Wisesel

Good post from @balajis on the "verification gap". You could see it as there being two modes in creation. Borrowing GAN terminology: 1) generation and 2) discrimination. e.g. painting - you make a brush stroke (1) and then you look for a while to see if you improved the... See more

Andrej Karpathy explains why human-AI collaboration often fails: we've got the workflow backwards and the bottleneck wrong. He points out that when working with AI, there's a clear pattern. The AI generates solutions quickly while humans verify the output. The goal is making this loop as fast as possible to get real... See more

Why the AI job loss narrative is overhyped: AI models still need to be prompted and verified, often iteratively, to drive business value. As @balajis says, AI is middle-to-middle, not end-to-end. Humans do the stuff at the ends (supervision); AI does the stuff in the middle.

Yes. A few miscellaneous thoughts. (1) First, the new bottleneck on AI is prompting and verifying. Since AI does tasks middle-to-middle, not end-to-end. So business spend migrates towards the edges of prompting and verifying, even as AI speeds up the middle. (2) Second, AI really means... See more

AI PROMPTING → AI VERIFYING AI prompting scales, because prompting is just typing. But AI verifying doesn’t scale, because verifying AI output involves much more than just typing. Sometimes you can verify by eye, which is why AI is great for frontend, images, and... See more

AI doesn’t do it end-to-end. It does it middle-to-middle. The new bottlenecks are prompting and verifying.