The Problem With AI Isn't Bad Code. It's Confident Code.
Lately, I've been thinking a lot about how we evaluate AI models.
Most of the benchmarks I see focus on output. How fast can it code? How much can it generate in one prompt? How close is it to building an entire product on its own? Those things matter, of course. They're impressive, and they make for great demos.
But what often gets missed is the experience of the person using these tools, especially when things move from "just testing" to "real people will use this."
That gap is starting to show.
We Measure Output, Not Responsibility
AI today is very good at helping you ship something that works. What it's not great at is helping you understand when what you've built carries real risk. Security, responsibility, and long-term system safety are usually treated as optional concerns, something the user should remember to ask about.
And that's where the problem is.
Recently, I saw a video that made this very real. Someone signed up for an AI platform on a free plan. Instead of upgrading, they opened the browser inspector, changed a few values on the client side, refreshed the page, and suddenly had access to a paid plan with thousands of tokens. The system trusted what the client sent instead of validating things on the server. The model kept running, costs kept adding up, and no one was paying for it.
That's not a clever hack. It's a basic failure in responsibility.
What's more worrying is that this isn't rare. You see it in products built with AI all the time. API keys exposed on the client. Auth logic handled entirely in the frontend. Sensitive data visible in network requests. Not because people are careless, but because they're moving fast, trusting the code they're generating, and not always knowing when they've crossed from a prototype into something that behaves like a production system.
"Prompt Better" Isn't a Real Safety Strategy
We like to say, "prompting matters," and that's true. If you ask the right questions, you can get better, safer results. But that also means we're putting all the burden on the user. We're assuming they know what to ask, what risks exist, and when something needs extra care. That assumption breaks down very quickly, especially as AI tools are used by more non-technical builders.
In most products, we don't design for perfect users. We design for real people who forget things, who rush, who don't know what they don't know. For some reason, we've decided AI should be different.
What makes this more frustrating is that the alternative doesn't have to be heavy or annoying. I'm not talking about long security checklists or forcing people down rigid paths. I'm talking about small, contextual cues. The kind we already accept everywhere else.
If someone is building a signup flow, the system could simply ask, "Do you want me to do a quick security check for this?" If user data enters the picture, maybe there's a gentle nudge that says, "This usually needs a bit more care in production." No blocking. No shaming. Just awareness.
The decision still belongs to the user, but it's no longer a blind one.
When Everything Looks Finished, Mistakes Slip Through
Right now, everything AI produces looks finished. A prototype feels deployable. A demo feels ready to ship. But production isn't about whether something works. It's about who can misuse it, who can access it, and who pays when it goes wrong. AI tools don't help users make that mental shift, and that's where a lot of silent mistakes happen.
I understand why things are the way they are. From a business perspective, speed and output are easier to sell. "Look what it built" sounds better than "look how responsibly it built it." But as these tools become more powerful, the cost of small mistakes grows. Leaving everything to the user starts to feel less like flexibility and more like avoiding responsibility.
Vibe Coding Should Be Forgiven, Not Blind
Vibe coding should be forgiven. Experimentation should be easy. But risk shouldn't be invisible.
This isn't something bigger models will magically fix. It's not about more intelligence. It's about intent, context, and product design. It's about knowing when to stay out of the way and when to gently step in.
If AI is going to help us build real products, not just impressive demos, then responsibility has to be part of the experience, not something we remember to ask for after things break.
As AI becomes more powerful, reducing human error matters more, not less. And that's where designers, product thinkers, and builders come back into the picture.
Because someone still has to decide when speed is enough, and when responsibility needs to show up. If AI is going to help us build real products, not just impressive demos, responsibility can't be an afterthought. It has to be part of the experience.