The Question · Beginning with a discussion of AI Agents
I recently read
“Two and a Half Years into Building a Company: Seven Things I Want to Share About AI Organizations,” by Digital Life Kha’Zix.
It brought to mind a striking paradox:
the same AI that can reduce repetitive work and expand an individual’s capabilities and creative room
can also make task allocation, performance measurement, and process management more granular than ever before.
At first, it is easy to read this difference as a matter of one company’s management style,
or of how a certain kind of manager understands technology.
But on further reflection, perhaps it is not merely a choice made by a handful of individual firms.
It may have just as much to do with industry competition, pressure for growth,
organizational governance, and the wider way our era structures work.
If this is a question larger than the distinction between a “good company” and a “bad one,”
then how should individuals make sense of this change?
What can we do within different kinds of organizations?
And how ought we to plan the long arc of our professional lives?
AIhas brought one question increasingly into focus as it enters the workplace:
once efficiency rises, where does the saved time actually go?
It may, of course, free people from repetitive labor.
Work that once took a full day can now be completed in a few hours—or even a few minutes—
leaving more room for judgment, creativity, communication, and exploration.
But another possibility exists as well.
Five tasks a day become twenty;
what managers once saw only at the level of outcomes can now be followed in real time;
work that used to resist quantification because management costs were too high can now be broken down,
tracked, and evaluated by agents.
AI does not, by nature, lead people toward freedom.
It can amplify the individual, but it can also amplify the organization.
And so the question shifts from whether a certain company “understands AI” to something larger:
In an age when AI is causing both organizational control and individual productivity to surge,
how can an ordinary person avoid becoming nothing more than a more efficient unit of production?
That, I think, is a question worth sitting with.
Chapter 01This Is Not Merely About One Particular Company
If we explain this phenomenon only in terms of organizational inertia,
management style, or even differences in how generations approach technology,
we may still be looking too narrowly.
A younger team, after all, can just as easily become a different kind of highly digitized,
highly quantified, highly controlling organization.
AI itself carries no moral preference for “empowering employees” or “controlling employees.”
It behaves more like an organizational amplifier:
In a trust-based organization, it expands personal autonomy;
in a control-oriented one, it lowers the cost of control.
In the past, a manager who wanted to know what employees were doing each day needed meetings,
daily reports, weekly reports, and endless process-tracking.
Now, agents can automatically read project records, analyze workloads, assess output, and generate rankings.
So with the very same technology:
One organization thinks: “If the work is done in two hours, the rest of the time can go toward something more valuable.”
Another thinks: “If it can be done in two hours, shouldn’t one day now hold four times as much work?”
This is not an imaginary risk. OECD research on algorithmic management has shown that AI and algorithms
are increasingly involved in task allocation, monitoring, and evaluation.
Where workers have little say in how these systems are implemented, the result often includes less autonomy,
heavier workloads, lower trust, and higher stress. Where employees do participate in implementation and decision-making,
those negative effects are significantly reduced.1
So if we want to call this the “background tone” of a certain industry or society,
we might go one step further:
It may not be the distinctive tone of one society at all,
but rather a managerial tendency that many organizations drift toward
when growth slows, competition intensifies, and workers lose bargaining power.
When growth comes easily, companies speak of innovation, experimentation, and exploration.
When growth becomes difficult, managers instinctively begin to focus on efficiency, cost, measurability, and traceability.
And agents make all of those things cheaper than ever before.
That is why the real dividing line ahead may not even be:
AI companies vs. non-AI companies.
It may be this instead:
companies that use AI to enlarge human capability vs. companies that use AI to enlarge organizational control.
In reality, of course, most organizations will fall somewhere in between.
Chapter 02Do Not Build Your Career Around the Hope of Finding a “Good Company”
When we are young, many of us quietly carry the same assumption:
find a company with sound values, a far-sighted leader, and a team that respects people—
and then do meaningful, creative work there.
And of course, if such a place can be found, all the better.
But to build a life around that expectation is more fragile than it appears.
Managers change.
Companies run into pressure.
Organizations expand.
Industries enter downturns.
Even a founder who begins as an idealist may, under cash-flow pressure,
turn obsessively toward managing efficiency.
So a steadier career strategy must shift from:
“finding an organization that will not compress me”
to:
building a structure of capability that still leaves me with choices
even when the organization begins to compress me.
The difference between those two ways of thinking is immense.
The former depends on others.
The latter builds one’s own bargaining power.
Chapter 03In the Age of AI, There Are Four Things Individuals Should Deliberately Accumulate
01 · Do Not Merely Accumulate “AI Skills”; Accumulate AI Leverage
Knowing how to use ChatGPT, Codex, or build agents will soon become ordinary competence.
NBER research on customer-service agents found that generative AI especially boosts less experienced
and lower-skilled workers: average productivity rose by about 14 percent, with even larger gains for newcomers.
In other words, one of AI’s most important functions is to rapidly replicate part of the method and experience
that once belonged only to high performers.2
In the past, people could say:
“I’m exceptionally good at making presentations.”
“I’m very good at organizing information.”
“I’m incredibly fast in Excel.”
“I’m excellent at writing reports.”
Those things once created clear advantages. They will become harder and harder to rely on.
What becomes truly valuable instead are different kinds of questions:
Why is this being done? What exactly should be done? Can the result be trusted?
What is the client’s real problem? How can a system continue to operate over time?
So do not train yourself to become:
the person who is best at operating AI.
Train yourself instead to become:
the person who understands a problem most deeply,
and who knows how to use AI to keep solving that problem.
The difference in professional value is substantial.
02 · Treat Judgment as a Core Asset
The stronger AI becomes, the more one quality stands out: judgment.
Judging which information deserves trust.
Judging what the real problem in a project actually is.
Judging the gap between what a client says they want and what they actually need.
Judging when automation is appropriate and when human intervention is indispensable.
Judging when an analysis is logically sound but impossible in the real world.
These things are exceedingly difficult to write into a prompt.
The ILO, too, does not frame AI’s impact on employment as a simple story of mass job disappearance.
Its argument is that a great many jobs will be reconfigured and decomposed;
what matters is how work is reorganized, how autonomy changes, and which tasks remain human.3
AI can spread possibilities across the table with extraordinary speed;
human beings remain the ones who must bear judgment.
And the people who bear judgment inevitably sit closer to the upper end of the value chain than those who simply execute it.
03 · Accumulate What You Can Carry With You
This is one of the easiest things for professionals to overlook.
Ten years into a career, the most dangerous situation is not weakness.
It is this:
you may be very strong,
and yet everything that makes you strong only works inside this one company.
You understand the company’s systems.
You understand its processes.
You understand managerial preferences.
You understand its internal relationships.
And once you leave, everything seems to reset to zero.
So it is worth asking yourself, over and over:
Of the things I am doing this year,
how much will still belong to me as capability three years from now, even if I leave this company?
This is not about taking company data or trade secrets with you.
It is about carrying away methods, industry understanding, problem frames, project experience, published work,
public expression, personal reputation, relationships, product thinking, and technical ability.
That is what really belongs on a personal balance sheet.
Salary is current income. Transferable ability is an asset.
04 · Most Important of All: Optionality
This may be one of the most underrated ideas in career planning.
Real security is not:
“The company probably won’t let me go for now.”
Real security is:
Even if I leave tomorrow,
I know where else I can go.
That power to choose can come from many places:
professional expertise, industry reputation, client networks, savings, a body of work, a small product,
a long-term area of study, a personal brand, or simply a handful of people who truly know what you can do.
Together, these shape one thing: the cost of leaving.
When the cost of leaving is high, people become captive to absurd organizational rules.
When the cost of leaving grows lower, one’s entire state of mind inside a company begins to change.
You may not feel a constant urge to resign. Quite the opposite—you may feel more at ease.
Chapter 04What Can One Do Inside a Company Where AI Is Increasingly Becoming a Tool of Control?
There is no need to elevate it immediately into a full-scale conflict of values.
Trying to “educate the company” is usually a very low-return activity.
A more realistic approach is to observe three things:
Has the efficiency brought by AI translated into more autonomy?
Has the improvement in capability led to more complex or more valuable work?
Has increased contribution brought more resources, income, influence, or room for growth?
If two of those answers are consistently “no,” it is time to become cautious.
Suppose, for example, a report that once took eight hours can now be finished in two with the help of an agent.
A good outcome would be this:
The saved time is used to research new problems, meet clients, build products, and develop methods.
A tolerable outcome would be this:
You take on more projects, but your position, compensation, and scope of ability expand at the same time.
The outcome that should raise concern is this:
one report a day becomes four.
At bottom, it is still the same question:
who ultimately receives the time that AI has saved?
You may use such an organization as a training ground for a while. But do not remain there indefinitely.
It may help to think of companies in two categories: training grounds and destinations.
Some companies may not be worth staying in for the long term, yet they are excellent places to train:
rich in resources, projects, data, and difficult problems.
Then use them precisely for that purpose: accumulate quickly.
The point is not to wait for an organization to become your ideal one,
but to complete your own accumulation of capability capital within it.
That shift in mindset matters more than it first appears.
Chapter 05Career Direction: Do Not Become Merely an “AI Operator”
In the age of AI, one easy trap is to imagine career growth as nothing more than learning a few more tools.
What is often more valuable is to form an intersection of capabilities:
industry understanding × data × AI × the ability to turn things into products and processes
Competition in pure AI technology will only intensify, while the models themselves continue to lower technical barriers.
But a person who understands real business and can turn AI from a demo into a stable workflow becomes harder to replace, not easier.
Especially in traditional sectors—hospitality, consulting, manufacturing, retail, culture and tourism, professional services—
what is truly scarce is often not “someone who knows the name of a model,”
but someone who understands how the business actually works, where the data sits, how decisions are made,
and how AI can be embedded into the flow of work.
If one were to sketch that career path as a line, it might look something like this:
business execution / data analysis / specialized professional roles
using AI to enhance concrete business functions
AI × industry product and process design
AI transformation / AI product / head of industry intelligence
an internal leader / AI consultant / independent product builder / founder
Its greatest advantage is this:
You do not need to place your bet on which company’s AI strategy will win.
What you are really betting on is:
over the next ten years, traditional industries will inevitably need a group of people
who both understand the industry and truly know how to embed AI into organizations.
People who understand only the industry are abundant.
People who understand only AI will become more abundant too.
People who understand the industry, understand data, understand agents,
and have actually carried out organizational implementation will be much rarer.
That is the kind of capability mix worth protecting over the long term.
Chapter 06Push the Original Question One Step Further
It is easy to understand “AI-ization” as a reactive move—something companies do because they fear being left behind by the times.
But if we stop there, the thought remains too shallow.
AI does not automatically make a company more advanced.
It simply gives that company greater power to become more fully what it already tends to be.
Control-oriented organizations gain stronger control.
Efficiency-oriented organizations gain greater efficiency.
Creativity-oriented organizations gain greater creative force.
Organizations that genuinely respect people may, for the first time, dramatically amplify ordinary human capability.
For individuals, then, the answer is not to wait for that last kind of company to appear.
The answer is this:
let AI amplify you first.
Let it accelerate how quickly you learn,
deepen your understanding of the industry,
expand your creative power,
extend the boundary of what you can accomplish on your own,
and ultimately enlarge your power to choose.
Then even if the background tone of the world does not change, your relationship to it will.
Over the next five or ten years, perhaps a better career goal is no longer the traditional one:
“What position do I want to climb to?”
But rather:
How many things can I still do,
even without this company?
As that answer grows, titles begin to matter less.
One Last Line
Do not stake your career on whether a company will respect you;
stake it on whether you can, little by little, become someone who truly has the power to choose.
That may be a more long-term way of thinking than simply trying to find a company that is younger, more AI-savvy, or more open.