Entrepreneurial Thought Leaders (ETL)
00:38:57 · Emily Ma, Brendan Foody, S5
[00:38:57] S1: Oh, yeah. You? Okay. Hi. I'm just curious. What are your thoughts on, like, the ethics behind AI and, like, recruitment? Specifically, just because it kinda goes based off of past information and then even more specifically for women or other minorities? [00:39:14] S2: Totally. Well, I think it's an incredibly important problem. And I have sort of two thoughts on this. First is that instead of using, like, heuristics for past things, I think grounding everything in performance data of how people actually do at the job, in the purest sense is the most effective way to make assessment of like people that have this skill or perform great on this assessment is the right way to do it rather than whatever arbitrary resume signals people have. The second thought is that the most unfair thing about recruiting processes is that the vast majority, 99% of people can't even engage. Right? Like the average one of us, if you submit an application to a hot company or Frontier Model Lab, they don't have the bandwidth to interview. They don't have the bandwidth to review resumes, to run you through all of their on sites. And the reason is that the entire process is manual, right? People have to manually review resumes, manually conduct interviews, and manually figure out who to hire. And so I believe that when you can solve this matching problem at the cost of software, it can enable everyone to be considered for every job and solve the largest inequity in every recruiting process. [00:40:31] S1: Okay. Thank you. [00:40:32] S5: Yeah.
00:40:32 · S5, S4, Brendan Foody, S6
[00:40:32] S5: Yeah. [00:40:34] S4: I was just curious. I think you guys went through prod if I'm correct. I was wondering like what your experience was with prod, not going to like YC or one of the traditional accelerators. It was quite early in in tune prod's history, right? [00:40:45] S2: I I would go so far as to say Merkur might not exist without prod. Yeah. It has been so unbelievably formative in everything. Like, all of our initial customers were prod teams. My friend Ben, who was the founding CEO of n eSphere, after he connected Michael and Aman with Arvid and Swale, a founder of this company Cofactory, they were an early customer, Rob and Gavin from Etched, and a bunch of others. So I I can't recommend them enough. Alright. Thanks. [00:41:16] S6: Hey. So a lot of people are... There... There's a lot of potential upside coming with AI, and we've been talking about some of that today. But there's a lot of people who are also worried about the potential downsides of AI. So just to you, what is your greatest fear? What keeps you up at night over the next ten years? Or what's in over the next ten years keeps... Or scares you the most, and what are you trying to do about it?
00:41:16 · S6, Brendan Foody, S4
[00:41:16] S6: Hey. So a lot of people are... There... There's a lot of potential upside coming with AI, and we've been talking about some of that today. But there's a lot of people who are also worried about the potential downsides of AI. So just to you, what is your greatest fear? What keeps you up at night over the next ten years? Or what's in over the next ten years keeps... Or scares you the most, and what are you trying to do about it? [00:41:45] S2: I think at a societal level and probably Merkur as well, there will be a lot of job displacement. Like, I say all of these optimistic things about how I think in five or ten years, it's it's clear that things will be dramatically better and there are gonna be lots of jobs that get displaced. I think in a lot of ways, most important problem in the economy is how do we navigate that in a few years, we might be able to automate 50% of the work that knowledge workers do. And I think that so much of my goal at Merkur is how do we help people navigate this problem? How do we build benchmarks that give us this window into the future of what are the jobs that AI can actually do and what are the jobs that it can't do for a very long time? How do we create these new job categories and how do we work with enterprises and policymakers to create awareness about both of those things. [00:42:41] S4: Great. Thank you. Hi.
00:42:41 · S4, S3, Brendan Foody, S5
[00:42:41] S4: Great. Thank you. Hi. [00:42:44] S3: Thank you so much for being here today. I was wondering, like, when you're hiring, like, these top consultants or top, like, investment bankers, did you ever have to, like, convince them to, like, you know, like, feed data to your models and also, like, potentially perhaps, like, lead to the downfall in the future? Like, was that ever an issue or is it more just, like, we'll pay them more? [00:43:07] S2: I think that one of the large motivations is people love being at the frontier of the technology using the unreleased models and finding the places where they fail. And the vast majority of people on the project share the mindset that we have that there's not a shortage of things to do in the economy. And when we make everyone twice as productive, we'll accomplish dramatically more. And so that tends to not be a concern for the people that we work with. Hi. Hey. [00:43:37] S5: My question is, do you think university is necessary or get big help on your entrepreneurship? [00:43:46] S2: I don't think that it was. I think that there are definitely communities of people that I met through university that were super impactful. Like, a great example is prod wouldn't have existed without sort of the Harvard network that Adarsh was in. And so I I think the communities that are formed around universities are super impactful, but I don't think that most of the value really came from, like, the academic structure, necessarily. [00:44:19] S5: Thank you. I have the same thought.
00:44:19 · S5, Brendan Foody, S11
[00:44:19] S5: Thank you. I have the same thought. [00:44:22] S2: There there are... I don't mean to say that, like, the entire academic structure is useless, but, there are there are... There's a lot of value to it. [00:44:31] S11: Hi, so I'm just wondering, you mentioned about leading indicators and looking for them. So I'm just wondering what are some concrete examples and what
00:44:31 · S11, Brendan Foody
[00:44:31] S11: Hi, so I'm just wondering, you mentioned about leading indicators and looking for them. So I'm just wondering what are some concrete examples and what [00:44:40] S2: are some patterns and what's the difference between something that's an actual leading indicator versus something that might be like red herring? So I think I'll say the best leading indicator is associated with dollars being spent because people can say something will happen or say they want something very often, but until they actually put their money where their mouth is, you don't know that that thing is true. And a great example of a leading indicator is what's happening in compute right now, right, where there's just, like, so much demonstration from the most sophisticated labs in the world that they will need to scale up the amount of compute that they have by orders of magnitude. And there's so many granular leading indicators. Like the fact that everyone is investing in alternatives to NVIDIA and we're moving to this multi chip future is really interesting, and there's gonna be a lot of technology built around routing between different chips. I think there's so much that's gonna happen in the data center build out, whether it's shortages of electricians or construction and all these different things. And so you can dig into a lot of the granularity of these, but I think that's one of the most interesting ones to look at.