. Yes.
Speaker 1:We're happy to give you these ramp ups. Oh, awesome.
Speaker 23:Thank you, guys. Enjoy. Right.
Speaker 1:Liberty put
Speaker 23:the money in ramp. Yeah.
Speaker 1:Yeah. Exactly. Exactly.
Speaker 2:Awesome. That's incredible. What's what's next for the company? You guys
Speaker 14:hiring, scaling? We're hiring very slowly.
Speaker 1:I'm helping this company right now. It's just the two
Speaker 2:of us.
Speaker 1:Just the two of you. Yes. We got
Speaker 14:a corporate old school YC.
Speaker 1:Yeah. Yeah. The way it was. Now there's folks coming through with 25 employees.
Speaker 14:Yeah. No. No. We incorporated right after getting into YC. No way.
Speaker 14:Awesome. We were like, alright. The two of us great.
Speaker 23:The MVP. Yeah. What were
Speaker 2:you doing before, by the way?
Speaker 14:So, I started a company called StackShare. It was a developer community. Yeah. We scaled it to over a million developers. By the end of the journey, was used by over 40,000,000 developers.
Speaker 1:Yeah. And we I think we did like a interview or
Speaker 14:of the Soylent Tech stack.
Speaker 1:Yeah. That's right. And I was like,
Speaker 14:how the hell are you shipping all these calories? And so we talked about all the tools.
Speaker 1:Yeah. So, yeah. I was a big developer community and then sold the company last year. Awesome. Congrats.
Speaker 2:That's amazing. You guys are in an incredible position. Yeah. I'm feeling feeling really good about this. Yeah.
Speaker 2:You you should be confident. It's gonna be hard, but I think the the confidence should
Speaker 14:be high. It's a good it's it's amazing how much we can get done now.
Speaker 1:Yeah.
Speaker 2:Just with like
Speaker 1:Oh my gosh. Yeah.
Speaker 14:Yeah. Right? It's a it's it's it's never been done before, so Yeah.
Speaker 1:We're excited. It also feels like an interesting market in that we've seen, like, it's just less monopolistic. It's not like trying to break through a social network where it's like you're either a trillion dollar company or zero. Yeah. Like, I feel like in DevOps, in enterprise, like, you can understand the the road map ahead, chop wood, create a great product, and carve out, like, a fantastic business.
Speaker 1:100%. That's why we see so many companies going public every year, so many Decacorns in this category. So congratulations. Thank you. I'm really
Speaker 2:really interested in this batch.
Speaker 14:Yeah. We're huge TV fans. Come on.
Speaker 2:Next thing you guys
Speaker 14:should be watching if you're a
Speaker 1:founder. There we go. Love you. Have a great rest your day. Of Good luck.
Speaker 1:Thank you, guys.
Speaker 11:Thanks a
Speaker 1:lot guys. Alright. And we will bring in the next crew.
Speaker 2:Stoked for
Speaker 15:you guys.
Speaker 1:Oh, yeah. Bring the phone out. Run out of these.
Speaker 2:We got you real quick.
Speaker 1:Run out of those. How many more do we have? We we we have a variety of cute goodies. Happy because Oh, I love the sweatshirts. We're owning a color.
Speaker 3:Much like There
Speaker 1:we go. Ramos yellow. You guys are wearing pink. You you can have those if you want.
Speaker 11:There go.
Speaker 1:What's happening? You can only get them this year. There we go. There we go. The colors are working together perfectly, roughly the same saturation.
Speaker 1:Everything's Roughly
Speaker 8:the same On the orange background.
Speaker 1:On the orange orange paint. Yeah. This is this is fever dream.
Speaker 2:It's jazz berry. It feels kind of vintage. Okay. It feels like I've known Yeah. Yeah.
Speaker 2:I've known it. What are
Speaker 1:you guys building? Bring it down.
Speaker 8:Oh, we're building an AI agent for bug finding. Okay. So
Speaker 15:right now, we have
Speaker 8:a PR bot. Yep. So you make a pull request. We'll take your code, clone it into a sandbox, and then we let an agent just go ham at it. Okay.
Speaker 8:And then we tell you how we break it.
Speaker 1:Okay. There we go. How much of this is just about speeding up the pace of development versus, like, is there a pen testing angle here? Is that just a completely separate cybersecurity play? No.
Speaker 1:I think
Speaker 8:we do some pen testing.
Speaker 15:Okay. So it's
Speaker 8:we want to basically find any kind of bug. Yep. So a lot of people take like a a limited a limited approach at bug finding. Mhmm. So they ever do like coverage testing or they try and find integration bugs.
Speaker 8:Yep. We really wanna basically build an agent that can do any of that or kinda what's best for your tool. Okay.
Speaker 2:Talk to So you like when people are vibe coding because they're just creating vibes all the time. Exactly. Yeah. Yeah.
Speaker 8:Vibes coders. We we're here to help you. We'll make
Speaker 28:better code.
Speaker 1:No problems with it whatsoever as long as you buy our software.
Speaker 8:Yeah. Yeah. You buy code, we'll test it. Yeah. Then you take our output, you put it back into cursor.
Speaker 8:Yeah. Just beat
Speaker 1:it back in. Yes. Talk to me about the pro the prompts that you're using to actually have the agent go and hammer it. Like, I imagine that that's not just try and find a bug. You've probably gotten very
Speaker 2:Don't make mistakes.
Speaker 1:Design like designing flow.
Speaker 2:It's vibe code on vibe code of ours.
Speaker 1:Goes into that. What what what goes into actually getting an agent to effectively hunt for bugs?
Speaker 29:Yeah. So, like, the most important part is just to have, like, a sandbox where it can, like, it can run code.
Speaker 13:Mhmm.
Speaker 29:It can compile your code. It can it can run, like, unit tests. Yep. And so then we just get the agent to, yep, go ham. And each time that it, like, runs a small experiment on your code, it learns a little bit more.
Speaker 1:Okay.
Speaker 29:And it's able to do run a better test the next time. And so it's able to search your repository, able to run commands to, you know, see if you've you know, changes you made actually were propagated through all the files. Mhmm. So recently we found a bug, which was that someone updated a path but didn't update it everywhere. So that was those these sorts of things.
Speaker 29:So the agent's able to run these, like, every any command that a person would.
Speaker 1:Yeah. Are you doing stuff like like trying to stuff multiple variables in a single function, like that type of stuff where, like, there isn't as much fault tolerance built into the code? Maybe they need, like, you know, if else try except clause in there or something like that. Is that the type of like bugs that you're trying to find or is it more about like scalability of code like, okay, you're making a database call right here. It looks fine now, but if we scale this up when there's and there's a lot of demand, you're gonna get cooked.
Speaker 1:I think it really depends. So we use
Speaker 8:the pull request as kind of the initial seed.
Speaker 1:Okay.
Speaker 8:So what change you make there kind of determines the path that we use for testing. Sure. So if you're trying to scale, then yeah, we'll we'll kind of test as if you're trying to scale. Sure. But if you're making path changes, we'll test as if you're making path changes.
Speaker 8:Mhmm. One of the things we find is like vibe coding often it it there's a different flavor of bugs that are that are happening because of vibe coding.
Speaker 1:Okay. Interesting.
Speaker 8:So they don't LMs don't make the same kind of errors that people do because people it with people code grows organically. Yep. LMs is like one shotting things. Yep. So it'll often like forget to add functions.
Speaker 8:Mhmm. So it's not as easy as pointing to a line and going, like, this variable is wrong. It's like, no, no, you you, like, fundamentally missed, like, this whole section of things you were supposed to implement.
Speaker 1:Got it.
Speaker 20:So, yeah, I think it's Okay.
Speaker 1:Talk to me about the go to market motion. Is this just a landing page you're driving traffic to? People sign up by themselves? Are you doing founder led sales, all of the above?
Speaker 8:No. No. We're we're landing page. Like, this is our go to market right now.
Speaker 1:Go sign
Speaker 8:Go sign up. Yeah. You can just go, you install the bot. We have a seven day free trial.
Speaker 1:Okay.
Speaker 8:So it's
Speaker 1:Consortium versus seat based pricing?
Speaker 8:What are you thinking? Yeah. It's seat based pricing. Okay. So for every developer, it's $20 a month.
Speaker 8:Okay. Just simple kind of flat rate.
Speaker 1:Are you running into cost problems? Because we've seen this, like, you know, the latest and greatest LLM comes out. It's really expensive. GPT o three just dropped by 80%. So anyone who is having a problem with their with their cost is probably fine right now.
Speaker 1:Yeah. Yeah. But how are you thinking about that side?
Speaker 30:Yeah. You go
Speaker 23:for it.
Speaker 29:Yeah. We found that, like, actually for just be like, you know, running lots of experiments on your code to find bugs, that it's actually better just to have a really fast and small model.
Speaker 1:Okay.
Speaker 29:And so we've actually yeah. We haven't had these sorts of problems yet.
Speaker 1:So what what does that mean? Like, Lama, fine tuned? Are you yeah. We we've talked to LLM training companies that have trained even smaller models, like, just JSON to, you know, for formulation or just translation models or just profanity finding. Are are are you thinking about going so small you could run it on a gaming GPU, or are we still talking about, like, the big boys?
Speaker 29:Yeah. So, like, right now, it's it's we've actually gotten like a lot of mileage out of Gemini Flash. Gemini. We started by, you know, we're we're fine tuned with RL a model that was specifically good at using tools.
Speaker 1:Yeah. Yeah.
Speaker 29:And and so, yeah, we're like we're getting ready to do that. Once we, like, find all our pain points exactly in our current architecture, we can train the exact right thing. The smaller, faster models that are targeted for the specific use case are better.
Speaker 1:So What
Speaker 2:were you
Speaker 8:guys doing before YC? We were both researchers. I was doing research and software testing using large language models. Cool. And Matteo was doing his PhD in reinforcement learning and formal methods.
Speaker 8:Very nice. Yeah. Nice. So our our kind of research has come together to make this happen.
Speaker 1:What
Speaker 16:are the
Speaker 1:How's the raise coming together? How's the pitch for demo day? What are the goals?
Speaker 8:We're slowly kind of growing. Well, I wouldn't say slowly. We're we've doubled our growth kind of every every week for the past kind of Nice. So that's a better way to frame it.
Speaker 1:There we go.
Speaker 8:But, yeah, we've gotten I think we're up to like 18 different kind of companies
Speaker 3:Okay. Using our tool.
Speaker 1:That's great.
Speaker 8:So, it's been awesome.
Speaker 1:Cool. Well, good luck to you.
Speaker 2:1,800 soon.
Speaker 8:Yes. 18 soon. After this, when all of you go subscribe, yeah, then we'll be in 1,800.
Speaker 2:Fantastic. Well, thanks, boys. Congratulations. Yeah. It's been a pleasure.
Speaker 1:Yeah. Cheers. We'll talk to you soon.
Speaker 2:Show the Crocs off too.
Speaker 1:Show the Crocs he's got the Crocs off. Woah. Completely done everything. Let's bring in the next team. How are guys doing?
Speaker 1:Welcome to the stream.
Speaker 2:We got they've got the shirts shirts are socks.
Speaker 3:You need one?
Speaker 31:I need one. Thank you. Welcome. Eddie. Nice
Speaker 1:to you. Nice to meet Welcome. To meet you.
Speaker 2:Welcome to Sharp. Pleasure for tucking your shirts in.
Speaker 1:Yeah. Look.
Speaker 2:We're keeping it we're keeping it respectful. Okay.
Speaker 1:You're ask the company. What are we building?
Speaker 31:So we're COTool. Okay. We are building AI agents for security teams.
Speaker 1:Cool. Sorry.
Speaker 31:We're building AI agents for security teams.
Speaker 1:Okay. Is specifically cybersecurity teams, are we talking DDoS? Are we talking somebody goes in and tries to steal secrets, steal data? What we
Speaker 27:talking about?
Speaker 32:So, like, know, you can think of cybersecurity as split into, like, AppSec and AppSec, where AppSec's, like, you know, defending, you know, the your deployed software out into the world. And then AppSec is, like, operational security, right, where it's, like, you know, protecting your your employees from phishing. We're definitely more on the automation side for the AppSec side of house. So think of of, like, basically, like, allowing security teams to, like, triage their tickets a lot faster for, like, impossible travel problems or for, like, you
Speaker 1:know Oh, impossible travel problems. That's like a that's like a buzzword for
Speaker 32:what happens. Like, you know, Eddie signed in from Singapore. Is that legit or not?
Speaker 1:Yeah. Typically Well, he was in the office earlier today. He couldn't have possibly gotten there because hyper sonic travel doesn't exist. Exactly. Exactly.
Speaker 1:Not quite yet. But not yet. Just that's gonna make it really complicated. No.
Speaker 26:No. It's gonna get
Speaker 1:a of You can get from LA to Tokyo in two hours. I'm gonna be like, well, I guess I did log in to Tokyo. Now, he's been going for sushi.
Speaker 32:It's funny because I think like a lot of people have like this like hacker aesthetic in their mind when they think of cyber security, which is like a lot of like, you know, in the movies, fucking around on a terminal
Speaker 1:or something like that. And it's
Speaker 32:just like when in reality, it's like most of time, it's like people triaging tickets day in and day out, that kind
Speaker 1:of thing.
Speaker 32:So, like, our goal is basically to, like, automate a lot of the BS that these teams have to go through and, like, a lot of, like, the annoying stuff and then, like, allow them to get back to
Speaker 1:work kind of thing. So what's the go to market motion? Are you doing enterprise deals, founder led sales, selling to other YC companies? What's the scale of company that
Speaker 2:needs to use product?
Speaker 33:Customer is Ramp? Oh, no way.
Speaker 1:Wow. Let's go. Wow. That's hilarious.
Speaker 2:I mean, we're I mean, that's tough. You're kinda starting that. So it's
Speaker 1:maybe We gotta keep it
Speaker 2:on family. Yeah.
Speaker 32:Time is money save both.
Speaker 1:Come on. There we go. No. Thanks.
Speaker 2:I mean, Eric Ramp is just an incredible CEO. Yeah. Yeah. He's a guy. He's a nice a
Speaker 31:a a a nice and so we're looking for, like, sort of tech forward. Enterprise is totally is like is would be awesome. But, yeah, anyone that's, like, you know, pushing the boundaries of what's, know, possible automation wants.
Speaker 1:Okay. And what what does the integration point look like? Is it is it a single, you know, security person at a company can get set up, or is it something that needs to be deployed throughout the enterprise and has a much more, like, like, staged rollout?
Speaker 32:Yeah. It's it's I I think, basically, where it is is, like, ideally, what we would have is is, like, we'd probably like, start working with, like, your detection and response team Mhmm. And, like, get that team plugged in. Basically, like, we're, you know, it's we're heavy heavily leveraging AI tool calling.
Speaker 1:So the
Speaker 32:idea is just, like, we plug into all the different points in your Slack, think, like, Okta, think, like, Panther, think, like, all of these different products kinda thing. And, like, basically, then what we're able to do is deploy out and, like, allow these teams to write their own agents. So they're in there customizing their own system prompts and all this kind
Speaker 33:of stuff to go out
Speaker 32:and, like, tackle their task day to day. So I think the initial go to market motion is work with these high-tech teams who are really used to automating already, and then basically kind of, like, build up a nice collection of agents, kinda start getting a good network effect, IKEA effect where, like, people are, like, building their own agents, sharing them out into the world. And then from there, we can kind of, like, move out to wider and wider and, like, less technical teams where it's basically, like, we can kind of, like, start to plug into people's stacks and, like, wholesale automate out of the gate and, like, solve a lot of these problems.
Speaker 1:How
Speaker 2:did how did you two meet?
Speaker 31:So we grew up together in Santa Barbara, California. Oh, good. We we have, you know, lived together for since, you know, we went to school. Yeah. Our cofounder is just outside.
Speaker 31:He's he's he's probably watching this
Speaker 1:right now. But That's great.
Speaker 31:But, yeah, we we we were really stoked when you guys watched our our launch video on Oh, yeah. On for the
Speaker 2:We did on the show.
Speaker 3:We're the mall.
Speaker 23:We're the we're
Speaker 1:the You guys are that company. Wait.
Speaker 2:Wait. They did the the rippling show. Single best
Speaker 1:That was incredible.
Speaker 2:Single best ad I've seen this year.
Speaker 1:At 100%. No. We were
Speaker 2:promoting that. Because because there's so many ways to do that and have it be just bad. Oh, It could
Speaker 11:have been that
Speaker 1:in a million ways.
Speaker 2:But it was it was tasteful. It was funny.
Speaker 1:It was funny.
Speaker 2:It was perfectly timed.
Speaker 1:Yeah. It did it it it it just made it funny. It is did where did that
Speaker 2:where did that come from?
Speaker 31:Well, so it's funny. We we were scared it was gonna flop, to be honest. Mean, like, we we we didn't know how it'd land, we're we're happy with how it did. But, no, I we we came with the idea. We worked as like, one of my friends growing up does comedy in in LA.
Speaker 31:Okay. I live in LA. But, like Yeah. She had knew a director who, like, was, like, amazing, and and his friends who were also, like, actors and stuff got in and Yeah. And and and acted for us.
Speaker 31:And so it's like it all came together with friends of friends.
Speaker 1:It looked incredibly polished. Can you give us an order of magnitude on the budget for that thing?
Speaker 31:It was it was it was, let's see, five digits.
Speaker 1:Five digits. But not six.
Speaker 31:Extremely low. Extremely
Speaker 1:low. Amazing.
Speaker 33:That's amazing.
Speaker 1:We were able to work. It looked like a it looked like a $100,000 project.
Speaker 31:But it was actually
Speaker 1:it was it was actually
Speaker 32:pretty funny. We, we showed the the original idea came from Gary, actually. We showed him the demo on the week of, and he's just like we started, like, showing him how you can, like, query Slack and that kind of stuff. He's like, oh, you
Speaker 2:guys gotta
Speaker 32:do the rippling thing.
Speaker 1:And we're like, oh my god. Yeah. We gotta do the rippling thing. So it
Speaker 31:was just like, literally and
Speaker 32:then we showed him, like, the final cut, like, the night before we went with it. He's like, you guys should tweak these these couple of things. And
Speaker 34:we're like,
Speaker 7:fuck. Okay.
Speaker 1:And so we, got back in real
Speaker 2:editing and stuff like that.
Speaker 1:But it
Speaker 33:was it was it
Speaker 1:was funny. It's funny because it's like y c on y c on y c violins. Oh, yeah. Yeah. But it's really just like, look, like, they're gonna sort their thing out.
Speaker 1:You guys are just having fun and
Speaker 2:Had catalyze around for you guys? It did.
Speaker 1:In some ways. Yeah.
Speaker 31:I think we yeah. I think the a ton of investor inbound off that.
Speaker 1:Yeah. Exactly. Yeah. No. No.
Speaker 1:It makes us it really grounds it because it it like, not everyone works in cybersecurity every day. It can be a little absurd. There we go. There we go,
Speaker 2:boys. Yeah.
Speaker 1:Let's go. What do you do with that? You push it up or something?
Speaker 2:Yeah. We're figuring
Speaker 1:out. We got a lot of these. Congratulations.
Speaker 2:Yeah. No. It's I'm super
Speaker 1:excited for
Speaker 2:you guys.
Speaker 1:Think we gotta give them a different hat for this. I mean, this is best.
Speaker 35:Oh, man.
Speaker 1:We can give you we yeah. We can give you two of these. Go. It's a special edition. Congratulations.
Speaker 1:Thank you so much. Really great chatting with you guys. Yeah. Great chatting
Speaker 2:with We're excited for you.
Speaker 1:We'll we'll follow-up. Yeah. We'll talk to you soon.
Speaker 33:Sounds good. Cheers.
Speaker 1:That was fantastic. I'm so glad we got to talk to those guys. That was such of year. Of of the year. And and and right at the perfect time because we we were just we're hitting, like, peak Vibrio.
Speaker 20:Right? Yeah.
Speaker 2:And if they dropped it even a month later,
Speaker 1:it would been would have
Speaker 2:been gone. Welcome
Speaker 1:to the stream. I'm John. Nice to meet you.
Speaker 10:Nice to
Speaker 1:Nice to meet you. I'm John. How are
Speaker 14:you doing?
Speaker 3:For. Or or dies since we're
Speaker 2:soon? They everyone died.
Speaker 1:Everyone died. You know the story about PMF or die? They died.
Speaker 16:They died.
Speaker 1:It happens sometimes sometimes. Never never lock yourself in a room for the ninety days.
Speaker 14:No. It's
Speaker 1:live stream. Move to sunny San Francisco. Do YC. That's our recommendation. The same.
Speaker 1:Yeah. From here on
Speaker 2:out. So
Speaker 1:Anyway, please introduce yourselves and the company.
Speaker 36:Yeah. I'm Tom.
Speaker 2:I'm Eric.
Speaker 1:Nice to meet you. Nice You
Speaker 2:guys look are are you you're not right?
Speaker 37:We're not really.
Speaker 1:You could go by brothers. People ask us people ask us brothers. Does the company do?
Speaker 36:We're doing a agentic people search.
Speaker 1:So Okay.
Speaker 36:We have a database people database of around 200,000,000 people and Interesting. We use agentic search to search over that for companies and businesses to do, like, sales recruiting, GTM,
Speaker 1:and So so are we talking about specifically, like, I am a recruiter and I need a salesperson Yeah. And I'm going to go to you to try and hire them?
Speaker 3:No. No. So basically, like, you can put in a a criteria. Like, essentially, what we do is
Speaker 2:Hold hold the microphone up. Yeah.
Speaker 1:Yeah. Push the microphone a little closer to you.
Speaker 2:Yeah. There you
Speaker 3:go. I mean, what we do is we, like like, deploy an LLM. Okay. Assign every profile to that LLM, and then given your criteria that could be, like, one paragraph long, we we just ask the LLM if if this profile, like, meets that criteria. And then and then we built, a load load distributor to run, like, 10,000 LLM calls in parallel.
Speaker 11:Wow. Do
Speaker 3:do that at scale. You know, we can we can search over, like, 5,000,000 pro pro profiles in, like, thirty minutes. Okay.
Speaker 1:Insane. So so walk me through one of the key examples. I'm sure this is live. You have customers. Play it on an Yeah.
Speaker 1:An example of someone using this
Speaker 3:So for example, you we had a courier come in yesterday, actually. It was just like every founder that was acquired that was a CTO of their startup was acquired by Databricks or Snowflake in the last three years, and and they and they still work there. And then there's only probably like 25 people in the world that fits that profile, we found all 25 of them Okay. Just by using because we can use and have an going. Yeah.
Speaker 3:It's like because we have like we can throw as much compute at the problem as we want. Interesting.
Speaker 1:And then
Speaker 3:and then and then the LMs get better the more compute
Speaker 2:you throw So it's interesting to be able to find you're able to find information that is historically effectively impossible to find without doing all the work that you guys did Yeah.
Speaker 1:Yeah. Ahead
Speaker 2:of time.
Speaker 1:Makes sense. Yeah. So who's who what buyer or what buyer archetype within an enterprise or a business is most excited to buy your product?
Speaker 3:Yeah. I mean, like, mega recruiters by far.
Speaker 1:Recruit recruiting firms? Yeah.
Speaker 3:No. It's just like people that,
Speaker 1:you know, Or like a big recruiter at Metairie.
Speaker 3:Mean, we work with Merkor right now, for example.
Speaker 2:Oh, Merkor. Okay. Cool.
Speaker 3:So, you know, like like just like people hunting for talent. But it's like a generalized type skill. Right? So, for example, you know, like in AI labs training a new voice model and they need, like, people that speak Cantonese. Sure.
Speaker 3:Then it's like, okay, find me every Cantonese speaker in The US that has, like, a podcast presence. Yeah. And then, okay, great. These guys can come train our voice models. Right?
Speaker 26:Like, that's kind of Or
Speaker 1:they might even need someone who speaks Cantonese and also is an expert in biology. Yeah. Yeah. They can so they can talk about the biology terms. Yeah.
Speaker 1:And that's something that how are you gonna search
Speaker 2:that right now? So it's so funny because I you I run these type of queries in my own head where I'm like, we need a videographer videographer who's in LA Yep. Yeah. That has experience in film, but, know,
Speaker 3:it's in like,
Speaker 1:but he's also part of teapot. Yeah. Yeah. That's basically what we need.
Speaker 6:You know, oftentimes, we're
Speaker 1:like, have sense of humor. It's like, how would you even know that? Well, if you look through their posts, I'm sure you
Speaker 6:could figure
Speaker 38:it out.
Speaker 3:Yeah. Mean, it's basically, like, like, as close as you can. If you just give, like, a criteria to human recruiter. Totally. So you can imagine, like, 5,000 human recruiters, like, manually looking over Yep.
Speaker 3:Profiles and then, you know
Speaker 1:Okay. Business model, are you most recruiting firms, they charge on, like, a per fee basis Yeah. $30,000 to place an engineer somewhere. Yeah. Are you are you doing, like, a seat based pricing, consumption based pricing?
Speaker 1:This sounds expensive if you're talking about running 20,000,000 LLM queries at the same time.
Speaker 3:It's actually not as expensive as people think because because because, know, open source models have gotten so
Speaker 30:good. Okay.
Speaker 3:Yeah. Like like, I'm guessing, like, know, any, like, deep research query cost is, $10 maybe. Okay. But then, you know, like, we we recharge for conversion with with our b to b
Speaker 1:conversion. Okay.
Speaker 3:Yeah. Yeah. We charge by conversion Cool. B to b
Speaker 1:that's probably pretty expensive. Exactly.
Speaker 3:Yeah. And also, we have, like, a platform that's that's just available to everybody. So they can pay us a subscription fee, search as as they want.
Speaker 1:Very cool. They do email enrichment phone number this. This is we might be
Speaker 2:Yeah. We need a guest that doesn't hate tech.
Speaker 1:Can care about this. Yeah. How
Speaker 2:did you two meet? What were you doing before YC?
Speaker 36:Yeah. So, we met in elementary school actually.
Speaker 1:So we
Speaker 36:were originally from Canada.
Speaker 1:Let's go.
Speaker 2:We're in
Speaker 36:an elementary school and we became close friends
Speaker 1:with Yeah. Yeah.
Speaker 36:It's been great here. But we became close friends when Eric, he uninstalled Windows on my computer. Nice. And I was really pissed at him for a day and then we fixed it. So then we became great friends after that.
Speaker 1:That's our
Speaker 2:great That's our
Speaker 1:great just couldn't load anything. Yeah. Yeah. Homework. Yeah.
Speaker 1:That
Speaker 36:was it was during school.
Speaker 15:And then
Speaker 1:Ultimate prank, uninstall Windows on your best friend's computer.
Speaker 2:Boys being boys. And then we spent
Speaker 3:one semester college each. So,
Speaker 1:So, I
Speaker 3:was at Penn. Was at UC San Diego. Yeah. And then and then come January, we were both like like, why are we even there?
Speaker 1:Let's drop
Speaker 2:Right. So you said it's 18, 19?
Speaker 1:We're both
Speaker 2:18 right now. Yeah.
Speaker 1:Wow. Go. Here we go. Let's pay down here.
Speaker 2:Yeah. What's what's the youngest team that you've met here besides yourself? 17. 17.
Speaker 1:17? A high school senior. Okay.
Speaker 21:I didn't.
Speaker 13:They got you.
Speaker 1:When I was
Speaker 27:in there. Okay.
Speaker 1:Or something. But yeah. Talk to me about traction, metrics, anything that you're sharing here at demo day, anything to get the venture capitalists excited.
Speaker 3:Yeah. 270 paying customers. Wow. version of so I don't know if you guys heard about Linked.
Speaker 1:Linked? No.
Speaker 3:Yeah. It's great. So so so so a very early version of this product relaunch, which is like Rank Stanford, like like like like Stanford Rank. Okay. We we will which was so we basically built this we basically scraped the entire alumni database of Stanford.
Speaker 1:I'm sure they love that. Yeah. They they they
Speaker 3:were okay with it. We we put it online and then and then and and then and then we had this we had we had this app where people can like see two random alumni next to each other Oh,
Speaker 1:it's a 100 and out basically. Then you
Speaker 3:vote for who's more cracked?
Speaker 1:Who's more cracked? Who's more
Speaker 36:cracked? And that version
Speaker 3:of the app picked up, like, 80,000 That's amazing. Users was how we got into YC and stuff.
Speaker 1:Very cool.
Speaker 11:Very cool.
Speaker 1:Yeah. And then yeah.
Speaker 3:And then, you know, we we came down here, 207 paying customers, about 16,000 monthly recurring revenue
Speaker 1:That's great. Since they all grew go. Yeah. Yeah. But Yeah.
Speaker 1:Wow. Is just you two
Speaker 3:right now? It it's a three so there's three of us. Three of you. We were two engineers from high school. Cool.
Speaker 3:Just got
Speaker 1:to go to high school. Yeah. We all
Speaker 36:went to
Speaker 15:the same high school.
Speaker 1:Yeah. Was amazing. Yeah. No.
Speaker 2:I'm super excited for you guys.
Speaker 1:Congratulations on product.
Speaker 2:Actually have a bunch of people to send this to.
Speaker 1:Yeah, is fantastic. You should give it a try. Fantastic. Thank you
Speaker 2:so much.
Speaker 1:Yeah. We'll talk to you Awesome, guys. Meeting you guys. Fantastic. Let's bring in the next team.
Speaker 1:Absolutely Come on down. How are doing?
Speaker 2:Aaron. What's up? Aaron. So this is a YC alumni. Oh, fantastic.
Speaker 2:You just raised the series a today.
Speaker 1:Congratulations. Yeah. We got it.
Speaker 36:This is series a.
Speaker 1:Here sit here.
Speaker 2:Yeah. So we were trying to make this happen for the last week. Amazing. But he just announces series a today, 17,000,000. Right?
Speaker 1:17,000,000. Congratulations. Thank you.
Speaker 2:Thank you. There we go.
Speaker 1:Well, I mean, we have stuff to celebrate. Already got the hat. Yeah. I got the hat. Hey.
Speaker 1:Hey. Congratulations.
Speaker 8:It seems like you've done that a couple of times.
Speaker 1:Yeah. Yeah.
Speaker 2:Who thought this is the biggest round of the day.
Speaker 1:This is the biggest round. Alright. Alright. Who did the round? How far along are you?
Speaker 1:How big is the company? Give me some
Speaker 13:stats.
Speaker 7:Yeah. Okay. So a ABC did the by the way, guys, this is awesome. I did like the interview with VentureBeat, and I'm way more starstruck by this. So, like, I'm not just saying that, but I'm like, alright.
Speaker 7:Yeah. So eight b c led the round. Cool.
Speaker 2:Let's go.
Speaker 1:Let's go. What's last partner? Dog.
Speaker 2:What what partner are you working with?
Speaker 7:Jack Moskowitz.
Speaker 24:Nice. Cool. Awesome.
Speaker 7:He is fantastic. Yeah. Yeah. So how far along? So we are
Speaker 2:And were you in the last batch?
Speaker 7:We no. We were we were a couple batches ago. We're summer twenty three.
Speaker 2:Cool. Great vintage. Was that still remote?
Speaker 7:Vintage. Or was
Speaker 1:that back?
Speaker 7:No. We were the batch.
Speaker 1:Back. We were yeah. There you go.
Speaker 7:We had an advantage. Very nice. But yeah. So outset does AI moderated research.
Speaker 1:Okay.
Speaker 7:So what does that mean? It's like when cuss when when big companies have to do a ton of research, like Nestle has to go figure out what, you know, whether they should launch this weird version of DiGiorno pizza or something. They go do research.
Speaker 1:Yep.
Speaker 7:Yep. So either they're running massive surveys and they're getting like very low fidelity data. They don't really know much. It's just a bunch of numbers. Or they're running interviews and that's where it's a one on one interview with everybody.
Speaker 7:Right? Which is not actually cost effective.
Speaker 2:So Yeah.
Speaker 7:Totally. So now you can run AI led interviews. AI is leading the conversation, digging in, following up, probing deeper, and synthesizing all that for you.
Speaker 1:Got it.
Speaker 7:And we work
Speaker 2:How big is that? How big is the legacy market in
Speaker 7:that space? So the research market is like a 140,000,000,000. It's it's actually a really big one.
Speaker 39:Yeah.
Speaker 7:Wow. Just like yeah. There's, like, huge companies that you probably may have never heard, like, Ipsos and Kontar and,
Speaker 1:like Yeah.
Speaker 7:Yeah. These, like, big guys that are just kinda sitting there. So so there's a lot of opportunity. And, yeah, we work with, like, Microsoft, Nestle, Weight Watchers. Who've been super enterprise focused.
Speaker 7:And, yeah, we're couple years in.
Speaker 1:What are the interaction patterns that you like or you think are kind of unlocked by AI? I imagine that voice phone calls is top of mind versus, like, forms, which was always, like, available and already computerized essentially?
Speaker 7:It's actually I think the the cooler thing is what you can get from participants and So it's like, so now we do video interviews Okay. And they do audio and they can even share their screen.
Speaker 1:Yeah. Right?
Speaker 37:All of
Speaker 7:that is being ingested. Interesting. And then like people ask like, to say I have an avatar when it's interviewing. And we actually did research and people don't want that.
Speaker 1:They don't want the avatar?
Speaker 7:No. Don't want the avatar. Yeah. Was an Something like weird uncanny valley
Speaker 1:stuff where I'm like
Speaker 2:you're just like, okay. You're to phone calls.
Speaker 8:Yeah. People used to phone
Speaker 7:calls and they're fine sharing their video. But if you suddenly see an AI, you're like, I'm trying to make $15 doing some research. Totally. Like, suddenly see that, you're gonna be all weirded out. So it's actually much better to have AI come through with voice and text.
Speaker 1:Yep. Talk to me about recruiting. You said $15. That's enough to get someone to jump on, but how do they even find out that $15 is on the table?
Speaker 7:Yeah. Yeah. So we partner with a number of different, like, companies that do nothing but panels. Panels. Recruiting.
Speaker 7:Nothing but recruiting. Got So you have, like, user interviews Yeah. And prolific are,
Speaker 1:like, partners demographics and some base
Speaker 24:They get
Speaker 7:all that. And they have millions of people that are, like, ready to it's like gig work. Right? And and so they
Speaker 1:get it. Okay. And you have a 100,000 people that drink energy drinks to give me feedback. Exactly.
Speaker 7:And you could You could do all that through our platform.
Speaker 2:Yeah. Is there a certain unlock when an AI doesn't need to, like, you know, if a human is scheduling research calls, you know, you can imagine they do like four Yeah. Thirty minute blocks and then they take a little break and they do some more or something like that. I I don't know how it works, but an AI could hypothetically talk for hours and hours and hours and hours, like, kind of
Speaker 7:a 100 So there's like, you know, I I think a lot of AI stuff is like, oh, where can it replace the human thing? And now it's cheaper. But actually, like, this this is taking on the stuff that, like, humans can't physically do. We only have twenty four hours in a day. And so what happens is people use it to like interview 500 people, right, in a day or two.
Speaker 7:And that's like it's literally not possible. It's like just laws of physics don't allow it. Yeah. And so they're able to do that. They can do multilingual.
Speaker 7:They can kind of do it all at once. Mhmm. So you can wind up like unlocking actual like stuff you've never heard. I don't can I curse on this?
Speaker 1:Oh, we usually don't, but Alright.
Speaker 7:We're free to you. Stuff that they like would not have uncovered anywhere else.
Speaker 1:Yeah. Yeah.
Speaker 7:And they actually are able to get that like
Speaker 1:Got it. In this Yeah.
Speaker 2:What's your what's your stack under the hood? What models are you using getting the most value out
Speaker 7:of? Yeah. We're using a combination. So it's, you know, it's like we're hitting multiple models constantly. But Azure we we're using a lot of Azure, actually, to open AI models under
Speaker 15:the hood
Speaker 7:to Azure.
Speaker 1:Yeah. And
Speaker 7:it's So
Speaker 1:Is their model router helpful? I saw they announced that it builds. I haven't talked to anyone that's actually used it.
Speaker 7:I I don't know yet. Okay. Actually, I so I I don't think we've typed that yet.
Speaker 1:Rolling it out.
Speaker 7:Yeah. So, basically, like, what we need is just incredible amount of reliability because what we'll have is, like, three customers are all running, like, 500 simultaneous interviews.
Speaker 1:Sure. Sure. Sure. So we
Speaker 7:have to actually scale very, very quickly.
Speaker 1:Yeah. Yeah.
Speaker 7:Yeah. And also, like, a lot of our customers, like, Nestle of the world, like, they care a lot about, like, super safe, super reliable.
Speaker 1:Sure.
Speaker 7:Sure. Kind of, like, wind up needing the kind of Azure.
Speaker 1:Yeah. And then you're also probably going through like peak LLM usage hours
Speaker 7:too because they're not doing
Speaker 1:it in middle of the night.
Speaker 7:No. No. We can't just like process it on our
Speaker 1:own time.
Speaker 7:I mean, like, to happen in that moment. But then we also fall back to OpenAI. We use Gemini for stuff. So we're like hitting a lot.
Speaker 1:Yeah. Talk about the the ease the voice modality beyond the uncanny valley at this point. Is that why voice is valuable or using voice a lot? Because you could imagine that this might have been possible via text interactions. You're texting your Yeah.
Speaker 1:Your responses back and probably get more out of a voice interaction. Are we just well, if we if we talk to you again in, two years, do you think it'll be like, okay. Yeah. Now I'm a believer in the in the Avatar thing because it is photoreal.
Speaker 7:Okay. So so two things. So one, right now what matters most is obviously the modality of the participant. Like, if you think about like all we care about is the most deep, like like thoughtful, in-depth data that we can like Put it that responds. Yeah.
Speaker 7:So that matters way better than text. And that's way better than that. Sure. Sure. Our product that we You gotta get them talking.
Speaker 7:Yeah. Our like, pilot with Weight Watchers back in 2023 was, like, all text, and it was actually pretty good, but, like, it's nothing compared to what people actually say.
Speaker 1:Sure. Sure. Sure.
Speaker 7:Yeah. And so that's what matters most is, like, video screen share voice from the participants. From AI, yeah, like, so we use voice a lot. So there's a voice to voice mode where there's like no buttons, it's just conversation. And like, people like it, but like not as much.
Speaker 7:And I think there's still just if it's not a person, then it's still got this the minor imperfections, the kind of slight bits of latency.
Speaker 1:Mhmm.
Speaker 7:And so I do think in two years, we'll talk more about that. But ultimately, like, people know it's a computer. Like, they know it's AI. And, like, they're kinda cool about it.
Speaker 20:Like, we
Speaker 7:we've done a bunch this research. We're like, do you care that it's AI? They're like, no. Like, I I get it.
Speaker 1:Yeah.
Speaker 7:Or or like or like the real reason I'm not using your product is this.
Speaker 2:Yeah. Yeah.
Speaker 7:Yeah. I'm not gonna say that to a PM or a researcher who's like at a company. Yeah. Yep. Right.
Speaker 7:But instead, you'll tell the real truth.
Speaker 1:Yeah. Yeah. Yeah.
Speaker 7:And that was like
Speaker 1:Very interesting.
Speaker 7:Our initial pilot with Weight Watchers was all about weight loss and like Yeah. That was like a thing people don't wanna share
Speaker 1:a lot about. Totally.
Speaker 7:But with AI, they like shared everything about their lifestyle.
Speaker 1:Yeah. Yeah. I mean, it sounds like you've done like well, you've worked with really big companies and that's where the money is. But when you went through YC or if you're here, you know, there's a big theme of talk to your customers.
Speaker 25:Yeah.
Speaker 1:Has there been any pull at the lower end of the market?
Speaker 7:Yeah. It it it's funny. I I sometimes you get a founder reaching out, like, I I really wanna use this because I don't wanna talk to my customers. Yeah. And my voice is, totally like
Speaker 1:Don't know. Talk to your customers.
Speaker 2:We don't want
Speaker 1:you as a customer. Intelligent. You don't
Speaker 40:want you Yeah.
Speaker 13:Exactly. Exactly. Yeah.
Speaker 7:There there's basically I like I think a good a good reason not to use an AI moderator is like if you could sell to that person
Speaker 1:Exactly.
Speaker 7:Probably don't outsource that. Right? Like, maybe to build the relationship yourself.
Speaker 1:So Yeah.
Speaker 7:Yeah. So that the truth is with especially all the b two b stuff, there's not as like, you usually turn around and say come back when you're like a couple 100 people and like you're kind of scaling that out. But, yeah, with with some consumer stuff where you really have millions of people you're trying to learn from, it can make a ton of sense.
Speaker 1:Talk to me about the, like, data processing and the intelligibility that goes into once you have all that data. Yeah. It's very nice to be like, hey, have, like, you know We prepared this site. Hours of video.
Speaker 2:Prepared a 200 page report for you.
Speaker 1:Yeah. But nobody reads that. So how are you thinking about actually compressing that down? Because that seems uniquely suitable for AI. That's But there's still a lot of art in terms of, like, a lot of people say, oh, yeah, I did a deep research report and then I the next query was, give me 10 bullet points about that.
Speaker 1:Yeah. Because I didn't actually want the full report.
Speaker 7:Yeah. Yeah. So so alright. So so our vision here is like, we should be building we are building the like deep research, but for primary research.
Speaker 1:Sure. Sure.
Speaker 20:So you
Speaker 7:think about deep research is like, alright, all desk research is now commoditized. Yep. Yep.
Speaker 8:So what
Speaker 7:if you just ask a question, you just want to like learn from real people
Speaker 1:Yep.
Speaker 7:That's much more up to date than whatever, like your own proprietary data, it should be the same kind of idea.
Speaker 2:Right? Yep.
Speaker 7:End to end agents are doing that. But today, like, the premise of AI kind of, scaling your qualitative research is, like, you also have to help them do something with all that conversational data. Right? Because otherwise, you're, like, left with just, hours and hours of transcripts. I'm like, I'm not gonna review that.
Speaker 2:Yep. But implication of this is I know this is not what you're focusing on, but if I was a VC that wanted to write a a multi $100,000,000 growth check, being able to like get live interviews with like 500 customers.
Speaker 1:People already do this GLG
Speaker 26:and Yeah.
Speaker 7:That's right. No. This this is a huge opportunity actually. So so I have a whole side thought. Like, I think one of the challenges there is expert networks is all about finding the right people.
Speaker 1:Sure.
Speaker 7:Yeah. But if you're in a situation where you can find the right people and you can find a 100 of the right people
Speaker 2:Well, we just had Plato AI on. You you should talk to them. They're in this batch. They do, like, LLM based people search. So you can find, like, every person that at at Databricks that was a former founder.
Speaker 7:Connect all of that through
Speaker 1:Yeah. Through that.
Speaker 41:But what I was
Speaker 7:gonna say with the synthesis is is what we do is basically take in all the video stuff and then we like process it and we give you basically reports. But it's like not just a like, here's what it's telling you like deep research style. But what you could do is like start slicing and dicing it. You could like we quantify it, we give you breakdowns, we like build highlight reels for you, and then like people can like cut it and segment it. And so it's like a whole analytics suite Yeah.
Speaker 7:On top of qualitative data, which is like not a thing that has ever been done.
Speaker 16:Yeah.
Speaker 42:Yeah.
Speaker 16:Because you
Speaker 2:know were you doing
Speaker 20:before this? So before this, I
Speaker 7:was VP of product at TripleByte, if you know the
Speaker 1:Oh, yeah.
Speaker 2:At Fudge Tigar.
Speaker 7:Yeah. Harsh Harsh Wired Murder. Yeah. Exactly. He Yeah.
Speaker 7:Yeah. He he, anyway, so so I I worked at Triplebyte at Cool. Jumpstart, another company. So I was, like, leading product and design teams. But earlier in my career, I was, like, a I was a consultant where I was doing, like, this work nonstop.
Speaker 7:Where I
Speaker 1:was, like,
Speaker 2:are you a are you a nominative determinism guy?
Speaker 1:Yeah. Yeah. He's looking
Speaker 2:for capital You're an absolute canon.
Speaker 1:To fire a billion dollars into research.
Speaker 7:Yeah. Have not capitalized on that.
Speaker 1:I was looking at that. But We're big we're big We're big into the names or the truth. The names
Speaker 7:are like that.
Speaker 1:Yeah. It works well for you.
Speaker 25:I have
Speaker 7:a friend who always calls me the loose cannon.
Speaker 40:Yeah. Yeah.
Speaker 7:Yeah. Yeah. A
Speaker 1:little bit too much. Dial it into capital cannon.
Speaker 18:Exactly. Capital.
Speaker 1:You can trust it with your capital. You just give it
Speaker 38:to him. Oh, yeah.
Speaker 1:Fire it in the capital cannon and boom.
Speaker 7:Yeah. I'm gonna pivot off of loose cannon on onto capital
Speaker 6:or capital cannon.
Speaker 1:Yeah. It's capital cannon. There you go. Anyway, this has been fantastic. Thanks so much, man.
Speaker 24:I'll do it. Congratulations.
Speaker 6:Congratulations. We'll have you
Speaker 2:back on sometime.
Speaker 1:Yeah. $17,000,000 series, ay.
Speaker 2:Let's go.
Speaker 1:Fantastic. Biggest round we've heard yet. Let's bring in the next guest, Operative. Oh. There we go.
Speaker 1:The pins are going on. The
Speaker 2:pin the pin vipers are going
Speaker 1:vipers. Welcome to the stream. How you doing?
Speaker 5:Hey. How's going?
Speaker 1:Good to meet you. I'm John. I'm here.
Speaker 20:I'm here.
Speaker 1:Eric, nice to meet you. Meet you guys. Hey. Good to meet you. You're wanna keep those mics close to you because it's noisy here at YC demo day twenty twenty five.
Speaker 1:Good to meet you. Can you introduce yourself and the and the company that you're building?
Speaker 31:Yeah. We're we're Operative.
Speaker 26:I'm I'm Chris. I'm Chris Settles. This is my co founder Eric Quintanilla. Very nice. You can introduce yourself as well.
Speaker 26:We're we're friends from high school. We met at West Aurora High School in Aurora, Illinois Oh, boy. 14. Cool. We were doing our Java programming class together.
Speaker 26:Wow.
Speaker 2:Wow. And,
Speaker 26:ten years later, here we are.
Speaker 2:Deep, deep. What what did you guys do between, then and now? Yeah. Do you
Speaker 43:wanna tell We're
Speaker 1:another programming languages. Do you
Speaker 5:wanna tell more
Speaker 31:of the story, Eric?
Speaker 1:Yeah. I mean, we went
Speaker 5:to college Maybe
Speaker 1:you still write Java. I don't know.
Speaker 2:Yeah. I mean, we
Speaker 5:went to college. Worked at a couple of big tech companies. Worked like a couple of
Speaker 1:startup tech. Yeah. Yeah. Big tech.
Speaker 2:That's here. Amazing. Yeah.
Speaker 5:Chris was at Uber.
Speaker 15:You can
Speaker 1:talk about that. There we go.
Speaker 2:Nice. Did you wear the pit vipers during your your main pitch?
Speaker 21:Sorry? Oh, are you you're
Speaker 1:asking why
Speaker 5:I wear the suns?
Speaker 2:Did you wear them while you were pitching the the photo?
Speaker 1:Oh, no.
Speaker 5:I just got these from Marco. I don't know.
Speaker 1:There we go.
Speaker 26:We saw Marco in here wearing We're like, Marco, you look so cool. Yeah.
Speaker 1:Yeah. Yeah. Yeah. You gotta put them on. So, break down the company.
Speaker 2:Yeah. What are you guys doing?
Speaker 26:Alright. So, we're working on web app cogeneration for internal APIs.
Speaker 15:Okay.
Speaker 26:Awesome. So, you can imagine having like a lovable plus a retool for your company where you can on demand just create any kind of application that you're thinking about, whether it's like a new dashboard Yeah. Or it's some kind of like a a app to manage your airflow and you wanna have a nice UI with it and you wanna connect it to all these different pipelines. And I mean, that's just one use case you can think about, but you can build any kind of front end application that connects to existing APIs that you have in your company.
Speaker 1:Yeah. I remember, like, when you when you set up, like, a Django website, you kinda get, like, the Django admin, like, out like, out of the box, and you can just in in, you know, in in investigate all the different classes that you've kind of defined in the database. What are some examples you could give us of these internal APIs that typically float around in companies? Or you can either give, a precise example or just a general example. Yeah.
Speaker 26:Do you wanna give one?
Speaker 5:Yeah. I mean, a a lot of the use cases are centered around, like, customer service. So people, like, wanna interact with, a database. People who are doing support wanna wanna, like, change, like, payments and stuff like that
Speaker 1:Yep.
Speaker 5:Data visualization, stuff like that.
Speaker 1:Okay. So yeah. I mean, it it it it is it is it unique about, like, bringing different services together? Because a lot of, like, smaller companies would say, you wanna you wanna look at the payments admin? Head over to your Stripe dashboard because Stripe's already built that.
Speaker 1:Where where are where are companies currently falling short? Is it that they're they're not bringing the data together across services? Or is it that they're developing, like like, their own databases and their own their own tables that just don't have a don't have an API out of the box or don't have a don't have a web app in front of their API out
Speaker 26:of the box? Yeah. Yeah. So as so definitely start ups, it's super easy to just say, okay. Yeah.
Speaker 26:Check out their Stripe dashboard Yeah. Manage that. And then that's what Stripe
Speaker 1:typical customer service flow. It's like you go to Shopify for this, and then you go to the the the CMS for this. Yes. Then you go to Stripe if there's a problem over there. Yes.
Speaker 1:Yeah. Payroll. It's all SaaS products.
Speaker 26:Yeah. But one of the things we're like we we noticed especially at our our jobs in big tech Yeah. Is that eventually your product gets really complicated and you actually don't there's not a SaaS product that those that those services offer. And so you end up needing to build something on top of it in order to manage it. And then you end up building needing to build all these different internal tools to Yep.
Speaker 26:Like have all of that organized in a nice way where you can connect it all together because Mhmm. The traditional SaaS doesn't support the features that you're looking for. Got it. And so then, as a result, you hire a team to build out those kinds of applications and then
Speaker 5:Spend a lot of money.
Speaker 26:Yeah. And that's what we're trying to bridge that.
Speaker 2:Yeah. What was the moment for you guys? Were you seeing the the kind of explosion of tools like Lovable and things like that and then and then you saw, wait, why aren't people doing this internally or what what was the kind of moment that you guys decided to focus on this?
Speaker 5:Yeah. I mean, I think for us we saw like we launched this like Lovable type product and we saw a lot of people from companies using it.
Speaker 26:As a consumer product actually at To build internal
Speaker 5:tools for that company. So we actually we were like, oh, we should just bring this directly b to b.
Speaker 2:Cool. Very cool. Yeah. In in some ways, makes sense being able to, like, quickly generate something, test it, and then and, like, it's very different than sort of these, like, ephemeral products that exist and, ultimately will need to be I don't know. Yeah.
Speaker 2:Sometimes rebuilt.
Speaker 1:Yeah. Talk about the go to market motion. It sounds like this is not a company where you need to sell to every other startup in YC. Who are you selling to? How are you actually convincing them to take the leap and go with you?
Speaker 26:Yeah. So we're we're starting from the initial sort of traction we saw with the consumer product, we decided we wanna work on this enterprise direction and or like larger organization direction Mhmm. Because of these like use cases we saw with people signing up all of their work emails and building apps. So we're starting some design partnerships with large organizations. I can't say
Speaker 1:Yeah.
Speaker 26:Yeah. I won't say the name exact name. And we're planning to kind of just leverage our our warm in warm intro outbound network Sure. Our warm intro network to be able to talk to some more organizations. And I think like there's some like there's actually a large percentage of companies that even will try to use like retool to build internal apps.
Speaker 26:And so we like, we think we can work with some of the other YC companies that are Okay. Doing things like that or using that and Yeah. But maybe have
Speaker 1:demo day. Are you guys are
Speaker 2:you guys working with Retul or you you you compete with Kinda competing
Speaker 26:have a pretty cool direction that's like pretty unique at least at the moment.
Speaker 1:We're Talking about the actual instantiation of the web app. I'm sure you're using AI and code generation. What's working? What how much is are you leveraging o three or Claude or or Gemini? Source or Gemini?
Speaker 1:What's what what are you looking for? Where are the models falling short? Yeah. Where are you kind of, like, trying trying to stay ahead of the puck because everything's developing so quickly? You wanna take it?
Speaker 5:Yeah. So I think, on the platform, I think the foundation models provide like a nice foundation. Yeah. But like when we try to leverage, good tools, so like viewing a file tree, we had a browser agent on top to test and validate the app.
Speaker 1:Okay.
Speaker 5:The model's far short in I think they're good at, like, going zero to one. But I think, like, if you go zero to one and then there's a bug, going back and finding what file it is, what's implicated in it Yep. I think it's very hard for the models, and it's better to just start over.
Speaker 1:Okay. Yeah. Have you benchmarked against how long it takes someone to build a web app for a on top of a private API using Cursor and just saying, I'm gonna vibe code this myself versus your product? Because it feels like it's getting faster and faster, and there's a lot of I mean, there's, like, there's whole there's whole open source projects for, like, build out the API docs around an API just from, you know Mhmm. This this was programmatic.
Speaker 1:This wasn't even this wasn't even AI. You know? You just have, like, you wanna you wanna put this you wanna instantiate this as a blog. Okay. Here you go.
Speaker 1:Yeah. Oh, what are you benchmarking against?
Speaker 26:Yeah. Actually, maybe, like, cool story, like, on the on the backstory of how we came across this is that we actually started with people wanting to develop apps with like inside Cursor. And so one of the kind of unique insights we had is that today, like it it require like developing any kind of like front end requires that you ask Cursor for a prompt and then you open up the web page and go click on it and just verify that it looks how you want it to.
Speaker 1:Sure. Sure.
Speaker 26:And you can go and like then tell Cursor like, okay, I wanna, you know, move the blue button inside the black box.
Speaker 1:Yeah. And then
Speaker 26:you have to like just keep prompting it until it do does that. So actually one thing Eric and I worked the thing Eric and I worked on was like a actually a MCP tool
Speaker 1:Oh, yeah.
Speaker 26:That allows people to like opens up a browser agent that can go and view changes that a coding agent makes.
Speaker 13:Mhmm.
Speaker 26:And it will it will just test to see if the like, visually, if the app actually works. Actually, there's a there's one of our users over there, Neil. He's actually giving us feedback on it. So and we grew that to, like, around a thousand GitHub stars. Yep.
Speaker 26:And so, like, people got really excited about that, but we wanted to bring those same tools into a product like Lovable or like CodeGen
Speaker 1:Mhmm.
Speaker 26:And be able to have all of this code generation with testing built in. So that way we can allow users to, like, just go from prompt to a working web page without any interaction.
Speaker 2:Very cool. Are you guys still raising? Raising right now?
Speaker 26:We're still raising. We have we've been back by Weekend Fund
Speaker 1:Oh, good.
Speaker 26:Nice. And also a few other angel investors. And we are we're still raising and
Speaker 5:open to some retool actually.
Speaker 26:With other investors. Have an angel investor in retool
Speaker 1:investing in us. So Play the hits.
Speaker 2:Play the hits.
Speaker 1:Play the hits.
Speaker 2:Find something you love and just
Speaker 1:Do you have any metrics that you that you shared here at demo day?
Speaker 26:Yeah. As I mentioned, we had the GitHub repo. We we, like, scaled to a 100 or sorry. A thousand a thousand stars.
Speaker 1:That's pretty good.
Speaker 26:Yeah. We just launched the consumer product we have. It got around 2,500 different users Okay. Cool. Build it using Operative built apps.
Speaker 1:Crazy. Yeah. Yeah. It's
Speaker 26:great. And right now, there's, about a 4% conversion from free to pro pro plan. We have a pretty generous free tier, so you can go and use the app however out you want. But the for the the power users, like, there's around 4% of them converting. So we have about $1.1.5 k monthly revenue at the moment.
Speaker 26:Amazing. And we're that's our that's with the consumer product, but, we're planning to scale more, revenue
Speaker 2:for the enterprise. You could probably get that on your contract.
Speaker 26:Exactly. Well, thank you
Speaker 1:for stopping by. Please enjoy
Speaker 2:these nice guys.
Speaker 35:Thank you
Speaker 1:so much.
Speaker 2:Have fun out there. Was great having you on.
Speaker 1:Thank you. We'll talk to you soon. Alright. Let's bring in the next crew. We are at YC demo day.
Speaker 1:Hey. How you doing, miss? What's up? What What's up?
Speaker 2:It's time to talk sleep scores.
Speaker 1:Let's talk sleep scores.
Speaker 7:We we
Speaker 11:continue to sleep last night.
Speaker 1:It was brutal. I was I was at the Rosewood.
Speaker 2:The Rosewood doesn't have eight sleep.
Speaker 3:It's a big problem.
Speaker 1:You don't have eight problems. Not yet. We're not
Speaker 2:yeah. Yeah. 90% of the guests are. I'm good.
Speaker 11:That's a good fun stuff.
Speaker 1:Good to see you.
Speaker 31:Yeah. Great
Speaker 1:job. I wanna talk f one. I wanna talk f one. You just did a interview with, Charles Leclerc. Yep.
Speaker 1:Break it down. What did you learn from him?
Speaker 11:His obsession for, every single detail in his preparation. He goes for, two weeks at the mountain in January to prepare for the season.
Speaker 2:Is that for elevation
Speaker 11:or Elevation. And then he does every sort of skiing
Speaker 1:Okay.
Speaker 11:For, like, eight hours a day. And then he goes to the gym, and then he brings the pod. He sleeps on the pod while
Speaker 1:he's there.
Speaker 11:That is when he really prepares the season. Yeah. And and then his obsession for every single detail when he when he travels, all the gadgets that he brings with himself from, you know, devices for recovery Sure. Ice bath, obviously, the pod.
Speaker 1:Yeah. The next training. Is he doing a compression thing? That's a big one right now?
Speaker 11:And I don't know if you saw it. So we so Charles was talking to us about the fact that when he's on the grid, it's really hot. Yeah. Yeah. And a lot of athletes in Formula One now, they use this vest, cooling vest.
Speaker 11:Oh, interesting. But they don't really work. Okay. So the way you wanna really cool your body, you need to cool the palm of your hands.
Speaker 1:Oh, interesting.
Speaker 11:And so I was with my co founder Max and we said, what if we build cooling gloves for you? He said, oh, that would be interesting. So we built the gloves in three days. No. No way.
Speaker 1:It's awesome.
Speaker 11:And then one of us one of our people, they flew to meet him in Barca. In Barca, they were, like, 35 degrees Celsius. It was super hot. And so Charles started using these gloves, cooling gloves that we built for him. And then as he goes in the box, Ferrari takes a picture of him just, you for Scuderia Ferrari blah blah
Speaker 1:blah.
Speaker 11:And so he was on the cover of the Instagram account.
Speaker 1:With his
Speaker 30:With his sleeve. That's good.
Speaker 1:So it
Speaker 11:was free branding for us with the device we built for him.
Speaker 1:Right? And so now You see that with the tires. They have the tire warmers, then you have the coolers. Yeah. Every different part of the f one machine needs to be temperature controlled.
Speaker 1:But then have access
Speaker 11:to this. I him using this. Yeah. And so paddle players, tennis players, they all say, oh, can I use it? And said, but, man, we did we didn't Yeah.
Speaker 11:One off. Yeah. It's one And so now we have a team that is building this cooling globe except for a few athletes.
Speaker 1:That's that's incredible. Great partnership. What what has YC Demo Day been like for you? Are you here just trying to sell Eight Sleeps, or are you also doing some investing? What are you what what are you getting out of of YC Demo Day today?
Speaker 13:Yeah. Yeah.
Speaker 11:Yeah. I do a bunch of investing. Okay. I invested, like, probably 20 companies in this batch.
Speaker 2:Already? Wow.
Speaker 1:Amazing. Amazing.
Speaker 2:You gotta get it. Yeah.
Speaker 1:Mean, you gotta get it in here.
Speaker 11:You need to start early or or then you're out.
Speaker 1:Oh, yeah. You gotta get it early.
Speaker 11:You gotta get it early. And so in every batch, I try to invest in around 20% of the batch
Speaker 1:Oh, cool.
Speaker 11:More or less.
Speaker 1:That's great.
Speaker 11:And I have been doing this for two years.
Speaker 1:Have you seen anyone doing cool hardware, cool consumer devices? I know that there's a ton of AI. There's some hard tech. Defense tech's becoming cool. But I always like I like the Eight Sleep because it's a gadget.
Speaker 1:You can give it for someone for, you know, Christmas, and they, like, experience it every day in a very different way than just nap on their phone. Yeah. Have you seen anything?
Speaker 11:So the the I I I I'll answer your question, but it's cool because I walk around with my head. Yeah. And people stop me, and they say, oh, I sleep on your product. I just met two two founders. Yes.
Speaker 11:And one had a 70 five score, the other one at forty eight score.
Speaker 1:Oh, no. That's it. Forty eight the night. Yeah. That's
Speaker 1:it. Every day. Every That doesn't work. I've been smoking Jordy this week. I I beat him twice a
Speaker 2:I got it. Yeah.
Speaker 1:I'm up in the nineties. I'm doing great.
Speaker 2:I've had a rough one.
Speaker 1:But there is not
Speaker 11:a lot of consumer hardware. It's hard. It's so hard.
Speaker 1:Yeah. It's really hard.
Speaker 11:I was just talking to a few founders. Even when I look back, other companies starting with us, they all struggle. Yeah. So we have been so lucky to be here today. Yeah.
Speaker 11:But you see a lot of really cool stuff now in robotics.
Speaker 1:Yep. Yep. Matic robots. We've
Speaker 2:and at home.
Speaker 11:And so usually, there is an hardware section, and that is the section where I go. So hard tech or that is my passion.
Speaker 1:Yeah. Yeah.
Speaker 11:And then, yeah, AI is everywhere.
Speaker 1:Yeah. Yeah. I mean, you see a lot at CES. You'll see the AI connected oven and the AI connected toaster. And why YC's kinda stayed away from that Yeah.
Speaker 1:Because it feels like it's hard to build, like, an independent business around. But but I am optimistic that that once the software side of AI is so commoditized and so, just ubiquitous, we'll see another revolution in hardware,
Speaker 11:another turn. With the robots, things will get easier because robots will explode Yeah. And a lot of consumer will buy different shades of robot.
Speaker 1:Freden was talking about. He wants the the the robot that picks up the leaves one by one instead of leaflet. Fun. Yeah. And and it's like a funny idea, but it feels like, yeah, that's only a couple years away, then it'll just be
Speaker 11:a Coming for security. Have this idea about a robot that goes around your house Yeah. For security cameras everywhere. There's cameras and lights. Yeah.
Speaker 11:I I would immediately buy that. Totally. If you could go around my house and make sure
Speaker 2:that Especially if you could put a gun on it.
Speaker 1:Yeah. I mean, Amazon Amazon did launch a drone that will I Or maybe they they just launched a video about this. There's a drone that takes off from a base station where it charges and it can fly around your house and kind of and kind of patrol inside your house. But yeah I wanted that turtle
Speaker 2:the when the fires were happening, I figured that a lot of fires in LA are just started from a single ember landing in like a backyard Yep. Or or on a house
Speaker 1:or stuck.
Speaker 2:Yeah. And you just realize that, you know, there there's probably an opportunity for Yeah. Drone based.
Speaker 1:Yeah. There there's actually a couple companies that did, like water turrets that you mount to your to your roof. Uh-huh. Yeah. And then if they see fire, they
Speaker 18:can just spray.
Speaker 1:Yeah. Right there. It's a hose. It's literally just a hose on an articulated arm. It's like not that complicated, but you could What
Speaker 2:what about at eight? Any any any other hardware coming down the line that you can talk, you can kind of allude
Speaker 11:to yet? So, actually, we have an office in SF.
Speaker 1:So Oh, yeah.
Speaker 11:I just landed. I came here. I'll be here for a couple of hours, then I go to the office to see the new products.
Speaker 6:Very
Speaker 11:cool. I'm already on the next generation.
Speaker 2:Edge. That's such an edge. You're on pod five. I'm pod I'm I'm already on pod six.
Speaker 11:Yeah. All my friends, they they always now make jokes because I always sleep on the next generation and there are new devices. And so they buy the latest, but I'm already one one
Speaker 1:generation this
Speaker 2:is how you win.
Speaker 1:This is how you win.
Speaker 11:We started working with some new sensors that are incredible in terms of computer vision.
Speaker 1:Sure.
Speaker 11:So we really wanna double down on body scanning and scan your body while you're asleep to save your life. And that to me is the one of the most exciting things because
Speaker 1:if we
Speaker 11:can convert your bed in a health platform and save your life, that's that's pretty sick.
Speaker 1:Yeah. Yeah. That's amazing.
Speaker 2:What, what are the companies that you're most excited about in the batch? Without picking too many favorites out of the 20, any anything that you're most Yeah. Kind of excited about?
Speaker 11:At the end of the day, I just get excited with founders. I mean, I just met this guy, and he's a high school dropout. He's 18.
Speaker 1:And he's Yes. And what? Yeah. That's crazy.
Speaker 11:Yeah. Because I was at school. Decided to drop out. I thought there I thought there was this opportunity for AI to help teachers. And so I dropped out.
Speaker 11:I built it, and now I'm selling it to my teachers.
Speaker 1:I said, what? That's amazing.
Speaker 11:And so when you see these people, right, I don't even I don't care what happens with your company, but I admire you so much.
Speaker 1:But can
Speaker 11:I can I
Speaker 1:Of course? I wanna back up the founder. Right? Exactly.
Speaker 11:And so that is what excites me, like, to to stay young Yeah. And see these generations shaping the future.
Speaker 1:That's fantastic. Amazing. Well, thank you so much for stopping by.
Speaker 11:We will see you soon. Great job, guys.
Speaker 1:Thank Yeah. Yeah. Glad you did. It's been great. Let's bring in the next team.
Speaker 1:Welcome to the YC demo day
Speaker 38:twenty twenty
Speaker 31:five stream.
Speaker 2:Good. I put so much of the of the
Speaker 1:Yeah. Yeah. That's okay. I'm working through it. How you guys doing?
Speaker 1:Oh, you guys already got the hats on. We'll see if there's if there's other surprises in store. We might need to blow the whistle. That's all I have. Welcome to stream.
Speaker 1:How you doing?
Speaker 2:I like I like how you guys just said, made in SF, not made with love. Made with
Speaker 1:Made in SF. Mean Taking shots.
Speaker 9:We have a made in with love in San Francisco as well.
Speaker 1:Oh, you do? Okay. Well, what is Theroxy? Break it down for us. Explain the business.
Speaker 9:We're building AI agents that help people selling into traditional industries such as manufacturing Okay. Distribution. Okay. There are sectors in which selling is very hard
Speaker 1:Okay.
Speaker 9:Especially from a b to b perspective.
Speaker 1:Sure.
Speaker 9:So we're helping those companies prospect into legacy industries
Speaker 31:Okay.
Speaker 9:And we manage the entire outreach to those.
Speaker 1:Okay.
Speaker 9:Whenever someone is interested in speaking with my clients, I connect them with their buyers. Sure. And to help them grow.
Speaker 2:So And so that's like selling super technical products. What does that what does that look like?
Speaker 9:Yeah. It's selling super technical products, but also professional services such as consulting. The important thing is we help people selling into traditional spaces like manufacturing.
Speaker 1:Okay. So so there's a manufacturing company out there. They're making microphones, for example. I am a company that's gonna sell, you know, better software design software that runs on this or or or automates the the facility. You're gonna help me find clients to sell to.
Speaker 1:Is this more about the prospecting and developing a lead list, doing the actual outreach? Is this an AI, business development representative, SDR play? Are we gonna see a billboard on the 101 for you pretty soon? What's going on?
Speaker 44:So it's a bit of, like, everything you've mentioned. So, like, we don't like to micro ourselves as AI SDRs because they have, like, a bad reputation.
Speaker 1:They have a terrible reputation.
Speaker 44:And we think, like, focus is a multiplier on, like, the work we do. That's why we're, like, serving these traditional industries which are like traditionally like underserved. Yep. And everyone's ignoring them, but they're like huge market opportunity
Speaker 13:Mhmm.
Speaker 44:And like really high average contract values.
Speaker 1:Mhmm.
Speaker 44:So we can like have people serving these industries both with AI, but with a bit of human in the loop right now. And now we're automating those humans in the loop Yep. As models get better, as agents improve
Speaker 1:Yep.
Speaker 44:And, like, rolling them out.
Speaker 1:So talk about the copilot era. Where is it important for in the sales process to keep the human in the loop right now? What is the most automatable part of that process?
Speaker 44:So the most automatable part is qualifying the whole companies. So like
Speaker 1:Okay.
Speaker 44:Actually, like, we just like pull a list of companies from, like, our crawlers, and then we look for, like, specific stuff with AI agents and we just qualify them.
Speaker 1:So could they even be a buyer? Do they have budget? Are they big enough? Are they declining in sales? Are they about to get rolled up into some private equity thing?
Speaker 44:The thing is, like, because we're focusing only in manufacturing companies, we can check for very specific stuff for,
Speaker 1:like Okay.
Speaker 44:Does this machine, like, fit, like, any specific Yeah. Specifications? Do they have, like, this grind type? Do they do, like
Speaker 1:Sure.
Speaker 44:CNC milling? Yep. That kind of stuff Yeah. Which is really important where,
Speaker 1:like Yeah.
Speaker 44:Horizontal SDRs fail Sure. And they fail to serve them
Speaker 1:because they're too generic. Because if I'm selling CNC software and some manufacturer doesn't use CNCs, why should I even talk to them? So you're saving me time that way.
Speaker 2:How did you guys get into this?
Speaker 9:So I think it's I was in sales, I was doing a lot of manual work myself. I've been an SDR at three companies.
Speaker 1:Mhmm.
Speaker 9:Completely different value propositions but I think one of the big issues was actually finding the company I can sell to. I was doing all of that very manual research. Do they have buying power? Do they have this specific certification? When I was selling into hospitals, had to check what their team staff looked like to see where they fit my software.
Speaker 9:And I hated my life. I was like
Speaker 1:you were still performing. You
Speaker 2:were still performing. Right? You were putting up big numbers?
Speaker 9:Yeah. I actually was. And But but, yeah, I I generally felt like my my job was gonna be replaced soon, so I was like,
Speaker 2:let me be I gotta get
Speaker 1:ahead of this. Let me replace myself. Disrupt yourself. Talk to me about what it takes to actually qualify a lead. What are the data sources?
Speaker 1:I've heard LinkedIn is extremely rich, but it's very locked down. They don't let you play with the API anymore. Obviously, you can crawl around on Google search, and some companies have a lot of information on their websites. Some of these manufacturers barely even have websites at all. That How are you getting data?
Speaker 44:That's our insight. Like, our insight is, like, LinkedIn doesn't, like, serve these manufacturing people because the owner of a manufacturing company is not on LinkedIn. Their prospects are
Speaker 38:not there.
Speaker 44:Yep. So we've built our own crawlers
Speaker 1:Okay.
Speaker 44:Which are crawling the whole Google knowledge graph Mhmm. From scratch and then sending agents to qualify these people which have access to specific tools with directories of other manufacturing companies Mhmm. Or API, like, access to, like, machine specification Okay. Access to vision. So we can, like, check out the content of the website, like, take a screenshot.
Speaker 1:Oh, interesting. Take a Hey, that's a CNC. Exactly. This is a CNC company.
Speaker 2:Talk about traction. When Did you guys come into YC with this idea and you've just been working on it or did you iterate towards it?
Speaker 9:Yeah. We came in with this idea of in six months we've gone to 1,500,000.0
Speaker 1:in
Speaker 30:a year.
Speaker 1:Let's go. That might be the biggest one we've heard yet. That might Big one. Let's go. 1,500,000.0, baby.
Speaker 1:Let's go. Congratulations.
Speaker 2:That's sick.
Speaker 1:That's amazing. So the round's already closed, I imagine. Yeah. Totally.
Speaker 37:We got
Speaker 1:to do another round. Absolute Absolute dogs. Great. That's fantastic.
Speaker 2:What what what's your guys' backstory? How'd you get to YC?
Speaker 44:You were We met in high school, and we've always been, like, building stuff since
Speaker 1:we were Awesome.
Speaker 44:Love it. Pablo was like, I hate sales, but like, I love money. Was like Let's go.
Speaker 1:Let's go
Speaker 44:build a company together. I was working in AI at JPMorgan. Was like the most boring thing to do ever. Working in
Speaker 1:a So
Speaker 44:I was like, let's go
Speaker 1:do it. Super money. That's amazing.
Speaker 2:That's amazing.
Speaker 1:How big is the team? I mean, sounds like you've you've already scaled this business a little bit.
Speaker 9:Yes. So we're team of four right now.
Speaker 1:Okay.
Speaker 9:Focus is very important. Yeah. So when you're asking where does humans perform better than AI, that's what we're testing. Sure. Whenever AI does better, we'll automate it, but not we have us actually doing that work.
Speaker 1:Yep.
Speaker 9:We wanna be very thick into the workflow, understand the problem. And, at the end of the day, it's my reputation down the line. Yeah. It's not the AI SDR of Throxie. Yep.
Speaker 9:It's me. Yep. So we're showing face in front of our clients
Speaker 1:Yep.
Speaker 9:And ensuring that this works.
Speaker 1:Yeah. That makes a ton of sense. Well, good luck to you.
Speaker 2:Amazing, guys. Congratulations.
Speaker 1:Awesome. Enjoy the rest of your day. Cheers. Nice meeting you. Much.
Speaker 1:We will talk to you soon. Stuff. And we will continue our coverage of YC demo day twenty twenty five. We will bring in the next guest. I see some people out there.
Speaker 1:Come on down to the palace party rounds. Tell us about your company. How are doing? Morphix in the studio. I see one hat.
Speaker 2:Let's go.
Speaker 1:I gotta give out a hat. How are you doing? Welcome to the stream.
Speaker 2:So much. Good catch.
Speaker 1:Come on down. Good catch. Who are you? What do you do?
Speaker 38:We are Morphic. Okay. Avi, and we build open source multimodal search for AI agents and applications.
Speaker 1:Okay. Open source multimodal search. What are, give me some examples of the multimodality. What are we searching? Because some of these datasets, I'm imagining if you're trying to search over YouTube videos, YouTube is gonna shut you down if I'm trying to search across that.
Speaker 38:No. That's a great question. So for multimodality, it includes not only, like, just plain textual documents, but documents that might have pictures, photos, images embedded inside of them. Yeah. What a lot of other people approach it as is trying to do OCR parsing on top, but that doesn't work because documents are
Speaker 1:Yeah. PDFs are a nightmare. The PDF stack is a disaster. We know this. It's a disaster.
Speaker 1:We've known this for decades. It's not getting any better. Adobe, clean
Speaker 38:it up. What what we do is we
Speaker 1:Take pictures.
Speaker 38:Pages directly as images Yep. And retrieve over those. Is much higher accuracy, much much better performance.
Speaker 1:Yep. Very cool.
Speaker 2:Are you guys gonna be able to fix the photos app? Searching photos. Very
Speaker 38:if Apple gives us a chance.
Speaker 1:Yeah. Yeah. Yeah. Give them
Speaker 2:a shot.
Speaker 7:Give a
Speaker 1:shot too. It does seem like they're trying to do that type of multimodal search on CrossOfIt, but they're just not there yet in terms of, like, I'll be searching for, you know, a dog jumping on a couch. I know that I have it in my camera roll, but it's a video. And that scene happened later, they haven't indexed it properly. So Yeah.
Speaker 1:Lots of opportunities. Talk about where people are implementing this. Is it enterprise private datasets? Is it public scrape the web? I wanna search everything.
Speaker 1:Narrow it down for me.
Speaker 38:Mhmm. It's a little bit of both. The most adoption is in the legal tech space. Oh, interesting. Also in the health tech space.
Speaker 38:They have, like, lots of documents with, like, tables, charts. Patent drafting, for example, has, like, a lot of technical diagrams in there.
Speaker 1:Yeah. Of course.
Speaker 38:And then it works really well for those people as well.
Speaker 1:Okay. Talk to me about the other major players in the space. We saw Glean yesterday raised, what, a 150,000,000 at 7,200,000,000.0. This feels somewhat adjacent. You're kinda eating off their plate a little bit.
Speaker 1:Is that a direct competitor, or is there something different where you can play nicely with them?
Speaker 22:Yeah. So, I mean, the kind of benefit for us is that we provide APIs and much more of a developer tool. Sure. Yeah. And the way we see this going is actually not as a competitor to Lean, but, like, more of, like, a provider to Lean.
Speaker 1:Okay.
Speaker 22:Right? And not just Lean, but to people that are building, like, CoreServerX and you need to deal with multimodal documents. Sure. That's exactly what we can help you with. So we're starting with search, but that's not it.
Speaker 22:Right? One of the things that cursor the reason why, like, tools like cursor and, like, coding agents have become really good is because code is low entropy Mhmm. Which means you can predict, like, well into the future what code is going to look like. Mhmm. One thing that we can do is help you get that low entropy with multimodal information.
Speaker 22:Mhmm. And so, like, what video editing looks like, kind of, like, three or four steps in the future if I, like, cut a clip and, like, cut another clip, I kinda know that I'm gonna delete the thing in the middle. Right? Mhmm. And so if you can use that and essentially provide that to models as, like, code, that ends up, like, performing a lot better.
Speaker 1:Mhmm.
Speaker 22:And you can start building cursor for video editing, and you can start building
Speaker 2:cursor videos. Mean? What were you doing before YC?
Speaker 38:We're both brothers.
Speaker 2:Oh, no way.
Speaker 38:Yeah. Before YC, I was software engineer at MongoDB.
Speaker 1:Oh, cool.
Speaker 38:And this guy
Speaker 1:dropped out of source thing tracks
Speaker 2:Dropped out of where?
Speaker 38:Right? Exactly. Cornell.
Speaker 2:Nice. Very cool.
Speaker 1:Yeah. Talk to talk to us about the open source strategy. How closely are you mimicking MongoDB? We heard earlier on the show that MongoDB didn't have a managed service, a SaaS product, until they almost went IPO. Are you planning to monetize the open source program earlier?
Speaker 1:What is the play between am I using the open source version, or am I paying you look like?
Speaker 38:Yeah. Good question. The way we see it is for people who if it benefits a
Speaker 22:single community member Mhmm. Then we want
Speaker 38:to open source it. Mhmm. If it benefits a team, then we want to leave it closed source. So things like SSO, things like connectors, like Google Drive, etcetera, or maybe, like, speeding it up for Sure. Like, making queries much faster.
Speaker 1:This is all gonna fall apart when people start building one person billion dollar companies because you're gonna be like, it all has to be open source because you only have one
Speaker 2:That's true.
Speaker 1:That'll be a
Speaker 2:good problem.
Speaker 1:That's a good problem to have. Yeah. But but but, digging in more into the open source, what's the traction been like? Do you have a GitHub project that has a lot of stars? What what are you what are you tracking in terms of rollout?
Speaker 38:2,600 stars
Speaker 1:today. Congratulations. That's fantastic.
Speaker 38:4,000 monthly downloads.
Speaker 1:Amazing. Thank you, Jordan.
Speaker 38:200 active deployments in production. Fantastic.
Speaker 1:There we go. Here we go.
Speaker 2:Audience is probably mad at me in
Speaker 1:the chat for that one, but it's amazing. Amazing. Yeah. Yeah. It's fantastic.
Speaker 2:You guys came into YC with this specific idea, or did you iterate to it throughout?
Speaker 38:We came in with a much broader idea. We weren't too focused or indexed on multimodal before. We were like, hey.
Speaker 22:Just we want to be the data layer.
Speaker 38:What sort of helped us was trying to narrow down on the multimodal aspect, seeing how people are building more and just, yeah, building on from there.
Speaker 2:Did you always wanna start a company together?
Speaker 1:Yes. That's been a dream since
Speaker 38:we were kids, but
Speaker 2:Incredible. Incredible. That's great. How's how's fundraising going? Fundraising's going well.
Speaker 2:We're kind of close, but Nice.
Speaker 38:Yeah. Looking looking to
Speaker 1:do it faster. Another one. Well, congratulations. It's been great chatting with you. Good luck on the next stage
Speaker 2:of your Very
Speaker 1:exciting. We'll be we'll be following along along Yeah.
Speaker 2:Excited to use Cursor for video whenever whenever that That would be the Send them our way.
Speaker 1:Yeah. They'll probably build it on top of your company. So thank you so much for stopping by.
Speaker 2:Sounds awesome, guys.
Speaker 1:We'll talk to
Speaker 2:you so much. Thanks for coming on.
Speaker 1:Bye. See you. Do we have anyone else? There's a little bit of lunch break going on, giving you some inside baseball here at Weissy. Coming in.
Speaker 1:Demo day 2025, but we have one more team. They don't need to take lunch breaks.
Speaker 2:They're owning purple.
Speaker 1:They're working. Owning purple. Purple. Purple. And and the vipers have made a return.
Speaker 1:I think it's all one pair of pit vipers in case recycling.
Speaker 2:We we know the pit viper found out.
Speaker 1:Color going on here. Let's put on some yellow hats as well. Let's just really get crazy with the the the the fever dream that's going on with the orange, the purple, the yellow. We love it.
Speaker 2:Quiety. Quiety. Quiety. Alright. What you guys up to?
Speaker 1:What are guys building?
Speaker 45:We're building automatic technical documentation for CodeBase. Okay. So saving 30% of, employee time.
Speaker 1:Okay.
Speaker 3:Yeah.
Speaker 1:Explain the customer. Are you going after larger enterprises? Are you selling to other YC companies?
Speaker 45:Yeah. Ideally, to enterprises. Okay. But we're starting off with series a b onwards companies. Okay.
Speaker 45:Majorly because that's when you start onboarding people. Yep. You need internal documentation to actually give, like, technical specification for your product, so on. So, yeah. Initial focus is series AAP on
Speaker 1:Specifically for companies that are delivering APIs that they sell access to?
Speaker 45:Not just APIs, but any any sort of, like right now, it's a web application, but any sort of Okay. In theory. So, like, API is part of
Speaker 12:it.
Speaker 1:Yeah. I I there's a number of open source projects that kind of allow you to, stand up a boilerplate for open source documentation or or API documentation. Mhmm. How is your product different? What what what are you hydrating?
Speaker 1:Because at a certain point, if there's kind of internal sacred knowledge around how an endpoint works, you have to get that from the person that designed it. Yeah. Or can you instantiate everything from the code?
Speaker 45:Yeah. That's the plan. Like, the idea is for us to not depend on one person. Yeah. The idea is, like, we actually go across the organization, all the code bases Mhmm.
Speaker 45:And, understand those, like, technical specifications for like, in the background.
Speaker 15:Yep. Right? So there's no dependency.
Speaker 1:How are you dealing with security? I imagine that if you're going across all the code bases, all of a sudden, there's, like, secrets that could leak. There's Yeah. There's internal tooling that they maybe they don't wanna have out there in their documentation. Yeah.
Speaker 1:How do think about that?
Speaker 45:So we make it completely self hostable. Okay. That's fine. So, especially for an enterprise when they have to be compliant and Yeah. Of course.
Speaker 45:Regulations and so on. Yep. So we completely make it self hostable. People can actually just use our product like it's packaged. So you can spin it up, put the repositories in, and nothing leaves their network.
Speaker 2:You guys get to YC?
Speaker 45:Like, in terms of the place?
Speaker 2:Yeah. What what's the backstory?
Speaker 1:You wanna go? Car, plane. What was it? I mean I mean, through plane, that
Speaker 43:was the last time I've
Speaker 1:known this guy. We've been through so many places. I've known him for about ten years. We met in uni, hackathons, tons of projects. Very cool.
Speaker 1:And to get to YC, just a couple failed applications, and here we are now. There's a story.
Speaker 2:Always a story. The
Speaker 16:one.
Speaker 1:So many. Everyone has one at least. How's the traction been? What are what are you sharing today?
Speaker 45:Yeah. So in the we launched about two and a half weeks ago. Right. We already are around 3,000 in revenue.
Speaker 1:Congratulations. Congratulations. There we go.
Speaker 45:Have we have about six six, seven customers now.
Speaker 1:Oh, amazing.
Speaker 45:So we are yeah. It's it's going well. We have we're increasing 30 over 30% week on week,
Speaker 1:actually. We go.
Speaker 2:Week on week. Go week. We go We week.
Speaker 16:Go week. Hopefully. Yeah. Never need to race again.
Speaker 1:You're good.
Speaker 2:Nice. How's how's fundraising going? You guys in the midst of it?
Speaker 45:Yeah. We are about 40% there. 50% there. So we're getting we're getting close Nice. Hopefully.
Speaker 1:Yeah. Yeah. What ways just you on the team right now? Do you have
Speaker 6:anyone else?
Speaker 1:We just do. Yes. Okay.
Speaker 2:Keep it that way for a while or you think you'll you'll start really
Speaker 17:I mean,
Speaker 45:we are thinking of getting people on board. But, yeah, we wanna make it we wanna keep it lean. We're we're trying to automate documentation, so Yeah. You got
Speaker 12:all that.
Speaker 1:Automate. Right? Yeah. Yeah. Yeah.
Speaker 2:You gotta live it.
Speaker 1:That's great. Well, thank you so much for coming on the stream. Yeah. Thank We'll talk to you soon. Thank you, guys.
Speaker 1:Bye.
Speaker 2:Good.
Speaker 1:And we are ready for our next guests coming into the palace of party rounds. Welcome to the stream. The the the of YC Demo Day has gone down as people move across street to lunch. Good to meet you. How are you doing?
Speaker 2:What's happening?
Speaker 1:Nice to meet you. I'm Jarrod.
Speaker 2:Nice to meet you. Pleasure.
Speaker 1:To meet you. Are doing?
Speaker 2:What's happening? What's happening? Big day.
Speaker 30:Picking up. Yeah. It's a big day
Speaker 8:for us.
Speaker 1:Yeah. How how did the pitch go? Smooth?
Speaker 25:We're gonna be in the in the afternoon today.
Speaker 1:Okay. In the afternoon.
Speaker 34:How are
Speaker 2:the nerves? Good luck. How are the nerves?
Speaker 25:Honestly, we are used.
Speaker 1:Yeah. This one, we
Speaker 25:are used.
Speaker 1:Alumni day of the day. They do a good job of kind of, like, getting you so many reps that it just feels like anything
Speaker 25:else. Honestly.
Speaker 1:It works you up to that very easily. Introduce the company. What are guys building?
Speaker 25:So I'm Francesco, the founder of Kuwa. Alessandro is the, COO. Mhmm. So we are building computer use AI agents
Speaker 1:Okay.
Speaker 25:Meaning AI agents that can solve any problems like a human would do in the terms of clicking, typing, scrolling.
Speaker 1:Okay. What at what layer of the AI stack are you working at? Are you sitting on top of just like a Chromium instance We or are you actually sitting on top of something like a browser based, or do you can keep
Speaker 25:people with them? We wrap an entire operating system on a isolated environment, kind of like a Docker for Yeah. The user agents.
Speaker 1:Okay.
Speaker 25:And that means that we can use system level events for injecting these commands, like click type and really any Bash or PowerShell commands.
Speaker 1:Yeah. Then and then what's working in computer use right now? There was theory that you just read the HTML at some point. Now there's more multimodal image generation, like, actually take a screenshot, process that, understand where to click. What's working?
Speaker 25:Yeah. So screenshot is working, way better Mhmm. Than accessibility tree for, operating system. In general, you don't type any HTML DOM to parts really.
Speaker 1:You don't parse any
Speaker 25:of that. System?
Speaker 1:You just throw it
Speaker 15:all away.
Speaker 25:We just, like, use screenshot. Yep. Screenshot and pixel based business models
Speaker 1:for that.
Speaker 25:Yeah. And it's also been proven by research that's working way better than Yeah. Interacting
Speaker 2:with What categories of agents are you guys seeing the most, you know, having the most excitement around traction? I think anytime you're building AgenTic infrastructure, you gotta have companies that are building great products on on top of you.
Speaker 1:It does.
Speaker 16:That can
Speaker 2:that can a challenge, but I'm sure there's a lot of other companies in the batch
Speaker 8:that Yeah.
Speaker 25:Yeah. So we haven't chased any verticals, meaning that
Speaker 1:You haven't chased any verticals yet?
Speaker 41:Okay. Meaning that
Speaker 6:we had the, the full one.
Speaker 25:Yeah. Yeah. Exactly. Because, like, actually, we month in YC, we were getting the most esoteric task from the users in terms of, hey. I have this bunch of contractors that are simply premium videos on video editing software on Mac OS.
Speaker 25:Can I use Kuo for that?
Speaker 24:Sure.
Speaker 25:And, like, really, we couldn't converge to, like, very common workflows, and that's why, companies that are our customers are chasing those verticals for us.
Speaker 1:Okay. Do you see the market fragmenting? Do you think you will find a vertical and niche down, or do you want it all?
Speaker 25:Honestly, we want it
Speaker 45:all.
Speaker 2:Okay. That's good. And we
Speaker 35:are here to
Speaker 1:Then then what is the, what are the the key deliverables that you have to optimize against? Is it speed, reliability, price, some sort of combination?
Speaker 24:How are you
Speaker 1:thinking about that?
Speaker 25:Like, probably for computer user agents Mhmm. We are still, like, six months away from the HTTP moment. Mhmm. Maybe for browser user agent, that's still the moment is today. So once we get that level of intelligence for models Mhmm.
Speaker 25:We need to come prepared with a a very good infrastructure to scale this isolated environment. Because, like, our leap of faith is that five years from now, most of the AI agent, a multi agent system will rely on API, maybe 80% of the scenarios, and the other 20% are gonna be based on browser and computer as tools.
Speaker 2:What were you guys doing before this?
Speaker 25:I was working on Microsoft for over five years.
Speaker 1:Oh, cool. Awesome.
Speaker 12:My myself, I was in Notion. I've been also a few startups in the
Speaker 45:Yeah. In the past,
Speaker 2:so Nice. Are you guys Italian?
Speaker 25:We are Italian.
Speaker 2:Nice. When did you come to The US?
Speaker 25:Three months ago now.
Speaker 2:Three months ago. Four YC.
Speaker 12:Yeah. My time in US.
Speaker 2:Crazy. Yeah. How's it been living up to expectations?
Speaker 25:Yeah. Definitely. People ask me
Speaker 1:Ferrari or Lamborghini? Yeah. People
Speaker 25:ask me how is San Francisco now? Is it better? And I don't
Speaker 1:have any comparison. I was But
Speaker 2:but how how old were you when you just when you knew you wanted to do YC? Was it
Speaker 25:I think it was pretty recently, like, three years ago now.
Speaker 12:Okay. Yeah. For for me, maybe since I was 16, and I'm 26 now.
Speaker 1:Wow. It's been,
Speaker 2:like, a long dream
Speaker 1:for me.
Speaker 12:That's remarkable. Since still, like, see
Speaker 1:I don't
Speaker 12:know if it's valid or not, but
Speaker 1:Yeah. Yeah. Living the dream.
Speaker 12:Living the dream. Yeah. Yeah.
Speaker 1:Yeah. What's the go to market been like?
Speaker 25:So the go to market right now, we've been focusing mostly on start up and scale up because we wanted to prove that we were on the right path and eventually fail also faster. Yeah. But we have an open source framework over eight ks stars.
Speaker 1:Woah. Eight k stars. Oh, you hid that. You buried the lead on us. Buried heard the lead.
Speaker 8:Please go and start
Speaker 1:our lead. Yeah. Yeah. Yeah. You head over to GitHub right now.
Speaker 1:Yeah. Give it another star. Yeah.
Speaker 25:Try Kuwa.
Speaker 44:That's amazing.
Speaker 1:Slash Kuwa. Slash Kuwa. Yeah. Yeah. So yeah.
Speaker 1:Mean, well, what's the monetization strategy around that open source So
Speaker 25:we like, month in YC, we were all these customer inbound that were basically asking us, how do I even productionize on our agents today? So we are providing a pathway for the user from the open source to bring in the same workflow that are working locally for them and scale them on on cloud. Mhmm. So we're really charging only based on Compute today. You simply have to input API key on our platform about what's gonna happen also for us.
Speaker 25:Are we gonna become an LM inference provider for these computer use AI models? Okay. Because maybe the the public sense is that there are only two computer use AI models, maybe the one from OpenAI and Entropic.
Speaker 1:Yep.
Speaker 25:And that's only because they have a better PR officer
Speaker 1:Sure. Sure. Sure.
Speaker 25:Than other models. Honestly, like, even on AgingFace, you'll find model from ByteDance, 1.5. That's also beating OpenAI and Anthropic
Speaker 1:On on computer use benchmarks?
Speaker 25:On computer benchmarks like OS Word.
Speaker 17:Sure.
Speaker 25:And they are so hard to set up and also hard to discover. And also we're gonna be the go to catalog and platform where
Speaker 1:Yeah. How how good are the evals right now or the benchmarks for computer use? It feels more abstract than just, you know, do some math problems.
Speaker 25:Yeah. So I was actually working with the Windows team when I was in Microsoft Okay. Doing evals for computer user agents on a benchmark called Windows Agent Arena, which is derived from OS word. Mhmm. I really like this benchmark.
Speaker 25:They like, tasks are not very meaningful. Like, for instance, you will find tasks like, hey, can you go and open VLC and add subtitles? Would you VLC anyway? Yeah. That's the question.
Speaker 25:Even, like, using LibreOffice instead of like Office or even Google Docs. So what's gonna happen? My my tip my leap of faith here is that the next generation of benchmark will measure like real world task.
Speaker 1:Yeah. Do you think there'll be a a sort of like LM Arena style benchmark where a human is watching two computer use models use computers and there's kind of like a vibe check almost?
Speaker 25:Yeah. Or even like a WikiRace where you have like
Speaker 1:A WikiRace? Yeah. Yeah.
Speaker 7:Yeah. Like,
Speaker 1:No. Okay, so Wiki race is you know you start with Y Combinator and you have to end at Christopher Columbus. Yeah. And how many clicks do you have to click to get from one to the other? So you might say YC has San Francisco, San Francisco's America, America's Columbus.
Speaker 1:Yeah. And and so you you try and race through. And and, yeah, it's an intelligence test, but it's also yeah. Great computer use test. That's hilarious.
Speaker 1:Yeah. But also, I mean, I imagine that there could be, a, like, big model smell, like a vibe check on the computer use. Because if it looks very jerky and it looks confused, like, that's something that might not even come across in a quantitative benchmark, but a qualitative human might might evaluate differently.
Speaker 2:Yeah. Interesting.
Speaker 25:Yeah. Yeah. And also, there is this whole problem. Okay. I have this workflow then now is working maybe 80% of the time.
Speaker 25:I'm make sure that I'm able to reproduce the same kind of workflow all over again. Yep. So we're also working on episodic memory that will let you basically use RPA one point o, the professional RPA new automation for workflows that are very deterministic Yep. And then say that you have a deviation from from one trajectory due to noise or changing on a web page
Speaker 1:Yep.
Speaker 25:Then that's when you use, like, full computer use, when it really makes sense.
Speaker 1:That makes a ton of sense.
Speaker 2:Any other Italian YC founders in
Speaker 25:this batch? We haven't met many.
Speaker 2:No. Are you guys hometown heroes back in the the Italian
Speaker 1:tech scene? You gotta go do that local press in Yeah.
Speaker 15:Do the press
Speaker 1:Yeah. That's true. Yeah. And over here, he's still gonna see you in the yeah.
Speaker 41:You know
Speaker 1:there is a
Speaker 25:there is a wise key, what's up group now.
Speaker 1:Okay.
Speaker 25:They're raging, like, these, week on the mountains.
Speaker 1:Oh, yeah. Yeah. Somewhere in the skies. Wise Wise skies. That that's reason enough alone to apply to white company.
Speaker 30:Yeah. Yeah.
Speaker 35:But then mostly
Speaker 2:in group chat.
Speaker 1:Yeah. Yeah. Yeah. Yeah. Anyway, thank you so much for your Congratulations, guys.
Speaker 1:Good to see you. For coming on. Thanks so much. Let's bring on the next. Do we have a who do we have next?
Speaker 1:Oh, Dalian. We were just talking about you. What's up? How are you doing? Oh, you got the hat on already.
Speaker 1:Got the hat. Oh, no. Oh my gosh.
Speaker 16:Let's go,
Speaker 1:baby. Shots fired. Let's go.
Speaker 2:Shots fired.
Speaker 1:Can't believe you hit them with that.
Speaker 2:We've been doing
Speaker 1:that when we hear it's like big numbers, big ARs. We didn't bring the gong.
Speaker 20:Should I, so I was a Y Combator summer, 14 company.
Speaker 1:Yeah. That's right. Do you want
Speaker 20:me to go through my summer 14? Please
Speaker 1:give us Give us the fish.
Speaker 20:So hi everyone. My name is Delian Asperojov. I'm the CEO of Nightingale.
Speaker 1:Okay.
Speaker 20:Build software that helps autism therapists that work with young children to basically help them capture data on those children's behaviors, pull that into reports that you then send off to your insurance companies to get reimbursed. Today, all this is done by paper and pencil, manual, takes these therapists 30% of their workday just doing a bunch of bureaucratic actions. We cut that down, make it super fast, they get reimbursed more quickly, and they get to spend more time with the kiddos rather than on a bunch of paperwork.
Speaker 2:It's a cursor for autism.
Speaker 1:I was about to say cursor
Speaker 13:for autism.
Speaker 1:It's cursor
Speaker 20:for autism. So if you ever wonder where all the autism came from, it's because my company literally all I did was spend time with autistic kids. Yeah.
Speaker 1:That's awesome. You know, if I
Speaker 20:had some in me already, it got amplified.
Speaker 1:How did your demo day go? Did you raise? Like what was it hard to raise back then? It feels like everyone here can put together a million dollar seed round previously.
Speaker 2:What was like the the comp that you used? A lot of a lot of YC companies obviously like to give some type Boober for it. For for dog walking.
Speaker 20:Yeah. Like summer fourteen, like you have to remember like at the time, it's not like there was a ton of like healthcare SaaS things that worked. It's like, you had, like, Epic as, like, an EMR
Speaker 7:that obviously done super well.
Speaker 46:But like
Speaker 1:Epic, the story fully.
Speaker 20:Yeah. And, like, we weren't really in So, honestly, I think, like, you know, I remember, like, Sam, you know, at the time, because he was, the head of YC back then, you know, two weeks before demo day, basically told me, like, your pitch is fucking trash. And then I went and, you know, I'm not gonna say this on livestream, but, yeah, I took some psychedelics on a weekend and, like, you know, really thought about my pitch, you know, for that weekend. Made a lot of improvements. And then Sam afterwards told me, was like, you know, relative to where your company's at in, performance, phenomenal presentation.
Speaker 20:Wow.
Speaker 1:Wow. The
Speaker 20:phenomenal presentation in 2014 turned into, like, 600 k seed round. That was a fucking grind.
Speaker 6:Grind to get you working.
Speaker 20:I was, like, still two months after demo day, like, you know, doing individual calls. Crazy. And it's like, when you look at, like, the amount of capital it's, like, you know, whatever, like, the next door building There was there there
Speaker 2:was, like, a two somebody said there was, like, a two hour wait this morning.
Speaker 3:If you tried
Speaker 2:to show up, like, right at the start, there was, like the line was, like, two blocks
Speaker 20:December 14, like, we, like, filled up, a small little like area, like cocktail area in like the computer history museum down there. So it's like, I I
Speaker 2:I I If if you just given $10 to like every batch member with you, you would have a billion dollars now.
Speaker 20:Think summer fourteen did have some hits. I'd to go back and remember
Speaker 1:who My batch had a ton. Coinbase, Instacart That was a good batch. Yeah. It was a good batch. There were a bunch of good ones.
Speaker 20:But, yeah, it's, like, interesting to study just, like feel like you can use Demo Day. This is my time coming to Demo Day in, like, I think seven years or something like that. Yeah. Partially, like, in 2018, I went, spent a bunch of time on it, then, like, honestly, it just, like, didn't lead to any investments. And so I
Speaker 1:was like, at some point,
Speaker 35:what am I, you know, sort of doing here?
Speaker 33:And then I
Speaker 20:was like, COVID really killed it off. And so, excited to be back, but it's also interesting just like marker of, like, the comp like, the industry's, you know, sort of maturity of just, like, the amount of companies, what the companies are working on. Now also the amount of comps that like you can like look at like Yeah. In 2014, if you had said like my comp is like I would like to be a $100,000,000,000 publicly traded company. It's like, okay.
Speaker 20:There's like basically like two of those in the entire history of technology.
Speaker 1:Well now you
Speaker 2:have to Google. Because people are doing Varda for X. Gotta
Speaker 1:A lot of
Speaker 20:fact, there's an Indian Varda.
Speaker 1:So, you
Speaker 20:know, gotta gotta track those guys down and figure out how to, like, acquire them, so we have, a Desi Varda. Yeah.
Speaker 1:That's great.
Speaker 2:Have you have you seen any of the hard tech companies yet? Any of the pitches? Gary said, like, 11 of the batch is hard tech.
Speaker 20:Yeah. Yeah. It's interesting to see, like I mean, I remember in, twenty eighteen, nineteen when I was doing, like, hard tech, industrials, defense, aerospace. It was just, like, so uninteresting to so many people. Like, they would all, like, literally, like, know we talked about this with, every my former colleague.
Speaker 20:Just, oh, like, if anything is, like, you know, negative gross margins Mhmm. And, like, highly CapEx intensive, send it to Dell. And he, like, said that as a joke. Yeah. And then everybody's realizing, like, CapEx intensity is like the best mode ever.
Speaker 20:Because like if you just build software, AI Slop can either replicate it basically overnight. Yeah. So yeah, definitely looked at it, know, shameful of them. Think it's cool to see that like YC's leaning into this because it's not something that they've
Speaker 1:They also had a couple hard runs with, like, Pebble was a bit was a really big, like, hard go because, like, they got so Sherlocked by Apple with the Apple Watch. Totally. Yeah. What are you gonna do? But at the same time, like, there's been now, like, Astronis and Oklo and, like, a few really even harder tech companies that have made it through and, like, scaled.
Speaker 20:So Astronis, do think, is, like, the best. Like, Oaklo, obviously, like, you know, sort of, know, sort of, you know, trading, but, like,
Speaker 1:you know, hard to figure
Speaker 13:out.
Speaker 1:Yeah. Hard to figure out, but
Speaker 20:still But Astronis feels like it's, got some, you know, sort of Yeah. Another one. Yeah. It's like 300 feet down. Yeah.
Speaker 20:Yeah. I admit that, like, I think, like, I mean, if you look at, you know, the, like, YC deep tech, you know, sort of portfolio and, like, hit rate and outcomes relative to, the FF Ordellian Sure. Seed Yeah. Deep tech industrial portfolio. Yeah.
Speaker 20:There's definitely one that I would wanna buy in the basket of and one that I wouldn't. And so, you know, I but you I mean, maybe I shouldn't be speaking. Maybe Gary's gonna fucking take the I
Speaker 1:think, like, the bull case is that, like like, it is great to have exactly what YC is, which is an incubator, an accelerator, like a pool for that type of talent to come out of. And if you wind up picking over at it at some point, like that's fine. And that actually part of the ecosystem. And yes, like you're gonna get earlier stuff, but it's nice that there's at least an ecosystem. Some of the people that go through YC with hard tech, they might become employees of these companies.
Speaker 1:Totally. They might become acquisition or acquihire or hard tech.
Speaker 20:It opens the Overton window for, like, the average day for grads and, like, not just
Speaker 2:more Exactly. How many multistage VCs have pulled back from from doing demo day investing at all and just saying, you know, we're not gonna try to go.
Speaker 20:I mean, I feel like in, like, you know, sort of 2019, it was, like, the consensus thing. Like, at some point, like, everybody was, why are we doing this? It's just, like, way too many companies
Speaker 1:Yep.
Speaker 20:That, like, you know,
Speaker 1:specific funds that would come in and just write a 100 check into tons of So like everyone was getting their rounds done. But yeah, I mean, yeah, it it it just speaks to like how they're playing. Also, there's this weird dynamic where YC used to say like, I don't know if you've got this advice, it was like, don't pitch any investors until YC Demo Day. Yeah. And then everyone was like, oh, well, like, the game theory is, like, if I'm the only one pitching before Demo Day,
Speaker 20:I'm doing all the interesting. And
Speaker 1:so and so, like, there's still, like, the I'm sure, like, you will you will take a pitch with a YC Demo Day founder a couple weeks earlier because they're inventive and creative and they got to you before.
Speaker 43:Totally.
Speaker 1:Totally. You just might not find them through this.
Speaker 20:I do think there's some amount of, like, a lot of the, like, you know, some companies have already closed their rounds by, like,
Speaker 1:the time
Speaker 20:this date happens, which was very much so not the case in 2014. 2014 is,
Speaker 1:Oh, yeah.
Speaker 20:Maybe one company in the entire batch. Yeah. And by the way, for what it's worth, like, you study those, like, early batches, there was basically, like, an extreme negative correlation between, like, the ones that, like, you know, sort of raised early Yeah. Yeah. And that actually ended up being, like, the, like, you know, sort of batch returning, you know, sort of outcomes.
Speaker 1:I remember there was one company in YC Summer twelve that had a, super viral video because they were in the viral video making business. Yeah.
Speaker 13:And so
Speaker 1:they got a ton of attention. And they'd also, like, kind of ramped revenue by, like, saying, like, yeah. Well, we'll produce a video for a $100,000. Like, that's not really, like, like, durable, scalable
Speaker 2:revenue Clearly. Clearly. Company that made the
Speaker 1:$5,000,000 round before demo day, and everyone was like, what's going on? The coin is sitting there at eight. It's
Speaker 3:like Did you
Speaker 2:see that did you see that ad? It was a company called Co Tool. They did, like, a skit based on the deal rippling thing.
Speaker 47:No way. Oh, yeah.
Speaker 1:They were gonna change crank on it.
Speaker 3:No. They didn't. You gotta see
Speaker 2:this video.
Speaker 37:This is hilarious.
Speaker 2:But they they're in this batch. Yeah.
Speaker 1:Yeah. Yeah. They they they have a they have a service that will monitor Slack who chats for Honeypots and create Honeypots for you and do all this different cyber security stuff. It's great.
Speaker 20:I mean, it is I I think back and like, I just had this memory from 2017 when I was at Coastal Ventures where like, you know, Vinod basically have like the junior team go through and like crawl through the entire batch of like everybody, like the week beforehand basically. Yep. On the Sunday night before like the like, know, she had a Tuesday demo day, we would invite like 15 companies to present at KV
Speaker 1:Yep.
Speaker 20:On Sunday from like noon until like literally midnight. We would be there. Like, I remember like letting founders in at 11:30 at night to like come pitch the firm. Wow. And it actually felt like we had this like really deep arb.
Speaker 20:It was Alpha. Yep. Nobody else was doing it, etcetera. Yep. And literally, it's like like you said, there now there's, these infinite, you know, sort of YC funds where, like, I actually just feel like the alpha there on, like, doing the pre work, etcetera, getting into it.
Speaker 20:It's not to say there might not be some good outcomes, but, like, many, many more people run that strategy. And then even the fact that, like, something like this exists, like, the idea in 2014 that there'd be, a live show that anybody would go fly about and actually wanna tune into to talk to
Speaker 2:a live
Speaker 20:broadcast. Bro, like, we could barely get anybody, like, you know, to any of us. Let alone the idea that there's, like, whatever. We're be, like, 15,000 people on Twitter or something. So, yeah, this entire industry has just gotten, like, you know I mean, like, this is we we are, like, the new Wall Street.
Speaker 20:It's like kids grow up, you know, know, in like 02/2018, like, wanna be like an investment banker.
Speaker 2:Yeah. The kid right here when 16. Yeah. He's 26 now. So he's like decades of
Speaker 20:making I mean, we talked about this in relation to the, like, Teal fellowship recently where it's like in 02/2012, the, like, off track thing to do was to, like, drop out Yep. Build a company, join an accelerator, work in technology. Now this is the track. Yep. And so we, like, need to practically find high school drop there's
Speaker 2:a high school dropout in this batch.
Speaker 20:Yeah. Yeah. I mean, at this point, like
Speaker 7:I mean, there's
Speaker 1:an article in Business Insider today by Julia Hornstein who wrote the article about the article about us talking about how, like, not just not even going to college at all is the new dropping out.
Speaker 20:It's like just the default.
Speaker 1:It's just the default.
Speaker 20:Versus like in my year to my team, there was three of us that dropped out of the was such a big deal.
Speaker 2:Is the he applied to college, get accepted.
Speaker 1:Yes. Yes. And then Get the branding. We'll be
Speaker 20:like, acceptee, but that's all
Speaker 1:they I I had a whole riff on this. It's like, yeah, Delian, you dropped out of MIT. I knew that I would drop out if I went there, so I didn't even apply. Yeah. Exactly.
Speaker 1:Exactly. It's like, yeah. Yeah. You're a sucker for even having gone for a day. You paid the
Speaker 2:You paid
Speaker 1:the fee.
Speaker 20:What a fucking fuck. Paid a full year of tuition. Boomer.
Speaker 3:Once I paying
Speaker 20:$5,000 back, I could have put that in fucking NVIDIA and I'd be a billionaire.
Speaker 1:I'm in the boomer mode. Anyway, thank you so much for stopping by.
Speaker 20:Good to see you, boys. Thanks for almost seeing you tomorrow.
Speaker 2:We got some big news, new investment from you coming on.
Speaker 13:Oh, yeah.
Speaker 1:Looking forward to tomorrow. Okay. Alright. We'll see you back to back days. Welcome to the demo day stream.
Speaker 1:What's going on? Five. What's going meet you. How are you doing? I'm John.
Speaker 1:Nice to meet you, Dave.
Speaker 15:Dave Muniquelo. Is it did you
Speaker 2:photograph us out there or
Speaker 15:was that somebody else at No. I didn't photograph you.
Speaker 2:What? Who was photographing your buddies? Someone else at GV, Han.
Speaker 1:Han. Han. A picture us. Put us on the
Speaker 2:screen. Yeah,
Speaker 1:we love the paparazzi. It's good. Good. Can you introduce yourself for the stream?
Speaker 15:Yeah, so Dave Muniquelo, Google Ventures, I'm a managing partner there. Been there about thirteen years.
Speaker 1:Yeah.
Speaker 15:Investing in AI and enterprise software from the early days.
Speaker 1:Lots of good stuff to do here.
Speaker 13:I was
Speaker 15:just standing in a room downstairs hanging with a bunch of folks. Yeah. And Gary came in and said, hey, you got to go do this.
Speaker 14:Amazing. Thanks for jumping on.
Speaker 1:I'm here. Excited to hang. Yeah. What what what trends are you following? What are you seeing that you like?
Speaker 1:What's interesting?
Speaker 2:Well, break break YC into chapters for us from your point of view. Sure you've in companies at this point, maybe not on right around demo day, but in every Yeah. Batch.
Speaker 15:I was in Cambridge in the early days in grad school when like Paul Graham and those folks were in Cambridge doing their thing. And so okay. At that point in time, was like the cool place to be connected to. Totally. And I think, you know, a lot of us, I met, you know, partner of mine that now runs our life sciences team and I co lead our digital team here at GV.
Speaker 15:We met in Cambridge and we used to like get really excited about any Cambridge YC events.
Speaker 1:That's amazing.
Speaker 15:So the place has totally evolved.
Speaker 14:Oh, totally.
Speaker 8:Right. It's a machine.
Speaker 15:It's a two chapters. Totally. Was fortunate enough to do GitLab in the early days. So like new domain from day one and like Sid has always shared his enthusiasm. Patrick Paulison is a portfolio founder comes in and talks to the batch and then we hear the cool stuff from him.
Speaker 1:What was the perception around GitLab at the time? I feel like I remember it and it was kind of like discounted because it was open source and GitHub was already such a success. Was like, how are they gonna do anything when there's already this winner?
Speaker 15:I literally just stepped out before this, stepped out to do the earnings call with the, you know, CEO and the CFO to, like, do the investor callback.
Speaker 1:Yeah.
Speaker 15:Yeah. Because we're public market shareholders Yeah.
Speaker 1:Of
Speaker 15:course, the company.
Speaker 1:Oh, wow.
Speaker 15:So we invested just after the series b. Yeah. We actually passed on the series b.
Speaker 1:Oh, no.
Speaker 15:Real. And we had a bunch of concerns.
Speaker 1:And if
Speaker 15:I look at those concerns, they were
Speaker 2:They're hold the mic a little bit closer or just angle it. Sorry. Yeah.
Speaker 15:They were one of the companies to be remote only.
Speaker 1:Oh, was the thing.
Speaker 15:WordPress and GitLab. Yeah. This remote only thing
Speaker 8:seems strange.
Speaker 1:How the hell
Speaker 15:are gonna run a company like this?
Speaker 1:Yeah, yeah, yeah.
Speaker 15:Sid is extremely technical.
Speaker 1:Sure.
Speaker 15:And I think remote only works really well for him because of the way that he shows up as a human. But he writes everything down. And so, that works for a subset of people in society.
Speaker 1:True, we're done, GV passed, but he's telling you in.
Speaker 15:Mean, we talked like, passing out a company, it's just like, it's like this stage isn't right for us, we wanna continue to talk to you over time, right?
Speaker 2:Yeah, yeah, What's your approach to demo now? I'm assuming you don't have a lot of FOMO, you guys write bigger checks.
Speaker 15:We've met something like 30 companies so far.
Speaker 2:Okay, so you are writing checks even at this early stage.
Speaker 15:We write checks into YC companies, we love YC companies. Amazing. Yeah, so even though we're multi stage,
Speaker 1:like
Speaker 15:$100,000,000 into a public company, as we were just talking about, we also do seed and everything in between. A is our sweet spot, A's and B's. Great. We do like an event a few weeks before demo day every year with all the YC founders. Nice.
Speaker 15:Then we know all the group partners. Right? So they like constantly ping us with ideas and stuff like that.
Speaker 1:That's great. What is the integration with Google like now?
Speaker 15:We're entirely separate. Entirely separate. Yeah. So we raise capital from Google.
Speaker 1:Yep. That's an LP.
Speaker 15:Yep. It's an LP relationship.
Speaker 1:Yeah. Because it's an interesting dynamic. Because on the one hand, that could be incredible value add. Is like, hey, we'll introduce you to you can sell into Google.
Speaker 15:The incredible thing is we have a stable LP that's there forever
Speaker 1:Permanent capital,
Speaker 15:with plenty capital and an appetite for risk. So we have a great LP that we have a half an hour conversation with every couple years
Speaker 1:Yep.
Speaker 15:And plenty of capital.
Speaker 1:Wow.
Speaker 15:We don't spend any time pontificating about where we think the market is. It's why I had to call our comms people before I came on here. It's like, don't usually you know, marketing
Speaker 1:or Sure. Sure.
Speaker 15:Like comms events or podcasts or stuff like this. Yeah. Excited to talk to you guys.
Speaker 8:Of course.
Speaker 15:But yeah, I think we're we're unique in that we spend all of our time with founders.
Speaker 1:Yeah.
Speaker 15:So I shared the like
Speaker 2:It's such a I was curious. A hack. I mean, I I think you're the envy of every no matter how successful our GP friends and the GPs on the show, having a dynamic where a 100 people could call you on a Saturday at 5PM, and you kind of owe them your time to some degree. And that's like that LP, you know, dynamic.
Speaker 15:I mean, I I talked to, obviously, tons of friends in at the GP level across different firms. It does keep them sharp. Like they get a lot of pressure from LPs, constant questions, deal flow, connectivity. I think that's really positive.
Speaker 1:Yeah.
Speaker 15:But we don't have to deal with a lot of that stuff. And so we time connecting with founders and other GPUs.
Speaker 1:Yeah. Do you think the nature of those questions usually are? Is it just like trying to understand markets and trends and how the funds That's just from Melchizedek? Yeah. I haven't really sat on that.
Speaker 1:So went to one I've been to like one or two LPDs for big funds.
Speaker 4:LPDs, what's strength of
Speaker 1:our thing? Keep talking about trends. I didn't wanna say
Speaker 2:it, but I've seen the movie,
Speaker 1:but That's hilarious.
Speaker 15:I I mentioned this idea of like we invested in, you know, Get Live in the early days, and then now they're a public company. Yeah. Hedge funds invest in the stock. Sure. They call us to ask us our perspective
Speaker 1:about the I'm sure.
Speaker 15:And they're thinking about like what happens in two weeks. And I imagine LPs are like slightly farther outside 90, maybe. They're like, you know, what's what's happening today? What's hot today? Yeah.
Speaker 1:Are you
Speaker 15:in the right deals? That sort
Speaker 30:of thing.
Speaker 15:Our LP doesn't care about any of that stuff, right?
Speaker 1:So we
Speaker 15:don't have that conversation about like what we're in, what we're not in, etcetera. It's
Speaker 1:like Do you have a
Speaker 15:talking generational founders.
Speaker 1:Do you have a particular understanding of how the startup landscape is interfacing with the mag seven, the big tech companies right now. We're in an interesting time. The big tech companies, they all kind of, like, went from a 100,000,000,000 in market cap to a trillion very easily. It
Speaker 7:was kind of like That's
Speaker 1:it was kind like the easiest 10 x of their entire career, you know, in kind of some sort of unexpected way. You would think that would be the hardest one, but a lot of them just did it. Mhmm. At the same time, it feels like there's more opportunity for startups than ever, but the big big companies have more resources than ever. What are you talking to founders about?
Speaker 15:And specifically around AI Yeah. We're wondering constantly, like, is AI going to make incumbents stronger? Yes. The mag setting a standard innovation? Totally.
Speaker 1:And yet I've been talking to companies that are doing millions of dollars in ARR over two days.
Speaker 15:That's right.
Speaker 1:That's
Speaker 15:right. And I think the question for us is, you know, does each incumbent, each big SaaS company, do they bolt on AI? Sure. Do they acquihire
Speaker 1:acquire I know, yeah, we're seeing this with the Windsor thing. Every big tech company needs a Yeah, it's a
Speaker 2:new hot structure.
Speaker 30:That's the
Speaker 1:new, yeah,
Speaker 15:the new M and A.
Speaker 1:This is the best venture. Trust me.
Speaker 2:I'll take 49 and all your best people.
Speaker 1:I could do this valuation or double it and you get half. It's like the same number.
Speaker 15:But I think having loads of capital right now in time when like the seats are shifting in tech is quite interesting. The mag seven might be the mag 70 in the next ten years. Right?
Speaker 2:You might have it. Add a zero to it.
Speaker 1:I would more big tech companies. We're on the side of big tech. And so we want there to be
Speaker 15:as much This is actually little tech. The 70 was a little drone.
Speaker 2:We're we're fans of all of it.
Speaker 1:All of it. Just all of
Speaker 3:it. High-tech.
Speaker 2:Defend tech. Yes. Totally. Yeah. I I I think this idea that, like, people wanna make people wanna say AI is good for big tech or it's an extending innovation, but it can be good for both.
Speaker 16:Right?
Speaker 2:It could be so transformative. Like the Internet was good for some big companies that adapted well to it. Totally. It was good for a bunch of completely novel ideas and so we don't have to like pick one and pick a side. It can be an extending innovation and it can also enable all of this.
Speaker 1:Yeah.
Speaker 15:I I think we're in this exciting place where like the most high agency humans in our lives that we're all connected to Mhmm. They're empowered not just to have like the underlying cloud be present for them, but intelligence be present for them. Yeah. And so now they're they're building you know, I met company out there that was like one person Yeah. No engineers Seriously.
Speaker 15:Already have tons of revenue. I think that, you know, he he told me it's gonna be the billion dollar company
Speaker 1:Oh, going for
Speaker 15:it. He's, right? Going for it.
Speaker 2:I always get I always get hung up on that because it's like the the if you attach if you attach
Speaker 1:It does feel vanity metric.
Speaker 2:It's a vanity it's a total I mean, it it'll be amazing when it happens Yeah. But a total vanity metric that can like guide you towards bad decision making.
Speaker 1:But it feels like it might like happen accidentally. Yeah, happens accidentally. Should be something that's maybe deliberate.
Speaker 2:Or the way to do it is just get to a billion and then lay off every other person but yourself.
Speaker 1:Yeah, yeah. The private equity guys are really gonna be the ones that do it.
Speaker 15:They're gonna be like, yeah. That's not how
Speaker 1:we build the company. We bought a 10,000 person company and we fired everyone except for one person.
Speaker 15:There are a few Yeah. VC companies right now looking at VC firms that are looking at, you know, the PE roll up.
Speaker 1:Sure. Yeah.
Speaker 15:Yeah. Something that has massive distribution.
Speaker 1:Have you looked at anything?
Speaker 15:We have at them. We have passed on a lot
Speaker 1:of them.
Speaker 15:Passed on lot of I think it's it feels like a PE play where you're trying to like get multiple change as a result of the industry that you're going
Speaker 2:a lot of the founders that are running that strategy, they just should change the structure and just say like we're gonna run a PE playbook here and we're gonna be
Speaker 12:They do.
Speaker 2:Two and twenty.
Speaker 13:We're gonna
Speaker 2:be two and
Speaker 1:Yeah.
Speaker 2:Because it's if you can raise if you can raise 20,000,000 on a 100 for the strategy and keep like a lot of the economics, sure, it's great for the team, but it should probably be like the basically the the comp incentive structure should be look more like private equity Totally Totally fair.
Speaker 15:Yeah. You guys are friends with a buddy of mine Sean Maguire's over here a
Speaker 2:couple times.
Speaker 15:Of course. Sean introduced me to a founder on Friday night at like 09:00 over text. Nice. And I respond back and the guy's like, do you wanna meet tonight? Nice.
Speaker 15:Do you want to meet tomorrow morning? Like, I'll come to you. I live like an hour away. I'll drive to you. Like, that kind of high agency human is going be empowered by an entire set of tools, a whole software suite.
Speaker 15:AI will be the thing that powers all of these incredible humans that we're meeting today.
Speaker 1:For sure.
Speaker 2:Yeah. The thing I I hope we get some more of these companies on. We've had a lot of developer tool teams on. I I think this idea of like business automation is being heavily explored around agents.
Speaker 1:When we
Speaker 2:were talking with Dylan Patel last week and he says like the the next category besides consumer tech, know, with like LLMs like, you know, ChatGPT and you have, you know, CodeGen
Speaker 1:Mhmm.
Speaker 2:As like big revenue categories and like this category of of of just actual like business automation. So like how do you just make the machine work? Yeah. And everybody's thinking about it in the concept of like agents, which is like kind of a I I think like almost a simplistic way to to view these things and like what what is the next what is the next iteration of that where it's like actually like an autonomous system that doesn't have to interact like a regular employee. It's sort of, so we'll see.
Speaker 15:Yeah, mean we're seeing it across an organization like a Sierra or a Decagon. Yeah. You know, Harvey is a legal AI company that we're invested in. Yeah. There's some medical AI companies.
Speaker 15:So like every vertical has AI, but it looks a lot like a human. It's like replacement of a human.
Speaker 2:It's a replacement of a human, but how do you replace 20 humans at once? Like whole forms Across cross functionally.
Speaker 1:Yeah.
Speaker 2:How do you worry when when a YC deal that you're looking at gets gets hot? Do you worry?
Speaker 1:Do you
Speaker 2:think it's a
Speaker 14:Oh, we
Speaker 15:don't worry at all. No. I I think, you
Speaker 2:know, yeah. I mean, historically, you know, if you I I think if you do look at the data, a lot of these companies that some of the hotter companies at demo day don't end up, you know, always
Speaker 15:I mean, we're investing in super hot companies at the a and the b Sure. And the the most sought after companies get marked up incredibly. Yeah. Right? So it's a sign of the heat, is a sign of enthusiasm from great people.
Speaker 2:Talent, opportunity.
Speaker 1:Totally, yeah.
Speaker 15:Totally. And so, you have to, if you're choosing to get exposed to that asset class, you have to be willing to pay the prices to enter that asset class and play in that asset class.
Speaker 13:Here,
Speaker 15:the challenge is that you have like three months of data. Yeah. And so, you're talking Yeah, it's
Speaker 2:like the company that grows revenue the fastest in two weeks, three months, whenever they actually launch Yeah. Is not necessarily the company that's gonna grow revenue the fastest over two years Yeah. Three years.
Speaker 15:We've seen that a bunch of times, where we invest in a YC company and then after demo day it kind of chills out a little bit. Mhmm. Yeah. And then it reaccelerates.
Speaker 1:Yeah. That's
Speaker 15:good. There's founder in this batch. Yeah, totally. Founder in this batch that raised, I think like almost $8,000,000 of uncapped convertible notes. And
Speaker 7:so it's just like
Speaker 2:humans Dog, an absolute dog. He's incredible
Speaker 15:founder. Incredible team. And like, I understand why people are sort of leaning in to working with him.
Speaker 2:That was pretty difficult. Yeah. Yeah. Just those are the You're thinking about that next call in a year or two with Google being like, know, trying to explain like, you know, entering a party round on an uncapped note. It's it's it's easy
Speaker 15:to do. And our team literally has no visibility Yeah. Sure. So maybe it's easier for But for us, the thing that we care about is we wanna be your lifelong partner. Like the thing that we think about for founders is how can we be for them there for them over every round and into the future.
Speaker 2:What's your messaging around signal risk? Because I think there's this like, if you're truly a multi stage fund, could do an early round
Speaker 1:Yes.
Speaker 2:And you could say, you know, we're we're good with our ownership right now and like we're gonna help you raise this round. But we actually like, I think this concept of of like signaling risk is like totally possible for you to wanna invest in a company today and three years from now. So like just because you don't do the in between rounds or Yeah.
Speaker 15:Or specific round. Conversation I was having when Gary walked up and said, hey,
Speaker 2:should we talk to these So
Speaker 15:I was talking to Niraj from General Catalyst.
Speaker 2:Oh, yeah. And we
Speaker 15:were chatting about like, do you play when the price gets to an uncomfortable place? What signals does that send? Like, you're essentially sending a signal to the market that your firm is excited about this company. They have a separate seed program. So they can kind of categorize this as like, this is a seed bet.
Speaker 15:We're going put $1,000,000 in this company. We'll see how it goes. We don't have a separate seed program. When we do an early stage bet, we use similar criteria to what we use in the A. Obviously, they don't yet have A metrics.
Speaker 15:But the question is like, if they had A metrics, would we lead the A in this company? Mhmm. So there is a big signal when we do it. Yeah. So it makes it harder for us to do like 30 or 40 companies.
Speaker 1:Mhmm.
Speaker 15:Yeah. We talk to 30 or 40, we might do a handful.
Speaker 1:Yeah, makes sense. Very cool. Well, thank you so much.
Speaker 15:Thank you, guys.
Speaker 1:You're welcome. Fantastic. Yeah. Great to
Speaker 25:have you on.
Speaker 42:Thank you.
Speaker 2:Take care. Good to meet.
Speaker 1:And we are ready for our next team or individual or investor or yapper. Who will it be? Who will be? Surprised because we are at YC demo day twenty twenty five. Welcome to the stream.
Speaker 1:How are you doing? Good to see you. You already have the hat. Hi. Hi.
Speaker 1:How are doing?
Speaker 2:And we're looking around for the camera.
Speaker 1:You're on live. You're on live stream. Here. You're looking here. Need to introduce yourself.
Speaker 39:This hat? Okay.
Speaker 1:You can wear your hat. You don't need to wear that. Whatever you whatever you do. Wear that. Yeah.
Speaker 1:Don't wear the hat.
Speaker 2:Love the
Speaker 1:Hey, guys. I'm John.
Speaker 39:John. What's up, Jordy, how are
Speaker 1:you guys? Introduce yourself.
Speaker 39:Hi. My name's Andrew Lee. I'm a partner at Andreessen Horowitz. Fantastic. Both working in the Games Fund and also at a sixteen z speedrun.
Speaker 1:Very cool. Alright. Are there any games companies here today?
Speaker 39:There are no games companies here
Speaker 1:None.
Speaker 39:Which is somewhat sad.
Speaker 1:Is this is this them all into
Speaker 2:speedrun? Yeah. Yeah. It's your fault. It's your There's about four
Speaker 39:or five of the games. Is. I do I would say that for for YC, there's about four or five Yeah. Consumer companies, which
Speaker 2:is pretty good.
Speaker 1:So you could look at those.
Speaker 20:Oh, for sure. For sure.
Speaker 2:Four or five in the whole batch.
Speaker 39:I mean, there's like five or six ish. Still. But here's the thing. I mean, like, there were a couple companies who previously in the past were like b to b companies that are like, you know what? This is really boring.
Speaker 1:Yeah. I don't wanna do this. It's really fun.
Speaker 39:And a number of them, like,
Speaker 37:ended up in gaming and
Speaker 39:they ended up in entertainment. So that was exciting. Yeah. And then also, I just generally think that, like, if you're gonna build something that's that, you know, your mom or your your your 70 year old degen friend is gonna go ahead and play, then, yeah, you know, it makes sense for
Speaker 19:them to go ahead and
Speaker 39:build b to b to b stuff as well.
Speaker 1:Yeah. Right. I wanna talk about the Games Fund. I I've been super interested in how hard it is to, get into the hot games because they kinda blow up out of out of nowhere. Yeah.
Speaker 1:Like,
Speaker 2:yeah. You're you're, like, getting on a plane to Sweden.
Speaker 1:It's it's it's more like investing
Speaker 2:By the time you get there, they've doubled their AR.
Speaker 1:Yeah. Yeah. Like like, there was this ballotro game that was, like, a massive Yeah. Thing. There was, Among Us.
Speaker 1:Right. Right. Was that the one?
Speaker 2:Yeah. Among Us.
Speaker 1:I mean,
Speaker 39:that was probably one of the top ones ever during COVID.
Speaker 1:Yeah. During COVID, like, these kind of, like, flash of the pan games that become very viral. Yeah. But I under I don't understand if they're, like, necessarily good businesses. Yeah.
Speaker 1:So, like, how are you thinking about finding great games or games related companies or even just consumer companies? Like, what are you looking for? Because the signal seems so much more noisy than in if there's noise around a, like, an enterprise dev tool company, like, they're probably selling that, and it's probably gonna be pretty sticky. Whereas, like, Bellatro, I don't know. It it seems like local funk, I think his name is, fantastic developer.
Speaker 1:But who knows if that's, like, a gonna turn into, like, Activision.
Speaker 39:I think fundamentally for us, the way that we tend to think about it is there's there's, like, a couple different things that they care about. One is that if there's a large audience that has a bunch of folks that are gonna play, if you do one innovation in that audience that makes sense. For example, big fans of League of Legends, if you're all out there.
Speaker 1:Oh, yeah. If you're
Speaker 39:trying to create like a MOBA that sort of makes sense. Sure. But I I think that for us, honestly, with the big wave we see is in the world of AI. Sure. Right?
Speaker 39:Whether it's like changes in three d animation. We fundamentally think that the future of entertainment is actually gonna be there's gonna be an AI Pixar. Mark my words. Sure. Someone's gonna make an AI Pixar.
Speaker 39:It may not actually even be Pixar. Mhmm. Right? Because usually what happens is we tend to think that, like, an incumbent, for example, like, you know, Disney or something like
Speaker 45:that Virtual Pixar.
Speaker 39:Exactly. Is is gonna make, like, the next thing or Yep. Or potentially take the next wave. But I don't know if you guys seen any of these video videos.
Speaker 1:Oh, incredible.
Speaker 39:I mean, the the Bible Bros, the whatever other, like, like, stormtroopers Stormtroopers.
Speaker 1:Vlogging all
Speaker 16:over the list.
Speaker 39:I'm like, it's astounding. And it's and don't think that's something that could ever occur. There's like a sort of innovator's dilemma that's occurring with the existing So
Speaker 1:Even just from like a PR pressure, like, I don't know if you saw that that show, the studio. There's this whole sequence where they're very worried about the casting for this new Kool Aid movie, and then it is revealed that no one cares about the casting. All they care is that they used AI a little bit, and it's this really hot button issue. There's massive PR backlash. And so that's this counter positioning where if you come to the market and you say, like, yeah.
Speaker 1:We're we're an AI Pixar. We're just making AI stuff. You you don't have the expectation. People expect Pixar not to use that.
Speaker 2:That's right. How how are you thinking about investing at the at the game layer versus kind of the infrastructure layer? Yeah. Feels like there's bunch of great questions. Feels like there's a bunch of new tooling around just creating these generative worlds that
Speaker 13:can
Speaker 2:turn into games. And is the is the Roblox of AI Roblox, or is it something, you know, new?
Speaker 39:Yeah. I it's interesting because I think the thing is what we've seen is well, we go where the founders go, and it seems the number one area that the founders are going is into more tooling and primarily using AI. I mean, was a bunch of stuff. There's still a ton of, I think, experiments happening in the web three layer, potentially in the content layer. Yep.
Speaker 39:But the main problem is, honestly, is that distribution stuff.
Speaker 1:I don't
Speaker 39:know if you guys have, like, invested in any consumer companies lately, but
Speaker 3:It's hard.
Speaker 39:It's hard. It's hard because there's a reason why every DDC company has basically had all their margin eaten by by all the big tech companies. Right? And fundamentally, think that's the same problem. And Steam, which is, like, usually, like, the platform for folks who want to build games Yep.
Speaker 39:Is not nearly as liquid as it possibly could
Speaker 35:be. Sure.
Speaker 39:So as a result, I think the thing is the one thing that I I think we've seen that's a very positive out there
Speaker 2:for those Liquid as in there's not enough demand coming from Steam itself.
Speaker 39:Yeah. Or or really that, like, your ability to get more bang for buck. Mhmm. Right? Like, that it's fast enough that you don't have to pay a huge amount of marketing spend.
Speaker 39:Yeah. It's it's gotten a lot tougher, and you
Speaker 20:just have to be very good
Speaker 39:at it. So there's great teams that are
Speaker 1:able to do that. Gaming going on on there too with, like, wishlisting, driving early sales, and so you're paying people to wishlist your product, and it's like there's kind of schemes on top of schemes. Right. Right.
Speaker 4:The thing
Speaker 39:is like, I think we could we could do it. I mean, like, there's definitely people who do that, but it just seems that if you create a 10x product
Speaker 1:Mhmm.
Speaker 39:That is able to grab some great cut consumers out there, that seems to be something that has natural growth. I mean, I'm sure we're all familiar with Midjourney. Mhmm. That was built it's built on Discord.
Speaker 1:Yeah. Yeah. It's amazing.
Speaker 30:It's still
Speaker 39:in building disc Discord. It's not as if it was something that naturally would have grown. Yeah.
Speaker 1:Yeah. Yeah.
Speaker 8:So the
Speaker 39:thing is, I think the the end, obviously, I'm talking about Vio because that's like my latest thing that I'm just doom scrolling on every single night. But when you can basically grow from zero to all of a sudden a 120,000 followers with four videos over the course of like two days.
Speaker 1:That's amazing.
Speaker 39:That's an astounding thing. Yeah. Right? And if someone can is able to do that, that shows me that there's, a natural consumer poll. Yeah.
Speaker 39:And that's the thing that's pretty interesting. So mark my words, I still think that there will be an AI Pixar. I also think that fundamentally, the world of AI, tech, and entertainment are gonna converge. We're gonna basically see somebody create that 10x product that then hopefully just basically skips all the distribution problems.
Speaker 1:Sure.
Speaker 39:But I don't know who might know.
Speaker 2:Are you are you seeing are you seeing enough are you seeing enough weird stuff? Right? Like, there's this concept of, like, you know, kind of, trying to leverage AI in the sort of existing paradigm. But when you think about the intersection of, like, tech and AI and entertainment and and sort of, like, you know, I can just imagine, like, entirely new ways of playing games. You already have kind of seen this on some of these, you know, people basically creating games within games.
Speaker 2:But Yeah. But are people being are people being, like, is the average pitch, you know, really trying to rethink things from from the ground up? Or is it, hey, we have a we think we can build the next Candy Crush?
Speaker 39:Yeah. I think a lot well, for us as a fund, we'd wanna obviously have people who are gonna be be willing to build the next unicorn company. Mhmm. But that doesn't mean that I think great developers out there won't create amazing experiences. Definitely agree with you that you have to basically aim for the weird.
Speaker 39:Like, you have to do things that are basically in the frontier of what's possible. Mhmm. Because otherwise, then what you're doing is, you're you're most likely just like basically doing a me too, which is then hard because in a distribution channel where you have to basically pay for your customer acquisition. That's, like, super hard. I think something that that's also pretty interesting that I was telling some folks about was that the the one thing that's interesting about both consumer experiences and games is that if there's a limitation take, for example, on let's say that, like, you can only network 20 people on a server together.
Speaker 1:Mhmm.
Speaker 39:Right? Well, the way you do that then is you just make it so your game has 20 people playing all at the same time, and there's a thing called Fortnite. And then 20 people have to kill each other all and then eventually ends up with only 20 that you'll be able to go network with each other.
Speaker 1:Sure.
Speaker 25:So the
Speaker 39:good thing is you can just basically change it's not like a bug. It's actually a feature. Yeah. That's kinda interesting. Yeah.
Speaker 39:Yeah. But I I think also, generally, what we're seeing in AI though is when it's you guys are probably familiar with this, which is the the cycle between B2B to the app side, which is basically, you have B2B infrastructure that allows you to, for example, to create really powerful video models. That then allows you to have apps that use those video models and then create content that create new network models, for example. And then that leads into other things happening with the infra layer.
Speaker 1:Yep.
Speaker 39:We're seeing a lot of that happening.
Speaker 1:Unreal Engine was a B2B play
Speaker 39:Exactly.
Speaker 1:Birthed Fortnite. And then there's another company in the Andreessen portfolio
Speaker 25:Yeah.
Speaker 1:That does kind of like world scale sharding so that you can like basically build like a big MMO that you can walk through. I forget what this company is, but they were doing like some scientific computing and some economic analysis. That was years ago. But there's obviously a lot of work done on the infrastructure layer. And are they gonna be the one to build the the the the next great consumer game?
Speaker 1:Maybe, maybe not. Maybe it builds maybe it's built on top of their platform.
Speaker 2:What are what are you seeing in VR?
Speaker 7:Oh, yeah.
Speaker 39:In VR let's see here. So, we have there is one great company that came through, before from from Speedrun in past and the very around the one was a company called Trask Games. Okay. So, you know, it's it's one of those things where basically, I think, just like people grew up in the mobile generation, you need to have people who grew up in the VR generation. Sure.
Speaker 39:So if you talk to anybody who's under the age of like 16 Yeah. Most of them will be like, I play a lot of VR. Really? And it's it's crazy. Like, they all go home.
Speaker 39:They just play VR a lot. Yeah. And they have to obviously have the device installed.
Speaker 19:Yeah. Of
Speaker 39:course. But for them, spend a lot of time there. So, we backed this team that was like a bunch of teenagers who like they won a bunch of App Awards. And then, they're just like, look, we just spent all our time in VR. So they built a game that basically is taking one of the bigger ones, attacking one of the bigger ones, which is Gorilla Tag.
Speaker 39:They made a game that is is astounding. Their their their their most recent game, actually, it's called Yeeeps. Yeeeps actually has you know, I I I can't describe it because I'm too old. But, basically, it's like, you know, we're all in a world together, and we get to go and do stuff together. Yeah.
Speaker 39:But then we all collect Yeeeps. We Yeeep at each other.
Speaker 1:Okay.
Speaker 39:And it's like We're Yeeeping. Yeah. We're Yeeeps all the Yeeeps. All the shout out to all the yeeeps out there.
Speaker 18:We're yeeeeping.
Speaker 39:But but but they're doing great because the thing is, I think, ultimately, it's not just a hangout spot for a lot of these kids Mhmm. But it is ultimately taking advantage of, you know, what VR and the the general install base. I think I still think that, like, VR still needs, like, a shot to the arm in terms of, you know, we all know this, that it probably needs more installs. It probably needs find a find a way there.
Speaker 1:But It it it needs, like, higher lower churn relative to the device sales.
Speaker 39:That's right. That's right. Or or the device sales have to be way less expensive. Yeah. Or find a way to, you know, just get as good of sales as the Meta Ray Ban glasses potentially.
Speaker 39:Right?
Speaker 1:Yeah. Totally.
Speaker 2:We need VR games that are truly addicting, not just novel.
Speaker 1:Yep. Yeah. I was
Speaker 2:really hoping for the You probably shouldn't
Speaker 1:you should
Speaker 15:try to
Speaker 1:I mean, I I I remember remember I like
Speaker 39:eaves though. And and the kids love it too.
Speaker 1:With the with the original, like, PlayStation, like, Metal Gear Solid, that game was like a hundred hours or like Final Fantasy seven.
Speaker 15:That was
Speaker 1:like a hundred hour experience. GTA four. Yeah. That was like a hundred hour experience. And and I've played a lot of VR games, have yet to find one that it's like, okay.
Speaker 1:The progression in this game is so addicting Yeah. That I need to keep putting it on to play. It's like it's like more like, okay. It's a cool demo. I set a high score.
Speaker 1:Okay. I'm done. Yeah. Yeah. I'm not like, need to finish this.
Speaker 1:I need to know where the story goes. And it's hard because it's a very expensive investment.
Speaker 17:I mean, I think that's
Speaker 39:why it's easier, honestly, if you're gonna innovate in the world of entertainment Yep. Innovate on that b to b layer Yeah. Which is where we're seeing it. We're seeing a bunch of AI sort of video creation tools. We had one company, Heidra, who who came through that who was honestly just astounding when the Studio Ghibli sort of content started exploding.
Speaker 39:People were like, well, how can I animate the Studio Ghibli content? And then everyone was using Heidra
Speaker 1:as as a I saw that.
Speaker 2:I saw your
Speaker 1:partner do that.
Speaker 2:Can a can we have studio the magic of Studio Ghibli was that it could one shot these beautiful outputs. Mhmm. And and are has there been are you anticipating that kind of moment for sort of ephemeral gaming where I could, like, take a picture of the three of us and say, like, make a boxing game where we can, like, fight and you get swords. And then it's, like, it creates that and it's, like, fun and viral. Can that happen in the near like, are are you expecting that at all?
Speaker 39:Well, never say never. I I think that ultimately, we have to get past this sort of distribution problem. Mhmm. But my hope is that it gets a mid journey moment like we've had in the past. And the good thing is, I mean, I've talked to a lot of investors about this that I think a lot of folks are in that cycle right between b two b back to the sort of, like, app layer Mhmm.
Speaker 39:Is a bunch of the investors are pretty interested in, obviously, the b two b AI side. And I think that will then drive a lot of innovation, which then gets you to 10 x here. Yep. And, hopefully, if someone builds a good network, then you have sort of, like, unwarranted or really sort of an differentiated customer acquisition.
Speaker 1:That makes a ton of sense. Well, thank you so much for joining. This was fantastic.
Speaker 2:Come back
Speaker 24:and talk
Speaker 2:to you guys. Come on when there's big
Speaker 7:game news.
Speaker 21:Of course.
Speaker 2:Of course. Games correspondent.
Speaker 6:I'm gonna take this hat.
Speaker 1:Please. Do it. Enjoy it.
Speaker 2:Alright. See you guys.
Speaker 20:Let's bring
Speaker 1:out the next the next person.
Speaker 2:This guy is
Speaker 1:actually eating data. Twenty twenty five. Eat data. Make chunks.
Speaker 2:Make chunks.
Speaker 1:Welcome to stream. I'm John. Nice to meet you. Nice to meet you, John. Can you introduce yourself?
Speaker 37:My name is Trayash. I am the cofounder of Chonky.
Speaker 1:Chonky? Chonky. Yeah. Chonky.
Speaker 2:That's the name
Speaker 1:of the company. What do
Speaker 37:you do? We take really complex documents.
Speaker 1:Okay.
Speaker 37:We split them up into meaningful pieces. That's just that one piece is one idea.
Speaker 1:Okay.
Speaker 37:And then we send your LLM only the data it needs to answer a question.
Speaker 1:Give me an example of a really complicated document.
Speaker 37:Financial reports.
Speaker 1:Okay.
Speaker 37:You've got graphs. You've got actual text data paragraphs. Yeah. You've got tables.
Speaker 1:Yeah. The pages are so annoying because there's, like, so much boilerplate you need to just skip to the right
Speaker 48:thing. Exactly.
Speaker 37:Okay. Yes. Exactly. And, like, most of the time when you're asking questions to an LLM Yeah. You really only need one table.
Speaker 1:Yes. Or
Speaker 13:maybe you
Speaker 37:need a summary, that's it.
Speaker 1:Why don't I just throw all of that in a big context window, Gemini, 1,000,000 tokens or something, and then just ask it what's
Speaker 37:the LLM? It'll work with, like, maybe one PDF, but you have a whole database of PDFs. You've got, like, 100 page PDFs, thousands of those, 10 thousands of those. You got schematics which are really complex. Models get confused.
Speaker 37:Mhmm. We actually ran this eval yesterday after the price dropped on o three Okay. Which is we took relatively simple documents. We took classic literature, know, David Copperfield, everything like that. And then we gave it to o three.
Speaker 37:We asked very pointed questions.
Speaker 1:Mhmm.
Speaker 37:O three got a retrieval accuracy of 75%.
Speaker 1:Okay.
Speaker 37:Great. We chunked the data through Chonky, then we asked you all to do the same thing. Always Chonky. A 100%.
Speaker 2:Always Chonky. Always Chonky. This is my favorite name since last YC batch, which was a company called Pig.
Speaker 1:Yeah. You that's setting a trend here. Just like large animals.
Speaker 2:Mean, it's just so it's it's just it's gonna stick. We're gonna we're gonna be talking about this next demo day.
Speaker 1:I'm talking about the pipeline. I'm I have a bunch of really huge PDFs on s three or something. I feed it into your, to your system. Am I getting Postgres table? Am I getting a MongoDB, like, unstructured You get embeddings out.
Speaker 1:Am I getting embeddings with these? So it's like a vector database?
Speaker 37:Yeah. So you get embeddings out. They can put it on your own vector database, or we can also wrap around your vector database. That's totally up to you. Okay.
Speaker 37:It's really developer friendly. The idea is to just make a dev tool that people just enjoy using and they can have it be two lines of code, five lines of code, whatever it So
Speaker 1:what's actually happening with it's not open source. Right?
Speaker 37:It is open source.
Speaker 1:It is open source. Yes.
Speaker 37:Okay. Have an open core strategy.
Speaker 1:Okay.
Speaker 37:Interesting. We are, we are, like, open source We started as a side project on the open source, and we love the open source.
Speaker 1:So so is this something that I should be running, like, in, like, an ingest process as I'm generating new large documents? I'm chunking them and then loading them into my deck vector database, which I may be also hosting
Speaker 37:on Yeah. And async chunk. But if if you're building a code gen tool, then you want a live chunk.
Speaker 1:Okay. Live chunk. Yeah. Okay.
Speaker 37:And so if you're doing like code gen on the fly or if you're like, you know, things that if you're working with a corpus that's changing all the time Yeah.
Speaker 2:Yeah. Yeah.
Speaker 37:Then you do wanna you wanna do it live.
Speaker 2:Did you come in You came into YC with this idea? You're already had it as an open source project? Yes.
Speaker 37:Yes. We had an open source project all set up in like February. Yeah. And we came into YC with
Speaker 15:this How
Speaker 1:many people on the scene?
Speaker 37:It's just me and my friend from grade. Now give us
Speaker 1:some chunk. He made
Speaker 2:it out of
Speaker 1:the GC. How many chunks have you chunked? How many stars do have on GitHub? How much revenue you're What do you get what do you got for us? And the quantitative metrics side.
Speaker 37:So, the metrics I really like are we've got over a 180,000 downloads.
Speaker 1:Very nice.
Speaker 49:And we've got
Speaker 37:over 200 projects using us.
Speaker 1:Wow.
Speaker 37:We're a core dependency on projects like Llama Index.
Speaker 1:Oh, cool. Nice.
Speaker 37:And we've got like 10 to 12 badge companies using us. Cool. We got inbound coming in from thereon.
Speaker 1:Fantastic. Round's
Speaker 30:already done.
Speaker 37:What was that?
Speaker 1:Round's already done.
Speaker 37:Round is almost done. We're we're trying to
Speaker 16:wrap it up this week.
Speaker 1:Very good.
Speaker 2:Thank you. Preliminary. Yes.
Speaker 37:Just just weighing our options and just like making sure if by Friday it'll be done.
Speaker 1:That's fantastic. Amazing. Well, good luck out there.
Speaker 37:Oh, thank
Speaker 25:you so much.
Speaker 1:Thank you for having
Speaker 2:me. Never stop chonking.
Speaker 1:Never stop chonking. We'll be following
Speaker 2:here soon. The domain?
Speaker 37:Ch0nkie.ai.
Speaker 2:If you want
Speaker 1:this merch,
Speaker 37:it's shop.chonkyai.
Speaker 1:Oh, he's already selling merch.
Speaker 2:He's selling merch. He's selling merch.
Speaker 1:Let's bring in the next Awesome.
Speaker 2:Great to meet
Speaker 1:you. The next participant of the demo day stream if we have one.
Speaker 2:We got
Speaker 1:Welcome to demo day twenty twenty five. We are live from Y Combinator
Speaker 2:Wow. Yeah. Hardware?
Speaker 1:Hardware. Hardware for Slack. Well
Speaker 15:Yeah. What do they have?
Speaker 50:RFX Way Pro Oh, sorry. The Stanford linear accelerator center.
Speaker 1:Oh, that
Speaker 40:not s l
Speaker 1:a c
Speaker 7:not s
Speaker 42:l a c k
Speaker 2:what what what hardware device Yeah. Cool.
Speaker 10:Awesome. So I'm Abhajid. I worked as a researcher at Stanford. I'm researcher at Harvard. I was second intern at Intel.
Speaker 10:Cool. Built a lot of stuff, I guess.
Speaker 1:Can you, pull up on the mic a little bit more? Yeah. And then tell us what your company
Speaker 40:Yeah.
Speaker 50:So we're Godela. We're building a frontier physics model for mechanical engineers. Okay. So currently AI models can't handle physics accurately Okay.
Speaker 1:Because a
Speaker 50:lot of them are language based. Yes. Ours are different. Ours are built to handle physics accurately
Speaker 1:Okay.
Speaker 50:Which means it can be used as a faster, cheaper replacement to simulations and physical prototypes.
Speaker 1:Okay.
Speaker 2:How This is something that I think a lot of labs like to to promise, right? Solving, you know, to bring up Yeah. This idea of of solving these problems in physics.
Speaker 1:So walk me through. It sounds like you're actually training a model. Is it all reinforcement learning with verifiable rewards? Or are you generating a whole bunch of training data? Is there a human late data labeling component?
Speaker 1:Like, what is the pipeline to create what you're creating?
Speaker 50:So at a core, we extract embedded physics from data, and that makes it generalizable. Emma, did you want to
Speaker 42:share more
Speaker 10:about Yeah. So we don't use reinforcement learning or anything. It's basically this encoding framework where we actually take the mesh itself. We work with meshes because we're in simulation and stuff. So we take the fluid mesh, and then we encore it into this lower dimensional space, and that allows us to learn the actual physics of the system.
Speaker 1:Interesting.
Speaker 10:And this allows and when you do symbolic regression on the latent space, you get a lot more generalizability than you would get with regular models.
Speaker 1:So
Speaker 2:When did you guys start working
Speaker 1:on this?
Speaker 2:Yeah. Did you bring it into YC or did you pivot to this at
Speaker 42:some point?
Speaker 50:Brought it into YC. So we were teeing a computational mechanics class together at Stanford, which was teaching undergrads in mechanical engineering how to use the traditional simulation software.
Speaker 2:Yes.
Speaker 42:Kind of bred
Speaker 50:a hatred for those softwares. Meanwhile, Abhij is doing insane research at Stanford to use ML to model a physical world in this crazy accurate way ways. He's like building ML models for Intel that Yes. Are replacing months of trial and error in their plasma etch process. And I realized, like, hey, there's this huge opportunity to out with the old out with these old simulators.
Speaker 50:Yeah. Let's bring physics informed ML to a broader group of engineers who could really benefit from these faster, cheaper answers.
Speaker 1:Okay. Try and make it a little bit more concrete for me. I'm familiar with, like, CFD, computational fluid dynamics. I have a I have an engine, and I'm trying to simulate how the air will flow over the jet that I'm building. Is that an example that we could use to kind of build off of is it just is it just faster inference than calculating everything deterministically?
Speaker 1:Is that
Speaker 50:the goal? Absolutely. Like, all of simulation every time you start with a simulation tool, the engineer is starting with with a question. Right? You've a question
Speaker 42:in your three d model.
Speaker 50:You wanna know how is what is drag?
Speaker 1:What's drag?
Speaker 50:Yeah, exactly. How is that gonna change as I change thickness or angle of attack of my airflow? Now, imagine instead of needing to learn a simulation software, you can ask with natural language, drop in your CAD and get simulation quality results instantly.
Speaker 1:Interesting.
Speaker 50:And we say instantly it's 4,500 times faster than
Speaker 1:Yeah.
Speaker 50:A benchmark GPU accelerated software that we tested against.
Speaker 1:Wow. Wow. Wow. Okay. Very cool.
Speaker 1:What's the go to market like? I mean, imagine you're selling to like very large aerospace defense companies or who else is building stuff? I mean, Google, these companies could buy this?
Speaker 50:Yeah, exactly. I think the huge benefit of physics informed ML is we can tackle problems that traditional simulators cannot tackle
Speaker 43:Mhmm.
Speaker 50:Multi physics, multi scale. Mhmm. We can extremely feasible to tackle those problems and we can also fill in the gaps where your idealized equations don't suffice to capture the complexity of problem. Sure. So these problems that Apple, Google, maybe aerospace are throwing millions at in terms of R and D and building and We can give you accurate physics models that can replace your need to physically build and test the product.
Speaker 1:And they can probably like reality check your faster results with the traditional system that they have in place whenever they
Speaker 2:need to Yeah. And build that confidence
Speaker 1:Run that overnight. Yeah. Exactly. While while we're
Speaker 2:live traction,
Speaker 1:selling
Speaker 50:it so It's been great. So we launched two weeks ago. We entered a twenty five k year contract with an engineering firm, Jervis ANSYS, is $30,000,000,000 incumbent. Wow. However, I think our stronger pull right now is we have some exciting opportunities with enterprise customers to, again, tackle those highest value problems where you don't there is no solution for.
Speaker 50:For modeling something like drop simulation, right, like there is no great simulator that gives you that fast accurate answers even though it's, you know, governed
Speaker 10:by definition. Gets like 70% accuracy or something. Yeah.
Speaker 15:Yeah. Interesting.
Speaker 50:And it's also very slow though.
Speaker 10:It's like two weeks to compute Exactly.
Speaker 50:A drop simulation on a 14 inch MacBook Pro today. So like these really high value problems for enterprise customers, we have the opportunity to go apply our software and they'll give give them more accurate answers.
Speaker 1:Very cool. How big is the team? Where are you going next? How's the fundraise going?
Speaker 50:Yeah. It's three core, one advisor. Yeah. The fundraise is going well. We're about well, it's very exciting.
Speaker 50:We're just,
Speaker 2:Yeah. Yeah. Yeah. Amazing. Whoever whoever's like, you know, bidding, I'm sure they're, you know, gonna watch this.
Speaker 2:Well, congratulations.
Speaker 1:Give us a fantastic
Speaker 2:demo day.
Speaker 1:We've been giving out hats to folks who come on the stream. Thank you so much.
Speaker 50:Thank you so appreciate so much.
Speaker 1:Congratulations. We will talk to you soon. Yeah. Let's bring in the next team or whoever it is and
Speaker 2:We got Dan.
Speaker 1:Don't forget to go to Vanta Dot Com, adio.com, numeralhq.com, adquick.com, aidsleep.com, wander Go to every website right Look at the bottom bars.
Speaker 2:What's going on, guys?
Speaker 1:Hey. How are doing? Dan.
Speaker 2:Dan. Dan. What's happening?
Speaker 1:Good to have you on the stream.
Speaker 2:Nice to meet
Speaker 1:you. Welcome to the show. Thank you. Can you introduce your yourself for the stream? I'm Linus.
Speaker 1:Linus? Pleasure.
Speaker 13:It is a pleasure. Is that do you
Speaker 6:want me to go into any more detail?
Speaker 1:Sure. Yeah. Well, let's get Justin's name.
Speaker 24:My name is Justin.
Speaker 1:And what are you guys building?
Speaker 13:We're building Den.
Speaker 1:Okay. What is Den?
Speaker 24:Den is cursor for knowledge workers.
Speaker 1:Okay.
Speaker 43:So break
Speaker 1:it down.
Speaker 24:We're an AI native Slack replacement. If you if you load up to the app, you'll see a bunch of AI agents
Speaker 1:and Slack replacements.
Speaker 24:Slack replacements.
Speaker 1:Okay. So I I'm not in Slack at all. I'm just in Den when I'm doing knowledge work. Mhmm. Okay.
Speaker 1:What is the typical knowledge worker, like, experience with Slack? Because I feel like there's lot of just, like, managerial overhead status updates that are going on. This sounds like something a little bit more mature than that. What what am I doing in Den?
Speaker 24:In Slack, there's a lot of kind of lost threads.
Speaker 1:Okay.
Speaker 24:A lot of like, kind of lost information Sure. And just a lot of communication
Speaker 1:Yeah.
Speaker 24:Ultimately, but no actions. Yep. With Den, we're all about actions and we work backwards from, you know, what what you need to get the task done.
Speaker 2:So Okay.
Speaker 1:Give me an example.
Speaker 24:Yeah. So I guess in Slack, you would be asking, you know, your coworker for how many how many users signed up last month. Mhmm. That's gonna be like an async process where I was man.
Speaker 1:Literally just happened in the Slack. Was like, in one of my Slacks I'm in. I was like, are we reviewing last week's numbers or the week before or the week before was a developer on a team talking to the CEO?
Speaker 24:Yeah. Sure. You know, in in Slack that task might just get lost in the background.
Speaker 1:Totally.
Speaker 24:You don't know who like, what the status is in Den. Mhmm. And AI agents just gonna pick that up. Mhmm. So, you know, we're all about like that multiplayer aspects.
Speaker 24:Your inputs are building the agents and kind of configuring your tools and configuring the tasks. And then we've kind of provide like the multiplayer environment where you might be orchestrating like thousands of AI agents. Mhmm.
Speaker 1:Yeah. Talk about the path of AI agents, long running agents. We have this idea of like ten minute AGI, twenty minute AGI, o three Pro seems to work for thirteen minutes every time you kick something off. Are you putting these things on cron jobs? Is there is there some sort of, like, long running process that can run through my den installation and say, hey.
Speaker 1:Like, every single hour, I want you to check on things that aren't getting done?
Speaker 24:Pretty much. Okay. Then we break it down into three things. Like, you've got your ad hoc tasks. Yep.
Speaker 24:You've got your, yeah, cron job tasks, your scheduled tasks,
Speaker 2:and then
Speaker 24:you also have things that kind of respond to triggers. It's like, oh, hey. Run this task whenever I receive an email.
Speaker 1:Got it. Okay.
Speaker 2:There there was this idea, I think it was an AI 2027 around the the idea that like an AI would just spin up a Slack instance and use that The
Speaker 1:AIs would use Slack to coordinate with each other.
Speaker 2:And and I can see a world where that makes sense. What what was the catalyst for you guys to realize that you you needed to kind of rethink the communication stack from the ground up
Speaker 1:Mhmm.
Speaker 2:To serve agents over, you know, and foremost over over humans?
Speaker 6:I guess what we realized was that there's tools like Zapier, tools like tools like Relevance AI, but they're external to your communication source. Knowledge workers spend more than 80% of their time in Slack and Notion. And so we wanted to bring the agents to where people actually did the work. Mhmm. And most important thing is now the agents can escalate tasks through, like, the CEO or the head of customer success, whereas they couldn't before because they were siloed.
Speaker 6:So we
Speaker 1:had to
Speaker 24:build it from
Speaker 6:the ground up for agents in that way.
Speaker 2:Do you think we'll still call ourselves knowledge workers in ten years if knowledge is instantly accessible by all
Speaker 1:machines? Workers? Is it taste maybe taste workers and agent when agency workers
Speaker 30:Yeah.
Speaker 1:Because knowledge is commoditized now and intelligence is too cheap to meet her.
Speaker 19:Like taste curators.
Speaker 1:Yeah, exactly. Taste makers. That's the only job that will remain in the future maybe. Knows?
Speaker 2:How how you guys are creating a platform that other agents can work on top of?
Speaker 18:Mhmm.
Speaker 2:How do you rate how do you rank the quality of different agents across categories? Obviously, there's like coding agents, we're friends with the the cognition team Mhmm. And and and the factory AI team and things like that. So coding agents are great. I I haven't heard a lot of people saying like I love my AI BDR yet.
Speaker 2:Yep. Right? Maybe there's some use cases, maybe they don't wanna talk about it, but like what are the categories that you guys are most excited about? Mhmm. Deep research is obviously another
Speaker 1:another category but Yeah.
Speaker 24:Think it's gonna slowly move down like the stack. I spoke with Sholto Douglas who is a researcher at
Speaker 1:Yeah. Recently. He's great.
Speaker 24:We have great, like, coding agents and and kind of math agents because research is like math and coding. Interesting. But, you know
Speaker 1:It's not just a verifiable reward thing?
Speaker 24:It's that as well. Okay. It's that as well. Yeah. I think we're kind of building in the infrastructure that's gonna allow those like iterations to happen where you do actually get like a really good BDR agent.
Speaker 24:Mhmm. We're already building customer success agents that haven't existed before because we're providing like the primitives and the building blocks. Yep. And I think the verifiability will come. Okay.
Speaker 24:It's just
Speaker 1:yeah. Interesting. How's the traction been? What's the rollout? What's the go to market?
Speaker 24:Go to market is handing out 500 business cards Yes. To everyone at demo day.
Speaker 1:So so you want every company here, all the small start ups, the the early stage companies. That feels easier than ripping out some massive Slack installation. Right?
Speaker 19:Exactly. Okay.
Speaker 24:We take a lot of inspiration.
Speaker 1:Start with Dan, stay with Dan forever. Got it. Exactly. Okay. Bro, seat based pricing?
Speaker 7:Yes.
Speaker 2:Interesting. Agent based pricing?
Speaker 1:Yeah. Do the agents have to pay? About the thousands of agents? Okay. So the one person company, you're done.
Speaker 1:You're cooked.
Speaker 20:Very warm. Exactly.
Speaker 1:Good luck, though. You'll figure
Speaker 13:it out.
Speaker 1:I'm sure I'm
Speaker 14:sure it'll be
Speaker 2:Alright. And have you shared any numbers? Before this.
Speaker 1:Yeah. Yeah.
Speaker 6:I previously started a company when I was 19. I would go to, zero to 5,000,000 ARR, 50 people Oh, that's good. Congratulations.
Speaker 3:Thank you. You know, I
Speaker 6:started this three months ago now,
Speaker 24:so it's been awesome.
Speaker 1:Awesome. Very cool. Right. Three months right before VIC. There we go.
Speaker 2:Yeah. Jasper, how were you
Speaker 3:doing?
Speaker 24:Yeah. Mean, Linus is a beast. He hired me actually at his previous staff, so I was able to go from, like, that 500
Speaker 1:Yeah.
Speaker 24:That's great. Thousand ARR to 5,000,000.
Speaker 1:Yeah.
Speaker 24:Yeah. And we just loved working together. That's awesome. This is what we wanted to spend the next
Speaker 1:twenty years on. Yeah.
Speaker 2:Yeah. Amazing. Well
Speaker 1:Fantastic. Thanks so much for helping on the stream. This was fantastic. Awesome. Good luck, guys.
Speaker 24:I'll do that.
Speaker 1:Thanks for See you on the next one. And we are ready for our next guest coming on in to the Palace of Party Rounds to YC demo day twenty twenty five. Welcome,
Speaker 2:JLIST three. Eloquent.
Speaker 1:How are you doing? Nice to meet you.
Speaker 42:Very nice to meet you.
Speaker 1:It's Pruce. Pruce.
Speaker 2:Hey, Jordan.
Speaker 15:Nice to
Speaker 1:meet you. Good to meet I'm John. Pleasure.
Speaker 2:What's happening?
Speaker 1:I'm I'm kicking this over. Would you mind starting with an introduction on yourselves and the company you're building today?
Speaker 42:Absolutely. This is Tuche. I'm the CEO of Eloquent AI.
Speaker 46:And I'm Aldo. I'm the chief AI officer of Eloquent AI.
Speaker 1:Fantastic. And what are you building?
Speaker 42:We are basically building an AI platform for financial services Okay. To automate complex regulated operations.
Speaker 1:Okay. What's an example of that?
Speaker 42:For example, in a bank, you if you want to unfreeze an account Mhmm. Or handle a regular dispute Mhmm. Run KYC, KYCB Sure. This usually goes currently to support teams. Sure.
Speaker 42:And you have a big queue. Right?
Speaker 1:Yep. Yep.
Speaker 42:It might take up to two weeks
Speaker 1:Yep.
Speaker 42:To open a new business account.
Speaker 1:Happens all the time.
Speaker 42:So we solve that problem. Rather than that's going to support team, it comes to our dedicated AI operator.
Speaker 2:Sure.
Speaker 42:Our AI operator takes on the job. Yep. And here's the real magic. It basically navigates your existing core banking portal just like your support team does. Yeah.
Speaker 42:We don't need any APIs or any engineering. Exactly right. So this is coming from Aldo's research for more than five years. They developed this technology that allows us to do computer use and browser use in a reliable way for very specific tasks. Mhmm.
Speaker 1:How much of what you're doing is purely enabled by the advances at the foundation model labs versus fine tuning or any sort of scaffolding that you're doing on top of the the state of the art models?
Speaker 42:That's a fantastic question. Do you wanna tell a
Speaker 46:little Of course. It's a little bit of both. Right? Mhmm. Foundation model, of course, gives us a lot of synthetic data for which we can on which we can train on.
Speaker 13:Sure.
Speaker 46:But, you know, we leverage a multi genetic, architecture to actually perform the actions reliably.
Speaker 1:Mhmm. What's the response been from big regulated financial institutions? They are not traditionally the earliest adopters of new technologies, but everyone's talking about AI every day, so I'm sure they're excited to at least talk to you. What's the response to this?
Speaker 42:Know, things have changed. Okay. We are finding it's actually reached half a million ARR in four weeks.
Speaker 1:Love that. Congratulations. And
Speaker 42:the reason is because I think all these banks now have the mandate.
Speaker 2:Sure. Wait. Did you say 4 and a half?
Speaker 1:4 and a half. It was it was four weeks.
Speaker 42:4 Half a million ARR.
Speaker 1:Million weeks.
Speaker 35:There we go.
Speaker 26:Right. Alright.
Speaker 2:Just do that every four weeks for the rest
Speaker 1:of the year.
Speaker 42:Well, this stage, we actually have a waiting list. Customers than we can onboard. Because as I was saying, things changed in the financial industry. Sure. They want to bring this cutting edge AI in house as quickly as possible.
Speaker 2:That's amazing. Very cool. How's how's YC been?
Speaker 42:Honestly, in any metric, it's massively exceeded my expectations.
Speaker 2:That's amazing. Really amazing.
Speaker 42:I'm a time founder.
Speaker 18:Okay.
Speaker 42:So I thought, okay, do we really need to come to YC? You know, we already have the investor networks. But what was really beyond my expectations is the customer network Mhmm. Especially in financial institutions is incredible. Sure.
Speaker 42:Yeah. A lot of YC alumni are now running big fintech companies Yep. Banks. Yep. And they have been very welcoming.
Speaker 42:We can definitely feel the love.
Speaker 1:That's amazing. That's great. Awesome. How big is the team? Where are you going next?
Speaker 1:I'm sure you're raising money. What's happening down the down the road? What's in the next twelve months?
Speaker 42:Absolutely. So we raised the big seed round. We closed last week.
Speaker 2:Congratulations. We
Speaker 1:did it. Dike in another That's a big one. We closed the seed round, everyone. I love that.
Speaker 42:I love the spirit here. That's amazing.
Speaker 1:And That
Speaker 2:still surprises me every
Speaker 1:time. It's amazing. Yeah.
Speaker 42:It does. It does still surprise me. I love it.
Speaker 40:And, yeah,
Speaker 42:we close the seed round.
Speaker 10:It's much At
Speaker 42:the moment, it's basically heads down. We are building. We are six people. Yes. Aldo is leading our technical team.
Speaker 42:Fantastic. I do sales. Congratulations. We are hiring 10 more people.
Speaker 1:10 more people.
Speaker 42:If anybody is watching who is looking for a new role, they are very welcome
Speaker 1:Well, thank you so much for
Speaker 2:stopping by. It was nice to
Speaker 1:meet you. Take care. Soon. Let's bring in the next guests. We are live from YC demo day twenty twenty five.
Speaker 1:Book a wander. Find your happy place. Find your happy place. Bring him on in. Go to wander.com.
Speaker 8:Hey, guys.
Speaker 1:What's happening? To the stream. How are doing?
Speaker 30:Happening? I'm good. Good.
Speaker 1:Good. Good to meet you. How are doing? Achoo. Good to meet you.
Speaker 1:Nice to to Why don't you kick us off with an introduction on yourselves and the company you're building?
Speaker 30:Sure. I'm Somi, and I'm Achooq, and we're building YouLearn.
Speaker 1:Okay. It's an
Speaker 30:AI tutor for students.
Speaker 1:Very cool. What's the go to market? I I I built I tried to build a ed tech company back in 2012. I I was actually the company I applied to YC for.
Speaker 38:Oh, wow.
Speaker 1:It was a disaster. It was extremely hard. Never really made a dime off of it. They had, 500 installs on the iOS app I built.
Speaker 30:Yeah. Let's give
Speaker 1:it to 500 installs. Yeah. Yeah. It's not exactly one of these. How are you solving that?
Speaker 1:Like, it's notoriously hard to sell into
Speaker 30:It is. But I think education. We're seeing a change Okay. Especially with AI. Yeah.
Speaker 30:We have over 200,000 students active on the Wow.
Speaker 1:We go. And our 2,000. A thousand more than what I got during my wife's event.
Speaker 13:Thank you. Thank you.
Speaker 44:You guys can
Speaker 1:keep talking. Okay. 200,000. I love it. Absolutely.
Speaker 1:So so, well, what is the channel? Is it
Speaker 2:Let me guess. You got a bunch of TikToks. Yep.
Speaker 1:Yep. Okay. There we TikTok.
Speaker 3:Yep. YouTube shorts.
Speaker 30:We have, I think, over 500,000 followers on Instagram. That's amazing.
Speaker 2:And how
Speaker 1:how long do you take that We
Speaker 30:we did a
Speaker 3:side project
Speaker 47:for a while,
Speaker 30:but I think eight months is, like, lot like, for the past few,
Speaker 1:eight months.
Speaker 2:Awesome. And how do you, I'm picturing kind of like an LLM style chat interface. Is that the wrong
Speaker 30:Yeah. So essentially, like students upload their like learning material like textbooks and we give them like concise notes. Sure. Like you can have a conversation with an AI tutor
Speaker 2:as well.
Speaker 30:Sure. Or like, you can have quizzes, you can create exams. Yep. All the study tools
Speaker 1:And you
Speaker 2:create, like, podcasts based on Yeah.
Speaker 30:So right now, it's a conversational experience. We wanna make it, like, more proactive. Yeah.
Speaker 1:You can listen.
Speaker 49:Yeah. I remember
Speaker 2:And are you getting to point where teachers and schools are are kind of asking their students to get on the platform and use it?
Speaker 30:It's not yeah. So we're talking to a bunch of school districts and stuff. But I think our main focus for now is students. We just wanna, like, get the product down for them and then move up.
Speaker 2:And what what what what level are we talking? Middle school?
Speaker 31:Yeah. Where's the
Speaker 1:option in strongest?
Speaker 13:High school?
Speaker 30:Yeah. So undergrads Undergrads? Are, like, number one right now, and then it's higher than that. And now we're seeing a lot of high schoolers as well.
Speaker 1:How are you are are you monetizing it?
Speaker 13:Are you Yeah. Yeah.
Speaker 1:So we
Speaker 30:have a freemium subscription.
Speaker 1:Okay.
Speaker 30:It's free for, like, a limited amount
Speaker 6:of time Sure.
Speaker 30:And then $20 a
Speaker 1:month. $20
Speaker 2:a have a search based pricing, exam season.
Speaker 1:Before exam people. Just We
Speaker 13:actually have Yeah. We give
Speaker 30:a discount on exam season,
Speaker 21:and it
Speaker 1:worked really It worked well. Mhmm.
Speaker 48:Well. Even post exam, like, there's always a summer sale this
Speaker 30:summer. It's
Speaker 1:really good.
Speaker 13:I think it
Speaker 21:just works out really well.
Speaker 1:That's amazing. Wait. So how long have you actually been building this? I I I missed it.
Speaker 30:So we she started eight months ago.
Speaker 11:Eight months ago.
Speaker 1:Okay. And then YC.
Speaker 43:Uh-huh. Okay. Great.
Speaker 1:So have you been focusing on specific growth metrics during YC to kick off demo day? Yes. Just growing everything?
Speaker 30:Yeah. So MRR is, like, our key metric.
Speaker 1:Fantastic.
Speaker 30:And then retention as well. We wanna make sure, like, like a key part in learning is, like, retaining
Speaker 1:Did you share an MRR number today?
Speaker 9:Yes. Can you
Speaker 1:share anything with us?
Speaker 30:Yes. We can.
Speaker 1:Fantastic. Let's hear it.
Speaker 30:We are at 75,000.
Speaker 1:Let's go. Congratulations.
Speaker 13:Wow. Thank you.
Speaker 2:I mean, honestly, I gotta say sorry because just Yeah.
Speaker 1:I know. $83.83 would have been 1,000,000. You're close. Amazing. Nothing.
Speaker 1:You're close.
Speaker 2:Yeah. You're close.
Speaker 1:Next week.
Speaker 11:Honestly, next week.
Speaker 1:Put another sale on.
Speaker 2:Oh, yeah.
Speaker 13:Go to every person
Speaker 21:in the bat. Every Subscribe right now.
Speaker 3:He also subscribed right Let's
Speaker 30:get into
Speaker 1:one That's fantastic. You actually
Speaker 2:we're not gonna let you leave till you get two million of
Speaker 1:extra hours. Okay. Okay. You gotta stay in this room while Yeah. Yeah.
Speaker 1:I mean, last question. Obviously, tons of developments from all the foundation model labs. What's working? What's most exciting? What are you taking advantage of?
Speaker 1:Are you focused on cost optimization, looking for open source? Do you wanna use the best? Are are you a beneficiary of the o three pricing drop? Are you a beneficiary of o three Pro?
Speaker 30:A 100%. Every model improvement benefits us directly.
Speaker 13:Mhmm.
Speaker 30:Yeah. We wanna focus on accuracy the most right now. Mhmm. We wanna make sure everything that a student gets is as accurate as possible.
Speaker 2:Are students, cooked by AI? Are
Speaker 51:you are
Speaker 30:you cooking them? Leveraging it more just to study smarter and that's what our vision is. Yep. And also an interesting thing is they really like our voice mode. Yeah.
Speaker 30:So it's not just text based. They can have a, you know, like a natural like
Speaker 2:you leave it open on your desk when you're studying
Speaker 13:Yeah. You can do that. Continue
Speaker 30:it can like create mind maps for you. It can create like flow charts. You can create whatever you want.
Speaker 1:So it's very cool. Yeah. How are you seeing the the the the competition?
Speaker 2:Drop out?
Speaker 30:In the process.
Speaker 1:In the process?
Speaker 11:Legally? Legally not. Yeah.
Speaker 2:School for you, but not for me.
Speaker 1:Alright. How are you seeing the obvious competition between just, like, using ChatGeeBT, $20 a month, that's right at the pro tier or, like, the the the plus tier? Yeah. I there's a lot of Tyler Cowen's been writing about, I used to feed in a snippet from a book and ask ChatGeeBT about it. Now I just say, hey.
Speaker 1:Summarize this book, he already knows. Or he's gonna just go out and find it. Yeah. It that feels like the logical, competitor. Yeah.
Speaker 1:But how are you differentiating in terms of the actual UI design? Because it seems like as we move to the application layer narrative, there is a world where aggregating demand around AI tools around a specific niche works, but there's a lot of secret sauce that goes into the UI. What what are you doing to
Speaker 30:stay ahead? So, yes. Definitely, a UI is a big component, but the main thing is we just ask our users. Mhmm. Why do they use us or ChatGit TV?
Speaker 1:Sure. What what's the And
Speaker 30:they say like, we have a, like, a feature called the quizzes Okay. And the flashcards Sure. And we create that, like, very accurately for them. Interesting. And what they can do is they can upload, like, a thousand page textbook, even 2,000 pages.
Speaker 30:Someone uploaded, like, the entire
Speaker 2:imagine imagine how that HBT hallucinates and you just fail your exam. I remember so
Speaker 1:so so I remember in in college, I had a I had a textbook and I wanted a digital version. I took it to a a scanning, and they scanned it all, and they were like, do you have the right to do this? This sounds illegal. I would try and download illegal PDFs. It was very sketchy.
Speaker 1:How is how are college students uploading 2,000 page documents?
Speaker 30:If it's available digitally, we they can upload it.
Speaker 1:Okay. Like, ButtHub or anything Mobi? Yeah.
Speaker 30:So I mean, they can upload anything they want, but it's on them.
Speaker 1:Okay. Okay. Yeah. They have to figure out that part of Okay. Got it.
Speaker 1:But eventually, maybe you could do partnerships with publishers.
Speaker 30:Oh, yeah. Wanna, like, partner with, like,
Speaker 1:ebook companies and That'd be very,
Speaker 45:very cool.
Speaker 1:Yeah. Yeah. Amazing. Well, congratulations. Yeah.
Speaker 1:Thank you so much.
Speaker 33:Thank you.
Speaker 1:Yeah. Congrats on all the progress. You. Thank Yep. And let's bring in the next team.
Speaker 1:Let's do it. Lightning round. Let's speed these up. Right? Speed these up.
Speaker 1:There's demand. Up. Is there demand out there? Okay. Hey.
Speaker 1:We only have two What are you gonna take me? Oh my god. There's Oh, we got it. We are gonna be
Speaker 43:like okay. Alright. Come in here.
Speaker 1:Come in. In. Okay. Realistically, only one of you or two of you can talk because there's only so many mics. But, again, keep going.
Speaker 1:Can you guys move camera to show everyone for
Speaker 13:a
Speaker 1:We got a small army here. Look at this army. Yeah. Yeah. Yeah.
Speaker 1:Tilt over a little bit. Go to the wide. I wanna show
Speaker 2:these whole Are you
Speaker 42:guys is this
Speaker 2:the founding team?
Speaker 1:Are you Yeah.
Speaker 41:What's going founding team.
Speaker 2:Yeah. Wrong record. Who's who's the founding engineer?
Speaker 6:There's one guy in
Speaker 1:the one guy in
Speaker 18:the subject.
Speaker 1:That was Oh, well, are you building? Break it down for us.
Speaker 41:So we are building AWS for AI agents like Okay. Imaging. We are twenty years ago, it was mostly infrastructure for software as a service. Yep. Now, AI agents are becoming, like, the de facto new Yep.
Speaker 41:New new software. So we are building this infrastructure for the agents.
Speaker 1:What is what is important about infrastructure that needs to be different? Why would I not just deploy this on AWS? Yes. Or or Microsoft. I mean, you saw Satya Nadella at at Build.
Speaker 1:He was saying, like, we have model router. We have everything from DeepSea to Lama to they vend every model. How are you gonna stand out?
Speaker 41:Yeah. We we stand out because today, people who are building agents are not cloud architect, cloud, you know, infrastructure experts. Sure. So a new generation of of software developers
Speaker 1:Oh, okay.
Speaker 43:Software engineers.
Speaker 41:Yeah. The thing is agents are going to build themselves their infrastructure. Yeah. Yeah. Yeah.
Speaker 41:So they need new interfaces to interact with the infrastructure. Yep. And the type of workload that they are using, like the the technology they are using, the lifetime of the computing platform is not the same. K. So serverless is one thing.
Speaker 41:Mhmm. Additional virtual machine is one of those things.
Speaker 1:Even going back to the YC story, Heroku. Yeah. Exactly. Was that story of like, yeah, could always spin up EC two, but Heroku made it easier.
Speaker 41:Exactly. And right now, we are doing the same for agents. So when your agent is generating code, it need it needs to have access to some resources to run this code. So we provide these kind of resources called sandboxes to to help them to run this code for fifteen minutes, an an hour, or maybe months, or years.
Speaker 2:How did you when did you start the company? Did you did you start it three months ago when YC started, or you guys have been at it for a little bit?
Speaker 41:Yeah. So actually, we both all worked in my previous company. We sold to a cloud provider. Uh-huh. So we we know how to
Speaker 1:The traders hate over here. They all laughed. It's like Intel again. What happened? It pertain to any of you?
Speaker 1:You couldn't be bought. Yeah. I see what happened. You couldn't
Speaker 41:be No. It's the six of us.
Speaker 1:That's amazing. Congratulations. Yeah. You guys stick together. I love it.
Speaker 3:I love it.
Speaker 1:Yeah. How's adoption been? Who are you selling to? Who loves this product?
Speaker 41:So we launched we launched seven weeks ago. Wow. And like most of cogeneration companies from this batch, even in the next batch are starting using us. Really? Paying consumers in production right The
Speaker 1:economic model, I assume it's consumption based on top of the underlying cloud platforms that you're building on top of. Is that correct?
Speaker 41:Yeah, exactly. We started actually to do something with a unique subscription with monthly subscription just to be sure that people were serious about using us. Being sure that we get the right traction. Like, we we they are using actually in production our our our software. Now we want to expand, so we are basically offering more just as usage based model for the next batch.
Speaker 1:Do you
Speaker 2:guys live in the office?
Speaker 41:So we leave, we sleep. When we when we can sleep, yeah, but we
Speaker 1:leave in
Speaker 41:the office.
Speaker 1:Yeah. It's a lot mouths to feed. Have you raised money?
Speaker 41:Yep. Yeah. Round's close.
Speaker 1:Congratulations. Thank you. Thank you. Jordan's got stuff for you. Another one.
Speaker 1:Spray them on. Spray them on. On.
Speaker 6:Yeah. Thank you. So for
Speaker 13:coming. Thank you.
Speaker 6:Thank you, guys.
Speaker 1:Fantastic team. Look forward to following your journey.
Speaker 2:We gotta
Speaker 1:Have a great have a great time. We'll talk to you. Thank you. Bye.
Speaker 2:We got
Speaker 1:a good name.
Speaker 2:Coming in next, we got waffle.
Speaker 1:Let's bring in waffle. Waffle. Come on in to the Palace party rounds. Oh, yeah. They're customer.
Speaker 2:That's great. Customer. Love it.
Speaker 1:Grab some seats. Put the microphones as close to your face as you can because we are live from YCWA 2020 You
Speaker 2:guys are building agents for agents.
Speaker 43:We're building What building?
Speaker 35:We're building
Speaker 43:an AI operating system for small to medium businesses.
Speaker 1:Okay.
Speaker 43:So what that means is we help you build your website Mhmm. Set up your business email address, your phone number
Speaker 1:Mhmm.
Speaker 43:Your bookings, your payments, that kind of stuff.
Speaker 2:Okay. Are you guys replacing Google Workspace? Wix building on top of it?
Speaker 43:We're starting off replacing Wix. Wix. We just we've just launched our AI website builder three weeks ago. We've had 700 projects built on there. Okay.
Speaker 43:And yeah, people are building websites.
Speaker 1:Okay. So you build the websites and then you also are able to instantiate all the downstream stuff. But I I imagine you're not rebuilding everything, so who are you plugging into? You're not rebuilding Stripe. Right?
Speaker 43:No. We're not rebuilding Stripe. It's like wrapping on top of these developer tools.
Speaker 1:Because there's so many of them now.
Speaker 43:There's so many and they're so cheap. Yes. You know, like, if let's take just, like, outbound email marketing. Yeah. Someone could use something like Mailchimp.
Speaker 1:Mailchimp. Chimp.
Speaker 43:Yeah. And that's that's much more expensive than how much me and Diogo pay when we set up a project with Resend Yep. Because it's a developer tool. It's
Speaker 1:super cheap. Interesting.
Speaker 43:So then we
Speaker 2:can make money
Speaker 1:on the spread. Okay. Interesting. How's adoption been?
Speaker 43:Adoption has been really interesting these few weeks because we've done no marketing. We're just like full time coding, working on the product. Yeah. So we've had people come in from the YC launch Yep. From the Twitter.
Speaker 43:And now in the in the coming months, what we're doing is like going in and doing more go to market stuff. So one of the things we're doing is taking people's existing Wix or Squarespace website and just cloning it. Yeah. Putting it to Waffle.
Speaker 1:And then just sending it to them.
Speaker 43:Sending it They
Speaker 1:can do it. I've seen a lot of Wix and Squarespace. You see the web you see the ads, the Super Bowl ads, and and it's always a small business, a restaurant, or a pub. And do you have a a couple customer avatars that you really like or or wanna use to fuel your go to market?
Speaker 43:Yeah. Right now, I think what's really interesting is, like, these small family run businesses, like, the small family run travel agents or law firms.
Speaker 1:Mhmm.
Speaker 43:I think long term, ecommerce is really where it's at. Mhmm. They care a lot more about their website.
Speaker 1:Yeah. Yeah. Yeah. How important is design in that? Are agents good at design yet?
Speaker 1:It doesn't seem like it's something that would come native of yet. The a lot of the AI art that's being produced is beautiful.
Speaker 9:Yeah. Yeah. I mean
Speaker 13:yeah. It's interesting. The default Claude like, if you just ask Claude to generate some UI, it's actually quite good, but it's very unopinionated.
Speaker 1:Unopinionated. Yeah. It's just like
Speaker 13:some generic type website. Sure. I think if you start giving examples and just really working the system from, you can get it to a point which is pretty good. What we've been doing is using 20 dev. A it's a tool that have a lot of UI and they're building out their magic chat, allows you to kind of take a UI components and, like, have remixes of it.
Speaker 13:Very cool. So they're they're working on that specific technology and we're we're working with them. So, yeah, it's actually pretty good at UI, but it's making stuff which is still quite generic and we're trying to push the boundary.
Speaker 1:Very cool. What were
Speaker 2:you guys doing before this?
Speaker 43:Yeah. So we we've never worked full time jobs. Yes.
Speaker 1:Me neither. That's weird. That's weird. Weird.
Speaker 2:Guys are founders.
Speaker 43:Yeah. Yeah. Yeah. So we we both did computer science. I was at Oxford.
Speaker 43:Diego was at Eco Polytechnique Lausanne. Cool. Met up in London. Yeah. I moved in with him, and we've just been working on side
Speaker 1:of We got you
Speaker 43:to America.
Speaker 1:Thank goodness.
Speaker 2:Great brain.
Speaker 1:Great You said it is impossible to build a company in Europe, and you're testament to the fact that you gotta come to America. Congratulations.
Speaker 43:Yeah. It's been insane. I was listening to you guys two days ago
Speaker 1:Oh, really?
Speaker 43:When the Cluelly founder was on
Speaker 1:Oh, yeah.
Speaker 43:And now we're here.
Speaker 1:Yeah. It's important to get attention, but there is a limit. That's how the message is clear.
Speaker 2:Where's the line for you? Clearly has their line around what they're willing to do for attention.
Speaker 1:It's pretty crazy.
Speaker 2:Yeah. You know what? Willing to go that
Speaker 43:YC goes the other end of the spectrum where they're like, just don't focus on your pitch. Yeah. Let the numbers speak for you.
Speaker 1:Yes. Yes.
Speaker 43:Yes. They're like, just put your graph up and that's what matters.
Speaker 1:I think it's good. Did you have a number that you shared today?
Speaker 43:Yeah. Yeah. So we got 700 projects built in the last three weeks and then that's grown 14% over the last week.
Speaker 1:Congratulations. We're good.
Speaker 2:Here we go.
Speaker 1:With the rest of Demi Day, we have one last hat here. There might be more out there. You guys
Speaker 2:can share that.
Speaker 1:You can swap. Based on days. There's more hats. Let's give them two We're running out of Are we out of hats?
Speaker 2:Yeah. Yeah. We don't wanna run too much.
Speaker 1:Anyway, congratulations. Awesome. Let's bring in the next team. And let's, tell you about Linear. Linear app Used to make manage your projects.
Speaker 2:Half at least half the batch
Speaker 1:is on. I think so. Welcome to the stream. How you doing?
Speaker 35:Doing good.
Speaker 1:Sorry. I need
Speaker 2:to my hand in water. I'm gonna not shake your hand.
Speaker 1:Good to meet you. Let's start with an introduction. Yeah. Who are you guys? What are guys building?
Speaker 1:And please, microphone is closer to
Speaker 32:it as possible.
Speaker 35:Yeah. I'm Daniel. Cool. And this is Musa. Cool.
Speaker 35:We're the cofounders of Vibegrade.
Speaker 1:What do you guys do?
Speaker 35:So we help teachers save time grading papers Oh, directly in their existing
Speaker 1:Interesting.
Speaker 35:Learning management system.
Speaker 1:What's the response been like? Do the teachers love it?
Speaker 35:Yeah. Yeah. Actually, all of our customers are paying out of pocket.
Speaker 1:Wow. Crazy. Okay. Interesting. Yeah.
Speaker 1:You said in their LMS?
Speaker 14:Yes.
Speaker 1:So who are you plugging into? Who's the most dominant platform providers right now?
Speaker 35:So the main one, the best integration is on Google Class room.
Speaker 2:Oh, interesting. So a
Speaker 1:lot of people Is Blackboard not a thing anymore?
Speaker 35:Not that much.
Speaker 2:Oh, I'm excited. We we got a request for
Speaker 14:it, but most teachers are on Google Docs and Canvas.
Speaker 2:Sure. So, like, most k
Speaker 14:to 12 which we serve are on Google Docs and Classroom. Yeah. Yeah. We have Toddle as well, but it's mainly those few.
Speaker 1:Makes sense.
Speaker 35:Yeah. But for universities, they like Canvas is great like they can it'll go and highlight like actual sections.
Speaker 1:So can you add that on as like one of those like mean, if I'm in Google Docs, I can I can do an add It's a Chrome extension? Yeah. Okay.
Speaker 14:So, we realized that teachers, they don't wanna have to learn a new tool Totally.
Speaker 12:And go
Speaker 39:to a new platform Yeah.
Speaker 14:To do that. Okay. So, we built a integration that works directly with Google Docs Okay. Directly with Canvas. Yep.
Speaker 14:So, it's easy as clicking a button
Speaker 1:Yep.
Speaker 14:And we open up a window inside Google Docs.
Speaker 1:Yes. So, walk me through the typical like workflow homework, I
Speaker 13:assume.
Speaker 5:Yeah.
Speaker 1:It's like the student uses chat the teacher Daniel will just to write the questions. Yeah. The student uses chat GBT
Speaker 36:Right.
Speaker 1:To write answers. And then the teacher goes back and
Speaker 2:You guys
Speaker 1:grade it. You guys grade it. Yeah. But but what's the actual workflow? No.
Speaker 1:Are in the loop at all? Are students are students sending, Google Docs links to their Yeah.
Speaker 14:Teachers when they're done? So so Daniel was just in high school, so he really knows how this works.
Speaker 2:Oh. Yeah.
Speaker 1:High school.
Speaker 35:I dropped out of
Speaker 1:high school. Oh. Get the bottle Yeah. You're a high school dropout.
Speaker 2:The word is a chad. Yeah. You front ran everybody. Everybody that was, like, coming into demo day.
Speaker 1:Oh, yeah. Dropped out of college. It's, like, that's so played out.
Speaker 2:You had to wanna
Speaker 1:to go farther.
Speaker 35:Yeah. I mean selling I
Speaker 2:think we heard about you earlier. You're selling it back to your teachers now? Like, that you at the school you dropped out of?
Speaker 35:Sort of. I mean, they they don't really like me that much for some Yeah. Like, on my final exam well, I mean, I I skipped finals
Speaker 1:Yeah.
Speaker 35:Yeah. To go fly to We we went to this EdTech conference
Speaker 1:Oh, yeah.
Speaker 35:In Orlando. Nice. So, I guess they don't really like me because
Speaker 25:of that.
Speaker 1:Oh, no.
Speaker 35:But we we had to bring in some more teachers.
Speaker 1:Well, they'll like you when you're the commencement speaker in a couple years.
Speaker 44:Maybe. Yeah. Hopefully.
Speaker 14:So, the workflow is teachers usually they students submit in Google Classroom or like Canvas. Yep. They submit a Google Doc or a PDF and then the teacher opens it up, the Doc, and they grade it within by like highlighting certain sections Sure. Adding comments.
Speaker 1:Adding comments.
Speaker 14:Yeah. A summary of the feedback. So, we do the whole process
Speaker 1:Okay.
Speaker 14:Yeah. Right in the Google Doc. Then we have the teacher review everything. Mhmm. They can speak to our system to understand their tone and their style.
Speaker 14:Yep. So, then we add those comments
Speaker 1:How how is the actual, like, testing and grading and homework changing? Cause I imagine that the, like, the solution to ChatGPT homework is probably everyone's on a laptop Yeah. Typing the answers in the classroom and there's a monitor watching that you're not just AI ing it. Yeah. And then it can be AI graded and then that's a win for everyone.
Speaker 1:Yeah. Is that roughly what's happening?
Speaker 35:Yeah. Like, the thing that we've seen is like people say all the time, you know, like students are writing with ChatGPT, teachers are gonna grade with AI. Yep. Like, what's the whole point? But Yeah.
Speaker 35:Well, the objective of the student is to learn. Sure. And if you're just using like ChatGPT and mindlessly submitting something Mhmm. Well, you're not doing the right thing.
Speaker 1:Yep.
Speaker 35:There's always gonna be a way to get around it
Speaker 1:Yep.
Speaker 35:If you really don't wanna learn. Yep. But like for teachers, the main like objective is to give students the best feedback and instruction. Yep. So what we're doing is just enabling them to do that a lot better because even in school when like our teachers would give us just like generic copy paste feedback.
Speaker 35:Yeah. So we just wanna give students the best feedback possible. Okay. It gives teachers their time back and they really get the value because they're the ones experiencing the benefits.
Speaker 1:Yeah. Talk to me about the
Speaker 21:top of funnel.
Speaker 2:Why would you not at one point just give the tool to students Yeah. As well?
Speaker 35:Yeah. 100%. That's actually what we're working on next. So Mhmm. We just signed three contracts with with schools
Speaker 1:Oh, interesting.
Speaker 35:Last week.
Speaker 1:Okay. Congratulations.
Speaker 35:And they want to be able to give this tool to students so that while they're writing, they're gonna get real time feedback Mhmm. On their Google Doc as if the teacher were adding comments in real time. Mhmm. So it's sort of like, you know, you you submit a rough draft to your teacher. Mhmm.
Speaker 35:They have to grade it and then give it back to you and then you can do a final draft. Mhmm. Instead of that, why not just have like the assistant give you feedback in real time
Speaker 1:Oh, yeah. That makes sense.
Speaker 35:On the doc.
Speaker 1:Yeah. That's great. Very cool. Talk to me about the top of funnel. How are you telling teachers that this works?
Speaker 1:I imagine at the price point, you can't do Yeah. Hand to hand combat sales. Yeah. How are getting in front of teachers these days?
Speaker 35:So, all of like, we have a 100 teachers that are paying completely out of pocket And all of that has been organic through either the Chrome Web Store. So Sure. They find us when they install another extension like Grammarly. Yep. And then they tell their friends about it.
Speaker 35:Yep. So word-of-mouth has been really strong. We got 600,000 views
Speaker 1:Mhmm.
Speaker 35:On Instagram and Facebook
Speaker 1:Oh, wow.
Speaker 35:In the past thirty days.
Speaker 1:There we go.
Speaker 35:Nice. We just started posting like memes and things like that and teachers really Yeah. They share it and then they look at our page and so they find us there.
Speaker 1:That's great.
Speaker 35:So that's the top of funnel up until this point and we're gonna start pumping out more like UGC style content.
Speaker 14:We're actually going to the biggest ed tech conference in The US
Speaker 1:Let's go.
Speaker 18:At the end
Speaker 25:of this month.
Speaker 1:The Super Bowl of
Speaker 7:ed tech.
Speaker 14:Yeah. 20,000 teachers there and we got a big
Speaker 1:we bought a 400 square foot booth there. Wow. And
Speaker 16:we're going all out
Speaker 14:for it, trying to reach as many teachers as possible. Congratulations.
Speaker 2:How did you guys make your money on the Internet? Thank you.
Speaker 35:How did we make Like,
Speaker 1:like, back back yeah. Yeah.
Speaker 14:I I used to make I used to make websites for, like, random people. Charged, like, this pastor in New Jersey like $3,000 to make his church a website. Wow.
Speaker 16:There we That was back when I
Speaker 2:was maybe 16.
Speaker 1:Going. There we go.
Speaker 35:I mean like a mental health chat app in like grade 10.
Speaker 1:Nice.
Speaker 35:And like a couple of people like one person in Japan found it, and they installed it and paid for it.
Speaker 1:They paid for it. Yeah. Let's go. That's amazing.
Speaker 2:I'll take that.
Speaker 1:Congratulations. Congratulations. A great weekend. Meeting you. Thank you.
Speaker 21:Appreciate it. Really rooting
Speaker 1:for you cooked. Excited. Call us when you meet the yeah. We're good. Middle middle school dropout.
Speaker 1:Middle school dropout. That's where I was going with that. Welcome to the stream. We are live from YC demo day twenty twenty five. Good to meet you.
Speaker 1:I'm John. Nice to meet you. Welcome to the stream.
Speaker 6:Nice meet you.
Speaker 1:How are doing?
Speaker 2:How you?
Speaker 6:We're gonna
Speaker 1:have you down here. We're gonna have you pull this microphone as close as you can, talk directly into it, introduce your company. What do you do?
Speaker 18:We are building AI agents for insurance claims operations.
Speaker 1:Okay. How is that going? Are you is business ripping?
Speaker 2:Yeah. We're 200 k
Speaker 37:a r now.
Speaker 1:Let's go. Let's go. Let's go where the money is. Yeah. That's why people rob banks because that's where the money is.
Speaker 12:That's right, John.
Speaker 1:Go to the insurance industry.
Speaker 2:It's expensive. Who
Speaker 1:who are you selling to? Are there is it is I I assume that the insurance industry is very oligopolistic. There's a few big players, but is that not right?
Speaker 18:We're selling to party administrators, which are these, claims outsourcing companies Okay. Of which there is a surprising
Speaker 1:There's a ton.
Speaker 26:Number. Yeah.
Speaker 1:It's, like,
Speaker 18:42,000 across Europe.
Speaker 2:When did you realize that you guys loved insurance?
Speaker 1:It's like no.
Speaker 30:I've seen
Speaker 18:it in my job before. Yeah. Like, sort of had some exposure. My roommate is an insurance adjuster. Oh, interesting.
Speaker 5:Look over his shoulder
Speaker 14:I would put
Speaker 1:you on a job so I can hang out and go to more movies on the weekends. I like you working late. I wanna play video games with you. That's great. That's the best.
Speaker 1:That's awesome. So, yeah, walk me through what you're actually building, what the product experience is like, how it plugs in. I assume this is some sort of, like, copilot experience right now, or is it fully agentic?
Speaker 18:It's a it's a full fully agentic experience. So the goal is to replace, like, autonomously parts of the workflow for
Speaker 20:the adjuster.
Speaker 18:So the thing we're live with with customers is like a claim intake agent. So when you crash your car and you call in Yep.
Speaker 7:You basically ask you a set
Speaker 18:of structured questions Sure. And then create structured data and writes it into the system of record.
Speaker 1:Got it. Okay. And and previously that was handled with someone on the phone talking and typing everything in. My roommate. Your roommate.
Speaker 1:Yeah. There you go.
Speaker 2:Did you get him as a customer yet?
Speaker 18:No. Not yet. He he works a very big carrier,
Speaker 1:so that's gonna be a long Yeah. Game with that one. So
Speaker 5:yeah. Yeah. Yeah.
Speaker 2:And what were you doing before?
Speaker 8:I was doing product consulting and then worked as a software engineer at Agentive, which is another YC start Oh,
Speaker 1:very cool. Yeah. Awesome. How how's the round coming together? What's Closed
Speaker 24:it yesterday.
Speaker 1:Closed it yesterday. Congratulations. Well, thank you so much for hopping on. Good luck. Thanks,
Speaker 2:Zach. Good luck with everything.
Speaker 16:We are Congratulations.
Speaker 1:We're moving into lightning rounds.
Speaker 13:Nice to meet you.
Speaker 1:I'm gonna give you one
Speaker 2:of these. Okay.
Speaker 1:Let's bring in the next team. We're moving through these quicker. Thank you so much for hopping on. We are live from YC Demonday 2025. Welcome to the stream.
Speaker 1:Gentlemen. Come on. Sit down. What do we got?
Speaker 2:Text AI.
Speaker 1:Text AI. That's a good domain.
Speaker 49:Are we supposed
Speaker 1:to suit up for this? No. No. Of course. We
Speaker 16:don't have the right gear.
Speaker 1:Pleasure. Pleasure. Pleasure. Nice you. To meet you.
Speaker 1:Nice to Nice How are doing? Sorry. We don't have an extra
Speaker 6:chair. I
Speaker 2:I I briefed, like, caught, like,
Speaker 3:thirty seconds
Speaker 2:of your two minute pitch.
Speaker 1:What but but give it to us us again. Well, what's the pitch today?
Speaker 16:Yeah. We're AI agent right in your group chats.
Speaker 1:Okay. In your group chats.
Speaker 16:Yes, sir.
Speaker 1:How are you plugging in? Because I feel like iMessage, they really don't like when other companies plug in.
Speaker 16:A 100%. We're carrier based.
Speaker 2:Okay. So
Speaker 16:as long as you have a number that you can just add
Speaker 15:to
Speaker 16:a group chat instantly Yeah. It's right then
Speaker 2:and there.
Speaker 1:Are you a beneficiary of the RCS update?
Speaker 16:Yes. We are.
Speaker 1:We are Explain how?
Speaker 16:Yeah. We're working through a couple of providers that give us a number. So, all working with all the carriers like Verizon team all the way AT and
Speaker 1:T. Okay.
Speaker 16:And then we're applying for the RCS. It's still in the beta stages
Speaker 1:Oh,
Speaker 2:okay. Even though it's
Speaker 17:rolled out.
Speaker 16:SMS infrastructure is one of the best infrastructure in the world. So, that's working. Woah.
Speaker 1:Woah. Hot take. I feel like everyone's annoyed by it. That's why Twilio exists. But, you know, is it is it good now?
Speaker 1:It feels like it was terrible for a long time.
Speaker 16:No. I think I feel like
Speaker 2:the big question around adding an AI to your group chat is security. Security. Things that happen in, you know
Speaker 1:Yeah. Signal group chats. You know, you wanna be very careful about who
Speaker 11:you are.
Speaker 1:Yeah. Journalists
Speaker 31:How are
Speaker 1:you handling security? Yeah.
Speaker 16:No. Great question, guys. It's we don't sell your data. Okay. You can kick out the AI whenever you don't
Speaker 1:want it.
Speaker 16:Right? You bring it in, it out, nothing compared to like med AI where it's always living in there ambiently. It's completely under your control. Mhmm. So, we don't sell any of that data either.
Speaker 1:What's the what's the most obvious use case? The family group chat, creating a grocery list or something? Like like, give me some examples of how people
Speaker 16:are using It's it's actually a lot of friends using this to get a restaurant recommendation Okay. Roasting each
Speaker 1:other. Okay.
Speaker 16:When do we need to actually all meet up? Have reminders in there. That's our agentic flow.
Speaker 2:Reminders. Okay.
Speaker 16:Yeah. Because in a and and when you're planning trips and trying to go out, there's always that one person that's like, no. I'm too busy or this doesn't work for me, and someone has to put in the screenshot. Someone's always taking a leave. Simplified.
Speaker 1:Of adding agents to Yeah. Group things, and it feels like you know, like, I saw this meme that was, like, I joined a Zoom call, and it was seven AI agents. Did you see this one? Everyone's seen this one. Yeah.
Speaker 1:Everyone's seen this. And it was, the one poor girl, and then, like, this person's notetaker and Fireflies and Lou and this one and this one and this one.
Speaker 15:Yeah. And
Speaker 2:how are you thinking about building the agents yourself
Speaker 1:It's definitely
Speaker 2:a good letting people build on top of TextAI?
Speaker 16:I think two things. Number one is, like, the quality control that has to come in along the things like you said on the data piece. Mhmm. We're we're a consumer company. We're like text dot ai.
Speaker 16:People are interacting with us. You wanna be very careful of allowing parties to come in here and see what they do with the data. Prevalent. And is like we build it ourselves so we can really figure out specific use cases like calendaring, for example, working with other people's calendars Sure. Giving you a exact answer like, hey, between all five of us, when's the best time where we can actually grab coffee later this evening?
Speaker 16:Yeah. And it can actually find it done and dusted right then and there.
Speaker 1:How much of the go to market is just pure viral growth because someone gets added, you realize that it's text AI and you add it to the next group chat and it just kinda
Speaker 2:goes add a Studio Ghibli machine to the group chat?
Speaker 16:Question. We have Ghibli images working. I wish I can put it on live stream right now, but we actually have collaborative AI where we can actually start doing images. People can
Speaker 5:edit it together.
Speaker 1:I mean, that was the beauty of mid journey is that they use Discord. Right? And then so, if Jordy writes a great prompt, I can kinda spin off that. Sense that would happen in iMessage and everyone
Speaker 2:so many fun, and I'm I'm already thinking, like, anytime somebody shares any photo, just immediately make it, a star trooper version of that.
Speaker 1:That that's, like, half of our group chat. It's like somebody should just be sending gibblies of all the other guys in the chat. It's like, great. Isn't exhausting that everyone, like, cuts and paste? Yeah.
Speaker 1:You go back and back and back and
Speaker 49:Yeah. Make it so that everyone is accessing it equally. Yeah. Yeah. Yeah.
Speaker 20:Yeah. And the beauty is
Speaker 47:you can create an image, he can edit it Yeah. And someone else can edit it on top of it.
Speaker 1:Yeah. Yeah. So it's much more collaborative.
Speaker 47:There is no need to really Yeah. Copy paste images.
Speaker 49:Yeah. Fun use case that we saw just developing. This is the
Speaker 1:best part of being consumer. Right? Yeah.
Speaker 49:Is that you get to put this out there and you get
Speaker 1:to see what people do it. Hinge dates.
Speaker 49:Oh. Someone goes in, brings them into a text thread says, hey, by the way, I have my AI staff here,
Speaker 1:digital staff. Yeah. Do you have
Speaker 49:any restrictions? Do you
Speaker 1:have any favorite interesting. Yeah.
Speaker 49:I can imagine, like, this is well above beyond use cases you can have imagined.
Speaker 1:Yeah. Yeah.
Speaker 49:Yeah. It's going to places where people are being really thoughtful about how do you bring it in Interesting.
Speaker 1:To make it
Speaker 49:more easy for us to have a human connection.
Speaker 2:Interesting. What were you guys doing before this? So,
Speaker 16:I was at Tesla
Speaker 1:Oh, cool.
Speaker 16:For four years leading there.
Speaker 2:Can explain what Tesla is?
Speaker 16:No idea. Your guess is best as mine. No. I was leading the digital supercharging team there and the vehicle subscriptions team.
Speaker 1:That's great.
Speaker 16:And then for our Yeah.
Speaker 25:I was, I was
Speaker 47:leading an engineering team at Walmart. And then before that, I used
Speaker 1:to Let's up to let's give it up to big retailers.
Speaker 47:I wonder what Walmart is. But before that, I was at a Eventbrite and OpenTable doing a lot of consumer personalization.
Speaker 1:Some of the same
Speaker 20:work So that's why I
Speaker 47:think one of the main reasons we founded TextAI was our rich sort of consumer background. Yeah.
Speaker 1:So yeah. That's very cool. Yeah. No.
Speaker 49:I've had a blast of being in media for a long time. Recently, I was the CEO of a b to b media company Okay. Down in Los Angeles. We exited. I sold it over in October.
Speaker 1:Cool.
Speaker 49:I've known these guys for eight years. Yeah. Nice. Rishi's job was at a crypto startup my wife was at. No way.
Speaker 49:She hired him, and then he wouldn't she wouldn't leave he wouldn't leave our house. He just, like, showed up. And so I've seen him do everything. And then, Perhar, of course, these guys
Speaker 23:went to school together.
Speaker 49:And so after going in and doing a lot of executive jobs at like publicly traded companies Sure. And whatnot. I think if you want to know what's happening, you gotta get hands on keyboard. Yep. There's no talk, the executive talk and pretend like you understand it.
Speaker 49:Yeah. You gotta get back on. And so for me, this was a great opportunity to go work with these two guys and start from scratch and just get to know what's happening because this is gonna be the platform for
Speaker 1:the next fifteen years. That's amazing. Well, congrats.
Speaker 2:Thank you very much. How's how's the Watch game at in in your guys' batches? See you guys each the Texas time
Speaker 16:He has a better guess.
Speaker 1:He's leading the way. My
Speaker 17:my wife's
Speaker 49:not the oldest founder ever to go through at YC, and so, yeah.
Speaker 2:32?
Speaker 1:Oh. Yes. Yes. Yes.
Speaker 16:Lucky. Yes. Lucky with that.
Speaker 49:Closer to 50 than I am to '32.
Speaker 1:Okay.
Speaker 25:Okay. Well,
Speaker 1:appreciate it. Pleasure. Pleasure, Dave. Thank you.
Speaker 2:Yeah. We'll see
Speaker 1:you soon.
Speaker 36:Thank you. Alright. Pleasure.
Speaker 1:Up next.
Speaker 2:Later, We
Speaker 1:have another team coming into the studio. Welcome. Cheers. Good to see you.
Speaker 13:Great energy.
Speaker 1:Come on. Welcome to YC demo day. I see these two sweatshirts. Hold up. Pull the mic up.
Speaker 1:Hey. Hey.
Speaker 2:What is that?
Speaker 1:Why? Preliminary preliminary. Just preliminary. You just assume that they're blowing out their metrics.
Speaker 2:That
Speaker 1:you're crazy. Pleasure. Good to meet you.
Speaker 5:What's up to meet you?
Speaker 1:Hey. Can you kick us off with an introduction on you and the company that you're building today?
Speaker 12:Hey, guys. We're, we're building ValuMate. We're automating real estate appraisals
Speaker 1:Okay. With AI. How's that work?
Speaker 2:What's up? You wanted to evaluate real estate appraisals?
Speaker 12:Yeah. So a little bit, you know, family background, like things of the sort. We kind of discovered this pain point. We both studied AI at CMU. Mhmm.
Speaker 12:We have families that had a background in it and, you know, seen appraisals and things of the sort, super inefficient process.
Speaker 1:Mhmm.
Speaker 12:And we were like, it's one of those industries just perfect. Like, just ready, you know, to, you know, to be to become a lot more efficient. And that's kind of how we how we got into the space.
Speaker 2:What is the what is the structure of the legacy market today? There's you guys are you're providing a tool for people that do appraising.
Speaker 12:Yeah. Exactly. So, like, the current, like, the current competition has literally existed for forty years.
Speaker 1:Mhmm.
Speaker 12:Right? And it's just like like, appraisal reports is like snapshot of, like, a property
Speaker 1:value Human goes in and looks at things, takes some pictures, and writes down
Speaker 3:Lot of
Speaker 1:quality of the picture. Exactly.
Speaker 12:Exactly. Boards and mold. So we bring this entire thing down to just a scan. Okay. Right?
Speaker 12:So you scan it. We build a three d model, two d floor plan. Our computer vision takes notes. Sure. And then we pull data from all these various sources.
Speaker 1:Got it.
Speaker 12:Right? And then, you know, we use AI, obviously, to fill out this kind of report. So it's it's it's a, you know, it's a pretty time consuming process Yeah. That we're about to that we're able to bring down.
Speaker 1:Is it a with Zillow to do a better Zestimate or or truly augmentation copilot for the existing true appraisal reports that are used underwriting.
Speaker 12:For the true appraisal reports that are using Got it. The issue with Zillow, and and every appraiser will kinda tell you this, this is, like, super inaccurate. Like, like, know, like, people are like, oh, like Yeah.
Speaker 1:It's mostly based on, like, recent sales in the area. It's not actually the quality of the building.
Speaker 12:Exactly. Right? So what
Speaker 1:we're able to do even take into account remodels. Exactly. Because they don't know.
Speaker 12:Right? They don't know. They they don't have the data. And what we're able to do is have this digital twin Okay. Literally of a property.
Speaker 12:So it's actually along the way, as we're, you know, we're building this and selling, know, software appraising and sort, We also have the most valuable property data set
Speaker 1:Mhmm.
Speaker 12:That anybody's gonna kind of have to build future models. It's super accurate.
Speaker 1:Give us
Speaker 13:the What were
Speaker 2:you guys doing
Speaker 1:before this? Sure.
Speaker 12:Before this, we were students at Carnegie Mellon University. We dropped out. Nice job.
Speaker 1:There Yeah.
Speaker 2:I'm sorry. There's a a couple guys
Speaker 1:Dropped out of high school. So it's not really special anymore, but Not cool. Yeah. Yeah. You did actually perform here.
Speaker 1:Talk to us about traction. How are things going?
Speaker 12:Things have been going super well. We started selling twenty days ago.
Speaker 1:Twenty days?
Speaker 12:And we're at a $124,000 in Last
Speaker 1:time, congratulations. You were correct. To bust the Yeah. Yeah. Guys the
Speaker 2:you guys get the round done already?
Speaker 12:We we are we are still filling out a round.
Speaker 13:Okay.
Speaker 12:Late stage talks with some with some leads.
Speaker 1:Good luck.
Speaker 12:But it's looking like we're gonna
Speaker 1:we're gonna When you last It's pleasure. When did
Speaker 2:you when did you discover YC? Oh, yeah.
Speaker 1:It's a good question.
Speaker 12:Discover YC. So my freshman year roommate from Carnegie Mellon was actually YCF twenty four.
Speaker 1:Oh, there you go.
Speaker 37:Told you about
Speaker 1:it.
Speaker 12:In my mind since then, I was like, okay. I have to do this.
Speaker 1:You got back? You got it And and then, know,
Speaker 2:we did it. So welcome.
Speaker 1:You're the
Speaker 15:you're in the
Speaker 2:league now.
Speaker 1:You're in the league. Playing the Welcome to the league. Welcome to Silicon Valley. Yep. And welcome to demo day.
Speaker 1:Thank you for stopping by. Thanks. We will talk to you guys soon. Good to meet you. Bro, let's bring you the next crew.
Speaker 1:Thank you so much.
Speaker 2:We got a line.
Speaker 1:We got
Speaker 33:a line
Speaker 1:out the door. We're gonna bang through these. We got
Speaker 2:These guys got QR codes on their sleeves.
Speaker 1:Bloom. Yes. Bloom. Are you building Bloom filters?
Speaker 21:No. No. No.
Speaker 1:What are you what what what
Speaker 21:So the easiest way to Bloomin' onions.
Speaker 1:Nope. No. Okay. Break it down.
Speaker 21:No. So lovable, but for native mobile apps.
Speaker 1:Okay. Cool. Oh, interesting. Interesting.
Speaker 21:We'd love to just, like, show you.
Speaker 1:Please. Please.
Speaker 21:Please. Okay. So have you guys tried to build mobile apps before?
Speaker 1:Touches. What is that? I've never seen that interaction before. Yeah. He's got a UI from the future.
Speaker 21:So have you guys ever tried to build
Speaker 1:Objective C. It was terrible. I wrote Xcode.
Speaker 14:Sucks. It's
Speaker 1:so We love Apple. But, you know, we we're very excited about the Anthropic partnership.
Speaker 21:Yeah. So, typically, you'll have to write code. You'll have to build the app in Xcode or something. You'll have to to get your users to download it and just, like, enter an invite code. Okay.
Speaker 21:Yep. So with us, I can literally just, like, talk into my phone Okay. Build a native app, and then I can send it to you via literally bumping phones.
Speaker 28:Oh, interesting.
Speaker 1:IPhone? Yeah. I do. I do.
Speaker 2:Wow. See
Speaker 1:this. Okay.
Speaker 2:Just load the app Yeah.
Speaker 1:Let's see.
Speaker 2:Right onto the home screen.
Speaker 1:Okay. Malware installed. All personal information extracted. Here we go.
Speaker 2:I don't
Speaker 1:know. I got I got one.
Speaker 6:Let me see.
Speaker 11:That's right.
Speaker 1:We got half.
Speaker 21:And this is just using AirDrop.
Speaker 1:I think do do you need to turn it on again? No? Oh, no. Live down.
Speaker 21:Yeah. AirDrop. Like, maybe we need
Speaker 1:another. Are you
Speaker 2:on the same
Speaker 21:Wi Fi? You wanna try?
Speaker 2:Yeah. I'll try.
Speaker 1:Let's see. AirDrop. AirDrop is on. Oh, contacts only. Yeah.
Speaker 1:That's what it Okay. I got it.
Speaker 21:I it. Cool.
Speaker 1:Let's try it now. Here we go. Alright.
Speaker 2:Go ahead. Skill issue.
Speaker 1:Skill issue, baby.
Speaker 21:Okay. Oh, Jesus.
Speaker 1:Is it going? This is
Speaker 13:I got
Speaker 16:the wave.
Speaker 1:You saw the wave. Right?
Speaker 21:Yeah. Yeah.
Speaker 1:Yeah. I only have RAM public AdQuick eight sleep wandered puzzle installed right now. Those are gonna be apps
Speaker 2:one more shot.
Speaker 30:Can take you
Speaker 1:can try it.
Speaker 21:Otherwise, we'll have to try on your phone.
Speaker 1:Yeah. It's okay. It's not like this is live or anything.
Speaker 6:Okay. You try.
Speaker 1:You try. We'll give
Speaker 2:it a Anyways, keep keep yeah. So So Try it.
Speaker 21:So when someone, like, tries
Speaker 1:to build an
Speaker 21:app with Bloom
Speaker 2:Right. It's doing
Speaker 1:the way
Speaker 17:it's but it's not doing
Speaker 26:that? Don't think the Internet. Internet just sucks.
Speaker 43:I think
Speaker 1:the Internet is the Internet.
Speaker 21:Anyway, so what what you would see is you'd be able to open this app on your phones, like, instantly. Right? Okay. And it uses App Clips Yes. Under the hood.
Speaker 1:Oh, yeah. Oh, okay. Right. So that's how you're getting the app on without me actually installing
Speaker 21:the app. Cool.
Speaker 3:And then
Speaker 1:Oh, so really hacking the app quickly. That's awesome. Okay. Yeah. Yeah.
Speaker 1:Cool. And
Speaker 21:so the other thing is like when you prompt to build an app with us, we also automatically deploy a back end for you. Yeah. Yeah. Real time syncs between devices.
Speaker 1:I as I understand app clips, it's like it's like, there's there's like one app, like the Uber app exists, and then there's an app clip that ties to to Uber. Are you creating, like, custom app clips? It's all it's all around one iOS. Oh, wow. It's one
Speaker 21:Bloom app clip that then
Speaker 1:loads, loads, whatever. Yeah.
Speaker 2:Anyone like
Speaker 1:So it's like an app store within an app store.
Speaker 21:Yeah. Kind of.
Speaker 1:Yeah. Thing like We we we we
Speaker 2:talked on the on our show before about this idea of, like, ephemeral apps Yep.
Speaker 1:Yep. Like
Speaker 2:memes. Exactly. Like, there's
Speaker 1:a lot
Speaker 2:of things that should exist, but only for, a day.
Speaker 1:Yeah. Yeah. Exactly. We had so many ideas for, like, funny apps, but we don't wanna actually go pay, you know, design something.
Speaker 13:Used to
Speaker 2:be there's so many people, like, people have an idea for an app, you're, like, you realize it'll cost like
Speaker 1:a million dollars to build that. Exactly. Got it.
Speaker 21:Like with us, we built an app for for demo day Yep. In five minutes that everyone in the audience could just scan to like vote on our valuation in five years. Yeah. And it like had a graph that updated in real time for everyone because
Speaker 1:it has
Speaker 21:a back end connected
Speaker 1:to it.
Speaker 21:And so what we wanna enable is a creator economy but for software.
Speaker 1:Okay. Under the hood, what are the best cogeneration LLMs that you're using? Yeah. What do you like? What's exciting?
Speaker 1:And how how is that market developing?
Speaker 21:Yeah. So I mean, now we're we're obviously for the smartest models, we're using Cloud for Sonnets. Cloud four. But we're also experimenting with, you know, a smart mode smart mode and a fast mode. Because sometimes people just wanna make like a quick edit to their And like literally, you can just speak into our phones.
Speaker 21:Right?
Speaker 1:Yep. You just
Speaker 21:go into like this like edit mode and then you can just type, hey, add this feature or whatever. Very cool. And so sometimes you just want those changes fast. Okay. And what's cool is like if you had this app open, it would also hot reload on your device.
Speaker 1:Yeah.
Speaker 13:Yeah. Yeah.
Speaker 2:Right? So Very, very what what use cases are you most excited about? What are you seeing? What categories broadly?
Speaker 21:Yeah. I mean, so right now, we're seeing people that already think of software as a creative outlet use this. So developers, designers, and entrepreneurs. Sure. So especially for designers and nontechnical entrepreneurs, it's great because they can just, like, be their creativity gets unlocked with this.
Speaker 21:Yep. And so we're seeing people build all kinds of things like personal apps, but also apps that I wouldn't have imagined before, like someone in Africa building like a wildlife tracking app for their conservation. That's cool. And then people building like funny apps for
Speaker 1:most delicious animals to go after. To conserve the animals for my for my own hunting. John. My next hunting expedition. I'm kidding.
Speaker 21:But but yeah, what I'm really excited about is like the apps that I can't even imagine. Right? Yeah. Of course. The YouTube put out YouTube, I'm sure they weren't imagining vlogging.
Speaker 21:Yeah.
Speaker 1:MKHD. Exactly. Just like, know, you put out images and chatty. But he didn't really imagine studio exactly happening.
Speaker 21:And for us, I mean, it's software,
Speaker 1:so you
Speaker 21:literally do anything.
Speaker 1:Congratulations. Thank you
Speaker 2:so much. How's how's your round going?
Speaker 21:Oh, and the round
Speaker 13:is closed. Oh,
Speaker 1:there we go. Let's go. Alright. Alright.
Speaker 13:Thank you. Pleasure.
Speaker 1:Nice to see you. Good stuff. Well, bringing the next team. Welcome to the breathing
Speaker 2:in the micro plastics.
Speaker 1:Oh, yeah. This is really this is violating everything I know about you. You're putting your life on the line.
Speaker 7:How's it going?
Speaker 2:It's great. What's happening?
Speaker 1:Hi. Nice to meet you. John. Pleasure. Hey.
Speaker 1:Hi. How are you? Don't have long Yeah. No worries. No worries.
Speaker 1:You introduce yourselves? What are you building?
Speaker 40:Sure. So we're Morpho AI. We're building a software tool for engineers that are building new robots and new machines.
Speaker 1:Oh, interesting.
Speaker 40:Yeah. So I came came from the manufacturing tech world. Okay. We both met at Harvard. I was an MBA.
Speaker 40:Cool. He was a postdoc, and, you know, you should talk about yourself too.
Speaker 23:Yeah. Absolutely. And, yeah, I met her once at Harvard. Like she said, we got a new introduction from a mutual mentor. I did my PhD at MIT focused on automating the design of robots.
Speaker 23:So it seemed like, you know, wanted to bring that into some sort of product, show people in the world how useful it was and how it could change the way people design. And she was super excited about changing all the pain points in manufacturing. So Sure.
Speaker 1:We're doing this. What is exciting in terms of robotics in manufacturing? There's lot of noise about humanoids, but we talked to a lot people who are just saying Robotics
Speaker 2:in manufacturing or is it a tool to accelerate the manufacturing of robots?
Speaker 23:Oh, one.
Speaker 2:Oh. The one. Yeah.
Speaker 23:But but the main app beachhead market is really in industrial robots.
Speaker 1:Okay. Yeah. Yeah. Okay. So so those are both those look like.
Speaker 40:Yeah. So the crazy stat that we found is, you know, if you're actually buying a robot off the shelf Yeah. You can't actually just, like, put it in the factory floor, like, 90 plus percent go through a customization process. Wow. So that's, like, six months of lead time.
Speaker 40:Yeah. Yeah. And, you know, it takes up a lot of engineering hours. So one of our customers, they were trying to build a full new industrial robot arm. Sure.
Speaker 40:Six months gone in, you know, arms not lifting. Yeah. They came to us, and in two days, we basically redid their entire hardware design. Interesting. So quite literally, we're allowing engineers to build new robots overnight is the hope, and in the future, in a matter of minutes.
Speaker 1:How concentrated is the robotic arm market? Are there just a few companies that you really need to integrate with deeply, or do you need to create something more generalizable out of the gate?
Speaker 40:So we're starting out with a little bit more of the OEM side of things Okay. But also these integrators that are buying these arms and saying, I now needed new hands.
Speaker 1:So Yeah. Yeah. Yeah.
Speaker 40:Out of, like, the, what, 50,000 robots that could deploy in a given year, most of them have a new hand that's made. And so now you have a generalist engineer trying to make a new hand for four to six weeks in a new design. So really plugging in in that early stage of what do I build Yeah. Before we even get to the programming part.
Speaker 23:Yeah. The whole idea is you just have to input the test specifications that you needed to solve, and then as much as possible, we're automating of the mechanical and somewhat the control design side of things.
Speaker 1:Is there is there a lot of data that you need to feed in? Is there a lot of data that you need to pull from the manufacturer before you can
Speaker 23:We work with parts that manufacturers have and that they like to work with. Okay. And aside from that, it's test specifications, but we unlike a lot of the Gen AI companies out there Yeah. We do employ some Gen AI solutions, but they're all trained from simulation and not from data sets. Interesting.
Speaker 23:And that's really important because the data for how do you go from design to some how well it's gonna work Yeah. That doesn't really exist. Nobody takes logs and curates data sets of things they built and how well they worked. Sure.
Speaker 2:Sure. Sure. About traction. Yeah. Sounds like you guys already are are in market.
Speaker 40:Yeah. So we actually just got a grant from the UK government, 2 and a half million British pounds Oh, fantastic.
Speaker 42:Past couple of months
Speaker 1:Non dilutive? Fully non dilutive. Congratulations. We love it for that journey. Journey.
Speaker 1:Oh my god. My ears. It's gonna help. Non dilutive. That's
Speaker 23:awesome. In here than out there.
Speaker 2:I mean, Europe just shooting themselves in the foot.
Speaker 1:Not they're gonna be like, why didn't
Speaker 2:we at least,
Speaker 1:like, a quarter point or something. Anyway, Thank
Speaker 40:you. None of us are British, but they basically
Speaker 23:We're setting up an office in London. Like, we're really excited about about about working with them and setting up
Speaker 1:a Yeah. Of course. Course, there's benefits to
Speaker 2:the research
Speaker 1:community. Yeah. Absolutely.
Speaker 44:We have lot
Speaker 23:of collaborators there as
Speaker 1:well. Yeah. That's fantastic.
Speaker 2:That's good. That'll be an amazing Yeah. Outcome for that. Last thing, bull bull bear on humanoids.
Speaker 23:Yes. Humanoids. What's your take? I'm bear, but I'm like medium bear. I don't know.
Speaker 23:It was it was just like, okay, you're gonna build a humanoid, which humanoid? Like, people come in all sorts of sizes and shapes. Yeah. A construction worker is not the same as like a like a toddler or something like ballerina. Exactly.
Speaker 1:Linebacker looks different than a So you're
Speaker 23:gonna need custom designs regardless of what you But I mean, the other side of things is just like, what is the application right now of the humanoids that isn't solved better by reengineering the things around the humanoid? Sure. Sure. There's a there's a long tail there. So we we hope we can help people to design the humanoids of the future.
Speaker 23:Yep. Just don't think it's here yet.
Speaker 1:Yeah. Yeah. Yeah. I think I
Speaker 2:I think there's a Yeah. You're not saying embarrassing.
Speaker 1:In a hundred year time. Yeah. Think it's a little I completely agree. Yeah. Very reasonable stance.
Speaker 1:Awesome. Anyway, congrats to you guys and congratulations on the line.
Speaker 11:Thank you.
Speaker 1:I'm gonna give you a solid progress. So we don't have to deal with the whole
Speaker 23:Or we could do like share a fist like
Speaker 1:you're Oh, okay. Perfect. Anyway, let's bring in the next team.
Speaker 2:We're putting the word out.
Speaker 1:So much. The table is now covered with confetti.
Speaker 2:The UK is giving out millions of dollars
Speaker 1:for free. Head over across the pond.
Speaker 16:This could
Speaker 13:be the
Speaker 1:greatest capital extraction event. The greatest wealth creation event in history
Speaker 2:In history.
Speaker 1:Since the Boston Tea Party. Anyway, good to meet you. What's up? Welcome to stream. My name is John.
Speaker 1:Yeah. How are you doing? Can you introduce yourself and the company?
Speaker 33:Yeah. Absolutely. Yeah. So, I'm Rajat. We're Prism.
Speaker 1:Okay.
Speaker 33:Prism is an agentic observability company.
Speaker 1:Okay.
Speaker 33:So we let developers configure agents to watch their production systems.
Speaker 1:Okay.
Speaker 33:So read logs for them, watch videos of their customers using software Mhmm. And then enable them to take certain actions. So, like, create issues on linear Sure.
Speaker 1:GitHub. Yeah. Lyrica. Sponsor the stream.
Speaker 33:Let's Exactly. Yeah. Yeah.
Speaker 1:I did
Speaker 33:that on purpose.
Speaker 1:And and send reports to Slack. So That's great. That's great. Well, how's adoption been? How are you selling in?
Speaker 1:Are you going medium sized scale ups, other startups, YC companies, enterprise? What are you thinking?
Speaker 33:Bottom up. So this is a tool that every developer needs and every developer can use. So we're starting with the YC community. Cool. We're completely self serve.
Speaker 33:So there's 23 people on the platform and counting.
Speaker 1:Yeah. More people sign up and start using it every time. Is there
Speaker 2:any reason? Were you guys iterating through the the batch Oh,
Speaker 21:yeah. Yeah.
Speaker 33:We were. So the version of the product Yeah. So it's like we watch videos of people using software.
Speaker 1:Right?
Speaker 33:Sure. So the version of the product was these two watching everyone's videos Yeah. And just giving them issues and like sending them Slack messages and stuff like, guys, this is broken. You need to fix this. Yeah.
Speaker 33:Yeah. So that we were basically like a services business, but we didn't tell everyone.
Speaker 24:We told
Speaker 33:them it was AI.
Speaker 1:And it
Speaker 43:was just
Speaker 11:these two.
Speaker 2:So that was
Speaker 1:the version of the business. That's good.
Speaker 2:Somewhere. That's awesome. Yeah. That's true. What how's how's fundraising going?
Speaker 2:You guys It's great.
Speaker 1:It's going great. We're in
Speaker 33:the midst of it. Still, you know, still trying to finish up the round.
Speaker 1:Yeah. Good luck.
Speaker 33:But, yeah, we're excited. Excited.
Speaker 1:That's fantastic. Awesome. What were you doing before?
Speaker 3:Johnson and Johnson.
Speaker 1:Oh, very cool. Alex Alex I'm from
Speaker 2:Big Pharma.
Speaker 1:Yeah. That's pretty good.
Speaker 33:Yeah. Yeah. So, Landon and
Speaker 3:I met
Speaker 33:in high school, and then all three of us ride Georgia Tech together.
Speaker 1:Oh, cool.
Speaker 33:Yeah. Alex actually had a post on LinkedIn this morning about how he left 850 k a year behind at Palantir to
Speaker 1:come work with us.
Speaker 2:So There we go.
Speaker 1:All Well, I think you'll make it all back. Burn the boats. Just burn the boats. Anyway, good luck with the rest of the day. Awesome.
Speaker 1:You much for having on. Cheers. Take care. We are ready for the next guest on the YC demo day, Street, Tyler's.
Speaker 2:Ten minutes left.
Speaker 1:Ten minutes left, and then we gotta go. Yeah. We have we can speed through this as much as we can. We got ten minutes until we gotta go to the airport. Is that right?
Speaker 1:Or start passing? Till wrap. Okay.
Speaker 13:Ten minutes.
Speaker 21:The the show
Speaker 1:is We got ten minutes more. Come on in. Don't even bother introducing yourself. Just tell us what you do. What company are you building?
Speaker 20:How are doing? Are Clarm.
Speaker 34:We are perplexity on internal documents. Search is being heavily disrupted by AI at the moment, as you know. Google is being disrupted by perplexity, and we're doing that for internal enterprise search.
Speaker 1:Okay. Competition with Glean. They just raised a bunch of money. How are thinking about that?
Speaker 34:We're gonna replace them. You're gonna replace them?
Speaker 3:I love it.
Speaker 1:They're just getting started.
Speaker 34:You're replacing replacing Google. We're replacing Gleam.
Speaker 1:Okay. Okay. As Got So
Speaker 38:how do
Speaker 1:you do it? What's different? How do you actually integrate? Are you building a bunch of integrations? Are you building on top of an integrator?
Speaker 34:We are we have our own integrations. Okay. We found that the the only way to do this
Speaker 1:Mhmm.
Speaker 34:Actually build AI agentic search Mhmm. Is to build it from the ground up. Yep. And that's why all these legacy players have to do that as well.
Speaker 1:What's more important in integration with Google Docs, for example, or integration with, like, the data lake that's maybe that maybe somebody has a Snowflake installation?
Speaker 34:What's more important? I mean, it's it's important to have both
Speaker 18:of those so that you can
Speaker 3:connect and
Speaker 1:integrate both in the can connect.
Speaker 34:We have about 45 connections.
Speaker 1:45 connections. Okay. How about customers? How many of those you got?
Speaker 34:We have eight. We only launched two weeks ago.
Speaker 1:Congratulations. Congratulations.
Speaker 2:What's the biggest challenge? Is permissioning hard? Maybe it's smaller companies. It's it's it's not as much of an issue, but as you go kind
Speaker 1:of market?
Speaker 34:Dealing with all the different types of data, and this is why b two c companies don't play in enterprise space. Because when you have to look at Salesforce data alongside messy Excel sheets Mhmm. Then it becomes, harder to to connect them together. But that's the value in it.
Speaker 1:Where can companies go to get started?
Speaker 34:They can go to climb.com. They can try our live demo now.
Speaker 2:Five letter domain already. Congratulations. Five letter demo domain
Speaker 1:on demo day.
Speaker 34:You can talk to us directly.
Speaker 1:Fantastic JLC reversal. Thank you very much. It's a fantastic Bye, guys. We'll see you soon. Congratulations for hopping on.
Speaker 1:Let's bring in the next team. We're doing lightning round. Lightning round. Lightning round. Lightning round.
Speaker 1:NYC demo day twenty twenty five. Welcome to the stream. What do you do? What are you building?
Speaker 2:AI copilot for solopreneurs. Wow. Exactly.
Speaker 1:Okay. Get out of here. Good job. No. How's it going?
Speaker 1:How many customers do have? What what data are you sharing today at YC demo day?
Speaker 2:Who's more cracked?
Speaker 28:He's the most cracked engineer we have we have ever seen. The most
Speaker 2:cracked engineer. He's the most.
Speaker 21:So we
Speaker 28:have gone from zero to 300 k ARR.
Speaker 1:It's just
Speaker 28:about nine
Speaker 1:weeks. So congratulations.
Speaker 28:Yes. So that's that's what we're doing.
Speaker 2:Absolutely, dogs.
Speaker 28:And, yeah, we're seeing this future where solopreneurs are going to completely wipe coat and run their entire business on Cactus.
Speaker 24:So right?
Speaker 28:So that's what we're building.
Speaker 2:Great name. Name.
Speaker 20:Thank you.
Speaker 3:What were
Speaker 2:you guys doing before this? We built
Speaker 28:a previous YC company. He was a founding engineer. We scaled it up to 2.5
Speaker 1:Founding
Speaker 2:engineer. Yeah.
Speaker 28:Yeah. That's
Speaker 1:I'm correct. He's the exact guy.
Speaker 38:He's the crack sales guy. I'm the crack engineer, and together,
Speaker 1:we are the crack founders. So crack sales guy, how are you actually selling this thing? Is it hand to hand combat with these solopreneurs, or are you doing, like, viral marketing? We've seen Levels get a lot of attention for solopreneur stuff. How are you actually attracting people?
Speaker 28:Absolutely. So it's mainly through word-of-mouth that's been spreading. What we also do is outbound where we call them. So think about a caterer, a private chef. They're busy cooking all day long.
Speaker 28:Mhmm. Call them. They don't pick up as expected. They're busy. We leave them a message saying, hey, you just missed an opportunity, guys.
Speaker 28:Mhmm. So, yeah, they get back and then they set up cactus. They get incremental 10 to 15 k in a month revenue. And the biggest thing is the headache for them is gone. Right?
Speaker 28:They don't have to answer the phone. Yep. That's the best thing.
Speaker 1:That's amazing. Yeah. 300 k r ARR. How's the fundraise going?
Speaker 28:It's going great. We just got completely oversubscribed.
Speaker 1:Woah. Exactly. Subscribed. Well, congratulations.
Speaker 17:Thank you.
Speaker 1:Thanks for coming
Speaker 15:on the stream.
Speaker 1:We're bringing in the next team.
Speaker 37:Next team.
Speaker 1:Have a great rest of your demo day. Come on down. We're live from YC demo day twenty twenty five, and we have our next team in the building. Hello. Nice to meet you, Anjan.
Speaker 1:Nice to meet you, Ashani. Welcome. Good to meet you, Hi. What's happening? Would you mind introducing your company?
Speaker 1:What are you building?
Speaker 52:Yeah. For sure. We are building Lemari, which is essentially helping go to market teams Mhmm. Build tools internally instead of having to buy super expensive SaaS.
Speaker 1:What what tools do go to market teams need?
Speaker 52:Like, from
Speaker 1:Like, a Yeah. Yeah. Yeah. Golf club buying Yeah. Machine on subscription and steak dinner booking.
Speaker 43:That would be nice.
Speaker 52:That would be nice. I wish we did that. But we are helping them, like, from anything from deal scoring, qualifying leads Sure. Making contracts, like, anything down, like, the sales pipeline.
Speaker 1:Got it. Got it. Okay.
Speaker 2:So Yeah. What what does the future of the stack look like? Are you guys gonna basic are you trying to verticalize effectively and allow people to build custom software at every point?
Speaker 52:At every point. Yeah. I really think we're gonna look back to this era of of SaaS of, like, having all these generic tools that you're using and think that was really silly because why wouldn't you have software that's custom built for your company, for your process? Yep. And so I think exactly that we're gonna start with replacing some of these really point solution software Sure.
Speaker 52:But I think in the future, every company's gonna have their own CRM.
Speaker 14:What were you
Speaker 2:guys doing before this? What
Speaker 52:were we
Speaker 1:What were you doing
Speaker 52:with OIC?
Speaker 13:I was
Speaker 52:at Stripe. Sam was at Google. Amazing. Yeah.
Speaker 50:We were
Speaker 2:Sort of a nontraditional background. Yeah.
Speaker 1:Yeah. Yeah. Exactly. Who who who's the key, person actually using the tools? Is this for a nontechnical person?
Speaker 52:It's a nontechnical person within these go to market teams, often like a revenue operation Sure. Sales operations
Speaker 2:How's traction?
Speaker 52:It's been great. We're at 90 in St. Louis. Congratulations. Yes.
Speaker 52:Obviously, we're out of confetti.
Speaker 6:Yes. Yeah. We
Speaker 2:are. We're we're pretty much We got we got a couple left.
Speaker 42:Okay. Okay.
Speaker 1:I'll I'll I'll
Speaker 38:let you see.
Speaker 2:Kinda busted. Yay.
Speaker 1:Anyway, thanks so much for coming
Speaker 52:on this so much.
Speaker 1:We will talk to soon. Yeah. Bye bye. Let's bring in the next team. We have five more minutes.
Speaker 1:Right? Something like that. Let's go. Five more minutes.
Speaker 2:Losing his voice.
Speaker 1:Let's go. I'm losing my voice. Welcome. Welcome to the stream. Introduce yourselves.
Speaker 1:Introduce the company. Good to meet you, man. What are doing?
Speaker 2:Shares all over your shirt.
Speaker 51:Yoav, and this is Shuria.
Speaker 1:Go ahead.
Speaker 51:Okay. We're time exited YC founders.
Speaker 3:Congratulations. Thank
Speaker 1:you. Addicted to startups.
Speaker 51:Addicted to startups. We're building ThirdShare. Okay. It's agents for in house legal teams and we're starting with media and entertainment companies.
Speaker 1:Oh, interesting. Very niche down, not just like legal AI, but you've
Speaker 2:actually wanna dominate a small market?
Speaker 51:No. It's a massive market. We're starting by dominating media
Speaker 1:and entertainment.
Speaker 15:Sure. Yes.
Speaker 51:So, we're starting by helping media and entertainment companies Yep. Find IP infringements Oh, interesting. Collect evidence around it.
Speaker 1:And then that's revenue driving immediately. Exactly. Is that the is that the business model? You take a cut of whatever you get or is it more seat based?
Speaker 51:We have a software model, but we also take a cut of
Speaker 13:what we
Speaker 26:get. So
Speaker 51:it's kind of
Speaker 1:a the apple. I like that. It works great. How's traction?
Speaker 19:What do you full
Speaker 2:stack, you're finding the IP infringements and then you're actually sending letter demand letters.
Speaker 48:Someday, we're gonna be a vertical AI law firm. And, yeah, there's a lot of we we are handling the entire workflow right now, and a lot of it is being done through agents. But our customers just think of us as people who get things done. Yeah. They don't care about how the other like, black box is working.
Speaker 1:That makes sense. Yeah. What metrics have you been sharing? How's demo day going? Does that mean is this your demo day then?
Speaker 11:It is
Speaker 37:demo day.
Speaker 1:demo day.
Speaker 26:Last time.
Speaker 51:Were you what's that?
Speaker 2:Were you cofounders together last time?
Speaker 1:No. We had
Speaker 51:separate startups. Separate startups.
Speaker 37:Rivals. Joining Social media analytics. Yeah.
Speaker 48:Now we're in legal.
Speaker 51:So why see alumni network is strong,
Speaker 13:you know?
Speaker 51:It's great. We teamed up and yeah, traction's going great. Cool. We just crossed a 100,000 ARR. Congratulations.
Speaker 51:We're working with the biggest media entertainment companies in the world now expanding to brands, doing stuff like marketing compliance.
Speaker 1:So, yeah. That's great. Well, congratulations on the progress.
Speaker 51:You guys so much.
Speaker 1:Thanks for stopping by the stream. We will talk to you soon. Let's bring in the next team.
Speaker 2:How are you doing? Q Q
Speaker 1:facts. Q facts. Facts. Hey, TC. Introduce yourself.
Speaker 1:How are doing? I think solo founder. We gotta give him a hat, a TVPN hat.
Speaker 19:Oh, yeah. Definitely. I'll
Speaker 1:I'll sell. Introduce yourself. Swap. Here.
Speaker 19:Perfect. Let's swap. Yeah. So hi, guys. I'm Ananya, I'm CEO of QFX.
Speaker 19:Cool. We're making a $24.07 Yeah. $24.07 stock exchange.
Speaker 1:Okay.
Speaker 19:So we're gonna let institutions and retail trade traditional assets like US equities and commodities real time twenty four seven without brokers, loads of leverage. Just so you
Speaker 2:Loads of Loads leverage.
Speaker 1:Of Right? How's how's the the traction been? It feels like it's really is
Speaker 2:this a a sneaky blockchain company?
Speaker 17:Yeah. There's
Speaker 19:no there's no blockchain. No chain. There's no blockchain.
Speaker 2:It's off the chain.
Speaker 19:It's completely off chain. Okay. It's basically it's it's like a it's a crypto exchange, but without the blockchain parts. We've taken those improvements and moved them over to the traditional assets world. Okay.
Speaker 19:And attraction is good. We've been running it well, we've not been running it to the YC batch because we're not licensed yet.
Speaker 1:Sure.
Speaker 19:But we
Speaker 2:You've been running some internal experiments.
Speaker 19:Yeah. Some internal experiments to the current YC batch. And due to those experiments, we're launching in a couple of months. Congratulations. With a license and
Speaker 2:Are you launching offshore or
Speaker 1:are you gonna
Speaker 19:Offshore. In Bermuda.
Speaker 1:We'll get
Speaker 2:to Bermuda. Bermuda is a nice place. Trips there.
Speaker 19:Yeah. Exactly. Free rum if you come visit our office.
Speaker 2:There you go.
Speaker 1:Fantastic. There you go.
Speaker 2:How how did you get into
Speaker 1:this? Yeah.
Speaker 20:I was
Speaker 19:a quant. Yeah. Tower research before.
Speaker 1:Okay.
Speaker 19:And my co founder was at Citadel.
Speaker 2:Well, you're our quant now.
Speaker 19:Yeah. Exactly.
Speaker 1:Well, congrats
Speaker 2:all the progress.
Speaker 1:Yeah. Great to meet
Speaker 19:you guys. Cheers.
Speaker 1:We'll talk to you soon. Thanks. Come on
Speaker 2:of leverage.
Speaker 1:Loads of leverage. We love to see it.
Speaker 53:Accounting for small businesses.
Speaker 1:Oh, okay. Cool. How how's it going? Do you have small businesses on the platform already?
Speaker 53:Yeah. Yeah. We have a few small businesses. We're just trying to automate all of their bookkeeping. Yeah.
Speaker 53:So, you know, chase people down for, receipts. Yeah. Make calls.
Speaker 1:I feel like there's so much that's already built into the accounting suites. Like, do you have to sit on top of QuickBooks? Or do you actually pitch people, hey, let's rip out with your existing accounting solution?
Speaker 53:No. We're sitting on top of ClickBank.
Speaker 36:There's no
Speaker 53:point replacing this software. Yeah. We're just replacing the service side of things. Sure.
Speaker 1:Oh, yeah.
Speaker 53:Yeah. Consolidating all the scattered data.
Speaker 1:That makes sense.
Speaker 53:You know? Yeah. Sits in all different kinds of places, your WhatsApp, your email, your Stripe Sure. Everything.
Speaker 1:How were
Speaker 16:you doing before this? Sorry?
Speaker 2:What were you doing before YC?
Speaker 53:I was building a health tech business. We're selling AI patient triage software to clinics.
Speaker 25:Awesome. Cool.
Speaker 1:How's traction been? How's the raise going? How's demo day been?
Speaker 53:Yeah. Demo day has been great. We are raising $2,000,000 That's and roughly, like, three quarters finished. So
Speaker 1:just trying to wrap it up.
Speaker 2:Congratulations. Classic. Two 0 20.
Speaker 7:That's really funny.
Speaker 1:Love it. Yeah. Exactly. Well, congratulations. Have a great rest of your demo day.
Speaker 46:Yeah. Cool.
Speaker 1:Cheers. Last one. Bringing it in, closing it out strong with
Speaker 2:What's going on?
Speaker 1:Sims Studio. Sims Studio. Are you simulating things?
Speaker 6:Yes. We are.
Speaker 1:I'm simulating.
Speaker 2:We're going
Speaker 37:on. How are you?
Speaker 6:Nice to meet you.
Speaker 1:I'm simming. Okay. Yeah. How are doing? Great.
Speaker 6:I'm I'm doing great. How are you?
Speaker 1:I'm great. Yeah. Break it down for us. What are you building?
Speaker 6:It's a open source platform to build AI agents.
Speaker 1:Okay.
Speaker 6:Yeah. So it's developer focused. It's like a Figma like canvas to build agents.
Speaker 1:Interesting. How many GitHub stars you got, we gotta ask?
Speaker 6:50 to 4,000 in the last two months.
Speaker 1:Wow. Wait. Wait. You you
Speaker 14:were to 4,000.
Speaker 11:And now
Speaker 1:you have 4,000?
Speaker 6:Yes.
Speaker 1:Wow. Congratulations. Thank you.
Speaker 6:I appreciate that. Thanks.
Speaker 1:We got boy. 4,000 GitHub Stars. You love to see it. Yeah. Congratulations.
Speaker 1:We got more.
Speaker 2:I can't believe Gary let us bring these in.
Speaker 1:Oh, yeah. He let us.
Speaker 14:We The cleanup after is gonna be interesting.
Speaker 1:Anyway Yeah. How's how's traction been in on the sales side? I I imagine you have a, you know, a product that you actually sell on top of it, not a nonprofit.
Speaker 6:Yeah. Yeah. So, yeah, we can't disclose revenue numbers, but Sure. We have a lot of great customers. Cool.
Speaker 6:So, yeah, Department of Defense Yeah.
Speaker 20:Oh, wow. Epic Global.
Speaker 1:Oh, wow. Yeah. A bunch of names. Huge. Huge.
Speaker 17:Huge. So the round's
Speaker 2:definitely done.
Speaker 1:Definitely done. Definitely done. Let's do another one. The round's done? No.
Speaker 1:It's done.
Speaker 2:Yeah. What what were you doing before this?
Speaker 6:So I was at Berkeley at my
Speaker 2:co founder. Berkeley?
Speaker 1:Let's go. Let's hear from Berkeley, baby. Right across right across the bay. Go Bears. Right across the bay.
Speaker 1:We love it. We love Berkeley. It's
Speaker 11:so much Yes.
Speaker 1:I'm completely covered. It's everywhere. Okay. Well, congratulations.
Speaker 6:I appreciate it. Yeah. Thank you.
Speaker 2:Yeah. That's great. What what's next for you guys?
Speaker 6:Next is just making our customers
Speaker 2:the the Department of Defense. Where do even go
Speaker 25:from there?
Speaker 6:Yeah. I mean, just making developers happy, like building the product out more and more. Yeah. You know? And I think, you know, developers love us and and big customers love us.
Speaker 6:So we're just, like, keeping that energy going, keep launching new products, building the team.
Speaker 1:Yeah.
Speaker 2:We have
Speaker 6:a really great, like, killer engineering team from friends at Berkeley. So, yeah, we're excited to just keep growing and keep building.
Speaker 1:What's the what's the key value prop for for, for building platform for AI agents? Is it, like, interoperability between different models? Is it the ability to scale? Is it just price and cost?
Speaker 16:I think the
Speaker 2:question I have is like Yeah. Even last YC batch, there was a bunch there was companies with this pitch and
Speaker 30:I think the
Speaker 2:challenge is everybody wants to build infrastructure and troubles for agents, but then I I it feels like you can build good Yeah. Software, but it's it's maybe even harder to get the kind of companies that can build high quality agents that actually have value. What's what's the secret to like finding even customers that are not just gonna sign up, but actually get value out of the product.
Speaker 6:Yeah. That's definitely true. I think a lot of people especially right now, it's very in vogue to adopt AI, and so there's a lot of push like top down push from companies to adopt these AI implementations. But I think the biggest thing for us is that we're focused on not creating easy abstractions to make it easy to use, and I think that's where a lot of people fell short. It's like you're creating these abstractions that make your platform easy to use, but it's actually not powerful enough to to really put AI into your into your production system.
Speaker 1:Mhmm.
Speaker 6:And so for us, we're really focused on actually be a little more like, might might be harder to use, but because we remove the abstractions, you can actually power relatively complex applications like real world simulations, deep research, data transformations. Mhmm. Because a lot of companies are sort of going after the sales and marketing, you know, use case. And for us, we're more focused on the developers and actually production systems. Interesting.
Speaker 6:Awesome.
Speaker 1:How how how much is real world simulation, like, scaling right now? We've talked to some of these companies and it seems like it's almost like video generation. It feels like very nascent. We really haven't had, this breakout Studio Ghibli moment for real world simulation. Like, how is adoption going there?
Speaker 6:Yeah. It's actually going quite well. Okay.
Speaker 1:I think at What are the applications?
Speaker 6:Yeah. So the applications of that are essentially simulating like I I I guess like one that would be interesting would be like international affairs. Mhmm. So like understanding how like global events are gonna play out.
Speaker 2:Political conflict.
Speaker 6:We're agents. No way. Yeah. So that's like, you know, a broad topic that we're
Speaker 1:economic modeling Exactly. Like housing prices, you can actually have agents running and it was always like Yeah. Just like get Grand Theft Auto level.
Speaker 13:Right. Right.
Speaker 1:But now it's like, oh, what if each of them has like an internal reasoning engine powered by an LLM and has like a 130 IQ instead of like Yeah. Turn left, I'm running a star.
Speaker 6:Right. Like it's much better. Yeah. And I think the thing that really our our platform unlocked was being able to run thousands of agents in parallel Mhmm. At the same time.
Speaker 6:And I think that was one of our grounding thesis, like thesis was that, you know, we wanted to create this environment. Like, Sims Studio comes from building simulations of things.
Speaker 1:Yeah.
Speaker 6:Yeah. So launching, you know, 10,000 agents at a
Speaker 11:time Yep.
Speaker 6:And perhaps some of them are gonna come back and give you an accurate result or some of them, you know, might inform you in an interesting way. And so you take those aggregated results and you go do something with it.
Speaker 2:Very cool.
Speaker 1:Yeah. Thank you so much
Speaker 6:for joining pleasure talking you. Me. Yeah. Thank you
Speaker 26:to both. Fantastic.
Speaker 1:And that is the end of our demo day stream.
Speaker 2:Thank you to all of our partners.
Speaker 1:We will be back in Los Angeles tomorrow live
Speaker 2:from YC team.
Speaker 1:Yeah. Thank you to the Flight Combinator team. It is Fantastic. We're Major Whitehill. San Francisco's back.
Speaker 1:Gary Tan's back. He would he never left. YC never left.
Speaker 2:But The Rosewood is back.
Speaker 1:It's a it's just a fantastic time in San Francisco. Fantastic time to be here at YCWD. Have a great afternoon. Thank you for watching. We will see you tomorrow.
Speaker 1:Goodbye.