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The $1 Trillion Problem OpenAI Just Solved

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The $1 Trillion Problem OpenAI Just Solved

Transcript

146 segments

0:00

As you already know, I've been saying

0:01

the model itself is turning into a

0:03

swappable part. You just rent it, you

0:05

route it, and you move on. So, if models

0:07

keep getting cheaper, the big labs

0:09

should be stuck in a price war right now

0:11

watching their margin bleed out, right?

0:13

However, they did the opposite.

0:16

Instead of selling a model and walking

0:18

away, OpenAI and Anthropic reportedly

0:20

built teams that sit inside a customer's

0:23

business and ship a working system. The

0:25

industry borrowed the name from Palantir

0:27

and calls them forward deployed

0:29

engineers. So, what that means in plain

0:31

English is that they send their best

0:32

people into your office to build that

0:34

thing, not just to demo it. So, think

0:36

about how strange that is. The companies

0:38

that make the models decided the model

0:41

was the cheap part of the job. MIT

0:43

looked at the pile of company AI pilots

0:45

and demo and found something like 95% of

0:48

them produced no result you could

0:50

measure. Isn't that crazy? The model

0:52

worked fine, just [music] they just died

0:54

at the last mile, where somebody has to

0:57

put the thing inside the real job and

0:59

make it run on a Monday.

1:03

That old software deal where a company

1:05

hands you a login and wishes you luck is

1:08

dying with them right now. So, I'm

1:09

building a payroll and scheduling

1:11

accounting product right now. So, I'm

1:12

living this question, not theorizing

1:15

about it. The scariest thing was never

1:17

the intelligence. It was the person

1:19

willing to sit in your business until

1:21

the thing runs properly, which creates a

1:23

problem here because that is the one

1:25

thing you cannot check before you pay

1:28

for it.

1:30

Most of my revenue right now comes from

1:32

putting AI systems into real businesses,

1:34

and the demand [music] is real, like

1:36

people calling me real. But, a hot

1:38

market pulls in a crowd and not always

1:40

the good kind. There are teams who will

1:42

cash an owner's check and just disappear

1:44

once the demo looks impressive enough.

1:46

And the ugly part is that an owner

1:48

cannot inspect the system that does not

1:49

exist yet. So, the whole sales runs on a

1:52

slide deck and a confident voice. That

1:54

is not a small gap. That's just the gap

1:56

the 95% falls through. Now, obviously I

1:58

want to be fair because a course sells

2:00

you the exact same un-verifiable

2:03

promise. You buy the promise of a

2:05

result, only one or maybe two percent of

2:07

buyers ever get the result they want,

2:09

and nobody finds out which group they

2:11

landed in until the money is just gone,

2:13

right? I think that is why selling

2:15

courses started to feel bad to me. You

2:17

are handing someone a lottery ticket

2:19

[music] with your name printed on it,

2:21

right? Setting aside the work fixes that

2:23

though, because you own the outcome. It

2:25

runs or it does not, and both of you can

2:28

see which by Friday. So, you should only

2:30

sell what a client can inspect, and only

2:33

buy what you can watch running, which

2:35

leaves one question worth answering.

2:37

Which part of the business do you build

2:40

yourself into?

2:43

The labs go deep for a reason. Once you

2:46

are inside a customer's payroll data and

2:48

their weekly schedule, pulling you out

2:50

costs more than keeping you. Nobody

2:52

swaps out a six-month integration on a

2:55

Tuesday because a cheaper option just

2:57

showed up out of thin air. You know, my

2:58

take is that the boring operational core

3:01

is the safest place to stand as of now.

3:03

And that is why I go after payroll and

3:05

scheduling and accounting piece. The

3:06

cool, fresh stuff is for the other

3:08

geniuses who want it. I don't want it.

3:10

Think about your own business for a

3:11

second. You do not rip out the thing

3:13

that runs payroll and you do not wake up

3:15

excited to migrate your scheduling your

3:17

data again. Now, the labs won by going

3:20

one profession at a time. They started

3:22

with cybersecurity, then moved into

3:24

medicine, law, and whole lots of other

3:26

things right now. So, the lazy takeaway

3:28

that I want to kind of give out is

3:29

niching down, picking an industry, and

3:32

just be done with it. I do not fully buy

3:34

into [music] that. The trap is which

3:35

thing you niche. Is it the content

3:37

creation for roofers? I don't think it

3:39

makes sense to me. The content and

3:41

marketing are a stack of many different

3:43

skills. And honestly, most of us are

3:45

barely, barely scratching the surface of

3:47

any one of them, right? So, a bounded,

3:49

repeatable job is a different animal.

3:51

Payroll is payroll, onboarding is

3:52

onboarding, accounting is accounting,

3:54

scheduling is scheduling. These are

3:55

whole different things. And when the job

3:56

has clear edge and it repeats every

3:58

month, you can own it end to end. When

4:01

the job is a fuzzy pile of hand-creative

4:03

skills, you just simply cannot, and

4:05

neither can I.

4:09

So, it would be really bad for you to

4:11

watch where buyers are spending because

4:13

they spend in two places, and both of

4:15

them sit far away from the model, down

4:17

at the cheap infrastructure and the

4:19

routing, which was last week's whole

4:21

point, up inside the work itself, which

4:23

is this week's that I'm talking about.

4:24

The dangerous spot is the generic

4:27

middle. I use ChatGPT for my clients.

4:30

Just easier for your clients to do

4:31

without you every single month. That's

4:33

not something to brag about. So, my

4:35

whole take is, you know, you either go

4:36

big or go home, but with calculated risk

4:38

because your runway is real and one dumb

4:41

bet could end the game. So, sitting

4:44

inside the workflow only pays off when

4:46

the workflow is big enough to be worth

4:48

the wiring. A two-person shop doing five

4:51

jobs a week may not have the juice. Deep

4:53

integration ties your income to your

4:55

client's survival. Anyway, the model is

4:58

the cheap part now, and it gets [music]

5:00

cheaper every month. The boring seed you

5:02

built yourself into is the part that

5:04

nobody can copy. Think about that.

Interactive Summary

This video discusses how AI model providers like OpenAI and Anthropic have shifted from simply selling models to offering deep, integrated solutions through 'forward deployed engineers.' The speaker argues that the real challenge of AI is the 'last mile' integration into actual business workflows, where 95% of AI pilots fail. Instead of chasing generic models or ephemeral trends, businesses should focus on building themselves into the 'boring operational core'—such as payroll, scheduling, or accounting—to create lasting, defensible value that clients cannot easily swap out.

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