The Frontier with Brendan Foody
00:14:15 · Brendan Foody
[00:14:15] S0: The other thing that you guys have been posting about a bunch, which I'm really excited about, is all of your benchmarks for uh all of the different capabilities that you're prioritizing with indoor dash. Of course, one thing we think about a lot is that there's this giant disconnect between the academic benchmarks that everyone has on their leaderboards and model scorecards versus the actual capabilities that you all are prioritizing and how you allocate your inference dollars. And so, what are the top benchmarks that you guys have launched and which of those are you most excited about?
00:14:50 · Andy Fang, Brendan Foody
[00:14:50] S1: We need to like catch up to you guys because it's all the benchmarks you guys have, but we didn't publish the benchmark, but we uh publicized our benchmark, I guess, called Dash Bench, which is just our code review benchmark. Essentially, it's like we are able to test various, not only models, but also harnesses to see how well they perform on our internal code review agent. And I think what we found and finding that we published was basically that we were able to get Fable plus Kimi K 2.6 to perform uh just as well as our uh previous, I guess, combination of models and harnesses at a cheaper cost. And so, like, if we can actually get fable level intelligence, but actually like pay less. Yeah, so that's kind of like what our like kind of our first, like I would say, legit benchmark. And I think what we're trying to do is we're trying to expand that to other parts of the business. And you and I have talked about this in some of the meetings we've had, but you know, I think there's a lot of things that agents can be doing for companies and enterprises that would be really good to have bet benchmarks to be able to see like how do you measure the ROI basically of the agent on various tests. [00:15:50] S0: How do you think about the tri the mix of open source models versus frontier models and your either through the lens of your tokens or your dollars spent on each evolving over the next couple of years?
00:15:50 · Brendan Foody, Andy Fang
[00:15:50] S0: How do you think about the tri the mix of open source models versus frontier models and your either through the lens of your tokens or your dollars spent on each evolving over the next couple of years? [00:16:03] S1: I actually don't look that much at token spend. I look I look more at dollar spend actually. Um what I've actually heard is that um like from people on the team is that Fable, even though it's more expensive, it's more token efficient. So that actually the spend doesn't end up being as crazy for certain tasks. [00:16:18] S0: For software engineering, especially, I imagine. Yeah. Yeah. [00:16:20] S1: And so to your point, like the token spend is like a little bit of a vanity metric where it's like you really care about the dollar spent to perform the task. That's something that we pay attention to, I'd say. Um, but you know, I think we're trying to figure out how to, you know, outside of code review and coding, like how do we figure out the other opportunities there, basically?
00:16:20 · Andy Fang, Brendan Foody
[00:16:20] S1: And so to your point, like the token spend is like a little bit of a vanity metric where it's like you really care about the dollar spent to perform the task. That's something that we pay attention to, I'd say. Um, but you know, I think we're trying to figure out how to, you know, outside of code review and coding, like how do we figure out the other opportunities there, basically? [00:16:38] S0: It definitely feels like the frontier model demand will keep skyrocketing for a very long time. But I wonder if we'll get to the point where there's a specific amount of tasks that you guys have where there's a sufficiency threshold. Yes. Of like, hey, Kimi or GLM or whatever open source model has hit that sufficiency threshold and then you're able to move over. It's a fascinating thing to imagine what percent of these tasks within a firm have a sufficiency threshold versus what portion don't. Because there's obviously certain kinds of software engineering tasks where there's almost no sufficiency threshold or it's decades out, uh, while there's certain kinds of you know, basic search or tool use, as you were getting at earlier, that have a much lower sufficiency.
00:17:20 · Andy Fang, Brendan Foody
[00:17:20] S1: Like, you know, we have some like uh classification pipelines that for actually even like more than a year ago, we've actually used open weight models on and it's performed really well. I definitely think a lot of the workflows will have that good enough benchmark. But I think the thing that I don't know about is as AI and agents get smarter and as enterprises figure out more and more how to embed them, like, are there gonna be like these new types of workflows that people are gonna want to have agents do that aren't happening yet? Like I think one example I can talk about at DoorDash is you know, our accounting team is like trying to figure out ways to leverage AI to make things better. At the same time, we're seeing like, you know, the the labs are claiming that the tasks that our accounting team are doing are like easily solvable by the models, but then our accounting team is like they're saying the models aren't good enough. So, like for me, it's like a disconnect of like, are we not using the models well or is there like a capability gap? Um, and so I think that's something that's like interesting. [00:18:17] S0: We're launching an accounting benchmark shortly, is hopefully we'll be able to uh in partnership uh with Ramp. Are you able to share how much you guys are spending on inference right now? [00:18:28] S1: I'll say a stat that I think our inference spent has gone up 20x since the beginning of the year. [00:18:34] S0: Wow. So what how much do you expect it to go up over the next 24 months?
00:18:34 · Brendan Foody, Andy Fang
[00:18:34] S0: Wow. So what how much do you expect it to go up over the next 24 months? [00:18:41] S1: Well, I would say even the past like month and a half, it's like plateaued. Like it hasn't gone down, it hasn't shrunk, but it's definitely like flatlined. I think there's definitely like efforts we've had internally to curb the spend. [00:18:52] S0: It makes sense that would coincide with shipping your benchmarks and the open source model is starting to work. [00:18:57] S1: Yeah, it's just like hard to predict, like because maybe like and I was just talking to like Tony, our CEO, about this earlier today. It's like if we're seeing ROI from the spend, wouldn't we want to spend more? Yeah. Right. And so like I think that's the thing that I mean, I think everybody's trying to figure out is like where are those opportunities? Because let's say if you're generating revenue from token spend, you would you would spend that token spend all day. For sure. But like I think a lot of what we're focused on as an industry right now, quite frankly, is on like cost efficiency and all that stuff. But like software engineering is a great example of where like when an engineer could do the work of like, you know, five engineers pre-AI, like you would probably want to spend a lot more there.