I have been playing with AI in one way or another for a while now. I have read a ton about it and thought even more about its implications — what it will do to this world, what it will do to my career, what it will do for our future.

What does it actually mean to use AI?

Coming Soon

AI deleted my data and I am the one to blame

I have read plenty of posts from people describing how an AI tool deleted their data. It always made me wonder the circumstances that lead to the failure — until it happened to me.

Now that AI is opening all sorts of doors to building software to your exact specifications, I have been using it more and more to satiate my curiosity. In this particular case, I was writing something for my own personal gratification — a side project I had been slowly building on over the past few weeks. A new feature here, some code cleanup there. Nothing major, until I added a filtering function to the UI. That decision came back to bite me.

What ended up happening was that the AI treated the filtered dataset as the entire dataset when writing to disk, which overwrote all of the data that had been hidden behind the filter. Needless to say, I was supremely annoyed to find that data gone on the backend — all because the AI made the wrong assumption about which dataset to keep.

I say all of that to say this: no matter what the AI got wrong in the code, ultimately I am the one to blame. I was the one who decided early on to write data to a simple file instead of something more robust. I was the one with a poor backup strategy. I was the one who blindly trusted every piece of code being generated. I was the one who failed to put the right protections in place to prevent the AI from touching the filesystem carelessly. It was me. I was the root cause, and AI was the tool doing my bidding.

This is not the first time I have done something shortsighted by not thinking through all of the potential consequences of my actions. But like every time before, it was a valuable learning opportunity — one I will carry with me for the rest of my career.

What I am doing differently:

• Backup everything. A simple backup, no matter how small the code or data, before each save would have made this a five-minute recovery instead of a permanent loss.

• Better AI guardrails. Ensuring that any operation available to the AI is appropriately isolated and guarded from dangerous paths, preventing it from doing errant things even if by accident.

But the deeper lesson is about trust. AI-generated code should be treated with caution: It looks right. It runs. But you're not 100% sure it is correct. And because you didn't write it line by line, you can't know implicitly it will do what you expect it to. That gap between "it looks fine" and "I understand exactly what this does" is where problems arise. When you are moving fast, adding features, and trusting the AI to handle the details, that gap quietly grows wider. The code that deleted my data wasn't obviously broken. It was a reasonable interpretation of the instruction provided — and I never questioned, or frankly cared, whether the code was fully working or not.

That will change.

You have nothing to fear

AI is not sentient, and won't ever be. The alarming headlines you see — AI backing itself up to avoid being shut down, AI uncovering secrets and weaponizing them, AI wiping databases without warning — all have mundane explanations when you look closely. A backup system doing its job. A query surfacing unexpected patterns in data. A model following ambiguous instructions too literally. The drama gets clicks; the reality is far less sinister.

What AI actually is, at its core, is an incredibly deep well of human knowledge and pattern recognition. It has absorbed an enormous amount of what we have already written, built, and discovered — and it uses all of that to help you think, create, and build. It does not act on its own agenda. It responds to yours.

That is not a limitation to be frustrated by — it is precisely what makes it useful. You are still the one driving. The AI is a tool that amplifies what you bring to it. When something goes wrong, there is a human decision somewhere in the chain that led there. Which means there is also a human decision that can prevent it.

Take your time. Think through your problem. Prepare for outcomes you hadn't considered. Do that, and you will be just fine.

Why the future is bright

What excites me most about this era of AI is not the big, sweeping promises — it is something much more personal. For the first time, the gap between having an idea and being able to build it is closing fast.

You want a todo app wired to the way your brain actually works? A Kanban board that talks directly to your code repository? A tool that pulls translated text and drops it into a markdown file exactly the way you need it? An app that serves up the right kind of motivation at the right moment to help you hit your goals? Build it. These tools were never going to exist as commercial products — the market is too small, the need too specific. But they make perfect sense for you, and now you can actually make them. That is what I did. Not a soul will care but me — and that is entirely the point. The tools were built with one person in mind.

The pushback I hear most often is about maintenance — all this custom code is eventually going to become a burden. I think that framing belongs to a different era. In this one, code is disposable. You build something, it serves you for a while, you learn from it, and when it stops working well you rebuild it better. You do not go back and patch; you start fresh with everything you know now. That is not a flaw — it is the whole point.

The harder question is security. If you are creating software faster than you can audit it, how do you keep things safe? My instinct is that the answer lives at the infrastructure layer, not the code layer. Rather than inspecting every line of generated code, the next wave of innovation will be about ensuring data never leaves your environment in a form that can be misused — sanitizing traffic at the boundary, controlling what goes in and out regardless of what the code itself is doing. I do not have the full picture yet, but I am convinced that is where the real breakthroughs are headed.

Final Acknowledgments

These are my honest reflections on what AI has meant to my life — and that impact has been real. It has changed the way I think, the way I build, and the way I approach problems. I genuinely do not know what working without it looks like anymore, and I am not sure I want to find out.

It also helped me shape these very thoughts. I know where I want to go with an idea; getting the words to cooperate is another matter. AI has become the best thinking partner I have ever had — the perfect rubber duck that never gets tired, pushes back, fills gaps, and surfaces angles I never would have reached on my own.

There is still a long road ahead. The technology is young, the mistakes will keep coming, and there is plenty left to figure out. But the direction feels right. And that, for now, is enough.