A Singaporean software engineer who goes by Airing is writing for devs who can already see the AI curve from inside their own work: docs, code, review comments, all moving faster than the human capacity to verify them. The design translation is pretty direct. AI changes who can produce, but judgment stays human, and the more production accelerates, the more expensive weak judgment gets.
Over the past year, every piece of my work that I could hand off, I handed off to AI, piece by piece. The design doc — it wrote. The code — it wrote. The first drafts of documents and review comments — it wrote. What I can now run in parallel in one evening would have taken a full quarter two years ago. By rights I should be idle, but the truth is the opposite — I am busier than I have ever been. The content of “busy” has simply changed: I almost never produce anything with my own hands now. I spend the whole day reviewing what it produces.
This is the embryo of the end-state workflow. In it, there are only two things left for humans to do: review the design, and verify the result.
Notice what these two have in common: neither of them is production. They are gatekeeping.
That changes the clock as much as the craft: more time spent checking outputs, less time making the first pass by hand.
That gatekeeping word applies to designers too. When AI can generate the interface, the screen, the copy, and the prototype, the remaining work is deciding whether any of it should exist in that form. Airing says the temptation is simple: “lower your own standards.”
Read one fewer line of code. Skip one step of reasoning. Don’t ask one of the “whys.” When the little voice in your head says “eh, probably fine,” nod and let it pass. Project this forward and you can already predict the next few years: nearly every collapse in engineering quality will not be because one specific person was lazy — it will be structural. When production is infinitely fast, the verification standard becomes the only compressible variable in the system, and every incentive in sight pushes you to compress it: the boss’s expectations, your peers’ speed, the performance-review whip — they’re all telling you to let go.
But think clearly about what letting go means. The reason you have not yet been optimized away from the verification step is precisely that your standard is higher than the machine’s. A gatekeeper who keeps lowering the standard is personally building the case for their own replaceability. My stance on this has never moved: slower is fine. The standard, not one inch.
Airing refuses to make this only a productivity story. The professional risk is cognitive atrophy: forwarding the problem to the model, forwarding the answer back, and losing the stretch where judgment is formed. For designers, the warning runs through the same place: don’t outsource the taste-building, the principled refusal, or the logic of why a thing should be this way and not that way.
A real question, most of the time, does not have a deterministic answer. Cognition does not fall from the sky. It is ground out by humans through derivation, argument, and exchange — bit by bit, through a process that cannot be skipped, because that process is the human. If cognition is handed to you directly by AI — whether or not it is comprehensive, whether or not it is critical — the mere absence of that middle stretch of thinking is enough to let subjecthood retreat, inch by inch, until what is left is the silhouette of someone waiting in front of a Jenny to be retired.
AI cannot replace humans in perceiving and experiencing the world — and this isn’t a pep talk; it has a structural basis. I wrote, in an earlier essay on AI and psychological healing: human cognition is not just information processing — it is an experience interwoven from sensation and emotion. We perceive the real world through our bodies; we assign meaning through feeling. AI is unmatched at processing information and generating text, but what it lacks is precisely this dimension of experience — it can process data, but cannot experience the real world that the data represents. In one line: AI’s understanding is instrumental, not existential. It can help us understand certain facets of the human condition, but it cannot replace the insight we obtain through lived experience and inner reflection. A painting can imitate nature, but it can never become nature itself.
The reed’s entire dignity is in its thinking. Please do not hand it over.
