every 3 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 going to replace entire teams and functions and enterprises.

That progression is still mind-blowing to me. A lot of the foundation models are kind of coming head-to-head.

There used to be only one player in town with Open AI, but we've been seeing in the last batch this has been changing, thank God.

It's like competition is, you know, the soil for a very fertile marketplace ecosystem, uh, for which consumers will have choice and uh Founders have a shot and that's the world I want to 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 and today Jared is a man on fire and he's going to 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 vertical 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 billion plus companies started just in this one category.

Nice. I'm going to do it by analogy with SAS and I think in a in a similar fashion people don't understand just how big SAS 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 20 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 20-year time period, which is way more than every other category.

Software is 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 um the the real catalyst for for the SaaS boom was, do you guys remember XML HTTP request?

Oh my God, like I I'd argue that that was quite literally the catalyst for the S boom like uh Ajax.

Ajax, yeah. In 2004, browsers added this JavaScript function, XML HTTP request, which was 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 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.

Yeah, Paul Graham actually uh 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 computer program that would change a website.

So Viaweb was an online store, kind of 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 SaaS apps kind of sucked 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 2005 when XML HTTP request white spread.

Anyway, I I see this LLM thing as like actually very similar. Um, it's like it's a new computing paradigm that makes it possible to just like do something fundamentally different.

And in 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 are the good opportunities for startups? I was going through the list of like all the billion dollar companies that were created and I kind of had this realization that, um, you could kind of bucket the the different paths that people took into like three buckets.

Um, 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.

Um, so that's like docs, photos, email, calendar, chat, all these things that like we used to do on our desktop with that obviously could be moved to the browser and mobile.

And the interesting thing is, zero startups won in those categories.

100% of the value flowed 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 a second category, which was like mass consumer ideas that were not obvious, that nobody predicted.

Um, that's like Uber, Instacart, DoorDash, Coinbase, Airbnb. Those ones, those ones came out of left field. Like the the 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 B2B SaaS companies, and that's like 300 of them.

And so like, more 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 of 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. Um, and I remember Mark Benioff coming to speak at YC and he tells the story as just very early on people just didn't believe you could build sophisticated Enterprise applications like over the cloud or via SaaS.

It was just so um, there was just like a perception issue, right? It was like no, like you don't you buy like your box software and that's like the real software that you run the way we always do it.

It was it was quite contrarian because the early web app sucked.

They were like Viaweb, where you had to be a visionary like PG and understand that the browser was going to keep getting better and that eventually it'd be good, which feels quite reminiscent of today, right?

Where it's like, yeah, the same thing, like, oh no, like you won't be able to build like sophisticated Enterprise applications that use these LLMs or AI tools because they hallucinate or they're not perfect or they um they kind of 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?

Why is Siri so stupid still? What year is it? It makes no sense. I mean, it's like a counter to that is like the very obvious thing is search and maybe Google will still win um on search, but Perplexity is definitely giving them a run for their money, right?

Yeah, this is the classic innovator's dilemma at the end of the day.

I 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 a 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, uh, 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 they were going to work.

Who will never launch an Uber clone? They never launch an Airbnb clone.

Um, I was listening to this, uh, talk by Travis, and one of the things that he said that really stuck with me is that in the, in the first years of Uber, he was very scared that he would, 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 exact was going to do that.

What do you think about, um, why the incumbents didn't go into B2B 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'd 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 B2B 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 to 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 it's sort of about the unbundling, bundling of software argument that comes up a lot as well, I think, and why didn't why did all these vertical B2B SaaS products evolve versus just like Oracle or SAP or, um, NetSuite just owning like everything.

Um, and I think it might be, AI 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 had this box software that was really like expensive to install and you had 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 a UA 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 all of these like vertical SaaS solutions to emerge doing exactly what you're saying.

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, but master of none.

Yeah, so ends up being a bit of a kitchen sink type of experience and this is where if you go and build a B2B SaaS vertical company, you could do literally a 10x better experience and more delightful because there's this stark difference between consumer products and Enterprise user experience yeah well there's only uh what three price points in software it's uh $5 per seat, $500 per seat, or $55,000 per seat and uh that maps directly to consumer SMB or Enterprise sales and then I think time in Memorial has taught us that in the past and this is less and less true uh 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 Fortune 1000 is the person who's getting wined and dined for this, you know, mega seven-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 um I'm sort of curious to see how this changes with LLMs 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 uh even in today's YC portfolio uh it's quite routine to see a company that reached a hundred or $200 million a year in Revenue but they have like 500, a 1,000, 2,000 employees already and I'm just going to be very curious like uh even the advice that I'm starting to give companies that are, you know, a month or two out of the batch uh 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 you might say, you know, let me find the absolute smartest person uh in all of these other parts of the org, like customer success or sales or different things like that.

And uh, I want to find someone who I've worked with who is, I know, is great, and then I'm going to go sit on their, you know, uh, 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, uh, the meta is shifting a little bit, like you actually might want to hire more really good software engineers who understand large language models, uh, 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 going to build LLM 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 and the prompts.

Does 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 like build marketing or cust, like user acquisition basically, um, and especially after we raised a Series B, the like traditional way you were supposed to do that is to like hire a marketing executive and build out like marketing team and um and just like basically spin up this machine to do like sales and marketing, but I'd actually met like a Y Combinator founder, um, Mike, who was, his company was basically building like a smart frying pan.

Sounds like bizarre, but like he was an MIT engineer. Yeah, you remember this. Um, 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 um, uh, Google Ads and just a whole bunch of stuff.

And so he he'd taken this engineers mindset approach to it and 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 we were spending like 1.

like a million dollars a month on just marketing and various like campaigns and Triplebyte had great marketing.

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 it.

You could tell it was not being done by some like VP marketing person. Um, 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 so much yeah yeah people be like I thought this is 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 had which is 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 that got disrupted by a SaaS company, and you could easily imagine the same thing happening again where now basically every SaaS company builds some software that some group of people use, the vertical AI equivalent is just going to 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, um, 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 one of my companies called, um, uh, Vector Shift that I funded about a year ago.

They're two really smart like Harvard computer scientists and it's a 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 um internal LLM powered agents, but like enterprises often don't know exactly what they want to 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.

Um, 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 like make it easy for you to do LLM stuff and then 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 2005 to 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 you 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.

Um, and like the user experience not going to 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 going to 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 with 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.

Um, 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, uh, 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 100% 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.

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 would be like one-to-one mapping, but is also going to eat all of the, a lot of the payroll because we look 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.

Vertical AI Agents Could Be 10X Bigger Than SaaS

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Y Combinator

Rapid advancements in vertical AI and its potential impact

Significant progress in vertical AI, leading to AI agents capable of replacing entire teams and functions within enterprises.

Increased competition among foundation models, creating a more fertile market and offering consumers more choices.

Vertical AI agents are poised for significant growth, with potential for the creation of multi-billion dollar companies.

Analogy drawn between the growth of SaaS and the potential of vertical AI agents.

The SaaS boom as an analogy for the potential of vertical AI

Many underestimate the size of the SaaS market due to limited personal use of SaaS tools.

Silicon Valley's focus on SaaS companies for the past 20 years, resulting in numerous unicorns.

XML HTTP request as a catalyst for the SaaS boom, enabling rich internet applications.

Paul Graham's Viaweb as an early example of a SaaS application.

Early SaaS apps had limitations due to the need for page reloads with each button click.

Analyzing the paths to success in the post-XML HTTP request era

Examination of three categories of billion-dollar companies created after the rise of cloud and mobile technologies.

Incumbents dominated the market for obvious mass consumer products (e.g., email, calendar).

Startups succeeded in the mass consumer market with less obvious ideas (e.g., Uber, Airbnb).

B2B SaaS companies emerged as the most numerous category of billion-dollar companies.

Parallel between LLMs and SaaS, and predictions for future market dominance

Incumbents are likely to dominate obvious mass consumer applications of LLMs (e.g., general-purpose AI assistants).

Discussion on why incumbents didn't enter the B2B SaaS market, focusing on the difficulty of managing numerous specialized domains.

The unbundling of software and the shift towards SaaS contributed to the rise of vertical B2B SaaS products.

Vertical B2B SaaS companies offer superior user experiences compared to general-purpose enterprise software.

The impact of LLMs on startup growth and hiring practices

Traditional startup growth often involves scaling the number of employees with revenue.

Shifting hiring strategies towards software engineers who understand LLMs to automate tasks and reduce the need for large teams.

The possibility of future unicorn companies with significantly smaller teams due to LLM-driven automation.

Example of Triple Bite's successful use of an engineer for marketing tasks, demonstrating the potential of leveraging specialized skills.

The case for 300+ vertical AI agent unicorns

Prediction of numerous vertical AI unicorns, mirroring the success of SaaS companies.

Discussion on how enterprises are still determining their needs for AI agents, with some focusing on platforms for custom deployment.

The question of whether vertical AI agents will take off immediately or follow a similar path to SaaS, starting with general-purpose solutions.

Early success of vertical AI agents in enterprise markets.

Vertical AI's potential to surpass SaaS in scale

Vertical AI agents can replace not only SaaS software but also a significant portion of payroll costs.

Examples of vertical AI agent companies and their success in specific domains (e.g., surveys, QA testing, recruiting).

AI-powered solutions are eliminating the friction caused by employees fearing job displacement.

Example of Cap.AI, an AI chatbot for developer support, enabling smaller dev ops teams.

The open market for specialized AI customer support agents

The market for AI customer support agents is largely untapped, with most existing solutions being simplistic and unable to replace human teams.

Example of Gig ML, providing AI-powered customer support for Zepto, replacing a large team.

The need for tailored solutions in customer support, reflecting the broader trend of specialization in AI agents.

The impact of AI on the scale and management of firms

Discussion of Coase's theory of the firm and its relevance to AI's impact on firm size and efficiency.

Rippling's approach to building a suite of HR tools, aiming for horizontal integration rather than vertical specialization.

AI tools can expand the information processing capacity of managers, potentially increasing the scale of firms they can manage.

Example of a voice chat tool allowing CEOs to communicate with and gather information from a large number of employees.

The rapid growth of AI voice calling applications

Example of Salient, using AI voice calling to automate debt collection in the auto lending space.

Rapid advancements in voice infrastructure enabling quick development and deployment of AI voice applications.

Significant improvements in voice technology over the past six months, making AI voice apps more realistic and capable of replacing human interaction.

The evolution of LLM-powered applications and the emergence of vertical AI agents

Rapid advancements in LLMs have led to the development of sophisticated vertical AI agents capable of replacing entire teams and functions.

Increased competition among foundation models is creating a more dynamic and innovative market.

Advice for finding the right vertical for an AI agent startup: focus on boring, repetitive admin tasks.

Examples of successful AI agent startups focusing on government contract bidding and medical billing for dental clinics.