

I actually don’t need to know how my IDE works to use it.
Neither do I need to know how a transformer works to use an AI agent. What does that have to do with anything?
I need to know Java to recognize whether IntelliJ’s refactoring produced sensible Java, just as I need domain expertise to recognize whether an AI agent produced sensible output. “Requires oversight and expertise” does not mean “requires understanding the internal implementation of the tool.”
With increasing degrees of specificity, it kind of is, yeah.
No, it really isn’t, and this is probably the strangest part of your argument.
Specificity of requirements and effort of implementation are two completely different things.
“Take these 30 classes, rename this field, update its usages, add null checks at these boundaries, update the affected tests, and run the test suite” is a reasonably specific description of a task. It is quite obviously not equivalent in effort to manually performing every edit.
“Create a sequence diagram showing the interaction between these five services for this request flow” can take seconds to describe and considerably longer to manually construct.
“Take these documents, extract these specific metrics, compare them by quarter, and put the results into a PowerPoint using this existing deck as the visual template” is a perfectly comprehensible specification. Actually reading the documents, extracting the data, calculating the comparisons, creating the charts and assembling the slides is where the work is.
This distinction is the entire reason programming exists. A specification describes what computation you want performed. We don’t conclude that because SQL lets me precisely specify which data I want from a database, I might as well manually inspect every row.
And “it only saves time where you don’t care about shit-quality work” is just begging the question. You’ve defined AI output as shit and then concluded that anyone accepting AI output must therefore not care about quality.
The useful workflow isn’t “ask AI for something and blindly ship whatever comes out.” It’s “specify the task, let the machine perform the expensive mechanical portion, inspect the result, and correct or reject it where necessary.”
Sometimes that is slower than doing it yourself. Sometimes the output is shit. Sometimes AI is simply the wrong tool.
But the idea that describing a task with sufficient precision inevitably approaches the effort required to execute that task is just demonstrably false.
If that were true, half of software engineering wouldn’t exist.






I think the “very basic topological fact” you’re looking for is that you’ve discovered a continuum and then somehow convinced yourself that this means both ends of it are the same thing.
Yes, if you progressively specify every implementation detail until you’ve literally specified the complete implementation, then congratulations: you’ve eventually implemented it.
This is a genuinely fascinating discovery.
Unfortunately, absolutely nothing requires you to do that.
“Rename this field everywhere, update the tests and verify they pass” is more specific than “fix the code,” and considerably less specific than enumerating every character that needs to change in every file.
The entire useful space between those two points is apparently missing from your topology.
This might be my favourite part.
No, reviewing something does not require independently recreating it from scratch. I genuinely don’t know how you function professionally if you believe this.
I review other people’s code without first independently implementing their ticket.
I review pull requests without recreating every commit myself.
I review architecture diagrams without drawing a second architecture diagram and holding them up to the light.
I review PowerPoint decks without secretly making my own PowerPoint deck first.
I can inspect a sequence diagram and notice “service B doesn’t call service C there” without first spending twenty minutes lovingly dragging boxes and arrows around myself.
This is, in fact, one of the rather important properties of human cognition: recognizing whether something is correct can be dramatically cheaper than producing it.
Otherwise code review would involve two developers independently implementing every feature so one of them could check the other.
Your Suno example is equally compelling. You found a tool that couldn’t produce output meeting your standards for a particular task, so you stopped using it for that task.
Excellent.
I once encountered a screwdriver that was unsuitable for hammering in a nail. Thankfully I managed to resist developing a general theory of screwdrivers from the experience.
And this:
is at least refreshingly explicit. We’ve finally abandoned the argument and arrived at “I have decided AI output is shit, therefore AI output is shit.”
Which is perfectly fine as a personal preference.
It’s just considerably less interesting than the “very basic topological fact” you dressed it up as.