There is a scene in Idiocracy that has stayed with me. 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, and after hearing it often enough, it turns into 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 is impressive: Redis integration, TTL settings, cache invalidation, monitoring dashboards, performance metrics, tests, documentation, everything neatly assembled.
Except it does not solve the real problem. Because the real problem was not a missing cache layer. It was an N+1 query in the ORM that triggered 200 database queries per request. The cache hides the symptom. The cause stays where it is.
Without domain knowledge, you do not see the difference. With AI tools, you can now produce a very convincing solution to the wrong problem, faster and more convincingly than ever before.
The problem is not that the output looks bad. The problem is that it looks good, good enough that nobody asks whether it solves the right problem. The better the AI gets, the harder it becomes to separate substance from decoration.
How Reasoning Gets Corrupted
The Hacker News commenter names a second phenomenon that may be even 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 defined problem, something subtle happens: the model drifts. It suggests directions that sound plausible but do not actually match the problem. It offers solutions that feel elegant but smuggle in assumptions nobody asked for and nobody checked.
Because the model argues so fluently, it becomes difficult to notice the moment when you stopped solving the original problem and started following the model instead.
That is not a bug in the model. It is the nature of probabilistic text generation. It optimizes for plausibility, not correctness. And plausibility is a very poor compass when the work requires precision.
In another Hacker News discussion, someone put it well: AI is not a colleague. It is an exoskeleton. It amplifies what is already there. If you know where you are going, it gets you there faster. If you do not, it lets you walk faster in the wrong direction, with more force and less control.
The Electrolyte Argument
What worries me most is how criticism of AI-generated output gets brushed aside. The commenter described 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.
There is no discussion of whether the output solves the problem. There is only a reference to the supposed power of the tool. The tool knows Rust and TypeScript. The tool wrote the code in ten seconds. The tool has hundreds of millions or billions of parameters. Therefore the result must be good.
That is not an argument. It is techno-animism: the belief that a sufficiently powerful system will automatically produce correct results, and that the complexity of the tool somehow guarantees the quality of the output.
There is a specific mixture here: self-inflicted intellectual dishonesty, irrational belief, and performance for the audience. The output is not judged by whether it solves the problem, but by whether it impresses people.
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.
Not the model. The model 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, then the output will reflect that. Garbage in, elegant garbage out.
An LLM is trained to be helpful. It is not trained to tell you, “Your question is poorly framed. You do not understand the problem. Go back and think again.”
That is why insufficient instructions do not produce resistance. They produce a polished answer. Always. And that is exactly what makes these systems dangerous for people who cannot reliably tell the difference between “looks good” and “solves the problem”.
The Exoskeleton Counterargument
There is a strong counterargument here, and it deserves an honest answer.
There are junior developers who learn patterns from Copilot suggestions that they might otherwise not encounter for months. They see what a repository pattern looks like, how dependency injection gets structured, how a clean API endpoint is put together. AI can absolutely accelerate learning.
That works, but only when the person using it actively understands why the suggestion looks the way it does. When they read it, question it, modify it. When they treat the tool as a teacher, not as a ghostwriter. The suggestion is useful as a starting point, as long as it remains a starting point.
The problem begins where understanding ends and acceptance begins. AI tools are built in a way that makes that boundary almost invisible. There is no moment where the tool says, “This is where you stopped thinking.” The output looks the same whether the human understood it or not.
The Question of Responsibility
Follow that line far enough and you get to a question the industry still avoids:
If the human gives the instructions and the model writes the code, who carries responsibility when the code causes damage?
Today the answer is clear: the human does. Whoever ships code to production owns it.
But what happens when the chains get longer? When one agent delegates to sub-agents that make their own choices? When the human at the start of the chain can no longer reconstruct the result in detail?
An exaggerated but not absurd future question is this: if one day we have AGI, and a given result can no longer be traced back to a clearly identifiable human author, who receives the court order? AGI, Datacenter Oregon, floor 5, rack #10202?
The question sounds satirical. The issue behind it is not. Responsibility does not disappear just because understanding disappears. It is simply exercised irresponsibly. Whoever puts code into production remains accountable, whether they understand it or not. AI tools just make it much easier to generate outcomes you are responsible for without truly understanding them.
"But it has electrolytes" is not an argument. Not for plant food, and not for software architecture. The question is not how powerful the tool is. The question is whether you understood the problem before you picked up 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, not tools that replace it.
The difference between a good and a bad craftsperson was never the tool. It was knowing when to use it, how to use it, and when to put it down.
Plants need water. Not electrolytes.