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42:13
Transcript
0:00
every 3 months things have just kept
0:02
getting progressively better and now
0:04
we're at this point where we're talking
0:05
about full-on vertical AI agents that
0:07
are going to replace entire teams and
0:09
functions and Enterprises that
0:11
progression is still mind-blowing to me
0:13
a lot of the foundation models are kind
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0:15
of coming head-to-head there used to be
0:17
only one player in town with open AI but
0:19
we've been seeing in the last batch this
0:23
has been changing thank God it's like
0:26
competition is you know the the soil for
0:28
a very fertile Market Marketplace
0:30
ecosystem uh for which consumers will
0:33
have choice and uh Founders have a shot
0:36
and that's the world I want to live
0:39
[Music]
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0:44
in welcome to another episode of the
0:47
light cone I'm Gary this is Jared Harge
0:50
and Diana and collectively we funded
0:52
hundreds of billions of dollars worth of
0:55
startups right when they were just one
0:57
or two people starting out and and today
1:01
Jared is a man on fire and he's going to
1:04
talk about vertical AI yes I am I am
1:09
fired up about this because I think
1:11
people especially startup Founders
1:15
especially young ones are not fully
1:17
appreciating just how big vertical AI
1:20
agents are going to be it's not a new
1:22
idea some people are talking about
1:23
vertical AI agents we funded a bunch of
1:25
them but I think the world has not
1:27
caught on to just how big it's going to
1:28
get and so I'm going going to make the
1:31
case for why I think there are going to
1:33
be
1:34
$300 billion plus companies started just
1:38
in this one category nice I'm going to
1:40
do it by analogy with SAS and I
1:43
think in a in a similar fashion people
1:46
don't understand just how big SAS is
1:49
because most startup Founders especially
1:51
young ones tend to see the startup
1:53
industry through the lens of the
1:54
products that they use as a consumer and
1:56
as a consumer you don't tend to use that
1:58
many sass tools because they're mostly
1:59
built for companies and so I think a lot
2:02
of people have missed the basic point
2:04
that if you just look at what Silicon
2:06
Valley has been funding for the most for
2:08
like for the last 20 years like we've
2:10
mostly been producing SAS companies guys
2:12
like that's literally been like most of
2:14
what has been coming out of Silicon
2:16
Valley it's over 40% of all venture
2:18
capital dollars in that time period went
2:21
to SAS companies and we produced over
2:23
300 SAS unicorns in that 20-year time
2:26
period which is way more than every
2:28
other category software is pretty
2:30
awesome software is pretty awesome I was
2:32
thinking back to the history of this
2:35
because we we always like to talk about
2:37
the sort of how the how the history of
2:39
Technology informs the future and um the
2:42
the real Catalyst for for the SAS boom
2:45
was a do you guys remember XML HTTP
2:48
request oh my God like I I'd argue that
2:51
that was quite literally the Catalyst
2:53
for the S boom like uh Ajax Ajax yeah in
2:57
2004 browsers added this JavaScript
2:59
function XML HTTP request which was the
3:01
missing piece that enabled you to build
3:03
a rich internet application in a web
3:04
browser so for the first time you could
3:06
make things in websites that looked like
3:08
desktop applications and then that
3:10
created Google Maps and Gmail and set up
3:12
this whole like SAS boom essentially the
3:15
the key technology atlock was that
3:18
software moved from being a thing that
3:20
you got on a CD ROM and installed on
3:22
your desktop to being something that you
3:23
use through a website and on your phone
3:25
yeah Paul Graham actually uh shares in
3:28
that lineage in that he was one of the
3:29
first people to realize that he could
3:31
take the HTTP request and then actually
3:34
hook it up to a Unix prompt and you
3:37
didn't actually have to you know have a
3:40
separate computer program that would
3:43
change a website so via web was a online
3:46
store kind of like Shopify but way back
3:48
in the day yeah it was basically like
3:50
the first SAS app ever like like PG
3:52
actually invented SAS in like 1995 it's
3:55
just that those first SAS apps kind of
3:57
suck because they didn't have XML HTTP
3:59
request and so every time you would like
4:00
click a button you would have to reload
4:02
the whole page and so it's just a shitty
4:04
experience and so it didn't really catch
4:05
on until 2005 when X XML HTP request
4:08
white spread anyway I I see this llm
4:11
thing as like actually very similar um
4:14
it's like it's a new Computing Paradigm
4:16
that makes it possible to just like do
4:17
something fundamentally different and in
4:20
2005 when cloud and mobile finally took
4:23
off there is this sort of like big open
4:25
question of like okay well this new
4:27
technology exist what should you do with
4:29
it where is the value going to acrw
4:32
where are the good opportunities for
4:33
startups I was going through the list of
4:35
like all the billion dollar companies
4:36
that were created and I kind of had this
4:38
realization that um you could kind of
4:41
bucket the the different paths that
4:43
people took into like three buckets um
4:46
there's there's a first bucket that
4:48
people started with which was like I
4:50
would call them Obviously good ideas
4:54
that could be Mass consumer products um
4:57
so that's like docs photos email
5:00
calendar chat all these things that like
5:03
we used to do on our desktop with that
5:05
obviously could be moved to the browser
5:06
and mobile and the interesting thing is
5:10
zero startups won in those categories
5:12
100% of the value flow to incumbents
5:15
right like Google Facebook Amazon they
5:17
own all all those businesses folks
5:19
forget that like Google Docs wasn't the
5:21
only company that tried to bring
5:23
Microsoft Office online there were like
5:24
30 companies that tried to bring
5:26
Microsoft Office online but they all
5:27
lost Google one then there was a second
5:30
category which was like Mass consumer
5:33
ideas that were not obvious that nobody
5:36
predicted um that's like uber instacart
5:39
door Dash
5:41
coinbase th Airbnb those ones those ones
5:44
came out of left field like the the dot
5:47
dot dot between XML HTTP request and
5:49
Airbnb is like very not obvious yeah and
5:52
so the incumbents didn't even try
5:54
competing in those spaces until it was
5:55
like too late and so startups are able
5:57
to win there and then there's a third
6:00
category which is all the B2B SAS
6:02
companies and that's like 300 of them
6:04
and so like Mo like by by by number of
6:08
logos way more billion dollar companies
6:10
were created in that third category than
6:12
the first two I think one reason why
6:14
that happened is like there is
6:16
no like Microsoft of SAS like there is
6:19
no company that somehow does like SAS
6:22
for like every vertical and every
6:23
product like for structural reasons it
6:25
seems to be the case that like they're
6:27
all different companies and that's why
6:29
there so many of them I think Salesforce
6:31
is probably like the first true SAS
6:33
company um and i' I remember Mark benov
6:37
coming to speak at YC and he tells the
6:40
story as just very early on people just
6:42
didn't believe you could build
6:43
sophisticated Enterprise applications
6:46
like over the cloud or via SAS it was
6:48
just so um there was just like a
6:50
perception issue right it was like no
6:51
like you don't you buy like your box
6:53
software and that's like the real
6:54
software that you run the way we always
6:56
do it it was it was quite contrar cuz
6:58
the early web app sucked they were like
7:00
via web where you had to be a Visionary
7:02
like PG and understand that the browser
7:03
was going to keep getting better and
7:04
that eventually it' be good which feels
7:06
like quite reminisent of today right
7:08
where it's like the yeah the same thing
7:10
like oh no like you won't be able to
7:11
build like sophisticated Enterprise
7:13
applications that use these llm or AI
7:16
tools because they hallucinate or
7:18
they're not perfect or they um they kind
7:20
of like just toys but yeah that's like
7:22
the early SAS story exactly the same and
7:25
so when I think about the parallels with
7:27
LMS I could easily imagine the same
7:30
thing happening which is that there's a
7:31
bunch of categories that are like Mass
7:33
consumer applications that are obviously
7:35
huge opportunities but probably the
7:38
incumbents will win all of those so
7:39
that's something like a per like a
7:41
general purpose AI Voice Assistant that
7:43
you you know you can ask it to do
7:45
anything and it'll like go do that thing
7:46
that's an obvious thing that should
7:47
exist but like all the big players are
7:49
going to be competing to be that thing
7:51
right W Apple's a little slow on that
7:53
one why is Siri so stupid still what
7:55
year is it it makes no sense I mean it's
7:58
like a count to that is like the very
7:59
obvious thing is search and maybe Google
8:02
will still win um on search but
8:05
perplexity is definitely give them run
8:08
for the money right yeah this is the
8:09
classic innovators dilemma at the end of
8:11
the day I mean you could argue going
8:12
back to what you said about Uber or
8:14
Airbnb these were actually really risky
8:17
things from a regulatory standpoint so
8:20
if you're Google and you have basically
8:22
a guaranteed you know giant a pot of
8:25
gold that you know sort of comes to you
8:27
every single month like why would you
8:29
endanger that pot of gold to sort of
8:31
pursue these things that uh might be
8:33
scary or might ruin the pot of gold I
8:36
think that's I think that's like
8:37
probably the primary reason why the
8:38
incumbents didn't end up building those
8:41
products and didn't even clone them even
8:42
after they got big and it was obvious
8:44
that they were going to work who will
8:45
never launch an an Uber clone they never
8:47
launch an Airbnb clone um I was
8:50
listening to this uh talk by Travis and
8:53
one of the things that he said that
8:54
really stuck with me is that in the in
8:56
the first years of uber he was very
8:58
scared that he would was going to
8:59
personally go to prison for like a long
9:01
time like he was actually personally
9:03
risking going to prison in order to
9:05
build that company and so yeah no highly
9:07
paid Google exactly was going to do that
9:09
what do you think about um why the
9:12
incumbents didn't go into B2B SAS is it
9:15
part of the reason is that a lot of the
9:17
use cases are very there's a very wide
9:20
distribution I it's a great question I
9:22
love to hear what you guys think my take
9:24
is that it's just too hard to do that
9:28
many things as a company like each B2B
9:31
SAS company really requires like the
9:33
people who are running the product in
9:34
the business to be extremely deep in one
9:37
domain and care very deeply about a lot
9:39
of really obscure issues you know like
9:42
take like Gusto for example like why
9:44
didn't Google build a Gusto competitor
9:45
well there's no one to Google who really
9:47
understands payroll and has the patience
9:48
to like deal with all the nuances of all
9:50
these like stupid payroll regulations
9:52
and like it's just like like it's just
9:55
not worth it for them it's easier for
9:57
them to just focus on like a few really
9:59
huge categories in the B2B SAS world
10:02
it's it's sort of about the unbundling
10:04
bundling of software argument that comes
10:06
up a lot as well I think and why didn't
10:09
why did all these vertical B2B SAS
10:11
products evolve versus just like Oracle
10:14
or sap or um netet yeah netu just owning
10:18
like everything um and I think it might
10:21
be Al is another thing that's
10:23
attributable to the shift to like SASS
10:25
and the internet is in the old ways of
10:28
selling software again like you had this
10:29
box software that was really like
10:31
expensive to install and you had like a
10:34
whole ecosystem around it and anytime
10:35
you wanted something custom like the
10:38
integrators would just say oh no like we
10:40
can like just build a UA custom like
10:41
payroll feature or something like that
10:44
and then Salesforce comes along with
10:46
like a SAS solution and it just seems
10:48
like it could never be as powerful or
10:50
sophisticated as like the expensive
10:52
Enterprise installation you just paid
10:54
for but they Prov that it totally was
10:56
the case and I think that just like
10:58
opened the gates for all all of these
11:00
like vertical sash solutions to emerge
11:02
doing exactly what you're saying the
11:04
other problem is that with a lot of this
11:05
enterprise software if you're a user of
11:08
Oracle and a netw suite because they're
11:10
they have to cover so much ground the
11:13
user experience is actually pretty bad
11:15
they're trying to be jack of all trades
11:17
but master of none yeah so ends up being
11:19
a bit of a kitchen sync type of
11:21
experience and this is where if you go
11:24
and build a B2B SAS vertical company you
11:27
could do literally a 10x better
11:29
experience and more delightful because
11:31
there's this Stark difference between
11:33
consumer products and Enterprise user
11:35
experience yeah well there's only uh
11:37
what three price points in software it's
11:40
uh $5 per seat $500 per seat or $55,000
11:45
per seat and uh that Maps directly to
11:48
Consumer SMB or Enterprise sales and
11:51
then I think time in Memorial has taught
11:54
us that in the past and this is less and
11:56
less true uh with new software
11:58
thankfully
12:00
but Enterprise is terrible software
12:02
because it's not the user buying it you
12:05
know some high up Mucky muuk inside
12:08
Fortune 1000 is the person who's getting
12:10
whin and D for this you know Mega seven
12:13
figure contract and you know they're
12:16
going to choose something that maybe
12:18
isn't that good actually for the end
12:20
user the person who has to actually use
12:22
the software day-to-day and um I'm sort
12:25
of curious to see how this changes with
12:28
llms actually I mean to date one of the
12:31
more Salient things that we've seen for
12:33
both SMB and enterprise software
12:35
companies is that or all software
12:37
companies all startups period is like
12:40
you know there's a sense that as Revenue
12:42
scales the number of people you have to
12:44
hire scales with it and so when you look
12:47
at unicorns uh even in today's YC
12:51
portfolio uh it's quite routine to see a
12:54
company that reached a hundred or $200
12:56
million a year in Revenue but they have
12:58
like 500 a th000 2,000 employees already
13:02
and I'm just going to be very curious
13:04
like uh even the advice that I'm
13:06
starting to give companies that are you
13:08
know a month or two out of the batch uh
13:11
it's a it's feeling a little bit
13:13
different than the kind of advice I
13:14
would give last year or two years ago in
13:17
the past you might say you know let me
13:20
find the absolute smartest person uh in
13:23
all of these other parts of the org like
13:26
customer success or sales or different
13:28
things like that
13:29
and uh I want to find someone who I've
13:31
worked with who is I know is great and
13:33
then I'm going to go sit on their you
13:35
know uh on their doorstep until they
13:37
quit their jobs and come work for me and
13:39
I want them to be someone who can you
13:41
know build a team for me hire a lot of
13:43
people that might still be true but I'm
13:45
starting to sense that uh the Met is
13:48
shifting a little bit like you actually
13:50
might want to hire more really good
13:53
software Engineers who understand large
13:56
language models uh who can actually
13:59
automate the specific things that you
14:01
need that are the bottlenecks to your
14:02
growth and so it might result in you
14:06
know a very subtle but you know
14:08
significant change in the way startups
14:10
grow their businesses sort of post-
14:11
product Market fit it means that I'm
14:14
going to build llm systems that bring
14:16
down my costs that cause me not to have
14:19
to hire a thousand people I think we're
14:21
right at the beginning of that
14:22
Revolution right now I mean we talked
14:24
about this in a previous episode we
14:26
talked about there will be a future
14:29
unicorn company that's only run if we
14:31
take it to the limit with only 10
14:33
employees that's completely plausible
14:36
and they're writing the evals and the
14:37
prompts does it I think what you're
14:39
saying is like a trend that was already
14:40
underway pre llms like I remember when I
14:43
was running triple bite for example we
14:46
needed to like build marketing or cust
14:49
like user acquisition basically um and
14:51
especially after we raised a series B
14:54
the like traditional way you were
14:55
supposed to do that is to like hire a
14:57
marketing executive and build out like
14:59
marketing team and um and just like
15:01
basically spin up this machine to do
15:03
like sales and marketing but I'd
15:06
actually met like a y founder um Mike
15:10
who was his company was basically
15:11
building like a smart frying pan sounds
15:13
like bizarre but like he was a MIT
15:15
engineer yeah you remember this um he's
15:17
an MIT engineer and to sell the smart
15:19
frying pan he had to get really really
15:21
good at understanding like paid
15:22
advertising and um uh Google ads and
15:25
just a whole bunch of stuff and so he he
15:26
taken this Engineers mindset approach to
15:28
it and remember just talking to him
15:29
about it and realizing this would be so
15:32
much better to have an MIT engineer
15:34
working on like our marketing efforts
15:37
than any of the marketing candidates
15:39
I've spoken to and he was able to like
15:41
scale us up to like we were spending
15:43
like 1. like a million dollars a month
15:45
on just marketing and various like
15:47
campaigns and triple bite had great
15:49
marketing like I remember like the Cal
15:51
trained station takeover that you did
15:53
all the like out of home stuff that you
15:55
did it was like really high quality
15:57
stuff it stuck with it you could tell
15:59
was not being done by some like VP
16:00
marketing person um and that was all mik
16:03
and like the comment I would often get
16:05
when people would ask me around that
16:07
time like how big is triple bite and we
16:08
were like 50 people and so much yeah
16:11
yeah people be like I thought this's
16:12
like hundreds of people I was like no
16:14
it's all because if you put a really
16:15
smart engineer on some of these like
16:17
tasks they just find ways to make they
16:20
find leverage and now like llms can go
16:22
even Way Beyond like The Leverage you
16:23
had which is pure software okay so
16:25
here's my pitch for 300 vertical AI
16:28
agent unicorns literally every company
16:31
that is a SAS unicorn you could imagine
16:33
there's a vertical AI unicorn equivalent
16:36
in like some new universe cuz like most
16:39
of these SAS unicorns beforehand there
16:42
were some like box software company that
16:44
was making the same thing that got
16:45
disrupted by a SAS company and you could
16:47
easily imagine the same thing happening
16:49
again where now basically every every
16:51
SAS company builds some software that
16:53
some group of people use the vertical AI
16:55
equivalent is just going to be the
16:57
software plus the people in one product
17:00
one thing might be just Enterprises in
17:01
general right now are a little unsure
17:03
about what exactly they like what agents
17:05
they need and one approach I've seen
17:07
from especially more experienced
17:08
Founders like um Brett Taylor the CTO of
17:11
Facebook started his company Sierra I
17:13
don't know all the details but as far as
17:15
I can tell it's essentially more like
17:17
broadly about letting Enterprises like
17:20
deploy these AI agents and spinning them
17:23
up like custom for the Enterprise versus
17:26
like oh hey we have like this specific
17:27
agent to do this it's something I've
17:29
seen from one of my companies called um
17:31
uh Vector shift that funded about a year
17:33
ago they're two really smart like
17:36
Harvard computer scientists and it's a
17:39
that what they found is that they're
17:41
trying to build a platform to make it
17:42
easy for Enterprises to build their own
17:44
like use like no code or sdks to build
17:47
their own like um internal llm powered
17:50
agents but like Enterprises often don't
17:53
know exactly what they want to use these
17:54
things for and so bring it back I wonder
17:57
if like in like the box software world
17:59
you started off with just like a few
18:01
vendors who just basically were trying
18:02
to convince people to use software at
18:04
all and it was just like it does
18:06
everything um and then it gets more
18:08
sophisticated and higher resolution and
18:09
you get lots of like vertical SS players
18:12
we go through that same period with llms
18:14
where the early winners might just be
18:16
these like general purpose hey like we
18:18
like make it easy for you to do llm
18:20
stuff and then it the vertical agents
18:22
will come in over time or do you think
18:25
there's reasons it's different now and
18:26
the vertical agents will take off on day
18:28
one
18:29
yeah that's interesting because if you
18:30
think about the history of SAS the
18:32
consumer things worked first like 2005
18:35
to 2010 was mostly consumer applications
18:38
like email and chat and maps and people
18:41
got people as individuals got used to
18:43
using these tools themselves and I think
18:45
that made it easier to sell SAS tools to
18:48
companies because you know the same
18:49
people are both employees and consumers
18:51
yeah I I think the answer might just be
18:53
like this is this is all just a
18:54
continuation of software and just
18:58
there's no reason it has to reset back
19:00
like llms don't have to reset back to a
19:02
few general purpose like Enterprise llm
19:05
platforms doing everything because
19:07
Enterprises have already been trained on
19:08
like the value of Point Solutions and
19:11
vertical Solutions um and like the user
19:14
experience not going to be that
19:15
different these things will just be a
19:16
lot more powerful and so if Enterprises
19:19
have already built the muscle of
19:21
believing that like startups or vertical
19:23
Solutions can be better than like Legacy
19:25
broad platforms they are probably going
19:28
to be willing to take a bet on a startup
19:31
promising a very good vertical AI agent
19:33
solution today and I feel like we're all
19:34
seeing that in the batch now with some
19:36
of our companies are getting faster
19:39
Traction in Enterprises for these
19:41
vertical AI agents than like we've ever
19:44
seen before I think we're just early in
19:45
the game right like all software sort of
19:47
starts quite vertical and then as the
19:50
industries actually get much more
19:52
developed um then I mean I just answered
19:56
my earlier question it's like you know
19:58
why does company end up having a
19:59
thousand employees it's actually that uh
20:02
you know early early in the game
20:04
everyone's making these specific point
20:06
Solutions and then at some point you've
20:08
got to go horizontal like you're already
20:11
doing this crazy spend on sales and
20:13
marketing and then the only way you can
20:15
actually continue to grow once you sort
20:18
of get 100% or you know some large
20:20
majority of the market is you actually
20:22
have to do like not just a point
20:25
solution but things that sort of work
20:27
together the other point of why the bull
20:32
case for vertical AI agents could be
20:33
even bigger than SAS is that SAS you
20:36
still needed a operations team or set of
20:39
people to operate the software in order
20:41
to get all the workflows to be done I
20:43
don't know approval workflows or you
20:44
have to input the data the argument here
20:47
is that you will get not only replacing
20:50
all that set of SAS software so that
20:52
would be like one to one mapping but is
20:54
also going to eat all of the a lot of
20:56
the payroll because we look a lot of the
20:58
spend for companies big chunk is still a
21:00
payroll and software's Tiny exactly they
21:02
spend way more on employees than they do
21:04
on software so it'll be these smaller
21:06
companies that way more efficient that
21:07
need way less humans to do random data
21:11
entry or approvals or click the software
21:14
I agree I think it's very possible the
21:16
vertical equivalence will be 10 times as
21:18
large as the SAS company that they are
21:20
disrupting I mean there there's two case
21:22
it could be that the vertical Point
21:24
solution could be just big enough and
21:26
you don't need to do that bro breath
21:28
thing right it that could be a nice
21:30
scenario should we give some examples I
21:32
feel like we've all been working with so
21:34
many vertical AI agent companies we've
21:37
got like news from the
21:38
front how it's actually going well your
21:41
former uh head of product Aaron Cannon
21:44
is working on a YC company called outset
21:46
that I worked with and uh basically
21:48
they're taking llms uh to the surveys
21:51
and qual Trix space so qual Trix is
21:54
almost certainly not really going to
21:56
build the best of breed uh large
21:58
language model with reasoning and then
22:00
the funny thing about surveys is you
22:02
know who's it actually for it's for
22:04
people who run products for marketing
22:06
teams it's for people who are trying to
22:08
make sense of like what do our customers
22:10
actually want and what are surveys like
22:12
guess what that's language so um and
22:15
then I feel like these types of
22:18
businesses um actually have to thread
22:20
this needle um because Enterprise and
22:23
SMB software often is sold based on a
22:27
particular person who who is the key
22:29
decision maker and um you have to go
22:32
high enough in the organization so that
22:34
the people you're selling to are not
22:36
afraid that their whole their job Andor
22:38
their whole team's job is going to go
22:40
away totally that's kind of the move
22:42
that I seen that a lot of companies that
22:45
sell need to do because if you're going
22:46
to go and sell to the team that's going
22:48
to get replaced by AI they're going to
22:50
sabotage it man it just does not work so
22:53
I think this is an interesting way that
22:56
a lot of these are top down and you have
22:58
to go through at some point even get the
22:59
CEO to sign off on it a company I'm
23:02
working with u MCH that's sort of
23:04
essentially an AI agent but for at least
23:06
where they're starting is like QA
23:08
testing um they're getting really great
23:11
traction right now and it's interesting
23:13
because you remember a decade ago um why
23:15
can't we worked with rainforest QA like
23:17
rainforest was a QA as a service company
23:21
and that they had this exact tension of
23:24
where they couldn't actually replace
23:26
your QA team and so they needed to build
23:29
software that made the QA te more like
23:31
efficient but really that obviously
23:33
meant trying to replace as many of them
23:34
as possible they couldn't replace the
23:35
whole team and so they were always on
23:39
the sort of like tight rope between
23:40
trying to sell the software to like the
23:42
head of engineering as like this will
23:43
mean you'll need less QA people and
23:47
great but then you also have to go sell
23:48
that to the QA team who don't want to be
23:49
replaced and so I think that was always
23:51
like a friction for that business for
23:53
how it could like scale and grow but now
23:56
like mtic with AI can actually just
23:59
replace the QA people so their pitch is
24:01
not oh this like makes your QA people
24:02
faster it's like this just means you
24:04
don't need a QA team at all so they can
24:06
just focus the sell onto like
24:07
engineering and Engineering doesn't need
24:09
buying from QA at this point and you can
24:12
also go in I mean to start with you can
24:14
go and sell to companies that don't even
24:16
have big QA teams at the moment they
24:18
just use something like mtic and then it
24:19
will just like keep scaling with them
24:21
scaling and they'll just never build a
24:22
QA team ever yes that is a real life
24:25
case study of what Diana was saying
24:26
about why these vertical AI agent
24:28
companies going to be 10 times as big as
24:30
the SAS companies yeah I'm seeing this
24:31
interesting now um like in recruiting
24:33
too I had this exact same issue with
24:35
triple vet where to build the software
24:38
um to build software that makes it easy
24:40
to like screen and hire software
24:41
Engineers you need buying from both the
24:43
engineering team that they're joining
24:45
but also the recruiting team and
24:47
effectively the software we were
24:48
building was trying to replace the
24:49
recruiters but we couldn't completely
24:51
replace the recruiters but now with NYC
24:54
and so the recruiters were always like
24:57
oppos
24:59
opposing it CU it was a threat to them
25:01
yeah so it just always like friction on
25:03
like how um on like how far you can get
25:06
when the customer you're trying to sell
25:08
to is worried about being replaced um
25:11
but yeah I think now it's still early
25:13
days but now with AI you can build
25:15
things that do the whole stack like of
25:18
recruiting we have a company we worked
25:20
with last batch like Nico work with them
25:22
a priora which is actually just doing
25:23
like the full like technical screen the
25:25
full initial recruiter screen and
25:27
getting great traction so I think as
25:29
those things keep going like they won't
25:31
have they have the same thing you won't
25:32
have the friction of oh I need to
25:33
convince recruiters to use this you're
25:35
probably just like not build a
25:37
recruiting team in the same way that you
25:39
used to I mean other example is even for
25:43
de tool companies they have to do a lot
25:46
of a developer support and I work with
25:49
this company called cap. AI that
25:51
basically buildt one of the best chat
25:54
Bots that responds to a lot of the a lot
25:58
of the technical details that are hard
26:01
to answer and I think a lot of the
26:03
companies that started using them they
26:05
actually ended up having Dev rail teams
26:08
that are a lot smaller because it
26:10
ingests a lot of uh the developer
26:12
documentations even the YouTube videos
26:14
that Dev tools put up and even a lot of
26:17
the chat history so it just keeps
26:19
getting better and better and it's like
26:22
gives really good answers actually it's
26:23
one one of the best I've seen yeah I
26:26
also worked with a customer support like
26:28
an AI customer support agent company
26:30
called Power help well actually we both
26:32
did um last batch and I learned a couple
26:36
interesting things from parel um the
26:39
first is customer like AI agents for
26:42
customer support was like the category
26:45
that's like famously crowded where
26:46
there's like supposedly like you know a
26:48
100 of them and if you go and you Google
26:49
like AI customer support agent you'll
26:52
get like a 100 results on Google um but
26:54
what I learned through working with
26:55
parel is like it's actually kind of
26:57
like like almost all of those
27:00
companies are doing very simple like
27:03
zero shot llm prompting that can't
27:05
actually replace a real customer support
27:07
team that does a lot of really
27:08
complicated workflows it just kind of
27:10
makes for like a nice demo like to
27:12
actually replace a customer support team
27:14
for like an at Scale company that has
27:16
like 100 customer support reps that do
27:18
lots of complicated things every day you
27:19
need like really complicated software
27:21
that does all the stuff that like Jake
27:22
heler was talking about and there's
27:24
there were only like three or four
27:26
companies that were even attempting to
27:27
do that
27:28
and cumulative they had cumulatively
27:30
they had like less than 1% Market
27:32
penetration and so the market was just
27:33
completely open I could also see that
27:35
being another case of um hyper
27:37
specialization or hyper verticalization
27:40
like there's not going to be I mean
27:42
maybe eventually there could be a single
27:45
general purpose customer support agent
27:47
software company but we're like in in
27:50
you know that that'll be like a eighth
27:52
or ninth inning kind of thing and we're
27:54
literally in the first inning so you
27:56
know instead you know you're going to
27:58
have companies like gig ml that you know
28:00
it's doing it for zepto doing 30,000
28:03
tickets uh every single day and
28:06
replacing a team of a thousand people
28:08
and but it's very specific and it has
28:11
you know it's not a general purpose demo
28:14
Weare kind of thing like it's 10,000
28:16
test cases in a very detailed uh eval
28:19
set that you know is basically just for
28:22
zepto and things like zepto yeah uh but
28:25
if you are you know any of the other
28:28
Marketplace companies you're probably
28:30
going to use it cuz like that's a very
28:32
well-defined kind of marketplace that's
28:34
you know instant delivery Marketplace I
28:36
think this is the kind of dynamic that
28:38
led there to be like $300 billion do SAS
28:40
companies rather than like one like1
28:42
trillion do like meta SAS thing that
28:44
provides all the software for the world
28:45
it's just like the customers just
28:47
require really heavily like tailored
28:50
Solutions and it's hard to build one
28:51
that like works for every everyone
28:53
exactly I mean we already gave three
28:54
examples of customer support but there
28:55
are very different verticals it's like
28:57
de tool comp need very different kind of
28:59
support that you need and the training
29:01
set to marketplaces very different right
29:04
yeah I guess whether you have agents or
29:06
real human beings working for you you
29:08
end up with the same problem which is
29:10
every company bumps up against CO's
29:13
theory of the firm which says that any
29:15
given firm will grow only so much to the
29:18
point where it uh becomes inefficient to
29:21
be larger than that and then that's why
29:23
they sort of networks and ecosystems and
29:27
you know a full blown economy you know
29:29
like every firm will sort of specialize
29:31
to do what it is particularly good at
29:34
and then the limits the outer limits of
29:36
what those firms can be it's actually
29:38
based on uh your ability as a manager so
29:42
yeah that that part a little bit breaks
29:44
my brain because you know when we spend
29:46
time with Parker Conrad at ripling uh
29:49
one of his favorite points is actually
29:50
well you know everyone's very obsessed
29:52
with with the fact that the rocks can
29:55
talk and you know maybe they can draw
29:57
but the more interesting thing for him
29:59
you know running HR It software that uh
30:02
you know he spends a lot of time
30:04
thinking about HR like actually the
30:05
coolest thing about the LMS is that the
30:07
rocks can read and from his perspective
30:11
like you he's I think he has 3,000
30:13
employees he still runs payroll for all
30:16
3,000 employees through Rippling so I
30:19
think he spends a lot of time thinking
30:20
about like how can one person extend
30:22
their ability as a manager and uh I
30:26
think we're going to see a lot more
30:27
there
30:28
that would be an a reverse argument that
30:31
if we're at this moment where uh tools
30:34
for managers and CEOs are going to get
30:37
much more powerful um oh it could it
30:40
could it could increase the scale of the
30:42
firm that you can run right and that's
30:44
certainly what ripling is trying to do
30:45
like he's attempting to build this like
30:47
Suite of HR tools where if he wins he's
30:49
going to eat a whole bunch of billion
30:51
dollar SAS companies and like one one
30:53
giant company it's very interesting
30:54
point Gary I think what made me think
30:57
about this is that with having all these
30:59
AI SAS tools it's going to give the
31:03
ability to all these leaders and all
31:06
these Orcs to basically open the
31:08
aperture of the context window of how
31:10
much information they can parse because
31:12
there a limit of how much uh humans we
31:14
can have meaningful relationship there's
31:16
like the whole thing with the D Mar
31:17
number it's about 300 people that you
31:20
150 that you can have a meaningful
31:22
relationship with but with AI because
31:25
all of these rocks now can read I think
31:27
think we will be able to extend that
31:30
dumbar limit that we have yeah I think
31:32
uh Flo crell had this interesting post
31:35
on Twitter that went viral around um I
31:38
think someone had made a voice chat like
31:41
just weekend project as a CEO but it
31:44
would call uh all
31:46
1,500 of their employees yeah and uh you
31:50
know it was you know very short call
31:52
like kind of sounded like it was from
31:53
the CEO just asking kind of personally I
31:56
mean it sort of reminds of um that scene
31:59
in her where it zooms out and uh
32:02
actually you know you're following the
32:04
experience of one person using the her
32:06
OS but actually that her OSS is actually
32:09
speaking to 15 you know thousands or
32:11
tens of thousands of people all at one
32:13
time how many others
32:16
8,316 yeah I mean large language models
32:19
can talk and can have conversations and
32:21
then to what extent can uh you know this
32:25
power actually extend the capability of
32:28
one or a few people to uh understand
32:31
what's going on I I heard about that yuk
32:33
it got it definitely got me thinking
32:34
because as understood the product is
32:36
something like it just it will call up
32:37
all your employees and then your
32:39
employees can just like ramble about
32:41
what they've been doing and it will just
32:43
extract the meaning out of it and give
32:45
the CEO of like a like bullet point
32:46
summary of here the most important stuff
32:48
and there were a bunch of like SAS
32:50
companies that attempted to do these
32:51
sort of like weekly pulse Pulses from
32:54
employees using like traditional SAS
32:56
software but like that version is is
32:58
literally a 100 times better than the
33:00
pre- elm version of this idea but I
33:02
wonder with like that particular tool um
33:07
just like it's not it's going Beyond
33:08
just like reading and summarizing like
33:11
this this is the argument of like if
33:12
writing is thinking then like there's
33:14
actually just a huge amount
33:16
of work that's involved in the effort of
33:19
figuring out like who's an effective
33:20
communicator and like what are the most
33:22
important things to be like what what
33:24
are the key things to be focused on as a
33:25
company and I just wonder if that at
33:28
some point do the llms do like they go
33:30
beyond just like summarizing and reading
33:31
and doing actual thinking at which point
33:33
like who's actually running the the
33:37
organization an interesting
33:39
thought I guess the other thing that's
33:41
kind of interesting about how Parker
33:43
Conrad's thinking about it is um I found
33:45
out about this recently off a an
33:47
interview with Matt mcginness his coo
33:49
that uh there are more than a hundred
33:51
Founders who work at ripling now as sort
33:54
of specific people who run like an
33:57
entire SAS vertical inside Rippling it's
34:00
super cool the way he's built the team
34:02
har probably knows a lot about it
34:03
because you've done a bunch of
34:04
interviews with him um yeah I mean it's
34:06
definitely very focused on uh recruiting
34:10
Founders and I mean Parker like Rippling
34:14
is essentially the the case against
34:17
vertical like verticalization trying to
34:20
uh
34:26
horizontalization like lots of value and
34:29
he wants to recruit Founders and teams
34:31
that build on top of the platform like
34:33
it's almost a little bit more sort of
34:34
like amazones whereas like shared
34:36
infrastructure um yeah I think every
34:39
product that they've released I mean
34:40
things like time tracking and whatnot I
34:42
mean basically they launch a thing and
34:45
it hits like multi-millions of dollars
34:46
in ARR on day one of launching and
34:49
that's exactly what we were talking
34:50
about earlier like once you once you
34:53
have a vertical once you have a tow hold
34:55
what you're saying is Well I have to
34:56
spend this money on sales and marketing
34:58
anyway can I uh you know basically get
35:01
higher LTV and hold my CAC constant and
35:05
uh that's sort of what you if you look
35:06
at all the top uh software companies
35:09
today it's like that's what Oracle is
35:10
that's what Microsoft is that's what
35:12
Salesforce is ripling knock on wood
35:14
going to be the next but um it's it's an
35:17
interesting alternative to uh going from
35:20
zero to one totally on your own do you
35:22
guys want to talk about some of the
35:23
voice companies that we have I think
35:25
that's like an interesting like sub
35:27
category of this of this stuff is like
35:30
really blowing up now I have a company
35:32
that I work with called Salient that
35:35
basically
35:37
does AI voice calling to automate a lot
35:40
of that collection in the auto Ling
35:42
space which tradition so they like call
35:44
up people and they're like hey you owe
35:46
$1,000 on your car yeah which actually
35:50
up with that actually this kind of job
35:52
is one of those butter passing job it
35:54
kind of sucks because a lot of uh these
35:57
low wage workers work in all these call
35:59
centers and it's like a terrible boring
36:01
job so very high churn and giant
36:04
headcount to run these because there's
36:05
just so many accounts with these banks
36:07
that have to do that and this is a
36:10
perfect task that AI could automate and
36:14
what Salient has done is has been able
36:17
to actually get very very accurate and
36:19
it has been going live with a lot of big
36:21
Banks which is super exciting and this
36:23
was a company from last year and
36:25
demonstrating that that part of it that
36:28
they were able to get in because they
36:29
sold through top down I guess the space
36:32
feels like it's moving very quickly and
36:33
that we have incredible companies that
36:35
are voice infra companies like vapy and
36:38
then people can sort of get started
36:40
right away and Retail also I mean these
36:43
companies that have reached pretty fast
36:45
scale just because it's one of the more
36:48
exciting like mindblowing things that
36:50
you can get up and running within I mean
36:53
literally the course of hours um and
36:56
then some of the question that you know
36:58
remains unanswered and we hope they
37:00
figure it out is how do you hold on to
37:02
them especially as you uh run into
37:04
things like the new open AI voice apis
37:08
um you know do you go direct like you
37:11
Pro it's probably way more work to try
37:13
to use the underlying apis off the bat
37:16
but these uh platforms are clearly low
37:19
bar and then the question is can you
37:21
keep raising the ceiling so that you can
37:23
hold on to customers forever har you
37:25
were making an interesting point earlier
37:27
about like how the apps that people have
37:29
built on top of LMS has changed from
37:31
like early 2023 when it started until
37:34
now voice which we were just talking
37:35
about as a great example of this I think
37:36
even if you went 6 months back it felt
37:38
like the voices were not realistic
37:41
enough yet the latency was too high like
37:43
there was it felt like we were probably
37:45
a ways off having AI voice apps that
37:48
could meaningfully like replace like
37:51
humans calling people up and like here
37:53
we are and yeah I was just zooming out
37:57
thinking back to the first YC batch
38:00
where llm powered apps first came in was
38:03
probably winter
38:05
2023 you know almost 2 years ago now and
38:08
the apps were essentially just things
38:11
that spat out some text and not even
38:13
like perfect T Rockit talk that's about
38:15
it yeah sort of more like copy editing
38:17
marketing edit email edits it was just a
38:20
kind of more like just like incremental
38:22
yeah like I I had a company I mean the
38:24
one that sticks in my head is a company
38:26
Speedy brand and all what they did is
38:28
make it very easy for like a small
38:30
business to just generate a Blog and
38:32
spit out content marketing um it's like
38:35
very obvious idea and it wasn't perfect
38:37
but it was pretty cool at the time and
38:40
that's what we've talked about a bunch
38:41
of the show but that's like the chat gbt
38:43
raer turned out around that time hey
38:45
like this is what an llm app looks like
38:47
it's just a chat GPT rapper it does very
38:49
basic spits out some text like it's
38:52
going to get crushed by openi in the
38:53
next release like and it did yeah well I
38:56
I I don't know if that one did but but
38:58
the that that first that first wave of
39:01
llm apps mostly did get crushed by the
39:02
next wave of GPT I feel like we've had
39:06
this sort of boiling of the Frog effect
39:07
where from our perspect it's sort of
39:09
like every three months things have just
39:11
kept getting progressively better and
39:13
now we're at this point where we're
39:14
talking about like full-on vertical AI
39:17
agents that are going to replace entire
39:19
teams and functions and Enterprises um
39:22
and just that progression is still
39:23
mindblowing to me like with two years in
39:26
which is still relatively early and the
39:28
rate of progress is just like unlike
39:30
anything we've seen before and I think
39:33
what's interesting to see is we
39:34
discussed this in the last episode is a
39:37
lot of the foundation models are kind of
39:39
coming head-to-head there used to be
39:41
only one player in town with open AI but
39:43
we've been seeing in the last batch this
39:46
has been changing Claude is a huge
39:48
Contender thank God it's like
39:51
competition is you know the the soil for
39:53
a very fertile Marketplace ecosystem uh
39:57
for which consumers will have choice and
40:00
uh Founders have a shot and that's the
40:02
world I want to live in so people are
40:04
watching and thinking about starting a
40:06
startup or maybe have already started
40:08
and uh they're hearing all of this how
40:11
do you know what the right vertical is
40:13
for you you got to find
40:16
some boring repeative admin work
40:19
somewhere and that seems to be like the
40:21
common threat across all of the stuff is
40:23
if you can find a boring repetitive
40:26
admin task um there is likely going to
40:29
be a billion dollar AI agent startup if
40:33
you keep digging deep enough into it but
40:35
it sounds like you should go after
40:37
something that you directly have some
40:39
sort of experience or relationship to
40:42
that is a common like there there's
40:44
definitely a Common Thread I've seen in
40:45
the companies that are that I'm seeing
40:47
promis with and another one just pops
40:49
into my head sweet spot I think I
40:50
mentioned on this before like they're
40:52
basically building an AI agent to bid on
40:54
government contracts and the way they
40:56
found that idea and a year ago was they
40:58
just had a friend whose full-time job
40:59
was to sit there on like a government
41:01
website like refreshing the page like
41:03
looking for new proposals to bid on and
41:05
they they were pivoting they're like ah
41:07
like that seems like something an llm
41:08
could do um a company from a recent
41:10
batch which pivoted into a new idea
41:12
that's getting great traction like
41:13
they're basically building an AI agent
41:15
to do um process like medical billing
41:17
for dental clinics and the way they
41:18
found the idea was um one of the
41:21
founders mother is a dentist and so he
41:22
just decided to go to work with her for
41:24
a day and just sit there seeing what she
41:25
did and she's like oh like all of that
41:27
like processing claim seems like really
41:29
boring like an llm should totally be
41:31
able to do that and he just started
41:32
writing software for like his mother's
41:34
dental clinic so I guess I mean in
41:36
robotics the classic Maxim is uh you the
41:40
robots that are going to be profitable
41:41
and that are going to work are going to
41:42
be um dirty and dangerous jobs and in
41:47
this case for vertical SAS look for
41:50
boring butter passing
41:53
jobs well with that we're out of time
41:56
for today we'll catch you on the light
41:58
cone next time
42:01
[Music]
— end of transcript —
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