2025-06-24XThread

Source item

AI doesn’t do it end-to-end. It does it middle-to-middle. The new bottlenecks are prompting and verifying. AI will create many jobs in the broad area of verifying AI. Proof of human. Proof of authenticity. Proof the output is not AI. Proof the output is actually right. This is a great one-liner by @chamath. And matches my experience. The senior engineer is the goalie. They catch what AI missed.

3 segments

Segment 1 · characters 0–106 · Original segment ↗

Segment 2 · characters 108–271 · Original segment ↗

Segment 3 · characters 273–400 · Original segment ↗

Quoted context

These are the quoted author’s words.

@balajis Reminds me of a comment made by Chamath that "in the past, QA team members were the lower level IT staff, with AI, QA will be the elite IT staff"
DRWestWing ↗

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 video. But for anything subtle, you need to read the code or text deeply — and that means knowing the topic well enough to correct the AI. Researchers are well aware of this, which is why there’s so much work on evals and hallucination. However, the concept of verification as the bottleneck for AI users is under-discussed. Yes, you can try formal verification, or critic models where one AI checks another, or other techniques. But to even be aware of the issue as a first class problem is half the battle. For users: AI verifying is as important as AI prompting.
balajis ↗