Vertical AI Agents Could Be 10X Bigger Than SaaS

Every three months, things have just kept getting progressively better. And now we're at this point where we're talking about full on vertical AI agents that are gonna replace entire teams and functions and enterprises. That progression is still mind blowing to me. A lot of foundation models are kinda coming head to head.

There used to be only one player in town with OpenAI, but we've been seeing in the last batch this has been changing. Thank god. It's like competition is, you know, the the soil for a very fertile marketplace ecosystem for which consumers will have choice and founders have a shot, and that's the world I wanna live in. Welcome to another episode of The Light Cone. I'm Gary.

This is Jared, Harge, and Diana. And collectively, we funded hundreds of billions of dollars worth of startups right when they were just one or two people starting out. And today, Jared is a man on fire, and he's gonna talk about vertical AI. Yes. I am.

I am fired up about this because I think people, especially startup founders, especially young ones, are not fully appreciating just how big vertical AI agents are going to be. It's not a new idea. Some people are talking about AI agents. We funded a bunch of them. But I think the world has not caught on to just how big it's going to get.

And so I'm going to make the case for why I think there are going to be $300,000,000,000 plus companies started just in this one category. Nice. I'm gonna do it by analogy with SaaS.

And I think in a in a similar fashion, people don't understand just how big SaaS is because most startup founders, especially young ones, tend to see the startup industry through the lens of the products that they use as a consumer. And as a consumer, you don't tend to use that many SaaS tools because they're mostly built for companies.

And so I think a lot of people have missed the basic point that if you just look at what Silicon Valley has been funding for the most for, like, for the last twenty years, like, we've mostly been producing SaaS companies, guys. Like, that's literally been, like, most of what has been coming out of Silicon Valley.

It's over 40% of all venture capital dollars in that time period went to SaaS companies. And we produced over 300 SaaS unicorns in that twenty year time period, which is way more than every other category. Software's pretty awesome. Software is pretty awesome.

I was thinking back to the history of this, because we we always like to talk about the sort of how the how the history of technology informs the future. And the the real catalyst for for the SaaS boom was do you guys remember XML HTTP request? Oh my god. Like, I'd argue that that was quite literally the catalyst for the SaaS boom. Like Ajax. Ajax. Yeah.

In 02/2004, browsers added this JavaScript function XML HTTP request, which is the missing piece that enabled you to build a rich Internet application in a web browser. So for the first time, you could make things in websites that looked like desktop applications, and then that created Google Maps and Gmail and set up this whole, like, SaaS boom.

Essentially, the key technology at Lock was that software moved from being a thing that you got on a CD ROM and installed on your desktop to being something that you use through a website and on your phone. Paul Graham actually shares in that lineage in that he was one of the first people to realize that he could take the HTTP request and then actually hook it up to a Unix prompt.

And you didn't actually have to, you know, have a separate Yeah. Computer program Yeah. That would change a website. So Viaweb was a online store, kinda like Shopify, but way back in the day. Yeah. It was basically like the first SaaS app ever. Like like, PG actually invented SaaS in like 1995. It's just that those first SAS apps kind of suck because they didn't have XML HTTP request.

And so every time you would, like, click a button, you would have to reload the whole page. And so it's just a shitty experience. And so it didn't really catch on until 02/2005 when x XML HTTP request. Why it's red? Anyway, I I see this LLM thing as, like, actually very similar. It's like it's a new computing paradigm that makes it possible to just, like, do something fundamentally different.

And in 02/2005, when cloud and mobile finally took off, there is this sort of like big open question of like, okay, well, this new technology exists. What should you do with it? Where is the value going to accrue? Where are the good opportunities for startups?

I was going through the list of like all the billion dollar companies who were created, and I had this realization that you could bucket the the different paths that people took into, like, three buckets. There's there's a first bucket that people started with, which was, like, I would call them obviously good ideas that could be mass consumer products.

So that's like docs, photos, email, calendar, chat, all these things that like we used to do on our desktop, but that obviously could be moved to the browser and to mobile. And the interesting thing is zero startups won in those categories. 100% of the value flow to incumbents. Right? Like Google, Facebook, Amazon, they own all all those businesses.

Folks forget that, like, Google Docs wasn't the only company that tried to bring Microsoft Office online. There were, like, 30 companies that tried to bring Microsoft Office online, but they all lost. Google won. Then there was, like, a second category, which was, like, mass consumer ideas that were not obvious that nobody predicted. That's like Uber, Instacart, DoorDash, Coinbase, the Airbnb.

Those ones places. Those ones came out of left field. Like, the the dot dot dot between XML HTTP request and Airbnb is, like, very not obvious. Yeah. And so the incumbents didn't even try competing in those spaces until it was, like, too late. And so startups are able to win there. And then there's a third category, which is all the b to b SaaS companies, and that's, like, 300 of them.

And so, like, like, by by by number of logos, way more billion dollar companies were created in that third category than the first two. I think one reason why that happened is, like, there is no, like, Microsoft SaaS. Like, there is no company that somehow does, like, SaaS for, like, every vertical and every product.

Like, for structural reasons, it seems to be the case that, like, they're all different companies, and that's why there's so many of them. I think Salesforce is probably like the first true SaaS company. And I remember Mark Benioff coming to speak at YC. And he tells a story. It's just very early on.

People just didn't believe you could build sophisticated enterprise applications like, over the cloud or via SaaS. Was just so there's just like a perception issue. Right? Was like, no. Like, you don't you buy, like, your box software, and that's, the real software that you run your That's the way we always do. It was it was quite contrarian because the early web app sucked. Yep.

They were, like, via web, where you had to be a visionary like PG and understand that the browser was gonna keep getting better and that eventually it'd be good. Which feels like quite reminiscent of today. Right? Yeah. Where it's like the yeah. The same thing. Like, oh, no.

Like, you won't be able to build like sophisticated enterprise applications that use these LLM or AI tools because they hallucinate or they're not perfect or they they're kinda like just toys. But, yeah, that's like the early SaaS story exactly the same.

And so when I think about the parallels with LLMs, I could easily imagine the same thing happening, which is that there's a bunch of categories that are like mass consumer applications that are obviously huge opportunities, but probably the incumbents will win all of those.

So that's something like a per like a general purpose AI voice assistant that you you know, you can ask it to do anything, and it'll, like, go do that thing. That's an obvious thing that should exist, but, like, all the big players are going to be competing to be that thing. Right? Apple's a little slow on that one. Why is Siri so stupid still? What year is it? It makes no sense.

I mean, it's they count to that is, like, the very obvious thing is search, and maybe Google will still win on search, but perplexities definitely give them a A huge run for their money. Right? Yeah. This is the classic innovator's dilemma at the end of the day.

Mean, you could argue going back to what you said about Uber or Airbnb, these were actually really risky things from a regulatory standpoint.

So if you're Google and you have basically a guaranteed, you know, giant pot of gold that, you know, sort of comes to you every single month, like, why would you endanger that pot of gold to sort of pursue these things that might be scary or might ruin the pot of gold?

I think that's I think that's, like, probably the primary reason why the incumbents didn't end up building those products and didn't even clone them even after they got big. And it was obvious that that they were going to work. Google never launched an Uber clone. They never launched an Airbnb clone.

I was listening to this, talk by Travis, and one of the things that he said that really stuck with me is that in the in the first use of Uber, he was very scared that he was going to personally go to prison for like a long time. Like, he was actually personally risking going to prison in order to build that company. And so, yeah, no highly paid Google exec was gonna do that.

What do you think about why incumbents didn't go into b to b SaaS? Is it part of the reason is that a lot of the use cases are very there's a very wide distribution? I it's a great question. I love to hear what you guys think. My take is that it's just too hard to do that many things as a company.

Like, each b to b SaaS company really requires, like, the people who are running the product in the business to be extremely deep in one domain and care very deeply about a lot of really obscure issues. You know, like, take, like, Gusto, for example. Like, why didn't Google build a Gusto competitor?

Well, there's no one at Google who really understands payroll and has the patience to, like, deal with all the nuances of all these, like, stupid payroll regulations. And, like, it's just like like, it's just not worth it for them. It's easier for them to just focus on, like, a few really huge categories.

In the B2B SaaS world, it's sort of about the unbundling, bundling of software argument that comes up a lot as well, I think. And why did all these vertical B2B SaaS products evolve versus just like Oracle or SAP or NetSuite. Yeah. NetSuite, just owning like everything.

And I think it might be also is another thing that's attributable to the shift to like SaaS and the internet is in the old ways of selling software. Again, like, you have this box software that was really, like, expensive to install, and you have, like, a whole ecosystem around it. And anytime you wanted something custom, like, the integrators would just say, oh, no.

Like, we can, like, just build you a custom, like, payroll feature or something like that. And then Salesforce comes along with, like, a SaaS solution, and it just seems like it could never be as powerful or sophisticated as, like, the expensive enterprise installation you just paid for. But they proved that it totally was the case.

And I think that just, like, opened the gates for all of these, like, vertical SaaS solutions to emerge doing exactly what you're saying. I think the other problem is that with a lot of this enterprise software, if you're a user of Oracle and a NetSuite, because they're they have to cover so much ground, the user experience is actually pretty bad.

They're trying to be jack of all trades about master of none. Yep. So it ends up being a bit of a kitchen sink type of experience. And this is where if you go and build a b to b SaaS vertical company, you could do literally a 10 x better experience and more delightful because there's this stark difference between consumer products and enterprise user experience. Yep.

Well, there's only, what, three price points in software. It's $5 per seat, $500 per seat, or $5,000 per seat. And that maps directly to consumer SMB or enterprise sales. And then I think time immemorial has taught us that in the past, and this is less and less true with new software, thankfully, but enterprise is terrible software because it's not the user buying it.

You know, some high up muckety muck inside a Fortune one thousand is the person who's getting wine and dine for this, you know, mega 7 figure contract. And, you know, they're going to choose something that maybe isn't that good actually for the end user, the person who has to actually use the software day to day. And I'm sort of curious to see how this changes with LMs actually.

I mean to date, one of the more salient things that we've seen for both SMB and enterprise software companies is that or all software companies, all startups period is like, you know, there's a sense that as revenue scales, the number of people you have to hire scales with it.

And so when you look at unicorns, even in today's YC portfolio, it's quite routine to see a company that reached a hundred or $200,000,000 a year in revenue, but they have, like, 500, a 2,000 employees already. And I'm just gonna be very curious.

Like, even the advice that I'm starting to give companies that are, you know, a month or two out of the batch, it's it's feeling a little bit different than the kind of advice I would give last year or two years ago. In the past, might say, you know, let me find the absolute smartest person in all of these other parts of the org, like customer success or sales or different things like that.

And I want to find someone who I've worked with, who is I know is great, and then I'm gonna go sit on their, you know, on their doorstep until they quit their jobs and come work for me. And I want them to be someone who can, you know, build a team for me, hire a lot of people. That might still be true, but I'm starting to sense that the meta's shifting a little bit.

Like, you actually might want to hire more really good software engineers who understand large language models, who can actually automate the specific things that you need that are the bottlenecks to your growth. And so it might result in, you know, a very subtle but, you know, significant change in the way startups grow their businesses sort of post product market fit.

It means that I'm gonna build LM systems that bring down my costs, that cause me not to have to hire a thousand people. I think we're right at the beginning of that revolution right now. I mean, we talked about this in a previous episode. We talked about there will be a future unicorn company that's only run if we take it to the limit with only 10 employees. That's completely plausible.

And they're writing the evals in the prompts. That's it. I think what you're saying is like a trend that was already underway pre LLMs. Like, I remember when I was running Triplebyte, for example, we needed to build marketing or customer user acquisition, basically.

And especially after we raised our series b, the, like, traditional way you were supposed to do that is to, like, hire a marketing executive and build out, a marketing team and and just, like, basically spin up this machine to do, like, sales and marketing. But I'd actually met, like, a YC founder, Mike, who was his company was basically building, like, a smart frying pan.

Sounds like bizarre, but, like, he was a MIT engineer. Yeah. You remember this? He's an MIT engineer, and to sell the smart frying pan, he had to get really, really good at understanding, like, paid advertising and Google Ads and just a whole bunch of stuff. And so he'd he'd taken this engineer's mindset approach to it.

And I remember just talking to him about it and realizing this would be so much better to have an MIT engineer working on, like, our marketing efforts than any of the marketing candidates I've spoken to. And he was able to, like, scale us up to, like and we were spending, like, at one point, like, a million dollars a month on just marketing and various, like, campaigns.

And Tripleby had great marketing. Yep. Like, I remember, like, the Caltrain station takeover that you did, all the, like, out of home stuff that you did. It was, like, really high quality stuff. It stuck with the you could tell that it was not being done by some, like, VP marketing person. And the the and that was all Mike.

And, like, the comment I would often get when people would ask me around that time, like, how big is Triplebyte? And we were, like, 50 people. And I would It felt so much better. Yeah. Yeah. People would be like, I thought there's, like, hundreds of people. I was like, no.

It's all because if you put a really smart engineer on some of these, like, tasks, they just find ways to make they find leverage. And now, like, LLMs can go even way beyond, like, the leverage you have with just pure software. Okay. So here's my pitch for 300 vertical AI agent unicorns.

Literally, every company that is a SaaS unicorn, you could imagine there's a vertical AI unicorn equivalent in, like, some new universe. Because, like, most of these SaaS unicorns, beforehand, there were some, like, box software company that was making the same thing got disrupted by a SaaS company. And you could easily imagine the same thing happening again.

We're now basically, every every SaaS company builds some software that some group of people use. The vertical AI equivalent is just gonna be the software plus the people in one product. One thing might be just enterprises in general right now are a little unsure about what exactly they like, what agents they need.

And one approach I've seen from especially more experienced founders, like Brett Taylor, the CTO of Facebook, started his company Sierra. I don't know all the details, but as far as I can tell, it's essentially more like broadly about letting enterprises, like, deploy these AI agents and spinning them up, like, custom for the enterprise versus, like, oh, hey.

We have, like, this specific agent to do this. It's something I've seen from all my companies called Vectorshift that were funded about a year ago.

They're two really smart, like, Harvard computer scientists, and it's that what they found is that they're trying to build a platform to make it easy for enterprises to build their own, like, use, like, no code or SDKs to build their own, like, internal LLM powered agents. But, like, enterprise often don't know exactly what they wanna use these things for. And so bring it back.

I wonder if, like, in, like, the box software world, you started off with just, like, a few vendors who just basically were trying to convince people to use software at all, and it was just like, it does everything. And then it gets more sophisticated and higher resolution, and you get lots of, like, vertical SaaS players.

We go through that same period with LLMs where the early winners might just be these, like, general purpose. Hey. Like, we'd, make it easy for you to do LLM stuff, and then it the vertical agents will come in over time, or do you think there's reasons it's different now and the vertical agents will take off on day one? Yeah.

That's interesting because if you think about the history of SaaS, the consumer things worked first. Like, 02/2005 to 02/2010 was mostly consumer applications, like email and chat and maps. And people got people, as individuals, got used to using these tools themselves. And I think that made it easier to sell SaaS tools to companies because, know, the same people are both employees and consumers.

Yeah. I I think the answer might just be like, this is this is all just a continuation of software. And just there's no reason it has to reset back. Like, LLMs don't have to reset back to a few general purpose, like, enterprise LLM platforms doing everything because enterprises have already been trained on, like, the value of point solutions and vertical solutions.

And, like, the user experience is not gonna be that different. These things will just be a lot more powerful. And so if enterprises have already built the muscle of believing that, like, startups or vertical solutions can be better than, like, legacy broad platforms, they are probably gonna be willing to take a bet on a startup promising a very good vertical AI agent solution today.

And I feel like we're all seeing that in the batch now, where some of our companies are getting faster traction in enterprises for these vertical AI agents than, like, we've ever seen before. I think we're just early in the game. Right? Like, all software sort of starts quite vertical, and then as the industries actually get much more developed, then I mean, I just answered my earlier question.

It's like, you know, why does a company end up having a thousand employees? It's actually that, you know, early early in the game, everyone's making these specific point solutions. And then at some point, you've got to go horizontal. Like, you're already doing this crazy spend on sales and marketing.

And then the only way you can actually continue to grow once you sort of get a % or, you know, some large majority of the market is you actually have to do, like, not just a point solution, but things that sort of work together.

Maybe the other point of why the bull case for vertical AI agents could be even bigger than SaaS is that SaaS, you still needed a operations team or set of people to operate the software in order to get all the workflows to be done, I don't know, approval workflows, or you have to input the data.

The argument here is that you will get not only replacing all that set of SaaS software, so that will be like one to one mapping, but it's also going to eat all of the a lot of the payroll. Because if you look at a lot of the spend for companies, big chunk is still a payroll, and software's tiny. Exactly. They spend way more on employees than they do on software.

So it'll be these smaller companies that are way more efficient that need way less humans to do random data entry or approvals or click the software. I agree. I think it's very possible that the vertical equivalents will be 10 times as large as the SaaS company that they are disrupting. I mean, there's there's two cases.

It could be that the vertical point solution could be just big enough, and you don't need to do that bra breath thing. Right? It that could be a nice scenario? Should we give some examples? I feel like we've all been working with so many verticals, AI agent companies. We've got, like, news from the front Yeah. Of how it's actually going.

Well, your former head of product, Aaron Cannon, is working on a YC company called Outset that I worked with. And basically, they're taking LLMs to the surveys and Qualtrics space. So Qualtrics is almost certainly not really going to build the best of breed large language model with reasoning. And then the funny thing about surveys is, you know, who's it actually for?

It's for people who run products, for marketing teams, it's for people who are trying to make sense of, like, what do our customers actually want? And what are surveys? Like, guess what? That's language. So and then I feel like these types of businesses actually have to thread this needle because enterprise and SMB software often is sold based on a particular person who is the key decision maker.

And you have to go high enough in the organization so that the people you're selling to are not afraid that their whole their job and or their whole team's job is gonna go away. Totally. That's kind of the move that I've seen that a lot of companies that sell need to do. Because if you're gonna go and sell to the team that's gonna get replaced by AI They're gonna sabotage it, man.

It just does not work. So I think this is an interesting way that a lot of these are top down. And you have to go through, at some point, even get the CEO to sign off on it. A company I'm working with, Momentic, that's essentially an AI agent. But for or at least where they're starting is QA testing. They're getting really great traction right now, and it's interesting.

Because you remember a decade ago, Ycom, we said we worked with Rainforest QA. Like, Rainforest was a QA as a service company, and that they had this exact tension of where they couldn't actually replace your QA team. And so they needed to build software that made the QA team more efficient. But really, that obviously meant trying to replace as many of them as possible.

They couldn't replace the whole team. And so they were always on this sort of like tightrope between trying to sell the software to like the head of engineering. And so this will mean you'll need less QA people and great. But then you also have to go sell that to the QA team who don't wanna be replaced.

And so I think that was always like a friction for that business for how it could like scale and grow. But now, like, Momentic with AI can actually just replace the QA people. So their pitch is not, oh, this, like, makes your QA people faster. It's like, this just means you don't need a QA team at all. So they can just focus the sell into, like, engineering.

And engineering doesn't need buy in from QA at this point. And you can also go in I mean, to start with, you can go and sell to companies that don't even have big QA teams at the moment. They just use something like Momentic, and then it will just, like, keep scaling with them. Scaling, and they'll just never build a QA team Yep. Ever. Yes.

That is a real life case study of what Diana was saying about why these vertical AI agent companies can be 10 times as big as the SaaS companies. Yep. I'm seeing this interesting now, like, in recruiting too.

I had this exact same issue with Triplebyte where to build the software to build software that makes it easy to, like, screen and hire software engineers, you need buy in from both the engineering team that they're joining, but also the recruiting team. And effectively, the software we were building was trying to replace the recruiters, but we couldn't completely replace the recruiters.

But now with NYC, this And so the recruiters were always, like, opposing Yeah. Opposing it because it was a threat to them. Yeah. So there's just always, like, friction on, like, how on, like, how far you can get when the customer that you're trying to sell to is worried about being replaced.

But, yeah, I think now it's still early days, but now with AI, you can build things that do the whole stack, like, of recruiting. We have a company we work with last batch, Nico, work with them, which is actually just doing, like, the full, like, technical screen, the full initial recruiter screen, and getting great traction.

So I think as those things keep going, like, they won't have the same thing. You won't have the friction of, oh, I need to convince recruiters to use this. You'll probably just, like, not build a recruiting team in the same way that you used to. Alright. Other example is even for dev tool companies, they have to do a lot of developer support.

And I work with this company called Capa dot ai that basically built one of the best chatbots that responds to a lot of the a lot of the technical details that are hard to answer.

And I think a lot of the companies that started using them, they actually ended up having DevRel teams that are a lot smaller because it ingests a lot of the developer documentations, even the YouTube videos that DevTools put up, and even a lot of the chat history. So it just keeps getting better and better and is like, gives really good answers, actually. It's one one of the best I've seen. Yeah.

I also worked with a customer support, like, AI customer support agent company called PowerHelp. Well, actually, we we both did last batch. And I learned a couple interesting things from PowerHelp. The first is customer like, AI agents for customer support was, like, the category that's, like, famously crowded where there's like supposedly like, you know, a hundred of them.

If you go and you Google like AI customer support agent, you'll get like a hundred results on Google. But what I learned through working with PowerHelp is like, it's actually kind of bullshit. Like like, almost all of those companies are doing very simple, like, zero shot LLM prompting. They can't actually replace a real customer support team that does a lot of really complicated workflows.

It just kinda makes for, like, a nice demo. Like, to actually replace a customer support team for, like, an at scale company that has, like, a hundred customer support reps to do lots of complicated things every day. You need, like, really complicated software that does all the stuff that, like, Jake Heller was talking about.

And there's there were only, like, three or four companies that were even attempting to do that. And cumulative, they had cumulatively, they had, like, less than 1% market penetration. And so the market was just completely open. I could also see that being another case of hyper specialization or hyper verticalization. Yeah.

Like, there's not gonna be I mean, maybe eventually, there could be a single general purpose customer support agent software company, but we're, like, in inning you know, that that'll be like an eighth or ninth inning kind of thing, and we're literally in the first inning.

So, you know, instead, you know, you're gonna have companies like GigaML that, you know, it's doing it for Zepto, doing 30,000 tickets every single day and replacing a a team of a thousand people. And but it's very specific, and it has, you know it's not a general purpose demo ware kind of thing.

Like, it's 10,000 test cases and a very detailed eval set that, you know, is basically just for Zepto and things like Zepto. Yeah. But if you are, you know, any of the other marketplace companies, you're probably gonna use it. Because, like, that's a very well defined kind of marketplace that's, you know, instant delivery marketplace.

I think this is the kind of dynamic that led there to be, like, $300,000,000,000 SaaS companies rather than, like, one, like, $10,000,000,000,000, like, meta SaaS thing that provides all the software for the world. It's just, like, the customers just require really heavily, like, tailored solutions, and it's hard to build one that, like, works for every everyone. Exactly.

I mean, we already gave three examples of customer support, but they're very different verticals. It's like Yeah. Dev tool companies, very different kind of support that you need, and the training set to marketplaces, very different. Right? Yeah.

I guess whether you have agents or real human beings working for you, you end up with the same problem, which is every company bumps up against Coase's theory of the firm, which says that any given firm will grow only so much to the point where it becomes inefficient to be larger than that. And then that's why they're sort of networks and ecosystems and, you know, a full blown economy.

You know, like every firm will sort of specialize to do what it is particularly good at. And then the limits the outer limits of what those firms can be, it's actually based on your ability as a manager.

So, yeah, that that part a little bit breaks my brain because, you know, when we spend time with Parker Conrad at Rippling, one of his favorite points is actually, well, you know, everyone's very obsessed with with the fact that the rocks can talk and, you know, maybe they can draw.

But the more interesting thing for him, you know, running HR IT software that, you know, he spends a lot of time thinking about HR. Like, actually, the coolest thing about the LMs is that the rocks can read. And from his perspective, like, you know, he's yeah. I think he has 3,000 employees. He still runs payroll for all 3,000 employees through Rippling.

So I think he spends a lot of time thinking about, like, how can one person extend their ability as a manager? And I think we're gonna see a lot more there. That would be in a a reverse argument that if we're at this moment where tools for managers and CEOs are going to get much more powerful Oh, it could it could it could increase the scale of the firm that you can run. Right.

And that's certainly what Rippling is trying to do. Like, he's of the war. Right. He's attempting to build this, like, suite of HR tools where if he wins, he's gonna eat a whole bunch of billion dollar SaaS companies and, like, one one giant company. It's a very interesting point, Gary.

I think what made me think about this is that with having all these AI SaaS tools, it's gonna give the ability to all these leaders and all these orgs to basically open the aperture of the context window of how much information they can parse. Mhmm. Because there's a limit of how much, as humans, we can have meaningful relationship. There's, like, the whole thing with the Dunbar number. Oh, yeah.

It's about 300 people that you 50. Hundred 50 that you can have a meaningful relationship with. But with AI, because all of these rocks now can read, I think we will be able to extend that Dunbar limit that we have. Yeah.

I think Flo Crevello had this interesting post on Twitter that went viral around think someone had made a voice chat, like, just weekend project as a CEO, but it would call all 1,500 Right. Of their employees. Yeah. And, you know, it was, you know, a very short call, like, kinda sounded like it was from the CEO just asking kind of personally.

I mean, it sort of reminds me of that scene in Her where it zooms out and actually, you know, you're following the experience of one person using the HerOS, but actually that HerOS is actually speaking to 15, you know, thousands or tens of thousands of people all at time. How many others? 8,316. Yeah. I mean, large language models can talk and can have conversations.

And then to what extent can, you know, this power actually extend the capability of one or a few people to understand what's going on? I I heard about that use case.

It got definitely got me thinking because as I understood, the project is something like it just it will call up all your employees, and then your employees can just, like, ramble about what they've been doing, and it will just extract the meaning out of it and give the CEO, like, a like, bullet point summary of here's the most important stuff.

And there were a bunch of, like, SaaS companies that attempt to do these sort of, like, weekly pulse pulses from employees using, like, traditional SaaS software. But, like, that version is is literally a hundred times better than the pre LM version of this idea. But I wonder with, like, that particular tool. Just like it's not it's going beyond just, like, reading and summarizing.

Like, this this is the argument of, like, if writing is thinking, then, like, there's actually just a huge amount of work that's involved in the effort of figuring out, like, who's an effective communicator and, like, what are the most important things to be, like what what are the key things to be focused on as a company?

And I just wonder if that at some point, do the LLMs do, like, they go beyond just, summarizing and reading and doing actual thinking, at which point, like, who's actually running the the organization? Interesting thought.

I guess the other thing that's kind of interesting about how Parker Conrad's thinking about it is I found out about this recently off an interview with Matt McGinniss, the COO, that there are more than a hundred founders who work at Rippling now as sort of specific people who run, like, an entire SaaS vertical inside Rippling. It's super cool the way he's built the team.

Hart Hart probably knows a lot about it because you've done a bunch of interviews with him. Yeah. I mean, it's definitely very focused on recruiting founders. And I mean, Parker like, Rippling is essentially the the case against vertical like, all verticalization. Trying to horizontalize and take over all of HR and IT software. Yeah. Yeah.

Like, the whole thesis is basically there's this underlying platform that has, like, lots of value, and he wants to recruit founders and teams that build on top of the platform. Like, it's almost a little bit more sort of like Amazon esque, whereas like shared infrastructure. Yeah. I think every product that they've released I mean, things like time tracking and whatnot.

I mean, basically, they launch a thing and it hits like multi millions of dollars in ARR on day one of launching. And that's exactly what we were talking about earlier. Like, once you once you have a vertical, once you have a toehold, what you're saying is, well, I have to spend this money on sales and marketing anyway. Can I, you know, basically get higher LTV and hold my CAC constant?

And that's sort of what if you if you look at all the top software companies today, it's like that's what Oracle is, that's what Microsoft is, that's what Salesforce is, rippling, knock on wood, gonna be the next. But it's it's an interesting alternative to going from zero to one totally on your own. TS, wanna talk about some of the voice companies that we have?

I think that's, like, an interesting, like, subcategory of this of this stuff. It's, like, really blowing up now. I have a company that I work with called Salient that basically does AI voice calling to automate a lot of debt collection in the auto lending space, which traditionally like, call up people and it's like, hey. You owe a thousand dollars on your car. Yeah. Which actually with that.

Actually, this kind of job is one of those butter passing job. It kinda sucks because a lot of these low wage workers work in all these call centers, and it's like a terrible boring job. So very high churn and giant headcount to run these because there's so many accounts with these banks that have to do that. And this is a perfect task that AI could automate.

And what Salient has done is has been able to actually get very, very accurate, and it has been going live with a lot of big banks, which is super exciting. And this is a company from last year and demonstrating that that part of it that they were able to get in because they sold through TopDown.

I guess the space feels like it's moving very quickly and that we have incredible companies that are voice companies like Vappy. And then people can sort of get started right away.

And retail also, I mean, these are companies that have reached pretty fast scale just because it's one of the more exciting, like mind blowing things that you can get up and running within, I mean, literally the course of hours.

And then some of the question that, you know, remains unanswered and we hope they figure it out is how do you hold on to them, especially as you run into things like the new OpenAI voice APIs. You know, do you go direct? Like, you prob it's probably way more work to try to use the underlying APIs off the bat, but these platforms are clearly low bar.

And then the question is, can you keep raising the ceiling so that you can hold on to customers forever? Haj, you were making an interesting point earlier about, like, how the apps that people have built on top of LMs has changed from, like, early twenty twenty three when it started until now. Voice, which we were just talking about, is a great example of this.

I think even if you went six months back, it felt like the voices were not realistic enough yet. The latency was too high. Like, there was it felt like we were probably a ways of having AI voice apps that could meaningfully, like, replace, like, humans calling people up and, like, here we are.

And, yeah, I was just zooming out thinking back to the first YC batch where LLM powered apps first came in was probably winter two thousand twenty three, so, you know, almost two years ago now. And the apps were essentially just things that spat out some text and not even, like, perfect text. Rocks could talk. That's about it. Yeah. Sort of more like copy editing, marketing edit, email edits.

It was just, like, kinda more, like, just, like, incremental. Yeah. Like, I I had a company I mean, the one that sticks in my head is a company called Speedy Brand. And all what they did is make it very easy for, like, a small business to just generate a blog and spit out content marketing. It's like a very obvious idea. And it wasn't perfect, but it was pretty cool at the time.

And that's what we've talked about a bunch of the show, but that was like the chat GBT wrapper turned out around that time. So hey, like, this is what an LLM app looks like. It's just a chat GBT wrapper. It does very basic, spits out some text. Like, it's gonna get crushed by OpenAI in the next release. And it did. Yeah. Well, I don't know if No, she Cat one did.

But that first wave of LLM apps mostly did get crushed by the next wave of GPT as well. Feel like we've had this sort of boiling of the frog effect where from our perspective, it's sort of like every three months, things have just kept getting progressively better.

And now we're at this point where we're talking about, like, full on vertical AI agents that are gonna replace entire teams and functions and enterprises. And just that progression is still mind blowing to me. Like, we're two years in, which is still relatively early, and the rate of progress is just, like, unlike anything we've seen before.

And I think what's interesting to see is we discussed this in the last episode is a lot of the foundation models are kinda coming head to head. There used to be only one player in town with OpenAI, but we've been seeing in the last batch, this has been changing. Claude is a huge contender. Thank god.

It's like competition is, you know, the the soil for a very fertile marketplace ecosystem for which consumers will have choice and founders have a shot. And that's the world I wanna live in. So people are watching and thinking about starting a startup or maybe have already started, and they're hearing all of this. How do you know what the right vertical is for you?

You're gonna find some boring repetitive admin work somewhere. And that seems to be just like the common thread across all of that stuff is if you can find a boring repetitive admin task, there is likely gonna be a billion dollar AI agent start up if you keep digging deep enough into it.

But it sounds like you should go after something that you directly have some sort of experience or relationship to. There is a common like, if there isn't, there's definitely a common thread I've seen in the companies that are that I'm seeing promise with. And another one just pops into my head, sweet spot.

I think I mentioned on this before, like, they're basically building an AI agent to bid on government contracts. And the way they found that idea, and this is a year ago, was they just had a friend whose full time job was to sit there on, a government website, like refreshing the page, like looking for new proposals to bid on. And they they were pivoting.

They're like, oh, like that seems like something an LLM could do. A company from a recent batch which pivoted into a new idea that's getting great traction. Like, they're basically building an AI agent to do process like medical billing for dental clinics. And the way they found the idea was one of the founder's mothers is a dentist.

And so he just decided to go to work with her for a day and just sit there seeing what she did. She's like, oh, like, all of that, like, processing claims seems, like, really boring. Like, an LLM should totally be able to do that. And he just started writing software for, like, his mother's dental clinic.

So I guess, I mean, in robotics, the classic maxim is, you know, the robots that are gonna be profitable and that are gonna work are gonna be dirty and dangerous jobs. And in this case, for vertical SaaS, look for boring butter passing jobs. Yeah. Well, with that, we're out of time for today. We'll catch you on the Lite Cone next time.