The Frontier with Brendan Foody
00:26:27 · Brendan Foody, Andy Fang
[00:26:27] S0: How much of that is people using the Claude Cowork interface themselves versus a custom harness that you guys have set up that they're able to use so that you can swap between models? [00:26:37] S1: I would say on the non-edge side, a lot of it right now is through cowork. Like we want to build a paradigm where um everybody can build their own agents. And so we want cowork to kind of be that playground to start with. If we find things that we're really excited about, then that's kind of when we'll do the harness. [00:26:54] S0: That's definitely something I've been interested in how it develops because I think that most large companies are just using cowork directly or ChatGPT directly. Yeah. Whereas a lot of startups are starting to build their own harnesses. Like I know Sierra has posted a lot about this. We have our own harness. We still use Claude a lot of the time and that harness, but it at least gives a little bit of optionality with respect to how we change the model that we're using over time and build more customizations on top of it. So I think it'll be interesting to see how that development unfolds.
00:26:54 · Brendan Foody, Andy Fang
[00:26:54] S0: That's definitely something I've been interested in how it develops because I think that most large companies are just using cowork directly or ChatGPT directly. Yeah. Whereas a lot of startups are starting to build their own harnesses. Like I know Sierra has posted a lot about this. We have our own harness. We still use Claude a lot of the time and that harness, but it at least gives a little bit of optionality with respect to how we change the model that we're using over time and build more customizations on top of it. So I think it'll be interesting to see how that development unfolds. [00:27:26] S1: Yeah. I mean, we have our own harness as well that like only like the power user non-technical people are using right now. But like I think for me, what I'm optimizing for right now is like try to identify the use cases. Yeah. Cause I don't think we've seen enough use cases where we're like, oh my gosh, like like we gotta like kind of like really like supercharge this right now. It's more like we're seeing a lot of like 20, 30% type gains in various areas that people are able to kind of like self-sufficiently do. [00:27:53] S0: And when it's people building their own agents, would that mainly be them building a skills file and setting up like a cloud scheduled action so that they can run that agent on whatever cadence? [00:28:05] S1: Pretty much. And then, but we also have our own like harness where like we can also you can spin up like a agent on a cloud instance and kind of do stuff. But like I think that one requires a little bit more wrangling.
00:28:05 · Andy Fang, Brendan Foody
[00:28:05] S1: Pretty much. And then, but we also have our own like harness where like we can also you can spin up like a agent on a cloud instance and kind of do stuff. But like I think that one requires a little bit more wrangling. [00:28:18] S0: How do you think about incentivizing AI adoption? Because there's obviously one school of thought of token maxing and like how do you just get people making as many queries using as many tokens as possible? Yeah. Which I think there is some value in, but obviously there is often a disconnect between usage versus actual outcomes. And then the other school of thought is how do you just look at the outcomes that people are accomplishing? But it's hard to know exactly if you set higher expectations for someone. You know, it's hard it's hard to like calibrate those expectations to what they should be with and without AI. Yeah. So how do you how do you think about those two schools of thought of token maxing versus focusing on outcomes and how you guys incentivize AI adoption through DoorDash?
00:28:18 · Brendan Foody, Andy Fang
[00:28:18] S0: How do you think about incentivizing AI adoption? Because there's obviously one school of thought of token maxing and like how do you just get people making as many queries using as many tokens as possible? Yeah. Which I think there is some value in, but obviously there is often a disconnect between usage versus actual outcomes. And then the other school of thought is how do you just look at the outcomes that people are accomplishing? But it's hard to know exactly if you set higher expectations for someone. You know, it's hard it's hard to like calibrate those expectations to what they should be with and without AI. Yeah. So how do you how do you think about those two schools of thought of token maxing versus focusing on outcomes and how you guys incentivize AI adoption through DoorDash? [00:29:02] S1: I would say we definitely focus much more on outcomes. So, like, for example, like we will try to solicit like concrete anecdotes of where people have used AI to make their job a lot better. Whether it's, hey, I got this project done like three times faster, or it's like someone on the recruiting team like kind of vibe coded their own internal like pipeline uh website tool that like saved them a lot of time, or kind of like what you mentioned, like a schedule action type workflow. And so that's what we're really looking for is like, hey, let's try to like and what we also try to do is celebrate those. So like if we identify that, then we share it in a forum and like that way people can kind of learn from example. And so that's kind of where we're at right now.
00:29:02 · Andy Fang, Brendan Foody
[00:29:02] S1: I would say we definitely focus much more on outcomes. So, like, for example, like we will try to solicit like concrete anecdotes of where people have used AI to make their job a lot better. Whether it's, hey, I got this project done like three times faster, or it's like someone on the recruiting team like kind of vibe coded their own internal like pipeline uh website tool that like saved them a lot of time, or kind of like what you mentioned, like a schedule action type workflow. And so that's what we're really looking for is like, hey, let's try to like and what we also try to do is celebrate those. So like if we identify that, then we share it in a forum and like that way people can kind of learn from example. And so that's kind of where we're at right now. [00:29:42] S0: So sort of rewarding the behavior culturally through shout-outs, probably performance reviews, all that kind of stuff so that people focus on aiming higher and accomplishing more with AI. Yeah, exactly. Nice. Andy, there's so many people talking about recursive self-improvement and super intelligence within two or three years. How do you think about that and how much AI is overhyped versus underhyped?
00:29:42 · Brendan Foody, Andy Fang
[00:29:42] S0: So sort of rewarding the behavior culturally through shout-outs, probably performance reviews, all that kind of stuff so that people focus on aiming higher and accomplishing more with AI. Yeah, exactly. Nice. Andy, there's so many people talking about recursive self-improvement and super intelligence within two or three years. How do you think about that and how much AI is overhyped versus underhyped? [00:30:05] S1: You can't argue with the amount of spend that companies are I mean, for us 20x in like six months, like that's definitely not hype. But I don't know. This RSI thing, at least the people I've talked to, they don't think it's coming that soon. I do know for a fact that for some of these labs, it's like, I guess for all the like the top labs now, it's like a top priority for them. I think the potential there is obviously tremendous if we can get to RSI. Yeah, I don't know. It's just like things are changing so fast, it's kind of hard to like wrap my head around. [00:30:32] S0: So maybe two questions. First of all, is how long until AI will be better than humans at 50% of tasks in the digital world? And then second of all, how long until hu AI will be better than humans at 100% of tasks in the digital world? So like digital super intelligence.