Entrepreneurial Thought Leaders (ETL)
00:35:38 · Brendan Foody, S10
[00:35:38] S2: Definitely. Actually, I mentioned Yash, and I learned a lot from him in 2024. And one of the things... I guess I only talk about this because this is, like, over... Or two years ago at this point, and so it's not, like, you know, totally locked down confidential. But one of the things we saw with deep research was that the... It made very clear that the model is the product. Yes. And the way to solve these problems is not through stitching together a bunch of API calls with drag and drop processes, and it then statements. The way to solve the problem, in almost every knowledge work domain is how do we lay out the end goals for the models or the agents, the eval, And then how do we just train the model to get really good at those end goals? And, I think it was it was work... Looking at the leading indicator, working super closely with him, that gave us that window into the future before everyone else. And I think now since Opus 4.5, everyone is really realizing that because that's like a, you know, great example. The model is the product, and the models are just getting so good that it really makes sense to lean into that part then. Thank you. [00:36:56] S10: Hi. You mentioned that as you mentioned that as we automate most of today's tasks, humans will migrate to, like, other kinds of jobs. So I was wondering what you think those jobs will look like.
00:36:56 · S10, Brendan Foody, Emily Ma
[00:36:56] S10: Hi. You mentioned that as you mentioned that as we automate most of today's tasks, humans will migrate to, like, other kinds of jobs. So I was wondering what you think those jobs will look like. [00:37:08] S2: I think there's a lot in the physical world, but maybe actually I'll break it into two categories. The first category is new jobs that get created, and then the second category is things that as an economy we under invest in. In terms of things that get created, probably the top three that come to mind are data center creation, are for deployed engineers or deployment strategists that you probably see at like every AI startup to help enterprises and companies adopt agents. And then the third is training agents where we have huge volumes of people paying out many millions of dollars a day to create these data sets, our own environments, and evals for helping to improve model capabilities. So that's the first bucket of new jobs that get created. And there's also all these things that we just haven't been able to invest enough brainpower in as a society of how do we cure cancer and solve climate change. Right? Like, think about if we could have 40 or 50% of, like, Stanford's graduates working on stuff like solving climate change in ten years. That would be so, so impactful. And so I think freeing up people's bandwidth and economic resources to mobilize towards those things will also be another huge benefit. Another analogy it made me think about is two hundred years ago, 75% of Americans were farmers. [00:38:33] S1: That's right.
00:38:33 · Emily Ma, Brendan Foody
[00:38:33] S1: That's right. [00:38:34] S2: Right? And as we made farming dramatically more efficient, it just made it so that we could work on all of these other things like literacy and medicine, etcetera, that we had underinvested in as a society previously. [00:38:50] S1: We will take five more questions. We're going to try. [00:38:56] S2: I can answer quickly. [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?
00:38:57 · 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.