AI has taken my job, yours is next

In 1835, around 75% of cotton mills in Britain were steam powered.

There were over 50,000 power looms running across the country. And something profound had happened. Machines no longer assisted skilled textile workers. They replaced the need for textile skill altogether.

Factories became so efficient that Britain could outperform India, even though Indian labor was cheaper. British mills could produce in 2,000 hours what Indian producers needed 50,000 hours to achieve.

That is not incremental improvement. That is structural displacement.

I keep thinking about this because I think we are watching it happen again. Not in cotton. In knowledge work. And I am not saying this from the outside.

I am a two-time CTO. I have built products across AI, Web3, and SaaS at startups backed by Y Combinator, Techstars, and OpenAI. I founded a pan-African hackathon community of 12,000 developers across 22 countries. That is exactly the kind of work AI is now learning to do.

For years, AI felt like a helpful tool.

Now it is something else entirely.

The deliberate choice

The AI labs made a strategic decision that most people still have not fully processed. They made AI great at writing code first.

Not because they only cared about software engineers. Because code builds everything else.

If an AI can write code, it can help build the next version of itself. A smarter version writes better code. Better code builds an even smarter version. That is not theory. That is a feedback loop, and feedback loops compound.

Recently, OpenAI released a new coding model and stated something in the documentation that stopped me:

The model was instrumental in creating itself, used to debug training, manage deployment, and diagnose evaluations.

Read that again.

AI helped build AI.

This is not a prediction about the future. It is a description of the present. Intelligence is being applied to improve intelligence. And once that loop starts running, it does not slow down on its own.

I experienced this firsthand. I used Claude to migrate my entire website, 456 pages of content across multiple categories, from WordPress to Jekyll in one shot.

I'm still processing this shock 😮

Opus 4.6 just migrated my entire website, 456 pages of content across multiple categories, from WordPress to Jekyll in one shot.

this felt like watching an industry category boundary collapse in realtime 😵. pic.twitter.com/Z7gpyizSDO

— drew.sh (@letandrewcook) February 10, 2026

That felt like watching an industry category boundary collapse in real time. Not because the tool was clever. Because the economics changed underneath me while I was using it.

The cotton mill pattern

This is the part most people miss because they are focused on capability. They ask “can AI do my job?” when the real question is about economics.

AI does not need to be better than you at everything.

It needs to be 80% as good, 10 times faster, and 100 times cheaper.

That changes the market. Just like steam-powered mills did not need to produce perfect cloth. They just needed to produce it faster and at scale. The quality was good enough. The speed and cost made the comparison irrelevant.

When textile automation hit, skilled workers lost leverage. Semi-skilled operators increased. Output exploded. Entire industries reorganized around machine capability, not human craft.

We are watching the same pattern unfold in knowledge work.

The experience tech workers have had over the last year, watching AI go from “useful assistant” to “this can do parts of my job better than I can,” is about to spread to every knowledge industry. Law. Finance. Medicine. Accounting. Consulting. Writing. Design.

Not in ten years.

In one to five. Possibly less.

The AI labs are already working on it. The same reasoning capabilities they built for math and coding are being extended into every professional domain. Legal reasoning. Financial modeling. Medical diagnosis. The code-first strategy was the beachhead. Everything else is the campaign.

The honest objection

If you used early AI models in 2023 and thought “this makes stuff up” or “this is not that impressive,” you were right. They hallucinated. They were inconsistent. They were limited.

But in AI time, two years is ancient history.

The difference between those early systems and today’s models is the difference between a prototype and infrastructure. Judging today’s AI by your 2023 experience is like judging the modern internet by dial-up. The name is the same. The thing is not.

The window

There is a brief window right now that I think most people do not appreciate.

Most companies are still underestimating what is happening. Most professionals are still casually experimenting. Very few are deeply proficient.

That creates asymmetry.

The person who walks into a meeting and says “I used AI to run this analysis in an hour instead of three days” instantly changes their perceived value. Not eventually. Immediately. In environments driven by speed and leverage, the person who multiplies output becomes indispensable.

For decades, career growth followed a predictable pattern. Gain experience. Build expertise. Move up slowly. Now there is a new accelerant, and it is not experience or credentials. It is tool mastery.

The professionals who win in this era will not be the ones who fear automation. They will be the ones who orchestrate it.

The shift is not “AI will replace you.”

The shift is that people who know how to use AI will replace people who do not.

The hand-spinner question

Here is where the cotton mill parallel gets uncomfortable.

When steam power arrived, the question was not whether the technology was ready. It was whether the workers would adapt before the economics made their current approach irrelevant.

Most did not. Not because they were stupid. Because the change felt gradual until it was sudden. Because it is hard to abandon a skill you spent years building. Because the new way of working felt like cheating until it became the standard.

The question is not “will AI take my job?”

The better question is: am I operating like a hand-spinner in a steam-powered world?

Because the cotton mill of 1836 did not ask permission to disrupt.

And AI will not either.

The winners are not going to be the ones who resist the machine. They are going to be the ones who learn to run it. The window for that is open right now. It will not stay open.

Not because the opportunity disappears.

Because the advantage does.

When everyone is fluent, fluency stops being a differentiator. Right now it still is. That is the moment we are in. And I think most people are going to realize it about two years too late.