Good News For Startups: Enterprise Is Bad At AI

engineering teams at these orgs are

filled with people that themselves don't

actually really believe in AI, don't use

codegen tools, think it's all super

overhyped, are really excited when an

MIT study comes out saying that it's all

like hype and retweeted and and really

want cuz it's a narrative they want to

believe. But the consequence of that for

the companies is that they can't build

the product. So if your engineers don't

believe in this, then how are you going

to build a product that actually works?

The knock on effect for startups then is

if you can actually build something that

works, the enterprises will talk to you

because they have no other options.

can't build it internally, can't go to

an established company. Um, so the

startups are actually getting like the

shot that they never had before.

>> I guarantee you someone who's watching

this right now and uh you've just

horribly triggered them.

[Music]

Welcome back to another episode of the

light cone. One of the things that has

been really pissing me off is these AI

influencers. You see them on X, you see

them on YouTube, and they're claiming

that 95% of AI projects are failures.

And that's proof that AI is a scam.

What's the real story, Jared? You

actually dug in to the MIT report that

these people are grifting with. What

does the report actually say?

>> What really went viral was like tweets

about this study. And I think the tweets

are actually quite misleading. Diane and

I were talking to a bunch of college

students recently and they had concluded

just by reading like the tweet version

of the study that like oh all these AI

startups that YC is talking about like

must not be working because the study

says that they all fail. But actually

the more I read the study, the more I

realized it was actually confirming a

lot of the things we've talked about

here on this podcast about what AI

agents are really like in the real world

and what approaches and categories are

working. And so I thought it'd be

interesting for us to talk about what

the study really says

>> because it's a very different approach

to the go to market for all these AI

solutions is not just standard

enterprise sales. I think one of the big

things that we talk a lot about is this

aspect of um teams, startup and founders

embedding themselves into the business

processes and really groing a lot of the

internal systems of record and going

deep deep deep in the integration which

is not something that has been typically

done in the SAS world. SL. So it's like

very plug and play which is different

but when you do succeed and plug into

the systems of record the pot of gold is

actually quite big but it does take a

long time. We actually have a lot of

examples and work with companies that

have succeeded which we can talk about

later.

>> You had a really great way of like

having like a mental model of like what

typically happens when an enterprise

tries to adopt AI and like why the

failure rate is so high. Can you give

people some intuition? Yeah, if you

think about uh enterprises are trying to

get something done and they've got uh

internal IT or sometimes when internal

IT doesn't do it, they go out and they

get an Ernstston Young or they get some

much bigger consulting shop, a Deote to

come in. And uh it turns out if anyone

has ever used internal IT systems,

generally internal IT systems are bad.

And then not only that, if you decide

that, you know, you can't build it

in-house and you have to go to

consulting, well, now you've got two

problems. The output of the study is no

surprise to me in that the majority of

software that actually gets built in the

world is very, very bad. To be fair to

IT consultants, um, Apple is very bad at

software. You know, my favorite example

is Apple of the company that can have

infinite access to capital and infinite

access to the smartest people in the

world. All of us use uh iPhones and uh I

use the calendar app. I think you guys

do too. We use it many times per day due

to our schedules and uh even the

calendar app is a piece of trash. Like

how you know you probably run into some

sort of weird bug in that like almost

every single day. So Apple, a company

with infinite resources and infinite

access to the smartest people in the

world, cannot make a good calendar app.

So you know, if that's true for Apple,

how could any normal company, let alone

an internal IT system, let alone like

Deote or Ernston Young, like very

well-meaning people, but like you know,

most of the time the output of something

like that is bad. I think a lot of what

goes on is that in the big enterprises

to really deploy sophisticated software

it usually has to be used by multiple

teams across the org. And so big

enterprises like there's just always

going to be like political battles and

turf wars and various things going on.

And so part of the reason I think these

enterprises go to consultants is like

you can go to an Ernston Young and get

them to like meet with like the data

science team, the customer support team,

the like IT team and like write up a

bunch of docs about what everyone wants

and sort of almost play like some sort

of mediator role of hey like here's kind

of what we're aligned on and here's like

the spec that will work for everyone.

The challenge is like then you actually

have which I think is valuable but then

the next step is to actually like

implement that and at which point the

consultants don't have like the

technical expertise to build the

software and then often in the

enterprise even if they have an internal

software team like the systems are just

like so old and like siloed that you

actually need both like the external

consultancy expertise to bring everyone

together but then also the software

expertise to actually build the systems.

And is the thing that you end up with at

the end like basically like a camel, you

know, a horse designed by a Yeah.

committee.

>> I mean, I think Jared and I actually um

now summer 2020, a while ago worked with

a company called Tactile that's building

sort of like a high level like a

business decision engine for banks in

particular. So it does things like in

real time can help them go through like

uh KYC and AML to figure out um someone

who's applied for a loan for example and

then instantly figure out oh yeah like

does this person have the right credit?

Do they make the right business rules

and do that like you know millions of

times per day at scale? The banks

themselves like City Bank and JP Morgan

have tried to build this kind of

software themselves and it's in each

case it's taken years 3 to 5 years and

tens of millions of dollars to actually

get this implemented whereas Tactile was

able to build a REST API that makes

decisions in real time. You can plug the

latest AI models into it. uh and they've

been able to do that all for a fraction

of the budget and in way less time.

>> And there's a company that I worked with

called Greenlight that also sells AI

systems to banks. And they were telling

me a story that is exactly along the

lines of what Gary was talking about

where there is a bank that they were

trying to sell to and the deal fell

through because the bank had an existing

relationship with Ernston Young, who

apparently builds all the software for

the bank, which is apparently not that

uncommon. And they're like, well, you

know, we trust our vendor, Ernston

Young. We've been working with them for

years. They say that they're going to

build this AI system

>> and that's where they got it all wrong.

>> Yeah. And so Ernston Young spends a year

trying to build this AI system. It

doesn't work at all. And the bank comes

back to Greenlight is like, "Uh,

actually, could you guys build this for

us?" And Greenlight now has their system

like fully deployed at the bank and it's

actually working. An interesting thing

about the report is that of the projects

that they surveyed, twothirds of them

were projects where the enterprise

built an internal software project or

did it with the help of a consulting

agency and only one-third was ones where

they bought a product from an outside

agency like a green light. And so

enterprises are mostly trying to build

things inhouse, but the success rate of

the ones where the enterprise went with

an outside vendor like a green light or

a tactile was much higher than the

success rate of when they tried to build

stuff themselves. I mean, why do you

think this is like, you know, I

certainly have my theories, but you

know, going back to the Apple thing, my

sense is that there actually just aren't

that many people who uh are polymaths

enough to be good at product and good at

engineering to make things that actually

work. Like there are lots of people out

there who are really really good

engineers, but you know, maybe they're

just in like the coding cave all day and

they can't relate to, you know, someone

working at a bank cuz they just like

don't step outside of their coding cave.

And then way over on the other end like

you know that sort of goes back to the

user like why doesn't the user just do

this? there's some evidence that maybe

they will you know there are all these

examples of uh people like you know

Verun Mohan from Windsurf mentioned that

back when he was uh working on that with

his sales leader who might not have a

engineering degree they used windsurf to

create their own tools. So way out at

like the 150 IQ type uh organizations in

the world this is already happening but

for now like a lot of the people who

really understand the domain they can't

code or they don't understand tech or

they can't you know do design or product

and ship it. So, you know, for now

there's just this startupshaped hole in

basically uh every

process or every sort of annoying system

that should exist that doesn't exist

yet. It's a very rare breed of skill

sets where they have a lot of the

extreme up-to-date latest and greatest

AI understanding and product taste and

at the same time to some extent a lot of

the kind of humanity in a sense to

understand all the human processes to

then gro those into into a product and I

think there's a different permutation of

what you both have mentioned around uh

companies going after consultants as a

solution as vendor. This is company that

Jared and I were good partners with

Castle AI. They're also selling to banks

as a this a theme here.

>> They're basically building a AI mortgage

serer. There's a lot of uh vendors

around that have been around for like

decades with very old system and they're

catching on as well. They know that

their lunch is going to get eaten these

vendors and they are adding AI on top of

it. And what happens when castle goes

into all these sales conversations with

the banks? they have to do a bake off

with the current incumbent solution. And

turns out what they learn the the banks

still do it because they trust the

vendor. They've been around with them

for a long time. Not not just a

consultant but a regular old school

software system. And funny thing is a

lot of times these products are very

subpar. The particular customers they

work with, they like closed off the bake

off because it's like wow the this

vendor solution was just AI slapped on

top of it. So there's such thing as like

it could be AI on the vendor side and

they add it but is lacking this aspect

of being really native from the

beginning and having that really good

taste on the product. And this is how

Castle has closed some of these large

banks which is impressive just one year

after the batch now.

>> YC's next batch is now taking

applications. Got a startup in you?

Apply at y combinator.com/apply.

It's never too early and filling out the

app will level up your idea. Okay, back

to the video.

>> And Nana, you have another example of a

company,

>> speaking of Apple, that actually sold to

a fan company

>> that Oh, yeah.

>> had tried to implement an internal

solution and had it not work. Maybe you

can tell us about that.

>> This is a very impressive case study.

The company is called Reduct. They just

announced their series B recently and

they actually closed a fan company 154

days after the badge which is I haven't

seen that happen. And this big fan

company reached out to them because they

did a YC launch.

>> That's how they found them. So our

launches

>> get people watching them

>> and they reached out. It's like oh this

is interesting. We'll love to try it and

we've been working on a solution. Turns

out this particular company has been

trying to what what Redux does is uh

document processing for AI and this

company has been having a lot of

internal systems and build internal

solutions for years to run a lot of

their operations and a lot of the

solution they try they tried open source

they tried AWS tesarak all sorts of OCR

solutions and they were not cutting the

mark and this is where product

excellence really got reducted to win

the deal and be a pretty big one. But

the thing about this one that still took

time to go through it. I mean so

reductor had to compete with internal

team and they had to have a lot of

finesse to navigate a lot of the

politics which is actually one of the

aspects that the MIT paper does talk. So

we do agree with that. There's still a

lot of work to get there and but they

got it done and it's still like hard but

at the end of it they do have this

awesome deal with them and they've been

live in production for more than a year

or two now.

>> Yeah. What was the secret to avoid uh

pissing off the wrong people, you know,

still be in the running and uh

eventually win?

>> This is where you do things that don't

scale. One of the things that they did

is they became really good friends with

the champion and really building

friendship with them and they saw that

oh there's these really smart kids and I

want to take a shot on them and is uh

this is what I think a lot of the story

around YC founders selling to big

enterprises. I think there's something

about this ambition and really optimism

from founders that is contagious that

really gets people excited. is like this

is a bit of a boring problem to like

process documents but you're like super

jazz about it and I'll give you a shot

and then when they do they surpass a lot

of the expectations and it's cool.

>> I've heard it's it's actually a sort of

a particular archetype of big company

employee. It's someone that really wants

to do a startup or has always sort of

had dreams of a startup but it's not

they're not actually ever going to do

it. They're too riskaverse and so they

can kind of live vicariously through an

exciting startup with founders that they

get along with. And if you find someone

like that to be your champion, it's like

they want you to succeed because they're

going to feel like they're on the

startup journey as well.

>> I think so. I think it's finding more

people like that that want to nurture

that inner child that had this dream of

startups but they didn't get to do it.

>> What's funny is this is a good example

of uh when you meet uh especially young

founders often they try to like dress up

like they'll just dress up in a suit and

they like copy Microsoft's homepage or

something and they shouldn't do that.

like they should just try to be a little

bit more authentic. Like it's actually

fine to be a startup. Like it's

important to come off as smart and with

it, but you do not need to copy the

formalism of, you know, sort of wearing

a suit or the equivalent of that in like

your interactions with people.

>> Another good tactic is to find uh

founders whose companies were acquired

by big companies and get them to be your

champion. With Triple Bite, we

essentially we were able to work with

Apple and there were like almost no

recruiting companies working with Apple

and that was all because of a a YC

company Q started by Robbie Walker and

Danny Gross actually um that had been

acquired by Apple and then they helped

us get in there and then I actually I

remember we got like a pilot with Oracle

through a founder who had sold his

company to Oracle and was just pushing

for them to hire better engineers and

helped us through procurement and gave

us all the internal politics and

step-by-step playbook to the pilot

going.

>> I think that's a special thing about

being here in Silicon Valley is this uh

pay it forward aspect that I think it

you cannot measure in a study.

>> One of the other interesting things from

from from this paper that's also I think

like a very optimistic point that got

lost in the in the like tweet version of

this paper is that like there's

overwhelming demand from enterprises to

adopt AI and they're way more willing to

take bets on new startups. So, you know,

all these all these all these tricks are

helpful, but I I do think it's probably

much easier to sell to a fang company

now, some AI agent, than it was back

when you were running Triple Bite.

>> But it sort of ties back into this

original study, something we we've

talked about and maybe tweeted about is

that I think the enterprises would

certainly prefer to buy these solutions

from established software companies.

Even established startups like latest

stage startups that have been around for

a while and have lots of funding and

feel less risky but they fundamentally

can't build the products and I think

many of the YC partners feel that a lot

of the time it's just because the

engineering teams at these orgs are

filled with people that themselves don't

actually really believe in AI don't use

codegen tools don't think think it's all

super overhyped are really excited when

an MIT study comes out saying that it's

all like hype and retweeted and and

really want because it's a narrative

they want to believe but the consequence

of that for the companies is that they

can't build the product. So if your

engineers don't believe in this, then

how are you going to build a product

that actually works? The knock on effect

for startups then is if you can actually

build something that works, the

enterprises will talk to you because

they have no other options. Can't build

it internally, can't go to an

established company. Um so the startups

are actually getting like the shot that

they never had before.

>> I guarantee you someone's watching this

right now and uh you've just horribly

triggered them.

>> Oh yeah. No, I I I tweeted this and I

got lots of angry emails.

someone out there right now is going

>> and the message for you guys out there,

the irony is all you have to do is

literally just try it. Like if you code

and you're a great engineer or even an

okay engineer, honestly, if you just try

this stuff and get really good at it and

you know, give it a shot. Like it's not

like I try it once and it screwed up a

variable name and now I'm mad and I'll

never use it again, right? It's actually

like invest into a real project. It

doesn't have to be your main work. It

could just be a side project. Do

something super fun. We literally had a

um a vibe coding dad's night about a

month ago and you know people who are

not even technical. We had like a

landlord who was making a vibe code

thing for their tenants so that they

could like see if they had paid their

rent or something. It's like you will be

amazed. And so the people who like feel

this it's like just give it a shot cuz

you you know you are sort of the perfect

people in the world to use these tools

and even if I mean honestly it turns you

know 10x engineers into 100x engineers

and it turns 1x engineers into 10x

engineers. I mean I it's that's like

such a gift but it requires an

overcoming of like this very real

emotion that's inside of people.

>> Yeah. The the other instance over the

last week where I've just seen this sort

of like the the people waiting for the

it's overhyped narrative was after that

Andre Kapathy Dwarkish interview. Did

you guys see that?

>> Yeah, I told you 10 years.

>> Yeah, I saw I read the tweet. The tweets

are essentially oh like you know Kapathy

says agents are overhyped and can't do

the work. So then I listened to the

interview and it's like the point he's

making is you can't just like give an

agent a prompt and expect it to do

everything perfectly the first time.

like you still actually have to do lots

of work to provide the right data and do

all the correct context and actually do

the evals and all like the actual

tooling and my interpretation of that

was that's like a fantastic opportunity

for startups and anyone who can build

software

there's like tons of stuff that's still

yet to build and I just found it like an

interesting raw shark test almost of

it's like if you fundamentally want to

believe that everything is overhyped

you're going to read into that that oh

yeah like look like AI expert confirms

it's overhyped but if you listen to

actually what he's saying there's like

tons of opportunity to build really

great tooling. It's like these things

are a tool and you just have to help

them work better versus expect that

they're all just going to be absolute

magic and work without any help.

>> Well, I think the exciting thing is

basically there's just a lot of

opportunity to rebuild all these systems

to be AI native because software needs

to be completely be rewritten to work

with AI which is really just lots of

opportunities for for founders which is

cool. Here's one other point from the

study that I I also thought was really

interesting in terms of like why

enterprise is such a big opportunity for

startups. I'll actually read this quote.

This is from some enterprise buyer

person. We're currently evaluating five

different gen AI solutions. But once

we've invested time in training a

system, the switching costs will become

prohibitive.

>> That's the CIO of a $5 billion financial

services firm. Fantastic.

>> Right.

>> That sounds like a moat to me.

>> Right. Exactly. I hadn't heard such a

direct quote from like a legit

enterprise buyer about that before. So

all these people who are worried that

these like chatbt rappers won't have

moes like that's the moat.

>> So uh there you have it. The AI doomer

influencers have been leading you

astray. AI is hard to actually

implement. And it turns out it's so hard

to implement that only 5% of the time it

actually works. But it also turns out

that if you're a startup founder and

you're really good one at YC the

acceptance rate is under 1% now. So we

gave you a whole bunch of examples of

people who are in that 1% who then went

on to be a part of like probably that

top 1% of implementations that actually

work because some of the smartest best

product people engineers uh are actually

focused on it. Ultimately, it's about

people who really, really great at

technology, but also are polymaths.

They're, you know, understand other

people, can understand what that uh

bank, you know, $5 billion fintech CIO

really, really wants. That's the good

news. You should not look at these stats

and say, "I could never be a part of

that 5%." If you're actually really,

really good, you absolutely can be, and

we have so many examples of that at YC.

So with that, we'll see you guys next

time.

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