Entrepreneurial Thought Leaders (ETL)
00:31:14 · S3, Brendan Foody, S8, Emily Ma
[00:31:14] S3: The fellowship? Yeah. [00:31:15] S2: Yeah. It's actually funny because it came later. It... We didn't get the TL fellowship until March 2024, and that was after things were... The company was at, like, 2,000,000 revenue, but we knew that we would be at 50 by the end of the year. And so things were real... Just, like, on the dawn of starting to work. And probably the largest value add was, I think, the credibility stamp to investors and hires is meaningful, but also the community. Like, I met, some of my best friends. My friend, Yash, who's on there, who's the CEO of Applied Compute and was previously our customer at OpenAI. And so those those are the two things, probably the credibility stamp and then the community. [00:32:01] S3: Thank you. Mhmm. [00:32:03] S8: Thank you so much for being here today, Brennan. When you started Merkor, it was around a time when Scale already owned a lot of the narrative around AI data and training. [00:32:12] S1: Yeah. [00:32:12] S8: And I'm curious what gave you and your team the conviction to kind of go for it and assume that the market's not fully taken as opposed to go and work for, say, Alexander Wang. Uh-huh.
00:32:12 · S8, Brendan Foody, Emily Ma
[00:32:12] S8: And I'm curious what gave you and your team the conviction to kind of go for it and assume that the market's not fully taken as opposed to go and work for, say, Alexander Wang. Uh-huh. [00:32:22] S2: Well, it's funny because I skipped over part of the story earlier, which is before I met Sean, our initial intuition was that we should actually partner with scale and have them become a customer of ours. And so we hired over a thousand software engineers for them using our platform and our technology, and then we got flooded with support tickets that these people weren't being paid. And they were like, Merkur is a scam because they referred us to this opportunity that is Scale AI. And what we realized was that there was this gap in the market in part because it was changing really quickly. Like, scale grew up in autonomous vehicle labeling with people in The Philippines, and a lot of the infrastructure was built around that. And if we went directly to the labs, we had opportunity to build things from the ground up around exceptional people creating this new paradigm of agentic data. And so that was really the moment that it it all flipped for us. [00:33:28] S1: Thank you. Hi,
00:33:28 · Emily Ma, S9
[00:33:28] S1: Thank you. Hi, [00:33:30] S9: Brandon. Thank you so much for being here. So since you missed two years of college, I thought I'd ask you something related to the education system. What do you think that education systems around the world can do in order to cope with this wave of agentic AI? And do do we need to change the way we actually evaluate human skill at this base level? Or do you feel like the current system is preparing students to actually take on a world that will look vastly different to the one we inhabit during our schooling?
00:33:59 · Brendan Foody, S9
[00:33:59] S2: I think a lot about this because we do... I mean, we've done over 6,000,000 AI interviews. So I I think a lot about how do we effectively assess people, in all sorts of different scenarios. And I think the most important thing for educators to get right is to lean into assessing people using the technology rather than stopping the technology from happening. Like in the initial waves of AI, we saw all of these things of like GPT zero try to stop the use of the models, which is almost analogous, like stopping people from using calculators or computers. And the better way is how do we encourage everyone to use the technology as much as possible and see what they can do with it? Like, now all of you can build a production skill app using Claude code in a week. And so how do we... That can be, like, a one week project, which is insane and has never been possible before. And so how do we, you know, just set these really ambitious targets and goals for students that that force the adoption of the technology and the exploration of what's possible with it. [00:35:10] S9: And just another question to lead up on that. Was there a moment you had during your founding process that made you realize that the whole Pagentic AI landscape was actually moving in this direction? Because today, if we look at it, we have so many tools like Open Claw. That's just a list of them. So was there one moment that made you actually realize that, Okay, the whole AI agent revolution is actually gonna be the next kind of change in in the whole AI landscape?
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.