This Week in Startups
01:00:28 · Jonathan Siddarth
[01:00:28] S0: Yeah. Phenomenal question. What we with relatively high confidence is that when the models get better at coding and math, they seem to get better in a wide variety of other tasks that have nothing to do with coding and math. Something about coding seems to teach the models how to think in a more structured way, how to communicate with less ambiguity, how to think step by step. So coding and math, there have been some experiments in how they have out of domain performance, outside of just pure code generation. And coding and math is also interesting because sometimes when you ask complex questions, Alex, the sub steps involve being able to compute stuff or calculate stuff and pass the results forward. For example, Alex, if you had a question like, hey, what are some interesting themes in investing in AI? The answer to that might involve a model that knows how to write code to query PitchBook, write some Python code to analyze the data and say, you know, AI in health care seems to be spiking. AI in retail seems to be going down. That requires the model knowing how to write some Python code to analyze the data. It knows Matplotlib to plot the results and show you the results. So coding and math have broader applicability. But it's also true that this is one of the mysteries of these language models. The models seem to learn some representations about the world that seem to carry over to other areas. I can totally imagine you understanding human psychology. If the models were really good in human psychology, it could help the model write better website code that designs the website to be more persuasive, maybe more conversion optimized, maybe makes it better for generating copy. So it may be... Who knows? Maybe there is something to learn from studying the universe that helps people in their day to day. It could be some indirect thing, but we don't know for sure.
01:02:26 · Alex Wilhelm, Jonathan Siddarth
[01:02:26] S1: So, Jonathan, here's to a next great couple of years as the world gets bigger and faster and smarter. And just before I let you go, where can people find Turing on the internet? And is there a particular job that you're having a hard time hiring for that you wanted to shout out to the world? [01:02:41] S0: Great. Thank you, Alex. But If you're working on AGI research or in human data, come talk to us. We are hiring across the board. And if you're building a frontier foundation model and you need data to make the model smarter at coding, reasoning, STEM, etcetera, come talk to us. We're at Turing dot com and you can email me at, jonathanturing dot com.
01:02:41 · Jonathan Siddarth, Alex Wilhelm, Brendan Foody
[01:02:41] S0: Great. Thank you, Alex. But If you're working on AGI research or in human data, come talk to us. We are hiring across the board. And if you're building a frontier foundation model and you need data to make the model smarter at coding, reasoning, STEM, etcetera, come talk to us. We're at Turing dot com and you can email me at, jonathanturing dot com. [01:03:02] S1: Perfect. All right, Jonathan, thank you so much. We'll have you back on in another six months when AI is twice as smart. And until then, this is Twist. Bye. Alright. So here on Twist, we have talked ad nauseam about AI and the job market, mostly about how AI might impact the job market, change who does what job, change what jobs are done. You get the idea. People have a lot of concerns that AI might take all the jobs. We'll have to see what happens. Today, though, I wanna talk to a company that wants to use AI to help people not just get jobs, but get the right jobs and also to have companies find the right people. Because as it turns out, and you know this if you've done any hiring in your career, finding the right people pretty much is terrible even with the modern tools we have today. So to help explain how this is gonna work, please welcome to the show. It's Merkur CEO and cofounder, Brendan Foody. Brendan, hey. How you doing? [01:03:48] S5: I'm doing great. It's awesome to be here, and I appreciate you having me, Alex.
01:03:48 · Brendan Foody, Alex Wilhelm
[01:03:48] S5: I'm doing great. It's awesome to be here, and I appreciate you having me, Alex. [01:03:52] S1: It's my pleasure, man. I love doing these. Talking to people who are building what's actually next for the economy literally never fails to give me a jolt like a good espresso, so thank you. Mhmm. Now one reason why I wanted to talk to your company is because you have this amazingly huge vision. You guys wrote that you founded the company because, and I quote, the labor market is the largest, most inefficient market in the world, which is a pretty big claim. So before we dive into how Merkur is gonna approach this and how you're gonna get into the market, tell me why you think that and what brought you to that as the problem you wanted to solve. [01:04:24] S5: Yeah. It really comes down to this matching problem, where when you were describing earlier, the reason that it's painful is that when a candidate is applying to a job, they can only apply to a couple dozen jobs. And when a company is considering candidates in the market, they can only consider a fraction of a percent of the people available that are looking for work because they need to solve this matching problem manually. They need to manually review resumes, manually conduct interviews, and manually decide who to hire. But when you're able to solve this matching problem at the cost of software, it makes way for this global, unified labor market that every candidate applies to and every company hires from. And so that's the end vision of the company and the North Star that we work backwards from.
01:04:24 · Brendan Foody, Alex Wilhelm
[01:04:24] S5: Yeah. It really comes down to this matching problem, where when you were describing earlier, the reason that it's painful is that when a candidate is applying to a job, they can only apply to a couple dozen jobs. And when a company is considering candidates in the market, they can only consider a fraction of a percent of the people available that are looking for work because they need to solve this matching problem manually. They need to manually review resumes, manually conduct interviews, and manually decide who to hire. But when you're able to solve this matching problem at the cost of software, it makes way for this global, unified labor market that every candidate applies to and every company hires from. And so that's the end vision of the company and the North Star that we work backwards from. [01:05:10] S1: Okay. So essentially, when I go out there to look for jobs, I might look at my local area. I might look at jobs that... Companies that I've heard of. But no matter how far I cast my net from my perspective, I'm still missing out on most of the gigs, and therefore, most of the companies aren't hearing from as broad a talent pool as they might. [01:05:26] S5: Exactly. Yeah. [01:05:28] S1: Okay. So in a world in which we're dealing with kind of the demise of remote work, isn't the labor market necessarily constrained by geography?
01:05:28 · Alex Wilhelm, Brendan Foody
[01:05:28] S1: Okay. So in a world in which we're dealing with kind of the demise of remote work, isn't the labor market necessarily constrained by geography? [01:05:37] S5: Well, not precisely. I don't think we're dealing with the demise of remote work. I just think that in many ways, work might actually become even more prevalent. And part of this is that if you automate 90% of what it means to do remote knowledge work, that means that the bottleneck to productivity is the other 10%. And so for all the domains like software engineering, where demand is extremely elastic, we'll just produce 10 or 100 times more. And so I think that humans and the role that we play in knowledge work both remotely as well as in person is going to become amplified with these huge increases in productivity that are starting to happen. [01:06:20] S1: So jisting that down for folks, do you think that in time remote work is not going to go away, and therefore, there will be kind of a global talent pool. And therefore, if you want to access the best people, you're not only going to have to have a much larger pool, but you're also going to look more broadly, at the corporate level. [01:06:37] S5: This is certainly the case. And most importantly, that I think it'll be centralized, right? Instead of all these decentralized, fragmented job searches and companies that are looking to hire, I think it'll be one place that everyone goes, that every company hires from, that can facilitate all of these job matches that people love and companies are finding a lot of value in.