Entrepreneurial Thought Leaders (ETL)
00:18:20 · Emily Ma, Brendan Foody, S3
[00:18:20] S1: Yep. [00:18:21] S2: The most important is making sure, a, that they're really high caliber. Right? Of we want to get the top people from any given program or organization, and b, that they cover a representative distribution of what our customers care about. [00:18:41] S3: Okay. [00:18:42] S2: So say our customers care about being better at consulting, We would break that down into what are all the different kinds of consulting, whether it's, management consulting or operational consulting, etcetera. And then what weighting do we wanna have in each of those categories, and how do we build out the distribution that corresponds to all of that? And so it's really just, like, the bottleneck to AI labs automating everything that happens in the economy [00:19:09] S1: Yeah. [00:19:10] S2: And we can do in all of our jobs is how do they build this network of human knowledge that covers the distribution of all of the context, all of the prompts, and all of the responses that correspond to that. And so that's what we focus on doing. [00:19:25] S1: Amazing. Okay. We're gonna ask the hard question. [00:19:28] S3: Mhmm. [00:19:28] S1: Right? Once all of this expertise is, in essence, built into the large language models, what comes next? Let me put it this way. We have a lot of students here who are wondering why should they study CS, and how are they gonna find their first job or their first adventure out of college. So what would you say?
00:19:28 · Emily Ma, Brendan Foody
[00:19:28] S1: Right? Once all of this expertise is, in essence, built into the large language models, what comes next? Let me put it this way. We have a lot of students here who are wondering why should they study CS, and how are they gonna find their first job or their first adventure out of college. So what would you say? [00:19:50] S2: Well, I think that in every revolution in productivity, people have underestimated how elastic productivity is. And what I mean by that is, I think, like, as an example, as a company, if we make all of our software engineers 10 times more productive, we'll hire more software engineers. Sure. People might not just be coding. They might be doing a lot of the things adjacent to software engineering of designing architectures, of having taste in the product that we wanna use, and setting the right goalposts for the agents. But but I think that the investment in human labor to do all the things that models can't do is gonna be profound. And while I would guess that probably within a few years, agents will be able to do a significant portion, maybe even a majority of what humans do in our jobs, I think it is a multi decade process to actually achieve super intelligence where we automate absolutely everything that people can do because models inevitably will just struggle with these super long horizon challenging to verify tasks. [00:20:55] S1: Yeah. Yeah. Okay. This is a fun one. We talk about artificial general intelligence. I know there's AISI, AGI. There's many ways of going about it. When that day comes, what will you be doing?
00:20:55 · Emily Ma, Brendan Foody, S3
[00:20:55] S1: Yeah. Yeah. Okay. This is a fun one. We talk about artificial general intelligence. I know there's AISI, AGI. There's many ways of going about it. When that day comes, what will you be doing? [00:21:06] S2: Well, I think I'll be doing the same thing. But but I do think there is such a large nuance between, I guess, AGI people define usually as models being able to do most economically valuable work versus a 100% of economically valuable work. And a great proxy for this is that the Bureau of Labor Statistics analyzed that over the last two hundred twenty five years, we've made the average, person 20 times more productive. So that's equivalent to automating 95% of what you do in your job. Yet, at the same time, we have more jobs in the economy than ever before. [00:21:45] S3: That's right. [00:21:46] S2: And I think that that will continue where so long as we don't automate a 100% and we just automate 95% of what everyone does, we're not gonna run out of things to do as a society. We're gonna have so much more work. We'll build a thousand times more software. We'll build rockets. We'll cure cancer, go to Mars, etcetera. And I think there's gonna be immense human involvement in all of those processes.
00:21:46 · Brendan Foody, Emily Ma
[00:21:46] S2: And I think that that will continue where so long as we don't automate a 100% and we just automate 95% of what everyone does, we're not gonna run out of things to do as a society. We're gonna have so much more work. We'll build a thousand times more software. We'll build rockets. We'll cure cancer, go to Mars, etcetera. And I think there's gonna be immense human involvement in all of those processes. [00:22:09] S1: You know, it's funny. We're experiencing these moments. The one that comes to mind for me is always the ATM. I think the banking industry was having a moment, thinking all these tellers would lose their jobs, but actually, financial industry evolved and became much broader and stronger as a result of... I mean, ATMs were just one thing in in innovation in that field. So we're seeing that widespread now, and it's helping people through this transition. [00:22:34] S2: I totally agree. Yeah. And I think there's also enormous job creation associated with people building data centers, people training agents for deployed engineers that need to go deep with every Fortune 500 to implement agents. And so with every revolution, there's been some degree of creative destruction that happens, and ultimately, humans always have and always will, play a meaningful part of the economy. [00:23:00] S1: Let me zoom out a little bit. What is the role of policy and government in a transition like this?
00:23:00 · Emily Ma, Brendan Foody
[00:23:00] S1: Let me zoom out a little bit. What is the role of policy and government in a transition like this? [00:23:09] S2: I think that there can be a lot of value to regulators helping to internalize the externalities to markets. And what I mean by that is sometimes markets don't price in that there are certain, you know, negative, things that happen because of business outcomes. Like, the most common example is carbon, from burning fossil fuel. And, like, how could we price in that externality via a carbon tax? I think similarly, probably the externality that's, like, most top of mind for the average American right now is jobs. Yes. While jobs are, like, the largest positive thing for everyone's lives in the economy, they're also the largest thing that regulators disincentivize. And so I think figuring out how regulators can invest and set... Through, like, payroll taxes, income taxes, especially at the low end, I think those are are not great. But figuring out how regulators can better incentivize all these positive things that we want more of, like new job creation, I think is, one of the most important things. [00:24:21] S1: Amazing. Okay. So, in the next year, we'll end with this question, and then we'll hand it to students. Actually, we'll have one more question after this. What are you most excited about? You don't have to disclose anything, but, like, what what are you excited about in terms of where AI is going? [00:24:39] S2: I'm really excited about how it gets applied to the rest of the economy. [00:24:43] S1: Yeah.
00:24:43 · Emily Ma, Brendan Foody, S6
[00:24:43] S1: Yeah. [00:24:43] S2: Because I think it's easy to be in these, like, echo chambers in Silicon Valley where [00:24:48] S1: you think [00:24:49] S2: that, you know, AI has a 100% adoption when we talk with the average company in the Midwest. They, you know, just started using ChatGPT a few months ago. Mhmm. And I think that that getting, like, really permeated throughout every business workflow in addition to every consumer is gonna be really exciting. I think in the context of Merkur, that's expanding from just serving the frontier model companies to serving every enterprise in the world and helping them build, the evals that correspond to their value chain so that they're able to implement and apply agents throughout everything that they do. [00:25:31] S1: Alright. So final, final question, which is the classic question we always ask at the end of these chats, is from Tina Selig, who started the series about thirty years ago. She wrote a book called What I Wish I Knew When I Was 20. I know that wasn't that long ago for you. That was... You know, you just turned 23. Happy birthday. If you could go back to three years ago, I've actually... I have, like, two questions here. What would you tell yourself, and what would you tell your mom? [00:26:01] S6: What would