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 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 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 sass 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 SAS 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 SAS companies and we produced over
300 SAS 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 SAS 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 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 SAS boom essentially the
the key technology atlock 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 SAS app ever like like PG
actually invented SAS 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 2005 when X XML HTP 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 exist what should you do with
it where is the value going to acrw
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 one 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
door Dash
coinbase th 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 SAS
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 SAS like there is
no company that somehow does like SAS
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 so many of them I think Salesforce
is probably like the first true SAS
company um and i' I remember Mark benov
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 SAS 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 contrar cuz
the early web app 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' be good which feels
like quite reminisent 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 llm 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 SAS story exactly the same and
so when I think about the parallels with
LMS 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 W 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 give them run
for the money right yeah this is the
classic innovators 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 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 SAS 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
SAS 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 SAS 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 SAS
products evolve versus just like Oracle
or sap or um netet yeah netu just owning
like everything um and I think it might
be Al is another thing that's
attributable to the shift to like SASS
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 SAS 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 Prov that it totally was
the case and I think that just like
opened the gates for all all of these
like vertical sash 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 netw suite 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 sync type of
experience and this is where if you go
and build a B2B SAS 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 Mucky muuk inside
Fortune 1000 is the person who's getting
whin 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 th000 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
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 founder um 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 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
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 triple bite had great
marketing like I remember like the Cal
trained 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 mik
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'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
had which is pure software okay so
here's my pitch for 300 vertical AI
agent unicorns literally every company
that is a SAS unicorn you could imagine
there's a vertical AI unicorn equivalent
in like some new universe cuz like most
of these SAS unicorns beforehand there
were some like box software company that
was making the same thing that got
disrupted by a SAS company and you could
easily imagine the same thing happening
again where now basically every every
SAS 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 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 SS 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 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 SAS 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 SAS 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 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 SAS is that SAS 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 SAS 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 so it'll be these smaller
companies that 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 SAS company that they are
disrupting I mean there there's two case
it could be that the vertical Point
solution could be just big enough and
you don't need to do that bro 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 qual Trix space so qual Trix 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 Andor
their whole team's job is going to go
away totally that's kind of the move
that I 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 u MCH 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 te 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 tight rope 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 mtic 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 mtic 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 agent
companies going to be 10 times as big as
the SAS 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 NYC
and so the recruiters were always like
oppos
opposing it CU 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
days but now with AI you can build
things that do the whole stack like of
recruiting we have a company we worked
with last batch like Nico work with them
a priora 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 they have the same thing you won't
have the friction of oh I need to
convince recruiters to use this you're
probably just like not build a
recruiting team in the same way that you
used to I mean other example is even for
de tool companies they have to do a lot
of a developer support and I work with
this company called cap. AI that
basically buildt one of the best chat
Bots 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 Dev rail teams
that are a lot smaller because it
ingests a lot of uh the developer
documentations even the YouTube videos
that Dev tools put up and even a lot of
the chat history so it just keeps
getting better and better and it's 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
an AI customer support agent company
called Power help well actually we both
did um last batch and I learned a couple
interesting things from parel um 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
100 of them and if you go and you Google
like AI customer support agent you'll
get like a 100 results on Google um but
what I learned through working with
parel is like it's actually kind of
like like almost all of those
companies are doing very simple like
zero shot llm prompting that can't
actually replace a real customer support
team that does a lot of really
complicated workflows it just kind of
makes for like a nice demo like to
actually replace a customer support team
for like an at Scale company that has
like 100 customer support reps that do
lots of complicated things every day you
need like really complicated software
that does all the stuff that like Jake
heler 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 um hyper
specialization or hyper verticalization
like there's not going to be I mean
maybe eventually there could be a single
general purpose customer support agent
software company but we're like in in
you know that that'll be like a eighth
or ninth inning kind of thing and we're
literally in the first inning so you
know instead you know you're going to
have companies like gig ml that you know
it's doing it for zepto doing 30,000
tickets uh every single day and
replacing a team of a thousand people
and but it's very specific and it has
you know it's not a general purpose demo
Weare kind of thing like it's 10,000
test cases in a very detailed uh eval
set that you know is basically just for
zepto and things like zepto yeah uh but
if you are you know any of the other
Marketplace companies you're probably
going to use it cuz 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 billion do SAS
companies rather than like one like1
trillion do like meta SAS 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 there
are very different verticals it's like
de tool comp need 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 CO's
theory of the firm which says that any
given firm will grow only so much to the
point where it uh becomes inefficient to
be larger than that and then that's why
they 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 uh 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 ripling uh
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 uh
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 he's 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 uh I
think we're going to see a lot more
there
that would be an a reverse argument that
if we're at this moment where uh tools
for managers and CEOs are going to get
much more powerful um oh it could it
could it could increase the scale of the
firm that you can run right and that's
certainly what ripling is trying to do
like he's attempting to build this like
Suite of HR tools where if he wins he's
going to eat a whole bunch of billion
dollar SAS companies and like one one
giant company it's very interesting
point Gary I think what made me think
about this is that with having all these
AI SAS tools it's going to give the
ability to all these leaders and all
these Orcs to basically open the
aperture of the context window of how
much information they can parse because
there a limit of how much uh humans we
can have meaningful relationship there's
like the whole thing with the D Mar
number it's about 300 people that you
150 that you can have a meaningful
relationship with but with AI because
all of these rocks now can read I think
think we will be able to extend that
dumbar limit that we have yeah I think
uh Flo crell had this interesting post
on Twitter that went viral around um I
think someone had made a voice chat like
just weekend project as a CEO but it
would call uh all
1,500 of their employees yeah and uh you
know it was you know very short call
like kind of sounded like it was from
the CEO just asking kind of personally I
mean it sort of reminds of um that scene
in her where it zooms out and uh
actually you know you're following the
experience of one person using the her
OS but actually that her OSS is actually
speaking to 15 you know thousands or
tens of thousands of people all at one
time how many others
8,316 yeah I mean large language models
can talk and can have conversations and
then to what extent can uh you know this
power actually extend the capability of
one or a few people to uh understand
what's going on I I heard about that yuk
it got it definitely got me thinking
because as understood the product 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 of like a like bullet point
summary of here the most important stuff
and there were a bunch of like SAS
companies that attempted to do these
sort of like weekly pulse Pulses from
employees using like traditional SAS
software but like that version is is
literally a 100 times better than the
pre- elm version of this idea but I
wonder with like that particular tool um
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 like summarizing and reading
and doing actual thinking at which point
like who's actually running the the
organization an interesting
thought I guess the other thing that's
kind of interesting about how Parker
Conrad's thinking about it is um I found
out about this recently off a an
interview with Matt mcginness his coo
that uh there are more than a hundred
Founders who work at ripling now as sort
of specific people who run like an
entire SAS vertical inside Rippling it's
super cool the way he's built the team
har probably knows a lot about it
because you've done a bunch of
interviews with him um yeah I mean it's
definitely very focused on uh recruiting
Founders and I mean Parker like Rippling
is essentially the the case against
vertical like verticalization trying to
uh
horizontalization 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 amazones whereas like shared
infrastructure um 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 tow hold
what you're saying is Well I have to
spend this money on sales and marketing
anyway can I uh you know basically get
higher LTV and hold my CAC constant and
uh that's sort of what you if you look
at all the top uh software companies
today it's like that's what Oracle is
that's what Microsoft is that's what
Salesforce is ripling knock on wood
going to be the next but um it's it's an
interesting alternative to uh going from
zero to one totally on your own do you
guys want to talk about some of the
voice companies that we have I think
that's like an interesting like sub
category of this of this stuff is 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 that collection in the auto Ling
space which tradition so they like call
up people and they're like hey you owe
$1,000 on your car yeah which actually
up with that actually this kind of job
is one of those butter passing job it
kind of sucks because a lot of uh 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
just 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
was a company from last year and
demonstrating that that part of it that
they were able to get in because they
sold through top down I guess the space
feels like it's moving very quickly and
that we have incredible companies that
are voice infra companies like vapy and
then people can sort of get started
right away and Retail also I mean these
companies that have reached pretty fast
scale just because it's one of the more
exciting like mindblowing things that
you can get up and running within I mean
literally the course of hours um 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 uh run into
things like the new open AI voice apis
um you know do you go direct like you
Pro it's probably way more work to try
to use the underlying apis off the bat
but these uh 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 har 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 2023 when it started until
now voice which we were just talking
about as a great example of this I think
even if you went 6 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 off 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
2023 you know almost 2 years ago now and
the apps were essentially just things
that spat out some text and not even
like perfect T Rockit talk that's about
it yeah sort of more like copy editing
marketing edit email edits it was just a
kind of more like just like incremental
yeah like I I had a company I mean the
one that sticks in my head is a company
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 um it's like
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's like the chat gbt
raer turned out around that time hey
like this is what an llm app looks like
it's just a chat GPT rapper it does very
basic spits out some text like it's
going to get crushed by openi in the
next release like and it did yeah well I
I I don't know if that one did but but
the that that first that first wave of
llm apps mostly did get crushed by the
next wave of GPT I feel like we've had
this sort of boiling of the Frog effect
where from our perspect 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 going to replace entire
teams and functions and Enterprises um
and just that progression is still
mindblowing to me like with 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 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 Claude is a huge
Contender thank God it's like
competition is you know the 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 so people are
watching and thinking about starting a
startup or maybe have already started
and uh they're hearing all of this how
do you know what the right vertical is
for you you got to find
some boring repeative admin work
somewhere and that seems to be like the
common threat across all of the stuff is
if you can find a boring repetitive
admin task um there is likely going to
be a billion dollar AI agent startup 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
that is a common like there there's
definitely a Common Thread I've seen in
the companies that are that I'm seeing
promis 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 a year ago was they
just had a friend whose full-time job
was to sit there on like a government
website like refreshing the page like
looking for new proposals to bid on and
they they were pivoting they're like ah
like that seems like something an llm
could do um 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 um process like medical billing
for dental clinics and the way they
found the idea was um one of the
founders mother 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 and she's like oh like all of that
like processing claim 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 uh you the
robots that are going to be profitable
and that are going to work are going to
be um dirty and dangerous jobs and in
this case for vertical SAS look for
boring butter passing
jobs well with that we're out of time
for today we'll catch you on the light
cone next time
[Music]