Don't fall into the anti-AI hype

I suspect that we are going to have a wave of gurus who show up soon to teach us how to code with LLMs. There’s so much doom and gloom in these sorts of threads about the death of quality code that someone is going to make money telling people how to avoid that problem.

The scenario you describe is a legitimate concern if you’re checking in AI generated code with minimal oversight. In fact I’d say it’s inevitable if you don’t maintain strict quality control. But that’s always the case, which is why code review is a thing. Likewise you can use LLMs without just checking in garbage.

The way I’ve used LLMs for coding so far is to give instructions and then iterate on the result (manually or with further instructions) until it meets my quality standards. It’s definitely slower than just checking in the first working thing the LLM churns out, but it’s sill been faster than doing it myself, I understand it exactly as well because I have to in order to give instructions (design) and iterate.

My favorite definition of “legacy code” is “code that is not tested” because no matter who writes code, it turns into a minefield quickly if it doesn’t have tests.

I want to know if any content has been made using AI or not.

There really should be a label on the product to let the consumer know. This should be similar to Norway that requires disclosure of retouched images. No other way can I think of to help body image issues arising from pictorial people and how they never can being in real life.

Yep, they work especially if you instruct them to add into your program ways for them to "see" what it is happening. And the more embedding models are getting better, the better results we will get too, from their ability to "see". For now Gemini 3 is the best at this, but is not the best at coding as an agent, so we will have to wait a bit.

All the other tools before that made programming more efficient results in rising salaries. I imagine salaries would only fall if AI can 100% replace a human, which currently it cannot. It remains to be seen what happens in the future of course.

Remember that an average software engineer only spends around 25% of their time coding.

This article is not about whether programming is fun, elegant, creative, or personally fulfilling.

It is about business value.

Programming exists, at scale, because it produces economic value. That value translates into revenue, leverage, competitive advantage, and ultimately money. For decades, a large portion of that value could only be produced by human labor. Now, increasingly, it cannot be assumed that this will remain true.

Because programming is a direct generator of business value, it has also become the backbone of many people’s livelihoods. Mortgages, families, social status, and long term security are tied to it. When a skill reliably converts into income, it stops being just a skill. It becomes a profession. And professions tend to become identities.

People do not merely say “I write code.” They say “I am a software engineer,” in the same way someone says “I am a pilot” or “I am a police officer.” The identity is not accidental. Programming is culturally associated with intelligence, problem solving, and exclusivity. It has historically rewarded those who mastered it with both money and prestige. That combination makes identity attachment not just likely but inevitable.

Once identity is involved, objectivity collapses.

The core of the anti AI movement is not technical skepticism. It is not concern about correctness, safety, or limitations. Those arguments are surface rationalizations. The real driver is identity threat.

LLMs are not merely automating tasks. They are encroaching on the very thing many people have used to define their worth. A machine that can write code, reason about systems, and generate solutions challenges the implicit belief that “this thing makes me special, irreplaceable, and valuable.” That is an existential threat, not a technical one.

When identity is threatened, people do not reason. They defend. They minimize. They selectively focus on flaws. They move goalposts. They cling to outdated benchmarks and demand perfection where none was previously required. This is not unique to programmers. It is a universal human response to displacement.

The loudest opponents of AI are not the weakest programmers. They are often the ones most deeply invested in the idea of being a programmer. The ones whose self concept, status, and narrative of personal merit are tightly coupled to the belief that what they do cannot be replicated by a machine.

That is why the discourse feels so dishonest. It is not actually about whether LLMs are good at programming today. It is about resisting a trend line that points toward a future where the economic value of programming is increasingly detached from human identity.

This is not a moral failing. It is a psychological one. But pretending it is something else only delays adaptation.

AI is not attacking programming. It is attacking the assumption that a lucrative skill entitles its holder to permanence. The resistance is not to the technology itself, but to the loss of a story people tell themselves about who they are and why they matter.

That is the real conflict. HN is littered with people facing this conflict.