Logan Kilpatrick: prediction

Transcript context

…Awesome. Okay, that's great. I was going to get to that later, but I'm glad we touched on that. I imagine that's on the minds of many developers and founders. Kind of along the same lines, there's a lot of talk about how ChatGPT and GPTs and many of the tools you guys offer are going to make a company much more efficient. They don't need as many engineers, data scientists, PMs, things like that, but I think it's also hard for companies to think about what can we actually do to make our company more efficient. I'm curious if there's any examples that you can share of how companies have taken built a, say, a GPT internally to do something so that they don't have to spend engineering hours on it or generally just used OpenAI tooling to make their business internally more efficient? Yeah, that's a great question. I wonder if you can put this in the show notes or something like that, but there's a really great Harvard Business School study about... And I forgot which consulting firm they did it with. Maybe it was like Boston Consulting or something like that, but it might've been one of the other ones. And they talk about the order of magnitude of efficiency gain for those folks who are using AI tools, I think it was chat GPT specifically in those use cases that they were using, comparatively against folks who aren't using AI. I'm really excited, also, just as just more time passes between the release of this technology, for us to get more empirical studies. I feel just for myself, as somebody who's an engineer today, I use ChatGPT and I can ship things way faster than I would be able to. I don't have any good metrics for myself to put a specific number on it, but I'm guessing people are working on those studies right now. I think engineering is actually one of the highest leverage things that you could be using AI to do today and really unlocking, probably on the order of at least a 50% improvement, especially for some of the lower hanging fruit software engineering tasks. The models are just so capable at doing that work. And it's crazy to think... And I'm guessing, actually, GitHub probably has a bunch of really great studies they publish around copilots and you could use those as an analogy for what people are getting from ChatGPT as well. But those are probably the highest leverage things. I think now with GPTs, people are able to go in and solve some of these more tactical problems. I think one of the general challenges with ChatGPT is it gives a decent answer for a lot of different use cases, but oftentimes it's not particular enough to the voice of your company or the nuance of the work that you're doing. And I think now with GPTs and people who are using the teams in ChatGPT and Enterprise in ChatGPT, they can actually build those things, incorporate the nuance of their own company, and make solving those tasks much, much more domain specific. So we literally just launched GPTs a couple of months ago, so I don't think there's been any good public success stories, but I'm guessing that success is happening right now at companies, and hopefully we'll hear more about that in the months to come as folks get super excited about sharing those case studies. I'll share an example. So I have this good friend, his name's Dennis Yang, he works at Chime, and he told me about two things that they're doing at Chime that seem to be providing value. One is he built a GPT that helps write ads for Facebook and Google just gives you ideas for ads to run, and so that takes a little load off the marketing team or the growth team. And then he built another GPT that delivers experiment results, kind of like a data scientist, with here's the result of this experiment. And then you could talk to it and ask for like, "Hey, how much longer do you think we should run this for," or, "What might this imply about our product," and things like that. And I think it's really-…

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