Let’s discuss:

Would you call an AI hallucination a hypothesis?

We dismiss hallucinations as errors. But history is filled with ideas that initially looked wrong, absurd, or impossible.

If an AI generates a novel claim unsupported by its training data, is it merely failing or is it doing something closer to what humans call speculation?

At what point does a hallucination become a hypothesis worth investigating?

Or are we giving machines far too much credit?

I'm curious where you draw the line.

Agree with the core point, but I'd frame it differently: AI doesn't ruin your career — using it as a crutch instead of a tool does.

I've seen this play out on the operations side too, not just coding. When someone uses AI to draft a process doc without understanding the workflow, you end up with something that looks polished but falls apart the moment a real edge case comes up.

The developers and operators who use AI well are the ones who already have the fundamentals. They use it to move faster, not to skip learning. The ones who skip the hard parts just accumulate technical debt they can't debug themselves.

Best approach for juniors IMO: build something from scratch first, understand the moving parts, then use AI to accelerate the next project. You'll know when the AI output is wrong because you've done it the hard way at least once.

Yes, I totally agree with this kind of opinions about using AI. always remember you are the main programmer of your projects and AI can be a assistant to you, not your entire project buliding.

This resonates a lot. I'd add that the same pattern shows up on the ops side too — not just in code. Founders and small team operators who let AI handle everything without understanding their own workflows end up unable to troubleshoot when things break. The real skill isn't using the tool, it's knowing what good output looks like so you can spot when the tool gets it wrong. Whether that's debugging code or reviewing a process, the fundamentals matter more than ever.

Relying on AI makes you replaceable because anyone can write a prompt. What makes a junior dev valuable is their ability to learn the business domain, collaborate with a team, and understand the core architecture of the product.

The best way for a junior dev to use AI is to treat it like a code reviewer. Write your own solution first, then ask the AI how to optimize it or what edge cases you missed, rather than letting it generate the initial logic.

We are already seeing a shift where companies expect junior devs to ramp up faster, but those using AI blindly are plateauing early. You have to know how the engine works before you can automate the driving.

Learning to code requires frustration and failure because that is how retention works. When AI shortcuts that struggle, junior developers miss the core learning loops that actually make concepts stick in the long term.

The tech industry values engineers who can navigate ambiguity and legacy systems. AI models work best on clean, standardized codebases, meaning a junior dev who relies solely on them will struggle when faced with messy, real-world enterprise code.

If you copy-paste AI code without understanding the underlying security implications or performance trade-offs, you are creating technical debt. Junior devs should use AI as a tutor to explain concepts, not as a proxy driver for their IDE.

Software engineering is not just about writing syntax; it is about system design and understanding why a specific architecture works. Juniors who lean heavily on AI miss out on the critical thinking phase that transforms them into mid-level engineers.

The biggest risk for junior engineers using AI is the illusion of competence. Passing a prompt and pasting code makes you feel fast, but during a live technical interview or a critical production outage, that lack of deep understanding becomes obvious.

AI coding assistants are great for boilerplate code, but junior developers need to focus on problem-solving fundamentals. If you let an LLM write all your functions, you never actually learn how to structure logic or handle edge cases on your own.

Relying too much on AI tools during your first few years stops you from building the muscle memory required for debugging. When the AI gives a wrong answer, a junior who cannot read stack traces or understand core language mechanics will spend hours going in circles.

I personally think its a trade off we have to make... Its like sewing machines vs hand sewing... Where you give up your proficiency for time... End of the day, what matters really is who are you?