Prof G Markets
00:43:36 · Luca Ferrari, Ed Elson
And then if you do that right, hopefully a virtuous cycle is ignited where you offer better and better jobs, you can pay more and more as the company becomes more successful, and the track record speaks for itself at the beginning, it's more like you have to believe me, but now, you know, go, you know, read on Glassdoor, or go talk to 50 people who work here, you can establish the truth pretty easy. So that's, you know, lot of effort. And then, you know, you want to select well among anyone who applies, and I believe that's a question of science, and we have tried to make it that, we were data scientists and AI researchers, we've put in a lot of effort to detect signals in someone else's application that predict performance, and we keep iterating and improving. Not unlike I think a quantitative trader would, with algorithms trying to find signals in the market to tell them which stock is likely to go up and which stock to go down. We try to do the same thing with people and find people who are, will be amazing if provided with enough opportunity and coaching. [00:44:39] S1: What does that look like, that sort of more scientific approach to hiring?
00:44:44 · Luca Ferrari
[00:44:44] S2: You need to first establish an objective function, what you wanna optimize for. We found that in our case, at this stage, that's a performance as rated by one's colleagues after twelve months on the job. The more people you hire, the more, the deeper you can go into the funnel while still retaining statistical significance. We are currently hiring several 100 people a year, we can go fairly deep one year, it's just tends to be pretty good. And then we... So that's the... Your objective function. And then what what you do is you need to to identify as many signals as you can in someone's profile, even beyond the pure application. And and then you you just... You rigorously track them and see what, you know, how... What combinations of signals best predict that outcome at the objective objective function level, and that's a... That's not a difficult problem, that's pretty well established in statistics. Or The problem is, course, capturing that outcome at one year, and in a way that's reliable, all those in the same way, pre structured. And those signals, the signals are really the key. Some are pretty obvious, for example, GPA. It's not a perfect predictor, it's only a mild predictor, but it's... If someone has done really well in university, they're more likely than not to do well at least at Bending Spoons. Most people would believe that helps. If someone has gone to a more competitive selective university, that's a predictor too, a weak one, I agree. People think, oh, someone to MIT, they must be a genius. Actually, it's much less predictive than you would think, but it is predictive a little bit. Some other are quite amusing and yet quite predictive. For example, we found that if someone was politically active when they were a teenager, but stopped being politically active when they were like in their twenties, that's a very good predictor of drive, of agency entrepreneurship, which you, at least we definitely want at Bending Spoons. It would have never occurred to me we spotted it through with AI basically. But then you can rationalize it by saying if someone is 16 and and and they're not content enough with being a good student, they're looking for ways to change the world around them, that's a good sign of, again, productivity agency, a healthy level of distance instruction.
00:47:02 · Luca Ferrari
If they're still doing it when they're 24, it's great, but it's probably more of a life mission and their job is probably, you know, takes a backseat to it, and so they're unlikely to do super well relative to people who prioritize their job. So, you know, hundreds of signals like that, we keep finding new ones, many we generate through tests, we have people go through practical tests, but we try to... We look at, you know, what people write in their emails, what tone they use, how do how do they interact with people who are not in a position of power. We found that how people email are, let's say, our our support agents who help them just with the logistics of their application, and and clearly are not the person deciding whether whether they get hired is a very good predictor of whether they are a a collaborative, humble team member as opposed to... Because they're always nice to people late in the funnel when they know that the offer depends on that interview. But but those who are assholes in life, they will show up as assholes more often than not with people who are not perceived to be in a position of power. So hundreds of those, none is very predictive, but when you put them all together, we we're pretty predictive at this point.
00:48:17 · Ed Elson, Luca Ferrari
[00:48:17] S1: It sounds quite similar to the strategy that Ray Dalio used at Bridgewater for many years where there was a lot of he he wanted to track everything. He wanted to track every meeting, every interaction, sort of put everyone on a social graph, which I think in a lot of ways probably worked. But, also, he got a lot of criticism for it, and a lot of people did not like it because it feels like... It felt like to a lot of the workers in that culture that they were being surveilled and that every interaction was a test. And it sounds like in a lot of ways with the level of tracking that you find... That that that you are employing that you might run into potentially similar issues? I guess, how do you think about that issue and and do you think about it? Do you think of it as a potential issue? [00:49:05] S2: I didn't work at Bridgewater. I... I've read a few things about it, so I'm slightly familiar with it. What I just described only applies before you get hired. [00:49:15] S1: Okay.
00:49:15 · Ed Elson, Luca Ferrari
[00:49:15] S1: Okay. [00:49:15] S2: On the job, we actually don't do any, there's a just a review at the end of the year, which is based on input from your colleagues. We don't do any tracking of working hours, of anything really. So we... And precisely for what you mentioned, B, are staunch believers in the fact that most people react really well to being trusted. Yeah. And so we try to hire people who believe we believe are amazing or can be amazing given enough time, and then we give them uncomfortably high levels of trust and freedom, to the point that sometimes we've been criticized quite the opposite, sometimes we've had these situations where we acquired a business and then we established as a general manager, someone who's like 27 years old, and sometimes acquired team members tell us, are you crazy like this person, how can you trust she can do the job? And historically she has by the way, but I think if we were to monitor or surveil people, that would really destroy that trust that's such a motivating factor for people to come to work and do their best. [00:50:20] S1: I guess it makes sense. It's like you're extremely discerning before they're hired and then extremely trusting once they're hired, which I think makes a lot of sense, is probably the right strategy.
00:50:20 · Ed Elson, Luca Ferrari
[00:50:20] S1: I guess it makes sense. It's like you're extremely discerning before they're hired and then extremely trusting once they're hired, which I think makes a lot of sense, is probably the right strategy. [00:50:32] S2: Over time, you assess people's outcomes over a range of scenarios. You don't need to to surveil altogether if someone is consistently delivering, delivering quickly, high quality work. I don't I don't care to know really how you got it done, know, like if you use AI or didn't use AI, did you work late or you didn't like, ultimately we wanna work with people who are effective. And so we can just measure how we contribute to projects over time. And I don't think surveilling people would add in very much if anything to be honest. But pre hire you don't have, like pre hire you need to, you don't have any information, you need to look at in every niche and cranny to be able to figure out whether extending an offer makes sense. [00:51:09] S1: One of the big themes with your business is sort of using AI and using modern computer science and infusing it into these businesses. What does that look like? What what does it look like to take a company like Eventbrite and then give it whatever the AI bending spoons magic actually is? What are you attaching onto these businesses?