A scene from Idiocracy has stuck with me for years. In it, people water their plants with a sports drink called “Brawndo” instead of water. Ask them why, and the answer is always the same: “But it’s got electrolytes!” Nobody knows what electrolytes are. Nobody understands why plants need water. But the sentence sounds convincing. Hear it often enough, and at some point it turns into the truth.
That scene came back to me when I read this comment in a Hacker News discussion:
“LLMs empower those without the domain knowledge or experience to identify if the output actually solves the problem. I have seen multiple colleagues deliver a lot of stuff that looks fancy but doesn’t actually solve the prescribed problem at all. It’s mostly just furniture around the problem.”
Furniture around the problem. That is hard to improve on.
The Furniture Problem
I have been seeing this pattern for months. In day-to-day work, in open source projects, and in pull requests. Someone gets a task like “Implement a caching layer for the API.” What comes back looks impressive: Redis integration, TTL configuration, cache invalidation, monitoring dashboards, performance metrics, tests, documentation, all neatly assembled.
Except it does not solve the actual problem. A cache layer was never what was missing. The actual problem was an N+1 query in the ORM that fired 200 database queries per request. The cache hides the symptom, and the cause stays exactly where it was.
Without domain knowledge, you do not see the difference. With AI tools, you can now produce a convincing solution to the wrong problem, faster and more convincingly than ever before.
The problem is that the output looks good. Good enough that nobody asks anymore whether it solves the right problem. The better the AI gets, the harder it becomes to tell substance from decoration.
How Reasoning Gets Corrupted
The Hacker News commenter names a second phenomenon. I think it is almost more dangerous:
“The second major problem is corrupting reasoning outright. I see people approaching LLMs as an exploratory process and letting the LLM guide the reasoning.”
When you use an LLM as a thinking tool on a vaguely described problem, something insidious happens: the model slowly pulls the reasoning in a different direction. It suggests paths that sound plausible but do not fit the problem well. It offers solutions that seem elegant but smuggle in assumptions nobody asked for and nobody checked.
Because the model argues so fluently, you often notice too late when you stopped solving the original problem and started running after the model.
That is not a bug in the model. This is how probabilistic text generation works: it optimizes for plausibility. Correctness is not the goal. Plausibility, though, is a miserable compass when precision is required.
In another Hacker News discussion, someone put it well: AI is less a colleague than an exoskeleton. It amplifies what is already there. If you know where you want to go, you get there faster. If you do not, you walk faster in the wrong direction, with more force and less control.
The Electrolyte Argument
What worries me most is how easily criticism of AI-generated output gets brushed aside. The commenter describes it perfectly:
“And the retort when I have to evaluate what they have done is ‘but it’s so powerful’. I stopped listening. It’s a pure faith argument without any critical reasoning.”
“But it’s so powerful.” Those are the electrolytes of our industry.
Whether the output solves the problem no longer interests anyone at that point. All that counts is the supposed power of the tool. The tool knows Rust and TypeScript. It wrote the code in ten seconds. It has hundreds of millions or billions of parameters. So the result must be good.
That is techno-animism: the belief that a sufficiently powerful system automatically produces correct results, and that the complexity of the tool somehow guarantees the quality of the output.
What comes together here is self-inflicted intellectual dishonesty, irrational belief, and performance for an audience. The output gets judged by whether it impresses. Whether it solves the problem plays no part in that.
When the Output Is Nonsense
The more I work with AI coding agents, the clearer one point becomes:
If the output is nonsense, the human still has the problem.
The model does not. It is doing exactly what it was built to do: generate plausible text from the input it gets. If the input is incomplete, ambiguous, technically wrong, or simply too thin, the output reflects exactly that. Garbage in, elegant garbage out.
An LLM is trained to be helpful. It is not trained to tell you: “Your question is badly posed. You have not understood the problem. Go back and think again.”
That is why insufficient instructions meet no resistance. They produce a polished answer every time. That makes these systems dangerous for people who cannot reliably tell “looks good” from “solves the problem”.
The Exoskeleton Counterargument
Some junior developers do learn patterns from Copilot suggestions that they might otherwise not have encountered for months. They see what a repository pattern looks like, how dependency injection is structured, and how a clean API endpoint is put together. AI can speed up learning.
That only works, though, if the person actively understands why the suggestion looks the way it does. They have to read it, check it, and change it, using the tool as a teacher and not as a ghostwriter. The suggestion is useful as a starting point, as long as it stays a starting point.
The problem starts at the boundary between understanding and nodding things through. AI tools are built in a way that makes that boundary almost invisible. At no point does the tool say: “This is where you stopped thinking.” The output looks the same whether the human understood it or not.
The Question of Responsibility
Follow that thought to its end and you land at a question the industry is still dodging:
If the human gives the instructions and the model writes the code, who is then responsible when that code causes damage?
Today the answer is clear: the human. Whoever puts code into production is responsible for it.
But what happens when the chains get longer? Say, when an agent delegates to sub-agents that make their own decisions, and the human at the start of the chain can no longer reconstruct the result in any detail?
A pointed but not absurd question about the future: suppose one day we have AGI, and a given result can no longer be attributed to any clearly identifiable human author. Who then receives the court order? AGI, Datacenter Oregon, floor 5, rack #10202?
As satirical as the question sounds, the problem behind it is serious. Responsibility does not disappear just because understanding disappears. It only gets exercised irresponsibly. Whoever puts code into production stays responsible, whether they understood it or not. AI tools simply make it much easier to produce results you have to answer for without having grasped them.
“But it has electrolytes” is not an argument, whether for plant food or for software architecture. How powerful the tool is has no bearing on that. The question is whether you understood the problem before you reached for the tool.
What Remains
I am not against AI tools. I use them every day. But I use them as what they are: tools that implement my understanding without replacing it.
The difference between good and bad craftsmanship has always been a matter of knowing when and how to use the tool, and when to put it down again.
Plants need water. Not electrolytes.