we're cold chilling, while we're doing the do. And my argument and my experience is that the bottleneck to this is not intelligence. The models are intelligent enough. They know all kinds of things. They know the entire internet. But they can't reliably deliver outcomes. And so I can't trust them. So, as a very simple example, you know, one day I get almost a fully working SaaS app from a single prompt, granted a long prompt. The next day, and I swear this actually happened, cloud code empties the entire contents of my Solana wallet. Oops. Okay. So, that doesn't really instill trust. So, uh at the bottom here we got this this progression, okay? And we all want to move towards the the one on the right where we're just sort of sitting there and meditating and and things are manifesting. And so, where does that come from? This is from the AI engineer code. It's actually from the back of the t-shirt. AI engineer code, November 2025. Man, I hope I hope you're there. If you weren't, watch it on YouTube. It was it was amazing. So, here's the thesis. The thesis is today's agents are mismanaged geniuses. The intelligence is there, and the missing layer is how do we specify and manage and reuse and verify the work. So, this uh framing this phrase, the mismanaged genius, uh comes from Alex Zeng, Z Li, and Omar Khattab at MIT. Um and Alex and Omar are uh part of the authors of the original recursive language models paper. Uh I've also talked a little bit about this recently on Turning Post. I forgot to mention that these slides