“For this benchmark, for example, we interviewed hundreds of bankers. And one question we asked that I thought was really interesting is, would you rather start from this output or not? And the vast majority said that they would have rather started from scratch. And so this, I said, like, sit with that for a second.”

But for this, for the for this benchmark, for example, we interviewed hundreds of bankers. And one question we asked that I thought was really interesting is, would you rather start from this output or not? And the vast majority said that they would have rather started from scratch. And so this, I said, like, sit with that for a second, right? Like the vast majority say they would have rather started from scratch. I think you're totally right from the lab perspective, right? Having the eval there to be like, hey, you know, we got to get better at surfacing these tax treatments or whatever, whatever ways those manifest. There's tons of those examples, right? It can certainly help you direct where do I need to improve steerability of the model? Where do I need to improve, you know, where might I need more training data? Where might I need, you know, you can figure out the solutions and solve for those. But if you're looking at it from the perspective of like, how do I get this used in a bank? You're talking a lot more about. Interpretability and like this human-agent interaction that starts to matter…