Why the people building AI can’t tell you what’s next | Dianne Penn (Anthropic)

Dianne Penn: I think one of the moments where really we started to get into our groove was shipping things like Golden Gate Claude... when you actually essentially dialed up that feature, Claude would obsess about the Golden Gate Bridge. So meaning in every one of its responses, it would come back and talk about the Golden Gate Bridge... it made us feel like oh we can actually bring new user experiences, showcase our research in a way that's different and authentic to us and in a very startupy like pace. That to me was like one of those like maybe hidden inflection points of we were starting to find our identity that we could build products, build experiences that were different for what our competitors had seen, what was already out there.

Dianne Penn: The way to drive user value is to figure out the right user feedback, the evals, right? That then can be a personification of that user need. So like we do write some product documents and PRDs, but we actually have a saying on the team of evals are the new PRDs... Here, you have to sweat the tokens as much as you sweat the pixels. And so, one activity we have on the team is reading the transcripts and understanding what was the trajectories that failed very deeply to then say was this like a hallucination? was this claw being overconfident.

Dianne Penn: In order to be good managers of teams and PMs working with this technology, you have to be really hands-on yourself and have spent not just time tinkering but actually shipping with this technology and and and again being in the details and sweating the tokens along with your PMS and your engineer and your teams... even for folks that I hire who have more tenure PM experience, the onboarding plans are exactly the same as somebody who is like more early career and it's around understanding users, reading like consented user feedback, talking to customers... I do feel pretty strongly that if you're a manager, you have to be hands-on. You have to spend a portion of your time actually shipping.

Dianne Penn: We started to talk about making claude and models better at judgment especially in the last year or so. I think judgment is one and is an area where it's an accumulation of so much nuance and so much experience and these systems haven't experienced as much as humans have and so I think that hard-earned like judgment is a area for product leaders and just generally will continue to be really critical. There are so many things AIs can build. Which one are the things that you know an or like lab should build right a lot of that requires like human judgment persistence so proactivity.