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In 1997, something unusual happened. The best chess player in the world lost to a computer.
Garry Kasparov, known for his brilliant mind and deep focus, sat across from a machine called Deep Blue. IBM built it to do one thing only: win at chess: no face, no feelings, just code and calculation.
And it won.
Kasparov lost the match.
People were shocked. News headlines talked about the end of human intelligence. Some thought this was the start of machines taking over. The fear was not just about chess. It was about what it meant.
If a computer could beat the best human at a game of strategy and thinking, what would it do next?
But what happened after was more interesting.
Kasparov didn’t give up. Chess didn’t die. In fact, the game changed. Players started using machines as tools to train, explore, and play better. A new style of chess appeared. People began working with the computer, not against it.
That moment didn’t show the end of human skill.
Now, years later, we’re facing the same kind of moment again. But this time, it’s not about chess. It’s about work. About our jobs. Our value.
And the question is not just “Will AI replace me?”
It’s something deeper.
Why was my job so easy to replace in the first place?
People keep asking if artificial intelligence will replace them… But that’s not the right question.
The better one is this: why was your job so easy to replace in the first place?
Because when AI steps in and performs your work faster, cheaper, and with fewer mistakes, the point is not that AI is too powerful.
The point is that the job was structurally weak. It was never built to last. It was likely built for throughput, not for thinking. For predictability, not judgment. And now the system is revealing itself. Not because of a tech revolution. But the assumptions behind the roles we created are collapsing under inspection.
That’s what makes this shift uncomfortable. Not that something external is disrupting us. But many of our jobs were always fragile. We just kept them alive with meetings, rituals, and presentations. Now the mask is off.
Most jobs were not designed. They were accumulated. Say hello, Peter Principle…
Across decades, industries built roles around gaps in communication, technology, or accountability. We added coordinators to manage work between silos. We added specialists to fix recurring problems. We added layers of reporting to track everything from a distance.
Eventually, we had org charts full of people whose job was, essentially, to keep the structure from falling apart.
Now, software can hold the structure. That leaves a lot of roles exposed. Not just low-skill roles, but mid-tier knowledge jobs. The kind that involve cleaning up after bad processes, rephrasing other people’s insights, or passing information around like hot potatoes.
Those jobs didn’t survive because they were good. They survived because there wasn’t a better option. Now there is.
AI is not replacing humans. It is replacing inefficient systems that relied on humans to hold them together.
That’s what makes this transition complex. We are not witnessing a linear automation event. We are watching a rebalancing.
A redistribution of attention, effort, and value.
That means the threat is not the technology. The threat is what it reveals about our dependency on wasteful patterns of work.
Let’s be more precise. Jobs that focus on execution without context are the first to go.
If the output is the same no matter who presses the button, a machine will press it.
This is not theoretical. It’s already happening in operations, finance, customer service, legal reviews, and parts of product management.
Jobs built on coordination without ownership are also exposed. If your primary task is scheduling, updating, reminding, or handing off, AI will do that better. Not instantly, but eventually. Because those tasks follow rules. And systems love rules.
Even jobs built on strategy are not entirely safe. If your ideas are shaped by templates, past slides, or industry trends, AI can learn that too. The only protection is differentiation. You need to work in uncertainty. Ask new questions. Frame decisions with human variables. Not just copy from the last deck.
And then there is the human layer. The part that stays. That’s the layer AI still cannot reach.
It is the layer of insight, care, and emotional risk. The part where someone says, “Something feels off,” and chooses to act. The part where someone tells the truth in a room full of silence. The part where a leader holds tension without collapsing it into a premature decision.
That is not automatable.
But it is uncomfortable.
The people who will thrive in this shift are the ones who redesign their jobs before someone else redesigns them for them.
This requires courage. Not fear. Not hustle. Just a clear look at the system you are part of, and the willingness to challenge it. To ask:
What am I doing that actually requires a person?
Where am I just maintaining a process that no longer makes sense?
How would I work if I weren’t trying to defend my current title?
If you answer those questions honestly, your role will start to shift. You will begin to do more of the work that matters and less of the work that machines can mimic. It won’t be easy. But it will be worth it.
Because what AI is doing is not just optimization. It is interrogation. And it is asking all of us the same question. Do you still need to do what you’re doing?
Or is it time to let go of the old script and write a new one?
Not for fear of being replaced.
But for the clarity of finally doing something no machine can.
Project management is not about filling spreadsheets, and the difference between a PM who is “overwhelmed” and one who is “in control” often comes down to the systems they rely on.
Don’t just manage the timeline. Lead the outcome.
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There's a clean logic here: if a machine can do your job, perhaps the job was hollow to begin with, and exposing that is no bad thing. Fair enough at the individual level. The bit the "maybe it should" framing skips is what happens in aggregate. A job being fragile isn't the same as it being worthless. Plenty of resilient, judgment-heavy work sits right next to a fragile task that AI happens to swallow first. And when it does, the company rarely keeps the same headcount and reinvests the freed-up hours in higher-order work. It banks the saving. The productivity gain flows to capital, not to the worker who survived the cut. So "maybe it should" quietly becomes "maybe there should be fewer of you", which is a structural question wearing a moral costume. The honest version of this essay isn't "what work survives the machines". It's "how many chairs are left once the fragile tasks are gone", and that number is set by demand for labour, not by how good the people in the chairs are. Fewer chairs is fewer chairs.
"If AI Can Replace Your Job, Maybe It Should"
And what of the human cost, to livelihoods and families? To communities? We live in a system where the means of production and capital allocation are driven by capitalism. Capitalism is good at efficiently allocating resources, and when markets are competitive within environments of commonsense regulatory guardrails, capitalism is good at producing choice and benefit for consumers and supports innovation.
Capitalism is *not* good at providing people the means to support themselves. That's merely a byproduct of human work, which is valued by supply and demand. The system treats humans as an afterthought, because that's what they are to the system. If capitalism could function without humans, it would. We're now flirting with that.
I'm not entirely opposed to the intellectual position that your headline posits, inefficiency is a risk to the resilience and profitability of an organization (and thus, competition, which is good for consumers). But the wholesale replacement of human work by AI risks further exacerbating wealth inequality, which can erode the stability of society.
Yes, society can adapt to mass AI-related displacement of labour within a generation or so. The cost will be high, if the hype machine lives up its promises and the transition speed is what the leading frontier model developers want.
People who spent half their lives getting an education and progressive work experience who are too old to retrain as electricians, plumbers, and pipe fitters will be put in a tough spot in that scenario though. Some of them might fall on hard times of the kind not seen since the Great Depression.
What's the acceptable number of people to sacrifice in the name of "progress"? A thousand? A million? Ten million? What would the fabric of society look like if a million people within a wealthy country like the USA were out of work in the span of a few years without any prospects of replacing the income from that work? Would there be less inequality, or more? Less violence, or more? Less discord, or more? Less division, or more?
And what would the erosion of that discretionary spending power do to the US economy, which is heavily reliant on consumer spending?
Societies are also systems, and the businesses which operate within them are reliant upon society. It *is* possible for progress to extract too high a cost, and for the snake to eat its tail.