After AI Takes Everything | Airing

essays<br>After AI Takes Everything

Date<br>06/12/2026

Tags<br>#essays

Author<br>Airing

Read<br>33 min

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Related<br>On the Value of Existence and the Experience of Life<br>How to Communicate Effectively — Through the Lens of Zhuangzi's Qiwulun<br>The 'Zen' of Sky: Children of the Light<br>How Is Artificial Consciousness Possible?<br>My Approach to Reading Papers and Writing Essays

&larr; All Posts Over the past month I’ve received three letters in a row from strangers — all software engineers I’ve never met. One was a frontend developer in infrastructure, another did data ops, the third was somewhere in between. The three letters look different, but they ask the same question.

The first one asked me: “Do you think one day all of it will be handed over to AI? If so, do code reviews even still need humans?”

My answer: soon. By the end of this year or next, the year after at the very latest.

The second was more direct: “Is our future job going to be building AI and then letting it replace ourselves?”

The third was the longest. The writer listed every reason he was embracing AI-assisted coding — fewer rewrites, less communication overhead, more output — and then admitted at the end: if things keep going like this, what happens to my own growth? He hadn’t figured that part out.

These three questions are, fundamentally, the same question. On the surface it looks like an engineering question, or a career planning question, but underneath it is an existential question: once execution is fully taken over by machines, where does the human stand? Or more bluntly — once AI takes everything it can take, what is left for us?

I gave fragmented answers in my replies, but I knew they weren’t enough. The question deserved a more complete answer, so I wrote this essay. It’s a shared question and one I’ve been turning over in my own mind lately. Writing my thoughts down may help others who are wrestling with the same thing.

I. The Spinning Jenny

The blue mountains cannot hold it back — the river still flows east.<br>— Xin Qiji, Pusa Man · Inscribed on the Wall at Zaokou, Jiangxi

A little over two hundred years ago in England, a spinning frame called the Spinning Jenny came into the world. One person could now do the work of several. Yarn got cheap, and the hand-spinners who had made their living with a pair of hands and an old wheel saw their livelihoods collapse in a matter of months.

Later, some of them went out at night and smashed the machines. History calls them the Luddites. People usually treat them as fools who hated technology, but that’s a misreading — what they wanted to smash was never the machine itself, but the position they suddenly occupied in front of it: a position that had become worthless overnight. They weren’t blind to the technology; on the contrary, they saw what was happening earlier than anyone.

The day Meituan announced 30%–50% layoffs, a friend texted me asking what to do. I replied: we should not be the spinners replaced by the Jenny — we should be the operators who use the Jenny well.

He may have remembered that line for a long time. Today I want to correct it. It was only half right.

Because the next page of history reads like this: the skilled Jenny operators didn’t have it easy for long either. Faster machines were built, and the Jenny — along with all its skilled operators — was sent to the museum. Every machine is waiting for the next machine. “Learn to use the new tool” has never been an amnesty. It’s just a stay of execution.

So the real question is not “do you know how to use AI.” People who know how to use it today do hold an advantage over those who don’t, but the half-life of that advantage is maybe a year or two — and at the top of the field, possibly only one or two months. The pressure from each new model generation is mounting; the window for exploration and adaptation gets shorter every time. Every new model release brings another paradigm shift, and the workflow you painstakingly built, the prompting tricks you collected, the engineering scaffolding you accumulated — any of it can become a Spinning Jenny overnight.

My only method for dealing with this is what I call end-state thinking : don’t spend yourself on intermediate-state problems. Think and act with the endpoint as the premise.

An example. A while back I had a serious argument with a few peers about a question: as AI writes more and more of the code, do engineers still need to understand it line by line? There are plenty of partial answers — better documentation, better visualization, mandatory code walkthroughs. But from the end-state view, this isn’t the core question at all. Once intent can be translated directly and reliably into a working system, “humans reading the intermediate artifact line by line” loses its meaning — just as today, no one asks engineers to read every line of the assembly the compiler produces. Getting stuck on a question that is destined to...