Rebecca Hinds: observation

Transcript context

…Yeah, I like that notion of, boy, it's been a long time since I've worked at a big company. But when I did, I had a lot of outside the box ideas. briefly, when I did briefly, I should say, I had a lot of outside the box ideas of how to use things like internal markets or, auction mechanisms to figure out how to allocate people to the best and highest uses. And those mostly fell on deaf ears and for understandable reasons, because there was going to be, even if even if you assume buy in, which we didn't have, there was still going to be a lot of like time spent operating those mechanisms. And I could just see why, it just wouldn't happen. The younger me didn't understand those things quite as well. But this is, because you can have the AIs grind through. This is sort of the, in a sense, it's like the positive version of the mass surveillance use case, right? Like we used to be safe for mass surveillance because there just wasn't enough human brain power to process all the logs. Now we've got that. problem solved in a potentially very problematic way. But here you can actually imagine going through your full roster and really trying to tinker with all the different assignments and configurations that you might spin up. And you can imagine how that could really unlock a lot of potential that nobody would have had the time, certainly, perspective probably as well to be able to do in a pre-AI era. So I do think that is like pretty exciting. And there's a small finding in the report that I'm incredibly excited. We have one of our co-authors, Aruna from Berkeley, she made a note as soon as we put this in the report, wow, this is so exciting. I think so too. We don't foreground it because there's so much, but what we see in the most effective organization, so the 13% who have employees saying that significant productivity gains are occurring, They measure. They measure a lot of different things. In particular, they don't just measure productivity, but they also put that data disproportionately more in the hands of employees. And this is not new. I saw it with collaboration technology too. I saw it with remote and hybrid work and aging type situations. If employees have access to that data, it is transparent to them. Everyone is better off. Unfortunately, we're seeing cases of that not happening in organizations. Again, another benefit of the enterprise graph is everyone has access to it, right? Everyone is able to query the graph and understand the state of play within the organization in a way that I think is going to drive much better outcomes for both organization and individual. So speaking of measurement, one thing that I was kind of pondering myself as I was imagining myself leading an organization through these challenges based on all the findings in the report, and I don't know if we said this yet, but 69% of people admit to some form or some amount of bot ******** which I think has like a couple different definitions, but for me, it's like passing off AI work in, down the line somewhere where you yourself cannot defend the quality of the work. That's a very high rate. So it does strike me that, again, kind of my earlier thought, in a way, that's maybe a really optimistic thing for how well AI is working, because if you already have two-thirds of people just doing AI outputs blindly, sending them down the line, and the wheels aren't falling off entirely, that's like kind of an amazing finding. But then if I'm trying to manage that as a leader, I'm thinking, how do I detect who's doing it? And how do I detect where it's actually working? And maybe I do want to share that with with employees, maybe I do want to partially share it. I'm not sure exactly what the right level of transparency would be there. But have you seen anybody, you know, I've seen also recently, historically, my attitude or my synthesis of available information has been AI detectors don't work. So like when I go to present to a group of teachers, I'll say, don't do AI detectors, or at least if you do, you know, you certainly can't they can be wrong. So you can't trust them too much. These days, it does seem like Pangram Labs is getting a lot of praise in the general discourse around being pretty reliable. So I'm kind of wondering, should I be, if I'm a leader, adopting like something like Pangram Labs and having, you know, all these intermediate work outputs evaluated such that I can potentially both realize who's doing this, but also like maybe where which, again, this is kind of an angle on which nodes in my graph of activity that constitute my enterprise, which ones can I actually just have AI do? Like I potentially already have a lot of answer to that question in the work product that people have, the AI work product that people have passed off as their own. Do you see anything like that?…

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