Nathan Labenz: uncertainty

Transcript context

…Exactly. But more recently, GPT-55 has gotten really good. I think it's a huge step up for our use case over 5.4. By the time this podcast comes out, we'll probably have announced this publicly. So just, hey, it's going to be there very soon. And it can navigate our harness super well. I think it gives Opus 4.6 a run for its money for most use cases. I think that's really exciting. I'm pretty optimistic about the OpenAI roadmap this year. I think they made a huge bet on compute last year, and I think that's starting to show, and I think where they're going to have a lead for a while. It also is clear that they've refocused their business much more on these types of use cases. You saw the progress Codex made over six months, and if they put that level of effort into tuning the models around these types of agenda use cases, I think that'll be It'll be huge. So we signed a deal with OpenAI the other day, and we'll be launching stuff there, and we're making a pretty big bet there as well. There's progress in other places, though. The latest Google models are pretty solid. They're not at the level of anthropic OpenAI yet, in my opinion, but they are making very fast progress, and they're much closer. And then we've been playing with Deepseek and with Kimi. And the, like the latest Kimmy is, you know, as far as we can tell, like maybe better than a haiku and cheaper. So I think we're going to see those probably make it, make it, make their way into, into task up soon. So I would expect, you know, within the next few months, we are going to have, you know, anthropic models, OpenAI models, open source models, Google models. I'll bet you the anthropic ones will still be the, you know, the best and probably the recommended most cases. But people have a variety of choices and like some good cost cost optimization options for certain things. So many follow-ups there. Let's maybe start with your, what I imagine has been a little bit of a delicate dance with Anthropic, and then we can kind of compare and contrast that with what OpenAI is now bringing to the table. I don't know, you probably know what the ratio is of API cost to effective token cost when you buy a Claude Max subscription and max it out. And obviously in the intervening time since we spoke last, there's been the whole open claw phenomenon and that's had its own bunch of drama with you can, you can't, you sort of can. We got to pay the API price. We're lowering our limits. We're buying compute from xAI. We're raising our limits back again. What has it been like from your perspective to be building on a platform that you're also sort of competing with that is kind of undercutting you to various levels at different times on their pricing. Yeah, it's definitely a it's definitely an interesting relationship. So like on the one hand, The models are amazing. They're super good for our use case. Their team has been really helpful and responsive. We talk to them on a very regular basis, and they're trying hard to support us, and we get early access to stuff, and they take our feedback and all of that. They're definitely totally enabling our business, making it happen, and working hard to do it, which is wonderful. They're a great partner. I don't want anyone to think I think otherwise. But at the same time, if you look at our stats of when someone churns off Tasket, where did they Like 80% of those users go to an anthropic product. So they are like a very direct competitor. And I think there's different use cases where their products shine, but it's clear that they're a very direct competitor. The number one reason that they do that is because they already have a max plan, and they don't want to have to spend additional money on tasks. Basically, every time they release a new model update, we're like, This is great. This is awesome. Every time they send an e-mail being like, Now your max plan is even cooler, we're like, Crap. This is just going to make it harder. They definitely subsidize it, and it definitely has set some distorted expectations with the folks around like what you can get for a certain price. And so it's cost is like a constant, constant struggle for us to try to, you know, help users use the products more efficiently and help them understand, you know, hey, we're actually working at like some pretty razor thin margins here and trying to make this cheap for you guys. So yeah, it's an interesting dance for sure.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.