The Wisdom Wall

“Generally, when I talk to engineers that want to join startups, for example, and let's say they eventually want to start their own company, which is a very common, uh, profile, it's, in my opinion, it's like way more useful to join somewhere where they've already kind of got the commercials figured out and you can…”

“and for us, actually those things help, but they're actually not the biggest difference maker. So in our use case, the type of intelligence that matters the most, um, We would probably describe it as instruction following.”

“the use cases that emerged, like you have to have those two qualities. Like it has to be able to be something that can be rolled out slowly and doesn't have the perfect off the bat, but it's already providing value, right? Like I think coding agents is like a good example of this where, um, you can just like section…”

“Yeah, I mean, another specific dynamic is that at the enterprise, it's becoming a lot more of a top-down motion, so You know, in the past, any of these technologies could have been just, like, one team trying to vet it or decide it, but now it's like a, it's an AI transformation, and the C-suite, the board are all,…”

“one of my, um, I guess theses is that there is a lot of untapped potential and just like really strong technical folks in making them a bit more commercial because the types of problems on the go to market side there, I would say generally a little bit more hairy. And so a lot of folks Don't like the messiness, and…”

“I think at the beginning you have to short, you have to think short term. You're just optimizing for closing the deal or closing a couple customers, but once you have your legs under you, You both can think more long-term and also you have obligation to, because if you don't, then eventually you get to a point where…”

“what we found is even when the engineering teams are very much involved, they don't necessarily want to be on the hook for every little change, and so in that case, we can work very well with them, and you have them still owning how does the AI agent interact with the systems and connect APIs and so on, where, and then…”

“You also don't want to price per like, you know, minutes of the call either like that. That's just kind of weird. Uh, and also incentivizes the agents to just like have really long calls. So you, you price basically the number of conversations that it can have.”

“our current use case as maybe what we think is like the golden use case for, for these AI agents, which is customer interactions, customer service. Um, the, the use case is very tailor made for, for what LLMs are good at.”

“most of the sort of alpha or most of the specials stuff that you build is on top of models. It's either the orchestration layer or the software around it.”

“Voice-to-voice, latency is great. Sometimes, though, with these production use cases, you do need the extra computation cycles. So, you know, fetch data, do multiple model calls, um, or there's, there might be other reasons that you can't do voice-to-voice.”

“in the last few years, maybe it lasts like, you know, five, six years, like there's just, because startups have been a lot more mainstream, a lot of Folks in this demographic have gravitated towards startups as opposed to, you know, traditionally it'd be either academia or quant trading and things like that.”