David Hsu: uncertainty

Transcript context

…Yeah, that's interesting. I think it's probably going to be engineering driven. I mean, I think you two are very well, maybe you two even started some of this wave and sort of the AI engineer wave. I think the companies that adopt AI the best, it is going to be engineering driven, I think, rather than like operations driven or anything else. And the reason for that is, I think the rise of this like profile with AI engineering, like AI is very philosophical, like AI is a tool in my head. Like it is not a, in my head, I think we're actually pretty far from AGI. But AI is not like a, you know, thing that it's not like a black box where like it does everything you want it to do. The models that we have today require like very specific prompting, for example, in order to get like, you know, really good results. And the reason for that is, it's a tool that, you know, you can use it a specific way. So if you use it the wrong way, it's not going to produce good results for you, actually. It's not like by itself taking a job away, right? And so I think actually, to adopt AI, it's probably going to be going to have to be engineering first, basically, where engineers are playing around with it, figuring out limitations of the models, figuring out like, oh, maybe like using vectorized databases is a lot better, for example, maybe like prompting in this particular way, it's going to be a lot better, etc. And that's not the kind of stuff that I think like an operations team is going to really be like experimenting with necessarily. I think it really has to be engineering led. And then I think the question is, well, what are the engineers going to focus on first? Like, are they going to focus on design first or like operations first? And that I think is more of a business decision. I think it's probably going to be more like, you know, the CEO, for example, says, hey, we're having trouble scaling this one function. So like, why don't we try using AI for that? And let's see what happens, for example. And so in our case, for example, we are really we have a lot of support issues. So what I mean by that is we have a really, really high performance support team. But we get a lot of tickets. And the reason for that is, you know, we're a very dynamic product, you can use it in so many different ways. And we'll have a lot of questions for us, basically. And so we were looking at, well, you know, can we, for example, draft some replies and support tickets, you know, by AI, for example, can we allow our support agents to be, you know, hopefully, you know, double as doubly productive as before, for example. So I guess I would say it's like business needs driven, but then engineering driven after that. So like, you know, we the business decides, okay, well, this is where AI can be most applied. And then we assign the project to an engineer, and the engineer goes and figures it out. riven after that. So like, you know, we the business decides, okay, well, this is where AI can be most applied. And then we assign the project to an engineer, and the engineer goes and figures it out. I honestly am not sure if like the operation, we're gonna have much of a, like, if they accept or reject it, I don't know what's gonna change the outcome, if you will. So another interesting part was the importance of AI in hiring. 45% of companies said they made their interviews more difficult in the in the engineering side, made interviews more difficult to compensate for people using copilot and chat GPT. As they change every tool, like, have you? Yeah, have you thought about it? I don't know how much you're still involved with engineering hiring, I get the company, but I'm curious how we're scaling the difficulty of interviews, even though the job is the…

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