### Rise of Vertical AI Agents in Startup Ecosystem

Vertical AI agents are set to revolutionize industries by automating repetitive tasks, creating vast opportunities for startups.

### The Rise of Vertical AI Agents: Transforming Industries and Creating Billion-Dollar Opportunities
* 🚀 The evolution of AI technology has led to the emergence of **vertical AI agents** that are set to **replace entire teams and functions** within enterprises, marking a **significant shift in the industry**.
* 💡 With increasing competition in the AI market, particularly against OpenAI, the landscape is becoming **fertile for startups to innovate and offer diverse solutions**.
* 📈 The SaaS industry has historically dominated venture capital funding, with **over 300 SaaS unicorns** emerging in the last two decades, and vertical AI agents are expected to follow a similar trajectory, potentially creating **$300 billion companies**.
* 🔍 The success of vertical AI agents will hinge on identifying **repetitive, mundane tasks** within organizations, which can be **automated to enhance efficiency and reduce labor costs**.
* 🏢 Unlike traditional SaaS solutions that require extensive operations teams, vertical AI agents aim to **streamline processes**, allowing companies to **operate with fewer employees** while maintaining productivity.
* 📊 Examples of vertical AI applications are already emerging, from **automating customer support** to **streamlining medical billing processes**, demonstrating the **vast potential of this technology** in various sectors.
* 🔄 As the AI landscape continues to evolve rapidly, the focus on vertical solutions will likely lead to the development of **specialized applications that cater to specific industry needs**, creating a **more effective and efficient marketplace**.
* 💪 Entrepreneurs are encouraged to leverage their personal experiences and insights to identify opportunities for vertical AI solutions, as these are often rooted in **solving real-world problems** faced in specific industries.

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 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 Market Marketplace ecosystem uh for which consumers will have choice and uh Founders have a shot and that's the world I want to live [Music] 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 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 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 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 a do you guys remember XML HTTP request? Oh my God, like I I'd argue that that was quite literally the catalyst for the SaaS 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 via web was a 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 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 2005 when XML HTTP request became widespread 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 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 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 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 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 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 Mo 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 are so many of them I think Salesforce is probably like the first true SaaS company um and I 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 contrary cuz the early web apps sucked they were like via web 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 like quite reminiscent of today right 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 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? Well 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

like a count 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 the 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 exactly 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

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 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 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 yeah 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 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 immemorial 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

win and D 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 thousand, 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 a, 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 Met 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 Triple Bite 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 a 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 took this engineer's 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 Triple Bite 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

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 Triple Bite 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 cuz 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 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 bringing 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 an 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, a big chunk is still 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 the vertical equivalence will be 10 times as large as the SaaS companies that they are.

disrupting I mean there 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 broad 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 vertical AI agent companies we've got like news from the front how it's actually going well your former uh head of product Aaron Cannon is working on a YC company called Outset that I worked with and uh basically they're taking LLMs uh to the surveys

and Qualtrics space so Qualtrics is almost certainly not really going to build the best of breed uh 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 um and then I feel like these types of businesses um actually have to thread this needle um because Enterprise and

SMB software often is sold based on a particular person who who is the key decision maker and um 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 going to 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 going to go and sell to the team that's going to get replaced by AI they're going to 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, uMch, that's sort of essentially an AI agent, but for at least

where they're starting is like QA testing. Um, they're getting really great traction right now and it's interesting because you remember a decade ago, um, why can't 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 like 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

the sort of like tightrope between trying to sell the software to like the head of engineering as like, 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 want to 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 uMch 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 onto like engineering. And engineering doesn't need buying 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 mTurk and then it will just like keep scaling with them scaling and they'll just never build a QA team ever yes that is a real life case study of what Diana was saying about why these vertical AI agents

companies are going to be 10 times as big as the SaaS companies yeah I'm seeing this interesting now um like in recruiting too I had this exact same issue with Triple Vet where to build the software um to build software that makes it easy to like screen and hire software

engineers you need buying 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 AI and so the recruiters were always like

opposing it because it was a threat to them yeah so it just always like friction on like how um on like how far you can get when the customer you're trying to sell to is worried about being replaced um but yeah I think now it's still early

Vertical AI Agents Could Be 10X Bigger Than SaaS

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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.