Logan Kilpatrick: prediction

Transcript context

…It's funny. I feel like Chatbots, as a PM for many years, it feels like every brainstorming session we had about new features, it's like, "Hey, we should have built a Chatbot to solve this problem." It's like the perennial like, "Oh, Chatbot," or, "Someone's going to suggest we do a Chatbot," and now they're actually useful and working and everyone's building Chatbots, a lot of them based on OpenAI APIs. There's not really a question there, but maybe the question, I was going to get to this later, is just when people are thinking about building a product like, say, TL Draw, what should they think about? Where OpenAI is not going to go versus here's what OpenAI is going to do for us. We shouldn't worry about them building a version of TL Draw in the future. What's the way to think about where you won't be disrupted essentially by OpenAI, knowing also they may change their mind? That's a great question. I think we're deeply focused on these very, very general use cases like the general reasoning capabilities, the general coding, the general writing abilities. I think where you start to get into some of these very vertical applications... And I think a great example of this is actually Harvey. I don't know if you've seen Harvey, but it's this legal AI use case where they're building custom models and tools to help lawyers and people at legal firms and stuff like that. And that's a great example of our models are probably never going to be as capable as some of the things that Harvey's doing because our goal and our mission is really to solve this very general use case and then people can do things like fine-tuning and build all their own custom UI and product features on top of that. I have a lot of empathy and a lot of excitement for people who are building these very general products today. I talk to a lot of developers who are building just general purpose assistants and general purpose agents and stuff like that. I think it's cool and it's a good idea. I think the challenge for them is they are going to end up directly competing against us in those spaces and I think there's enough room for a lot of people to be successful, but to me you shouldn't be surprised when we end up launching some general purpose agent product because, again, we're sort of building that with GPTs today and versus we're not going to launch some of these varied verticalized products. We're not going to launch an AI sales agent. That's just not what we're building towards. And companies who are and have some domain specific knowledge and they're really excited about that problem space, they can go into that and leverage our models and end up continuing to be on the cutting edge without having to do all that R&D effort themselves. Got it. So the advice I'm hearing is get specific about use cases, and that could be either models that are tuned to be especially useful for a use case like sales or make an interface or experience solving a more specific problem.…

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