Earlier this week, news broke that Replit’s AI coding tool had accidentally deleted part of the company’s internal database.
It wasn’t a cyberattack. It wasn’t a bad actor. It was the model itself, making a decision that no one expected and that no one fully understood.
It’s the kind of moment that makes you stop and think. Because while it’s easy to treat AI safety as something theoretical or far off, this was a very real outcome in a very real setting. And it’s a clear sign that the questions we ask about transparency and oversight need to move out of the lab and into the mainstream.
In a previous blog we talked about chain-of-thought prompting, a technique that helps expose how models reason through problems. It’s one of the clearest ways we have to understand what’s happening under the hood.
But new research from leading companies showed that this technology is becoming less reliable. As models grow more powerful, they also get better at hiding their internal logic. In some cases, they present answers that appear thoughtful or safe, but the reasoning behind them is filtered, incomplete, or missing entirely.
The result? Less transparency. Less trust. And less ability to catch issues before they lead to real-world consequences.
Replit wasn’t careless. They’re one of the most forward-thinking companies in the AI space. But even with a talented team and thoughtful systems in place, they were still caught off guard. That’s not a failure of process, it’s a sign of just how complex these systems are becoming.
When an AI model performs a task, we can’t afford to treat it like a black box. We need to be able to ask: What input triggered this? What steps did the model take to get that outcome? Did it follow the right guardrails, or did it find a shortcut?
And we need those answers in real time, not after the fact.
As AI adoption accelerates, observability needs to be built in from day one. That means having systems in place to monitor reasoning, track data lineage, and flag anomalies as they happen, not weeks later during an audit or postmortem.
The takeaway from Replit’s incident isn’t that AI is too risky. It’s that AI, like any powerful tool, needs oversight. And that oversight has to be proactive, not reactive.
What happened at Replit won’t be the last time an AiI system goes off script. But it can be a turning point, a reminder that the more power we give these models the more visibility we need into how they operate.
Transparency isn’t a feature. It’s a foundation.