We might be the last generation of white-collar workers.
Doomsday news is coming out of Silicon Valley.
Last year,
in the code of top Silicon Valley labs,
70% to 80% was still written by humans.
This year,
that number has become
less than 1%.
Not a 1% drop,
but only 1% remains.
Within a single year,
human programmers went from the main force to mere reviewers.
And now,
even the ability to review is struggling to keep up.
These are first-hand data points from two podcasts by Xixiang Tech,
shared directly by Silicon Valley founders.
What sends chills down my spine is this next sentence:
The world's top AI researchers
are worried themselves
that they'll be out of a job in a year or two.
They say
the next year or two
might be the only remaining window of work for our generation.
Hello,
everyone,
I'm Wang Lijie.
In today's video,
I'm not just breaking down a news story,
but a timeline that is changing everyone's destiny.
From this Silicon Valley field report,
I've extracted six sets of data and facts.
On the surface, they seem unrelated,
but when connected with a specific logic,
I see a conclusion that makes me very uneasy.
Our generation
may very well be the last generation of white-collar workers in history.
This isn't a figure of speech,
nor is it alarmist nonsense;
it is a reality happening now.
Let's quickly go through these six sets of data,
so you can feel the gravity yourself.
Group 1:
Frontier labs and top programmers
have basically stopped writing code.
Last year, 70% to 80% of code in the system was human-written;
this year, it's less than 1%.
In many tasks, Claude Code and
Codex have already reached
Meta's internal L8
or L9 levels—
the level of Chief Architects and CTOs.
Several founders privately admitted,
"Claude is better than my own CTO."
Consider the resumes of these CTOs;
they fought their way up from Stanford and MIT.
It's like when the Go champion
first saw AlphaGo play that famous 37th move.
At that moment,
the feeling wasn't "I lost,"
but rather,
"the game of Go as I understood it
might not be what Go actually is."
Group 2:
Token consumption levels.
Top Silicon Valley engineers
consume hundreds of dollars in tokens daily,
thousands per week.
Big tech firms like Meta
give employees unlimited Claude access.
Not only is it unlimited,
but they track and rank each employee's token usage,
encouraging others to mimic the high consumers.
Some founders even use interview questions like,
"Here's $1,000;
let's see how fast you can burn through these tokens."
If you can burn them quickly,
it shows you know how to maximize AI productivity.
Group 3:
Research speed.
It used to take two to three weeks to go from an idea to working code.
Now, one or two days is enough.
Semi-automated or even fully automated experimental workflows
have already emerged within Anthropic and OpenAI.
Mathematical derivations and research breakthroughs at OpenAI
are already emerging directly from dialogues
with Codex and Claude.
They aren't being thought up by human engineers.
The data pipeline of a leading multimodal team
shrank from twelve months to just a few days or a week.
In over fifty working days, Anthropic released
more than seventy products and features.
This pace
was completely impossible in the internet era.
Group 4.
The most heart-wrenching set of data.
The top AI researchers,
the very people who built all of this,
are starting to worry they'll be unemployed in a year or two.
Because they know
the next generation of automated AI researchers is coming.
These are systems capable of replacing their own jobs.
Vitalbridge wrote in their 2026 internal forecast:
"Our generation might be the last of the white-collar workers."
It sounded too radical at the start of the year.
A quarter later, looking back,
they say this is no longer a prediction;
it is a reality in progress.
U.S. college grad employment hit a record low this year.
Potentially 30% of jobs could disappear.
Meta is still conducting massive layoffs.
The growth and training paths for talent
have been cut short by AI.
Group 5.
The macroeconomic level.
Knowledge and intelligence are being compressed into models,
becoming billable computing resources.
You used to trade reading, learning, and experience for a job.
Now, that intelligence is compressed into strings of tokens.
The U.S. is a nation of over 100 million middle-class people.
Coders, lawyers, doctors, agents, bankers—
these happen to be the jobs AI is best at replacing.
India's IT outsourcing industry may have entered a decline.
Demand for many SaaS companies is falling
because users find
that Claude alone is enough.
Group 6.
My advice to you:
AI replaces people who don't embrace it.
Those who do embrace it will be the beneficiaries.
This is like the wave of layoffs in 90s China,
forcing capable people out of their comfort zones.
AI drastically reduces the cost of starting a business.
One-person companies can now explode in scale.
The core value of humans in the future
will return to creativity and aesthetic taste.
That's all six sets of data.
It's very dense.
But if I just told you,
"So go learn AI quickly,"
I'd be no different from every other AI influencer.
I want to take you one level deeper
to see what is actually happening
beneath these six sets of data.
Here,
I want to bring up a few concepts we've discussed before.
The first is the Cantillon Effect.
I've explained the Cantillon Effect in previous episodes.
270 years ago, this economist discovered
that when new money is injected into an economy,
those closest to the injection point benefit the most.
Those furthest from the injection point
not only fail to benefit,
but also bear the cost of inflation.
This principle used to explain currency,
but today,
I'm giving you a new version:
The Intelligence Cantillon Effect.
You see,
AI isn't something that appeared out of thin air.
It is an "intelligence injection event."
Every intellectual output in human history—
every book, every line of code, every paper, every conversation—
has been compressed into something called a Large Language Model.
It has become a fluid, priceable resource,
capable of being redistributed like water among different people.
This is an unprecedented redistribution of intelligence.
So, who is closest to this injection point?
It's the researchers and engineers working inside
Anthropic, OpenAI, and Meta.
And the front-line Silicon Valley founders
who burn hundreds of dollars in tokens every day.
They aren't just getting 100% of average human intelligence;
they're getting 1,000% or 10,000% in external 'plugin' intelligence.
They finish three weeks of work in one day.
It's not because they've become smarter;
it's because they are standing right next to the injection point.
Who is second closest to the injection point?
Early adopters outside the San Francisco Bay Area
who remain hyper-sensitive to AI.
People like top-tier Chinese entrepreneurs, investors, and indie developers,
including myself.
We aren't at the injection point,
but we are close enough
to feel the direction of the flow immediately.
Who is farthest from the injection point?
The majority of white-collar workers who still work the way they did two years ago,
unwilling or afraid to deeply embed AI into their workflows.
It's not that they aren't smart;
they just haven't realized yet.
The current has already changed,
but they are still standing in the old riverbed waiting for water.
This is the first layer:
the Intellectual Cantillon Effect.
But let me pause here,
and tear open something deeper for you to see.
Because there is a second layer,
a much more brutal one.
In the traditional Cantillon Effect,
inflation devalues the purchasing power of the cash in your hand.
What you lose is the intermediary tool called money.
But in the Intellectual Cantillon Effect,
what is being devalued?
It is devaluing
you as a person.
Think about it.
How did society value a person in the past?
It was based on the knowledge they mastered, the skills they possessed,
and the cognitive tasks they could complete.
A doctor's value
came from studying for over a decade, rotating for tens of thousands of hours,
and storing hundreds of thousands of medical decision trees in their head.
A lawyer's value
was remembering thousands of precedents
and locating the right legal clause in seconds.
A programmer's value
was translating human business logic into machine language.
What is the common foundation of these values?
The scarcity of intelligence.
You study for twenty years to become a doctor—
those twenty years themselves are a massive barrier.
This barrier keeps others out,
which is why you, as a doctor, can command a high income.
You can understand this as
society setting a price for the scarce resource of intelligence.
Whether that price is high
depends on how scarce that intelligence is.
But what is happening now?
Intelligence is no longer scarce.
For a few dozen dollars a month,
you can access an intelligent system stronger than most professional lawyers,
doctors, or programmers.
For a few hundred dollars a month,
you can access a system stronger than a Meta L8 Principal Architect.
The barrier you spent twenty years building—
AI didn't just climb over it.
AI leveled it.
It turned a cliff into flat ground.
So, what's being devalued isn't your money.
[MISSING]
What's devaluing is the very foundation of your pricing as a knowledge worker.
Sixing says this might be the last generation of white-collar workers.
It's not that white-collar workers will be unemployed;
unemployment is just a surface-level symptom.
The deepest layer is that
the social role of 'white-collar'—
this identity category established in the industrial age—
has had its value foundation hollowed out from the bottom.
This is the second concept we've discussed on this channel.
In the trillion-parameter episode, I said
parameters are not knowledge itself,
but the geometric shape of knowledge relationships.
'Compression is intelligence.'
Now, this compression has taken shape.
What happens once it takes shape?
Formed intelligence is packaged into an API,
becoming an infrastructure like electricity.
Think about it—the same thing happened in history.
150 years ago,
what was humanity's most valuable skill?
Physical strength.
How much weight a strong man could carry,
how heavy a millstone he could push, or how long he could farm continuously—
these determined his social value.
Then the steam engine arrived.
Then the internal combustion engine,
and later the electric motor.
The resource of strength was compressed into machines,
turning into electricity priced by the kilowatt-hour.
Those who relied on physical labor for a living
didn't disappear,
they were forced to pivot.
They either became workers operating the machines,
or were squeezed into margin areas where machines couldn't replace them.
What's happening today is structurally identical.
Except this time, it's not physical strength being compressed;
it's brainpower.
Middle-class white-collar workers who live by their wits
are in the exact same position as the physical laborers
150 years ago.
You either become a new worker operating AI,
or get squeezed into the margins where AI can't replace you.
And,
the speed of this displacement
is more than ten times faster than last time.
The 19th-century Industrial Revolution,
from steam engines to full replacement,
took nearly a hundred years.
That's long enough for generations to adjust slowly.
In today's AI revolution,
the data we see is that
within a year,
Silicon Valley engineers went from the driving force to bystanders.
This isn't on a century scale;
it's on a yearly scale.
At this speed,
social systems simply cannot keep up.
The education system can't keep up.
You study Computer Science for four years,
only to graduate and find the market no longer needs what you learned.
Social security can't keep up.
Unemployment insurance and vocational training—
these were designed for the rhythm of the industrial age.
They can't catch an employment structure collapsing in a single year.
Here,
I want to tell a short story.
I know a CTO I really admire.
His resume is flawless:
PhD in CS from Stanford,
Head of Tech for a unicorn startup,
high six-figure salary,
managing an engineering team of dozens.
I used to see him as the template for the intellectual class—
the upper limit of what humans can achieve through education and hard work.
Recently, he spoke privately with an investor
and said one thing.
He said,
'The core of my daily work now
is no longer writing code;
it's breaking down tasks for Claude.'
Then I review the code it writes.
But the speed of reviewing
can hardly keep up with the speed at which Claude generates code.
Think about that.
He's not saying AI is assisting his work;
he's saying
his job has shifted from creating to chasing the speed of AI's creation.
His value no longer comes from what he knows,
but from how fast he can verify AI's output.
And now,
even verification is falling behind.
What he said next was even more heart-wrenching.
He said,
"I'm starting to wonder:
My PhD, all the coding experience I've built over 15 years—
how much is it all still worth?
If a junior engineer reporting to me,
equipped with Claude,
can finish in two days what used to take me two weeks,
what's my value as a CTO?
My management experience?
But
AI is also learning how to manage AI."
This is where that 'existential dread' comes from.
It's not just 'will I be replaced?'
It's 'is everything I've accumulated in the past
being systematically reset to zero?'
You might say,
"Leo, you're being too pessimistic.
Doesn't AI bring growth and new jobs?"
Let me break down this common logic for you.
The argument usually goes like this:
Every technological revolution in history has created new jobs.
Old ones vanish,
but new ones emerge.
Overall, it's progress.
So this time will be the same;
no need to panic.
This logic sounds very reasonable,
right?
It's based on historical induction.
It happened every time before,
so it will happen again.
But there's an equivocation here,
and I want to point it out precisely.
In past revolutions,
machines replaced manual labor.
The new jobs created
were higher-level cognitive roles.
Steam engines replaced porters
but created engineers.
Automation replaced assembly line workers
but created programmers.
Notice the pattern:
machines replace the lower levels,
and humans migrate to higher levels.
Humanity as a whole
has been migrating up a climbing ladder.
But this time is different.
This time, AI is not replacing
a certain level of labor;
it's replacing the ladder itself.
This ladder from lower to higher levels—
on the human side—
the top tiers are abstract thinking, creative problem-solving, and complex judgment.
And these
are exactly what LLMs are best at.
It's not their weakness;
it's their strength.
As we discussed in the Moravec's Paradox episode,
AI's capabilities are the inverse of humans'.
Things humans find hard—
playing Go, solving math Olympiads, writing code—
AI finds easy.
Things humans find easy—
picking up a pencil, walking on uneven ground,
detecting micro-expressions—
AI finds incredibly difficult.
So when AI starts striking from the top of the ladder,
where are you going to migrate?
Upward?
AI has already occupied the top positions.
Downward?
Lower positions are manual labor,
but AI robotics is also advancing,
blocking that path too.
This is why historical induction fails.
It's not just a cliché that "this time is different."
It's because the displacement is happening in the opposite direction.
In the past, jobs were replaced from bottom to top;
people could move upward.
This time, it's being replaced from top to bottom.
There is nowhere to run.
That's why I told ValueVerse we might be the last generation
of white-collar workers.
This isn't an emotional panic;
it's based on structural analysis.
The value foundation of white-collar roles is being hollowed out.
And this time,
no higher-level roles are waiting to absorb us.
You might ask,
"What about creativity and aesthetics?"
Aren't those the final bastions of humanity?
Let me pour cold water on that too.
Creativity and aesthetics
sound wonderful,
but they have one fatal flaw:
Demand for them
is very limited.
Think about it.
Modern society supports over 100 million middle-class people
because there's massive demand for medium-complexity intellectual labor.
Contracts, coding, accounting, diagnosis, design, analysis—
these tasks occur in every company,
in every industry, every day.
The demand is measured in the hundreds of millions.
But at the level of creativity and aesthetics,
what is the scale of demand?
For a hit movie,
tens of millions of people consume a single creative output.
For a viral song,
hundreds of millions consume one creative act.
Labor at the creative level
is a case of "winner-takes-all."
The top 1%
will capture 90% of the income.
The middle 20%
might barely survive,
while the remaining 80%
can't even get into the game.
So, saying human value lies in creativity and aesthetics
sounds like it's opening a door,
but it's actually opening a very narrow gate.
The number of people it can sustain
is orders of magnitude smaller than the current middle class.
This isn't pessimism;
it's an economic reality.
So,
in this structural dilemma,
as an ordinary person,
what should we actually do?
I think we should look at it in three layers.
The first layer,
and the most urgent:
Actively embrace AI.
Move yourself toward the injection points.
This is ValueVerse's advice,
and it's the core action of the Cantillon Effect I've been discussing.
AI doesn't replace white-collar workers;
it replaces those who don't embrace AI.
By embracing AI and embedding it in your workflow,
you move from being the one replaced
to the one replacing others.
This is the basic fundamental step.
Do it today,
or it'll be too late tomorrow.
But in all honesty,
doing this only buys you time.
It doesn't solve the problem,
because AI is also embracing AI.
The next generation of AI researchers are writing the next generation of AI.
Everything you do, AI is doing too.
And it's doing it faster than you.
So, embracing AI...
at most buys you a few years before being replaced.
It won't keep you on the safe side forever.
Now, the second level:
something more fundamental.
Rethink your relationship with work.
Xiang said this may be the last generation of white-collar workers.
Looking at it from another perspective,
this judgment can also be a liberation.
Our generation was indoctrinated with a life script:
study hard, find a good job, stay stable, get promoted, and retire.
The underlying assumption of this script is that...
your intellectual labor...
will be consistently needed by society,
and consistently priced.
Now,
this assumption is collapsing.
Collapse isn't necessarily a bad thing.
Think about it:
is this script itself even reasonable?
A person spends 20 years studying,
then 40 years in a cubicle repeating tasks AI can finish...
in a single second,
and finally retires.
This script is a product of the Industrial Age.
It's a compromise our species made for survival in that era;
it's not some eternal truth.
AI is forcing us to face a more fundamental question ahead of time:
In an era where intelligence is not scarce,
what do we actually live for?
What is the meaning...
of a person's existence?
These questions were masked by work in the past.
Because you had a job,
you didn't have to think about them.
Now AI has stripped away the fig leaf of "work."
These questions...
are being slammed right in your face.
I don't think this is bad.
I see it as a forced,
but potentially beneficial,
re-examination.
Third level:
something I've been thinking about lately.
Going inward.
We’ve talked many times on this channel...
about the relationship between 'Yuanshen' and 'Shishen'.
Shishen is the chatterbox analytical machine in your cerebral cortex.
Yuanshen is the quiet observer behind that machine.
Civilization over the past centuries...
has been a civilization of 'Shishen'.
We reward those good at analysis, calculation, and reasoning.
We crowned 'Shishen' as the king of intelligence.
But this 'Shishen'...
is exactly what AI is best at simulating.
The underlying logic of Large Language Models is:
receive input, call parameters, generate output.
This is exactly...
what your default mode network does.
The emergence of AI...
is telling us, in a way,
that the intelligence we've revered for centuries...
might never have been the full picture of intelligence.
It is just a subset;
and the subset most easily replicated by machines.
So where are the parts that are not easily replicated?
They are in 'Yuanshen'.
In awareness.
In direct experience.
In creative intuition.
In the deeper dimensions of consciousness.
These things cannot be tokenized.
They can't be compressed into a model.
They can't be called via API.
They are alive,
not just a combination of rules.
I feel more and more...
The impact of AI
may have an underlying purpose we haven't yet discerned.
It takes over everything the 'Conscious Mind' can do
in order to
force you to discover
that you are not just an ego-bound mind.
It forces you to recognize that which lies behind the ego,
the more fundamental 'you' that has always existed.
This direction isn't suitable as advice for everyone,
because not everyone is ready to go this way.
But I want to propose this possibility,
because I am on this path myself.
I believe this might be the most worthwhile path in the AI era.
At the very least,
it's the path that can bring you a lasting sense of stability.
On that note,
I'd like to pause here
and invite you to leave a comment.
From this firsthand report from Silicon Valley,
what do you feel?
Is it a sense of urgency?
Anxiety?
Or liberation?
In the work you do now,
do you think you'll be replaced by AI within five years?
If so,
what do you plan to do?
If not,
where does your confidence come from?
Also,
for the next video,
what would you like me to talk about?
Dig deeper into AI's impact on specific industries?
Or how to build AI-proof skills in this era?
Or perhaps how the 'Divine Spirit' awakens
once the 'Conscious Mind' is taken over by AI?
Tell me what you'd like to see.
Finally,
I want to end today's video with a certain image.
150 years ago,
how did the last generation that valued physical strength
view the steam engine?
They must have felt just as we do regarding AI today:
curiosity at first,
then shock,
then fear,
then anger.
Some smashed the machines,
some denied them,
some pretended they didn't exist,
others fought to become the ones operating them.
In the end,
the machines came anyway,
completely rewriting the world based on manual labor.
Today, we stand at a similar crossroads.
The difference is,
we are not bystanders.
We ourselves
are the ones relying on 'strength.'
Except,
this time it's mental strength,
and the steam engine is now a Large Language Model.
I don't know how this generation will get through it,
but I know one thing:
history doesn't give you the same chance twice.
Among those people 150 years ago,
those who first learned to operate the machines
became the first generation of elite workers.
Those who insisted they'd never use such things
were left behind by time.
Today's watershed moment
is already right in front of you.
Time
will not wait.
I'm Leo Wang.
See you next time.