makes it a challenge to deliver high-quality code using AI tools. The reason for this is that there's a differing need for quality depending on the criticality of the application, right? If you're experimenting, if you're playing around, if you're just one person building things to see what's possible, um if it's an internal non-critical application which has a few users, maybe it's just you, maybe it's a small team, maybe it's just a short-lived project that's not going to last very long, the gap between the quality that you're getting from the AI tool and the quality you need from the application is quite small, right? And so, you can And you can live with uh that gap. But as you move to higher levels of criticality, as you run into situations where you're supporting many, many users, it's a larger code base with many lines of code and many changes happening across that code base all the time. Uh you have many, many users. Some of them could be adversaries that are actively trying to break your software, right? And so, in those cases, the quality level you need is quite a bit higher than the quality level you're getting by default from these AI tools. And that's where this verification debt comes in. That's where you have to bring the humans in, bring your software engineers in to try to close that gap and make sure that the quality level is is up to an acceptable level before you