So, here is a headline that landed last

week. A Gartner study of 350 global

business executives, all at companies

with at least a billion dollars in

annual revenue, found that 80% of those

who have piloted AI or autonomous

technology have carried out workforce

reductions. 80% That is a staggering

number, and if that's where the finding

stopped, this would be a very short and

very depressing video. But, that is not

where the finding stopped. The study

also found that there was no correlation

between those layoffs and higher returns

on investment. The companies cutting

staff because of AI were not the ones

making money from AI. The companies

actually seeing high return of

investment were doing something

different entirely. They were keeping

their people and using AI to make them

more productive. Gartner's VP analyst

Helen Poitevin put it very plainly,

"chasing value only through headcount

reduction is likely to lead most

organizations down a path of limited

returns." So, companies are firing

people in the name of AI, and it's not

working. They're performing a human

sacrifice to a god that didn't ask for

one and isn't rewarding it. That raises

an obvious question. Why are they doing

it? My name is L. I have a PhD in

computer science, and this is the kind

of question I find it very difficult to

leave alone. So, let's get into it. A

quick note before we go further, given

some past comments on this channel, if

your first instinct is to dismiss survey

data, remember that this is the same

statistical methodology that approved

every medication you've ever taken.

Gartner's sample of 350 billion-dollar

companies uses the same representative

sampling as clinical drug trials. Yes, I

know it's not perfect, but dismissing it

means dismissing the foundation most of

modern science is built on. So, let's

engage with the data seriously. Now, why

are companies laying people off if it's

not generating returns? There seems to

be a few things happening

simultaneously. First, there is a

genuine belief, not necessarily

supported by evidence, but widely held,

that AI can replace human workers at

scale. Another gentler, but equally

accurate word for that is hope. The

logic sounds intuitive. If machine can

do what a person does, you don't need

the person. But the Gartner data suggest

this logic is incomplete because it

turns out the value of AI isn't in

replacing human judgment, it's in

amplifying it. The companies seeing the

highest returns are the ones treating AI

as a tool their employees use, not a

substitute for having employees at all.

Think of it less as we replace the pilot

with autopilot, and more we gave the

pilot better instruments, and now the

plane lands on time and nobody dies

ever. Both are automation stories, only

one of them ends well.

Second, there is

what Sam Altman, the CEO of OpenAI, has

described as AI washing. His argument is

that some companies are attributing

layoffs to AI when they would have made

those cuts anyway. AI becomes a

convenient narrative, it sounds

strategic, it sounds forward-thinking,

it sounds like you're positioning for

the future, it sounds a lot better than

we over-hired during the pandemic and

our CFO finally noticed. Now, Altman has

his own motivations for saying this. He

runs the most prominent AI company in

the world and has an obvious interest in

AI not being associated with mass

unemployment. That's like the CEO of a

fireworks company assuring you that the

house fire was probably electrical. I'm

not saying he's wrong, I'm just saying

that when anyone, CEO, analyst,

politician, literally anyone, makes a

public statement, they have reasons for

making it and those reasons are not

always the ones they state. That is not

cynicism, it is just how information

works. People have motivations, the

truth is hard to get to partly because

people might not always be forthcoming,

but also because even genuine beliefs

change constantly as new data emerges.

Which brings me to Dario Amodei, the CEO

of Anthropic, the cloud people. Last

year, he made a widely reported claim

that AI could eliminate roughly half of

white-collar entry-level jobs. This

year, he walked that back, citing

something called the Jevons paradox,

which I'm going to explain in a moment,

because it's generally fascinating, and

suggested AI might augment work rather

than eliminate it. Though he cautioned

that AI is evolving faster than previous

technologies and could produce different

outcomes. So, we've basically gone from

half your jobs will disappear to

actually maybe not, it's complicated, in

about 12 months. Which, to be fair, is

also how long it takes most companies to

finish an AI pilot program. So, at least

the timeline is consistent. Again,

motivations exist. Amodei runs a company

that builds AI and has a commercial

interest in that technology being

adopted widely. But, the fact that two

of the most prominent AI executives are

now publicly moderating their

predictions about job displacement is,

at minimum, worth noting.

Now, let's

talk about the Jevons paradox, because

it's one of those ideas that sounds

counterintuitive, but has been

surprisingly durable. Back in 1865, an

English economist named William Stanley

Jevons observed something strange about

coal. The steam engine had just become

dramatically more efficient, meaning you

needed less coal to produce the same

amount of energy. You would expect,

logically, that total coal consumption

would decrease, but it didn't. It

actually increased massively. Because

when coal became cheaper and more

efficient to use, people found more uses

for it. New industries became viable,

new applications emerged. The efficiency

gains didn't reduce demand, they

exploded it. The Victorians basically

invented, "We'll save money by spending

more," 160 years before every SaaS

company on Earth independently

rediscovered it. Apollo's chief

economist, Torsten Slok, has argued that

the same paradox now applies to AI. If

AI makes certain types of work cheaper,

faster, and more accessible, the demand

for that work will not disappear, but

rather it will expand. New applications

emerge, new roles get created, new

industries become viable that were not

viable before. Is he right? I don't

know. Nobody really does, but the early

data is very interesting. Let's have a

look at that. The World Economic Forum

reports that AI has already created more

than 1.3 million new jobs globally,

including roles that either didn't exist

or existed in negligible numbers before

LinkedIn's 2026 Jobs on the Rise

report ranked AI engineer as the number

one fastest growing job title in the

United States, with job postings rising

143% year-over-year. Four of LinkedIn's

top five fastest growing positions are

AI-related. AI and machine learning job

posting surged 163%

from 2024 to 2025. And these are not

just engineering roles, by the way.

Prompt engineers design inputs for AI

systems to produce reliable outputs. AI

trainers evaluate and correct

AI-generated content to improve model

performance. AI governance specialists

and model evaluators make sure the

systems don't do something catastrophic.

There is an entire ecosystem of roles

emerging around the question of how to

make AI systems work properly, roles

that require human judgment, domain

expertise, and contextual understanding

that the technology itself doesn't have

right now. Three years ago, prompt

engineer wasn't even a job title. Now,

it's one of the fastest growing

positions in the market with a 135.8%

growth rate. We have invented a

technology so powerful that it requires

a brand new profession dedicated

entirely to asking it questions in the

right way. That is either a breakthrough

or the most expensive auto complete in

history and honestly it might be both.

Even the companies building AI models

rely on enormous human workforces to

train them. Data labeling companies like

Search AI hit over 1.2 billion dollars

in annual revenue by connecting

thousands of domain experts, doctors,

lawyers, scientists, engineers with AI

labs that need human feedback to improve

their models. The AI isn't training

itself. It requires an invisible global

workforce of people teaching it how to

behave which if you really think about

it makes AI less like a brilliant new

employee and more like a very expensive

intern who keeps confidently doing

things wrong until a senior person

corrects them and teaches them better.

Except the intern costs 4 billion

dollars and occasionally hallucinates.

So the picture is more complex than AI

takes everyone's jobs. AI is taking some

jobs. AI is also creating new jobs and

nobody can really tell you yet which

effect will be larger in the long run.

That uncertainty is definitely generally

uncomfortable and I understand why

people find it frightening rather than

reassuring. It might work out as not

exactly a pension plan. But there is

another dimension to this that I think

deserves more attention and it has to do

with costs. There is a line that I came

across that stuck with me a lot. Workers

had salaries you could control. AI

agents have costs you cannot. And the

data backs this up actually. AI

operations fluctuate unpredictably.

Unlike a salaried employee whose cost is

fixed and known AI agent cost are tied

to token consumption, API calls, cloud

compute, and interaction volume. All of

which vary and are difficult to

forecast. Integration costs regularly

exceed initial estimates by 30 to 50%.

The initial development of a production

AI agent represents only 25 to 35% of

what a company will spend over 3 years.

The rest is tokens, infrastructure,

security, monitoring, retraining, and a

dozen other line items that weren't in

the original proposal. One SaaS company,

for example, discovered $280,000

in monthly unaccounted cloud spend from

23 undocumented AI services that various

teams have spun up without centralized

oversight. 23 rogue AI subscriptions,

that is not exactly a technology

strategy. It's more corporate haunting.

And then there's vendor lock-in.

Companies that build their operations

around a specific AI provider's

ecosystem, their APIs, their models,

their infrastructure, find themselves

locked into that vendor, reducing

flexibility and increasing long-term

costs. You fire the person whose salary

you negotiated annually, you replace

them with a service whose pricing model

changes quarterly at the vendor's

discretion, and whose cost scaling ways

you cannot predict until the invoice

arrives. You swapped unknown costs for a

mystery bill. Congratulations, you've

automated your budget into a slot

machine. This doesn't mean AI is a bad

investment, by the way. The companies in

the Gartner study that used AI for

people amplification are seeing strong

returns, but it does mean that the

calculus of replace workers with AI to

save money is far less straightforward

than the pitch deck makes it sound.

You're not exactly swapping a salary for

a subscription, you're swapping unknown

costs for an unknown one and hoping the

math works out.

Here is where I want to bring it all

together. AI was the leading reason

cited for layoffs in March and April of

Outplacement firm Challenger,

Gray, and Christmas found that 49,135

jobs have been attributed to AI so far

this year, nearly matching the entire

total of 2025, and it's only May. Those

are real people, real livelihoods. That

number is not exactly abstract, but the

Gartner study tells us those layoffs

aren't generating the returns companies

expected. The Jevons paradox suggests AI

might create more work than it destroys.

The job market data shows over a million

new AI-related roles already created

globally, and the cost data shows that

replacing humans with AI isn't a clean

savings exercise that the term

automation implies. The word automation

is doing an enormous amount of heavy

lifting in boardroom presentations right

now. It's carrying the entire pitch on

its back while the actual numbers

quietly file for workers compensations.

None of this, of course, means that

everything is fine. People are losing

their jobs right now, today, in this

economy, and telling them the Jevons

Paradox suggests your suffering may be

temporary is not exactly helpful when

rent is due on the 1st. The pain of

technological change is never

distributed evenly. It never has been.

We actually covered the parallels with

the former industrial revolution in a

separate video that I'm going to link in

the description. Some industries will be

hit harder than others. Some roles will

disappear entirely. Some people would

benefit enormously while others bear the

cost. That is not a prediction about AI.

It's just a description of how every

major technological transition in

history has played out. What the data

does suggest is that the most productive

path forward isn't replacing people with

AI. It's rather combining them. The

companies seeing the best results are

not the ones firing the workforce.

They're the ones arming their workforce

with tools that make them faster,

sharper, and more capable than either

humans or AI could be alone. The god

doesn't want your people. It wants a

competent operator. But if the data says

combining works, why are so many people

still furious about AI? It's not because

they haven't seen the data. It's because

the data isn't really the point. I made

a video exploring where that anger

actually comes from and why it might be

aimed at the wrong target. That's the

one that I would watch next. Thank you

all so much for watching this one. I'll

see you all in the next one.