Inside OpenAI | Logan Kilpatrick (head of developer relations)

[00:00] finding people who are [00:02] high agency, [00:03] and work with urgency. If I was hiring five people today, those are some of the top two characteristics that I would look for in people. Because you can take on the world if you have people who have high agency and not needing to get 50 people's different consensus. They hear something from our customers about a challenge that they're having and they're already pushing on what the solution for them is and not waiting for all the other things to happen that people just go and do it and solve the problem. And I love that. It's so fun to be able to be a part [00:36] Today, my guest is Logan Kelpatrick. Logan is Head of Developer Relations at OpenAI, [00:41] where he supports developers building on OpenAI's APIs and JATGPT. [00:46] Before OpenAI, Logan was a machine learning engineer at Apple and advised NASA on their open source policy. [00:52] If you can believe it, ChatGPT launched just over a year ago and transformed the way that we think about AI and what it means for our products and our lives. Logan has been at the front lines of this change and every day is helping developers and companies figure out how to leverage these new AI superpowers. In our conversation, we dig into examples of how people are using ChatGPT and the new GPTs and other OpenAI APIs in their work and their life. [01:22] on how to get better at prompt engineering. We also get into how OpenAI operates internally, how they ship so quickly, and the two key attributes they look for in the people that they hire. Plus, where Logan sees the biggest opportunities for new products and new startups building on their APIs. We also get a little bit into the very dramatic weekend that OpenAI had with the board and Sam Altman and all of that, and so much more. A huge thank you to Dan Shipper and Dennis Yain for some great questions, suggestions.

[01:52] Logan Kilpatrick, after a short word from our sponsors. This episode is brought to you by Hex. If you're a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of screenshots and CSV files. Hex brings everything together. Its powerful notebook UI lets you analyze data in SQL, Python, or NoCode, in any combination, and work together with live multiplayer and version control. [02:22] can generate queries and code, create visualizations, and even kickstart a whole analysis for you, all from natural language prompts. [02:29] It's like having an analytics co-pilot built right into where you're already doing your work. Then, when you're ready to share, you can use Hex's drag-and-drop app builder to configure beautiful reports or dashboards that anyone can use. Join the hundreds of data teams like Notion, AllTrails, Loom, Mixpanel, and Algolia using Hex every day to make their work more impactful. Sign up today at hex.tech.lenny to get a 60-day free trial of the Hex team plan. That's hex.tech.lenny. [02:59] This episode is brought to you by Whimsical, the iterative product workspace. Whimsical helps product managers build clarity and shared understanding faster with tools designed for solving product challenges. With Whimsical, you can easily explore new concepts using drag and drop wireframe and diagram components, create rich product briefs that show and sell your thinking, and keep your team aligned with one source of truth for all of your build requirements.

[03:29] from product leaders like myself, including a project proposal one-pager and a go-to-market worksheet. Give them a try and see how fast and easy it is to build clarity with Whimsical. Sign up at whimsical.com slash Lenny for 20% off a Whimsical pro plan. That's whimsical.com slash Lenny. [03:49] Logan, thank you so much for being here and welcome to the podcast. Thanks for having me, Lenny. I'm super excited. I want to start with the elephant in the room, which I think the elephant is actually leaving the room because I think this is months ago at this point, but I'm still just really... [04:05] Curious. [04:06] What was it like on the inside of OpenAI during the very dramatic weekend with the board and Sam and all those things? [04:13] What was it like? And is there a story maybe you could share that maybe people haven't heard about what it was like on the inside of what was going on? [04:20] Yeah, it was definitely a very stressful Thanksgiving week. I think in broad context, OpenAI had been pushing for a really long time since ChatGPT came out. And that was supposed to be one of the first weeks that the whole company had taken time away to actually reset and have a break. So very selfishly, I was super excited, spent time with my family, all that stuff. [04:43] And then Friday afternoon, we got the message that all of the changes were happening. And I think it was... [04:49] super shocking because I think, and this is a perspective a lot of folks share, everybody has had and continues to have such deep trust in Sam and Greg and our leadership team that it was just very surprising. We're also a very...

[05:04] as far as company cultures go, very transparent and very open. So when there's problems or there's things going on, we tend to hear about them. And again, it was the first time that a lot of us had heard some of the things that were happening between the board and the leadership team. So very, very surprising. I think my sort of [05:23] Bean. [05:24] someone who's not based in San Francisco, I was, again, very selfishly happy that it happened over the Thanksgiving break because a lot of folks actually had gone home to different places. So it felt like I had a little bit of comfort knowing I wasn't the only one not in San Francisco because everybody was meeting up in person to do a bunch of stuff and be together during that time. So it was nice to... [05:47] to know that there was a few other folks who were sort of out of the loop with me. I think the thing that surprised me the most was just how quickly... [05:56] everybody got back to business. I flew to San Francisco the next week after Thanksgiving, which I wasn't planning to do to deal with the team in person. And seeing literally Monday morning, I was walking into the office expecting something weird to be going on or happening. And really, it was like, [06:13] people laser focus and like back to work. And I think that that like speaks to like the caliber of, of our team and like everybody who's just so excited about building, [06:24] towards the mission that we're building towards. So I think that was the most surprising thing of the whole incident. I think a lot of companies would have had the potential to truly be derailed for some non-trivial amount of time by this. And everybody was just right back to it, which I love.

[06:39] I feel like it also maybe brought the team closer together. It feels like it was kind of [06:43] Hehe. [06:44] traumatic experience that may [06:46] bringing folks together because it was something they all shared. Is there anything along those lines that's like, wow, things are a little different now? [06:52] One of my takeaways was I'm actually very grateful that this happened when it happened. I think today the stakes are... [07:00] They're still relatively high. People have built their businesses on top of OpenAI. We have tons of customers who love ChatGPT. So if something bad happens to us, we definitely impact our customers. But on the world scale, somebody else will build a model if OpenAI disappeared and continue towards this progress of general intelligence. [07:21] Fast forward five or 10 years of something like this would have happened, and we hadn't gone through the hopeful upcoming journey. [07:30] word transformation and sort of all those changes that are going to happen, I think it would have been a little bit or potentially much worse of an outcome. So I'm glad that things happened when the stakes are a little bit lower. And I totally agree with you. It's like, [07:41] the team has been growing so rapidly. [07:45] over the last year since I joined. It's been crazy to think about how many new folks there are. And I really think that this really brought people together. Because most folks, historically, many of the folks when I joined, what kind of banded us all together was the launch of JGBT, the launch of JGBT4. And for folks who weren't around for some of those launches, it was perhaps Dev Day. For folks who were around for Dev Day, it was probably this event. So I think we've had these events that have really brought the company together cross-functionally.

[08:15] all the future ones will be like really exciting things like, you know, GPT-5 whenever that comes and stuff like that. Awesome. We're going to talk about GPT-5. [08:23] Going in a totally different direction, what is the most mind-blowing or surprising thing that you've seen AI do recently? [08:30] The things that are getting me most excited are these new interfaces around AI, like the Rabbit R1. I don't know if you've seen that, but it's a consumer hardware device. This company called TLDraw. I don't know if you've seen TLDraw. I think you sketch something and then it makes it as a website. Yeah, and that's only a small piece of what TLDraw is actually working on. But there's all of these new interfaces to interact with AI. And I think I was having a conversation with the TLDraw folks a couple of days ago. [09:00] my mind to think about how chat is the predominant way that folks are using AI today. And like, I actually think like, and this is my, you know, my bull case for the folks at TL Draw, I'm super excited for them to build what they're building, but they're sort of building this infinite canvas experience. And you can imagine how, as you're interacting with an AI on a daily basis, like, [09:20] You might want to jump over to your infinite canvas, which the AI has sort of filled in all the details. And you might see a reference to a file and to a video and all of these different things. And it's such a cool way. It actually makes a lot more sense. [09:33] from us as humans to like see stuff in that type of format than I think like just listing out a bunch of stuff in chat. So I'm really, really excited to see more people. I think like 2024 is the year of multimodal AI, but it's also the year that people really push the boundaries of some of these like new UX paradigms around AI.

[09:52] It's funny, I feel like chatbots, like 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. [10:02] It's like the perennial, like, oh, chatbot 4, someone's going to suggest we do a chatbot. [10:05] And now they're actually useful and working, and everyone's building chatbots. [10:09] a lot of them based on OpenAI APIs. [10:12] There's not really a question there, but maybe the question I was going to get to this later is just, [10:15] When people are thinking about building a product like, say, TLDraw, [10:20] What should they think about where OpenAI is not going to go? [10:24] versus like here's what OpenAI is going to do for us. [10:26] We shouldn't worry about them. [10:28] building a version of TLDraw in the future? What's the kind of the way to think about where you won't be disrupted, essentially, by OpenAI, knowing also they may change their mind? [10:36] 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, it's actually like 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.

[11:06] 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. And I think that's the... I have a lot of... [11:21] empathy and a lot of excitement for people who are building these very general products. I talked to a lot of developers who are building just general purpose assistance 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. 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 building [11:51] that with GPTs today and versus like, we're not going to launch like some of these like very verticalized products. Like we're not going to launch like an AI sales agent. Like 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. Like, [12:08] they can go into that and leverage our models and like end up continuing to be on the cutting edge without having to like do all that R&D effort themselves. [12:16] Got it. So the advice I'm hearing is get specific about use cases. [12:20] And that could be either models that are [12:23] tuned to be especially useful for a use case like sales. [12:26] Or make an interface or experience solving a more specific problem. And I think if you're going to try and solve this very general, if you're going to try to build the next general assistant to compete with something like ChatGPT, it has to be so radically different. People have to really be like, wow, this is solving these 10 problems that I have with ChatGPT, and therefore I'm going to go and try your new thing. Otherwise, we're just putting a ton of engineering efforts and research effort into making that an incredible product.

[12:56] building companies. It's just hard to compete against somebody like that. 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. [13:05] 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. [13:18] But I think it's also hard for companies to think about what can we actually do to make our company more efficient. [13:25] I'm curious if there's any examples that you can share of how companies have [13:30] built, 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. [13:42] Yeah, that's a great question. And 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 have 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. And I think it was ChatGPT specifically in those use cases that they were using comparatively against folks who aren't using AI. [14:12] excited also just like as this more time passes between the release of this technology for us to get more like empirical studies because like I feel this for myself like as somebody who's an engineer today like I use chat gpt and like I can ship things way faster than I would be able to I don't have any like good metrics for myself to put a to put like a specific number on it but I'm guessing like people are working on those studies right now I think engineering is actually like one of the highest leverage things that you could be using AI to do today and like really unlocking like

[14:42] 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 published 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... [15:06] leverage things. I think now with GPTs, people are able to like, [15:10] go in and solve some of these more tactical problems. I think one of the general challenges with ChatGPT is like, it gives like a decent answer for like a lot of different use cases, but oftentimes it's not like particular enough to like the voice of your company or like the nuance of the work that you're doing. And I think now with GPTs like, and people who are using the teams in ChatGPT and enterprise in ChatGPT, I can actually build those things, incorporate the nuance of their own company and make, [15:40] more domain specific. So we literally just launched GPTs a couple of months ago. So I don't think there's been any like good public success stories, but I'm guessing that 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. [15:58] I'll share an example. So I have this good friend, his name is Dennis Yang. He works at Chime. [16:02] And he told me about two things that they're doing at QIIME. [16:05] that seem to be providing value. One is he built a GPT that helps write ads. [16:11] for Facebook and Google. It gives you ideas for ads to run.

[16:15] And so that takes a little load off the marketing team or the growth team. [16:18] and then he built another GPT that [16:20] delivers experiment results, kind of like a data scientist would like, here's the result of this experiment. [16:25] And then you could talk to it and ask for like, hey, how much longer do you think we should run this for? [16:30] What might this imply about our product and things like that? [16:33] I love that. Like you said. Is there anything else that comes to mind, just like things you've heard people do, just like, wow, that was a really smart way of, so I get there's like engineering copilot-y type tooling. Is there anything else that comes to mind just to give people a little inspiration of like, wow, that's an interesting way I should be thinking about using some of these tools? [16:50] I've seen some interesting GPTs around like the planning use cases. Like you want to do like OKR planning for your team or something like that. There's I just actually saw somebody tweet it like literally yesterday. I've seen some cool like venture capital ones of like doing diligence on like a deal flow, which is kind of interesting and like getting some different perspectives. I think all of those like. [17:10] horizontal use cases where like you can bring in a different personality and like get perspective on different things I think is really cool like I've personally used in a GPT the private GPT that I use myself that like helps with some of the like [17:24] planning stuff for different quarters and just making sure that I'm being consistent in how I'm framing things, driving back to individual metrics, stuff that when people do planning, they often miss and are bad at. And it's been super helpful for me to have a GPT to force me to think about some of those things. [17:43] Wait, can you talk more about this? What does this GPT do for you and how do you...

[17:46] What do you feed it? [17:48] Yeah, I forgot what article I saw online, but it was some article that was talking about what are the best ways to set yourself up for success in planning. And I took a bunch of the... I'll see if I can make it public after this and send you a link. But I took a bunch of the examples from that and went in and put some of those suggestions into the GBT. And then now when I do any of my planning of like, I want to build this thing, I put it through and have it generate a timeline, generate all the specifics of what are the metrics and success that I'm working for, like... [18:17] who might be some important cross-functional stakeholders to include in the planning process, all that stuff. And it's been it's been helpful. [18:25] Wow, that is very cool. That would be awesome if you made it public. [18:28] And if we do, we'll link to it, and we'll make it the number one most popular GPT in the store. [18:34] I love it. Going in a slightly different direction, there's this whole genre of prompt engineering. [18:40] It feels like it's one of these really emerging skills. I actually saw a startup hiring a prompt engineer. [18:45] one of the startups I've invested in. And I think that's going to blow a lot of people's minds that there's this new job that's emerging. [18:51] And I know the idea is this won't last forever, that in theory, [18:54] AI will be so smart, you don't need to really think about how to be smart about asking it for things you needed to do. [19:00] Can you just describe this idea of what is prompt engineering, this term that people might be hearing? [19:04] And then even more interestingly, just like what advice do you have for people to get better at writing prompts for, say, ChatGPT or through the API in general? [19:12] Yeah, this is such an interesting space. And I think it's like another space where I'm excited for people to do like more like scientific empirical studies about because there's like so much like gut feeling best practices that like maybe aren't actually true in a certain ways. I think the reason that prompt engineering exists and comes up at all is because the models are so inclined because of the way that they're trained to give you just an

[19:42] basic question, you're going to get a pretty basic response. And actually, the same thing is true for humans. And you can think of a great example of this. When I go to another human and I ask, how's your day going? They say, it's going pretty good. Literally, absolutely zero detail, no nuance, not very interesting at all. Versus, again, if you have some context with a person, if you have a personal relationship with them, and I ask you, hey, Lenny, how's your day going? How did the last podcast go? Et cetera, et cetera. You just have a little bit more context [20:12] my whole position on this is like prompt engineering is a very human thing like [20:18] When we want to get some value out of a human, we do this prompt engineering. We try to effectively communicate with that human in order to get the best output. And the same thing is true of models. And I think it's like, [20:30] Again, because we're using a system that appears to be really smart, we assume that it has all this context, but it's really like... [20:38] Imagine a human level of intelligence, but literally no context. It has no idea what you're going to ask it. It's never met you before. It has no idea who you are, what you do, what your goals are. And it's the reason that you get super generic responses sometimes is because people forget they need to put that context in the model. So I think this thing... [20:58] that is going to help solve this problem. And we already kind of do this in the context of Dali. So when you go to the image generation model that we have Dali, and you say, I want a picture of a turtle, what it does is it actually takes that description, it says, I want a picture of a turtle, and it changes it into this high fidelity, like, you know, generate a picture of a turtle with a shell with a green background and, you know, lily pads in the water and all this other,

[21:28] the model is trained. It's trained on examples with super high fidelity. This will happen with text models. You can imagine a world where you go into chat to you and you say, write me a blog post about AI. It automatically will go and be like, let me generate a much higher fidelity description of what this person really wants, which is generate me a blog post about AI that talks about the trade-offs between these different techniques and some example use cases and references some of the latest papers. And it does all that for you. And then you as the user will [21:58] yep, this is kind of what I wanted. Let me edit this. Let me edit this here. [22:02] And again, the inherent problem is like, we're lazy as humans. We don't want to type all that. We don't really want to type what we mean. And I think AI systems are actually going to help solve some of that problem. [22:12] So until that day... [22:14] What can people do better when they're prompting, say, ChatGPT? And I'll give you an example. [22:19] Tim Ferriss suggested this really good idea that I've been stealing, which is when you're preparing for an interview, go to ChatGPT. And so I did this for you. I was like, hey, I'm interviewing Logan Kilpatrick. He's head of developer relations at OpenAI on my podcast. [22:34] Give me 10 questions to ask him in the style of Tyler Cowen. [22:37] who I think is the best interviewer. He's so good at just like very pointed. [22:42] original questions. [22:43] So what advice would you have for me to improve on that prompt to have better results? Because the questions were like, fine, they're great. They're like interesting enough. [22:51] But they went like, holy shit, these are incredible. So I guess what advice? [22:55] Would you give me that example? [22:57] Yeah, that's a great example where thinking in context of who it is that you're asking questions about, I'm probably not somebody who has enough information about me on the internet where the model actually has been trained and knows the nuances of my background. I think there's probably much more famous guests where it might be that there's enough context on the internet to answer the questions. You actually have to do some of that work. You need to say like...

[23:21] If you're using Browse with Bing, for example, you could say, here's a link to Logan's blog and some of the things that he's talked about. Here's a link to his Twitter. Go through some of his tweets. Go through some of his blogs and see what his interesting perspectives are that we might want to surface on the blog or something like that. Again, giving the model enough context to answer the question. I think, again, that prompt actually might work really well for somebody who has it. If you were interviewing Tom Cruise or something like that, somebody who has a lot of information about them on the internet, [23:51] little bit better. [23:51] So the advice there is just give more context. It doesn't tell you, hey, I don't actually know that much about Logan, so give me some more information. It's just like, here we go, here's a bunch of good questions. [24:00] Exactly. It so deeply wants to answer your question. It doesn't care that it doesn't have enough context. It's the most eager person in the world you could imagine to answer the question. And without that context, it's just hard to give anything of value. If we got t-shirts printed, they should say, context is all you need. Context is the only thing that matters. It's such an important piece of... [24:23] getting a language model to do anything for you. Any other tips? Just as people are sitting there, maybe they have ChatGPT open right now as they're crafting a prompt. Is there anything else that you'd say would help them have better [24:35] results. [24:37] We actually have a prompt engineering guide, which folks should go and check out, and it has some of the examples. It depends on sort of the order of magnitude of how much performance increase you can get. There's a lot of really small, silly things like... [24:50] adding a smiley face increases the performance of the model. Like telling the, you know, you've seen, I'm sure folks have seen like a lot of these like silly examples, like telling the model to like take a break and then answer the question, all these kinds of things. And again, if you think about it, it's because the corpus of information that's,

[25:07] that's trained these models is the same things that is that humans have sent back and forth to each other. So like you telling a human, like when I go take a break and then I come back to work, like I'm fresher and I'm able to answer questions better and like do work better. So very similar things are true for these models. And again, when I see a smiley face at the end of someone's message, like I feel empowered that like, this is going to be a positive interaction and I [25:29] be more inclined to give them a great answer and spend more effort on the thing that they asked me for. [25:34] Wow, wait, so that's a real thing. If you had a smiley face, it might give you better results. [25:39] Again, it's like the challenge with all this stuff is it's like it's very nuanced. And it's also like it's a small jump in performance. You could imagine like on the order of like one or two percent, which for a few sentence answer is like might not even be a discernible difference. Again, if you're generating like an entire saga of text, like the smiley face could actually make a material difference for you. But for like something small and tactical, it might not. Okay, good tip. [26:04] Amazing. Okay. [26:05] We've talked about GPTs. I think maybe it might be helpful to describe what is this new thing that you guys launched, GPTs? [26:11] And I'm curious just how it's going, because this is a really big change. [26:17] element of OpenAI now with this idea that you could build your own mini, and I'm almost explaining it, your mini OpenChatGPT. [26:24] And then people can, I think you can pay for it, right? Like you can charge for your own GPT or is it all free right now? [26:29] It's all free right now. Okay, it's all free. Okay, in the future, I imagine people will be able to charge. So there's this whole store now. Basically, it's the whole app store that you guys have launched.

[26:38] How's it going? What's happening? What surprised you there? What should people know? [26:41] Yeah, it's going great. And again, historically, the thing that you would have to do, let's say, for example, you have a really cool ChatGPT use case, what you would have to do to share it with somebody else is actually go in and start the conversation with the model, prompt it to do the things that you wanted to, and then you would share that link with somebody else before the action has actually happened and be like, here, now you can essentially finish this conversation with ChatGPT that I started. [27:11] important context, you put it into the model to begin with, and then people can go and chat with essentially a custom version of ChatGPT. And the thing that's really interesting is you can upload files, you can give it custom instructions, you can add all these different tools. A code interpreter is built in, which allows you to do math, essentially. You have browsing built in, image generation built in. And you can also, for more advanced use cases, if you're a developer, you can connect it to external APIs. So you can connect it to the Notion API or Gmail or all these [27:41] to have it actually take actions on your behalf. So there's so many cool things that people are unlocking. And what's been most exciting to me actually is the non-developer persona is now empowered to go and solve these really, really... [27:56] really more challenging problems by giving the model enough context on what that problem is to be able to solve it. Going back to context is all you need. This is very true in the context of GPTs. And if you've given enough context, you can solve much more interesting problems.

[28:11] There's so many things that I'm excited about with this. I think monetization when it comes to the store later this quarter, I think is going to be extremely exciting when people can get paid based on who's using their GPTs. That's going to be a huge unlock and open a lot of people's eyes [28:26] here. I also think continuing to push on [28:30] making more capabilities accessible to GPTs for people who can't code is really exciting. Like having to, even for me as like someone who is a software engineer, like it's not super easy to like connect the Notion API or the Gmail API to my GPT. And like, really, I'd love to just be able to like one click sign in with Gmail. Then all of a sudden it's like my Gmail is accessible or like someone else can sign in with their Gmail and make it accessible. So I think over time, like all those types of things will come. But today it's really like, [28:58] Custom prompts is essentially like one of the biggest value adds with GPDs. [29:03] Awesome. I have it pulled up here on a different monitor. [29:06] And Canva has the top GPT currently, and I was trying to play with it as you were chatting just to see... [29:11] I was going to make a big banner that said, it's the context, stupid. [29:14] And it doesn't, I'm not doing some right, but I'm not paying that much attention to it because we're talking. But this is very cool. Just maybe a final question there. Is there a GPT that you saw someone built that was like, wow. [29:25] That's amazing. That's so cool. Something that surprised you. And I'll share one. [29:29] That was very cool, but is there anything that comes to mind? I think my instinct is the Zapier, all of the stuff that Zapier has done with GPTs is like the most...

[29:41] useful stuff that you can imagine. You can go so far with what... And I don't know how it's packaged for Zapier's GPT right now, but you can actually, as a third-party developer, integrate Zapier without knowing how to code into your GPT. So they're pushing a lot of this stuff. And then basically all 5,000 connections that are possible with Zapier today, you can bring into your GPT and essentially enable it to do anything. So I'm incredibly excited for Zapier [30:11] unlock using that platform. So I think that's probably the most... [30:16] the most exciting thing to me for people who aren't developers. [30:19] Awesome. Zapier's always in there, getting in there, connecting things. [30:23] Yeah, they're great. [30:24] So the one that I had in mind, so I had a buddy of mine, Siki, who's the CEO of a company called Runway, built this thing called Universal Primer. [30:32] which helps you learn. [30:34] It's described as "learn everything about anything." [30:36] And it basically, I think, is kind of this Socratic method of helping you learn stuff. So it's like, explain how transformers work in LLMs. [30:44] And then it just kind of goes through stuff and then asks you questions, I think, and kind of helps you learn new concepts. [30:48] And I think it's the number two education GPT. [30:51] I love that. Siki's incredible. Yes, it's true.

[31:21] documentation. But don't just take my word for it. Quantum Metric, the leading digital analytics platform, created an interactive product tour library to drive more prospects. With Arcade, they achieved a 2x higher conversion rate for demos and saw 5x more engagement than videos. On top of that, they built the demo 10x faster than before. Creating a product demo has never been easier. With browser-based recording, Arcade is the no-code solution for building [31:51] options, designer-approved editing tools, and rich insights about how your viewers engage. Every step of the way. Ready to tell more engaging product stories that drive results? Head to arcade.software.lenny and get 50% off your first three months. That's arcade.software.lenny. [32:10] you [32:11] I want to talk about just what it's like to work at OpenAI and how the product team operates and how the company operates. [32:16] So you worked at your two previous companies were Apple and NASA. [32:20] which are not known for moving fast. [32:23] And now you're OpenAI, which is known for moving very fast, maybe too fast for some people's taste, as we saw with the whole board thing. [32:30] And so what I'm curious is just what is it that [32:34] OpenAI does so well that it allows them to build and shift [32:38] so quickly and it's such high a bar like is there a process or a way of working that [32:42] You've seen that you think other companies should [32:44] try to move more quickly and ship better stuff. [32:48] You know, there's so many interesting trade-offs and all of this like tension around like how quickly companies can move. I think for us, like, again, if you think about Apple as an example, you think about NASA as an example, just like older institutions, like lots of like, you know, over time, the tendency is things slow down. There's like additional checks and balances that are put in place, which sort of drag things down a little bit. So we're young and like a new company. So like we don't have a lot of that like institutional legacy.

[33:16] barriers that have been put in place. I think the biggest thing, and there's a good Sam tweet somewhere in the ether about this from, I think, 2022 or something like that. But like, [33:26] Finding people who are high agency and work with urgency is like one of the most, you know, if I was hiring five people today, like those are like some of the top two characteristics that I would look for in people because it's you can you can take on the world if you have people who have high agency and like not needing to either like. [33:48] you know, [33:50] get 50 people's different consensus because you have people who you trust with high agency and they can just go and do the thing, I think is one of the most... [34:00] It is the most important thing, I'm pretty sure, if you were to distill it down. And I see this in folks that I work with. Folks are so high agency. They see a problem and they go and tackle it. They hear something from our customers about a challenge that they're having, and they're already pushing on what the solution for them is and not waiting for all the other things to happen that I think traditional companies are sort of... [34:22] stuck behind because they're like, "Oh, let's check with all these seven different departments, so try to get feedback on this." People just go and do it and solve the problem. And I love that. It's so fun to be able to be a part of those situations. [34:35] That is so cool. I really like these two. [34:37] characteristics, because I haven't heard this before, is the two, maybe the two most important things you guys look for. High agency, high urgency. [34:44] To give people a clear sense of what these actually look like when you're hiring,

[34:48] You shared maybe this example of customer service, someone's hearing a bug and then going to fix it. [34:52] Is there anything else that can illustrate what that looks like, high agency, and then [34:57] Similar question on urgency other than just like move, move, move, ship, ship, ship. [35:00] I think like the assistance API that we released for dead day, like, [35:05] We continued to get this feedback from developers that people wanted these higher levels of abstraction on top of our existing APIs. And a bunch of folks on the team just came together and were like, hey, let's put together what the plan would look like to build something like this. And then very quickly came together and actually built the actual API that now powers so many people's assistant applications that are out there. And I think that's a great example of... [35:31] It wasn't this top-down, oh, someone's sitting there being like, oh, let's do these five things. And then like, okay, team, go and do that. It's like people really seeing these problems that are coming up and like, [35:41] Knowing that [35:43] They can come together as a team and solve these problems really quickly. And I think the assistance API, and there's like a thousand and one other examples of teams taking agency and doing this. But I think that's a great one at the top of my head. [35:55] that [35:56] makes me want to ask just how does planning work at OpenAI? So in this example, it was just like, hey, we think we need to build this. Let's just go and build it. [36:03] I imagine there's still a roadmap and priorities and goals and things. [36:06] that that team had. How does [36:09] How does roadmapping and prioritization and all of that generally work to allow for something like that? [36:13] I think this is one of the more challenging moments

[36:19] pieces at OpenAI, like there's so many, like everyone wants everything from us. And like today, especially in the world of ChatGPT and how large and well used our API is, like [36:30] people will just come to us and say like, Hey, we want all of these things. I think there's like a bunch of like core guiding principles that we look at. Like one thing, [36:38] going back to the mission, like, is this actually, like, going to help us get to AGI? So there's a huge focus on, like, you know, there's this, like, potential shiny reward right in front of us, which is, like, you know, like, optimize user engagement or whatever it is. And, like, is that really the thing? Like, maybe the answer is yes. Like, maybe that is what is going to help us get to AGI sooner. But, like, looking at it through that lens, I think, is, like, always the first step of deciding any of these problems. I think on the developer side, there's also these, like, core tenets of, like, reliability. Like, [37:08] "Hey, it would be awesome if we had additional APIs that did all these cool things like new endpoints, new modalities, new abstractions." [37:16] are we giving customers a robust and reliable experience on our API? And that's often the first question. And I think there have been times where we've fallen short on that. And like, [37:25] There was a bunch of other things that we've been thinking about doing and really bringing the focus and priority back to that reliability piece. Because at the end of the day, nobody cares if you have something great if they can't use it robust and reliably. [37:38] core tenets, and I think, again, we have very other than [37:42] all the principles about [37:45] how we're making the decision. I think the actual planning process is pretty standard. We come together, there's H1, Q1 goals,

[37:53] We all sprint on those. I think the real interesting thing is how stuff changes over time. You'd think we're going to do these very high-level things, new models, new modalities, whatever it is. And then as time goes on, there's all of this... [38:08] turmoil and change. And it's interesting to have mechanisms to be like, hey, how do we update our understanding of the world and our goals as everything sort of the ground changes underneath of us as is happening in the craziness of the AI space today? [38:21] It's interesting that it sounds a lot like most other companies. There's H1 planning, there's Q1 planning. Are there... [38:28] metrics and goals like that? Do you guys have OKRs or anything like that? Or is it just here, we're going to launch these products? [38:33] I think it's a much higher level. I actually don't think OpenAI is a big OKR company. I don't think teams do OKRs today, and I don't have a good understanding of why that's the case. I don't even know if OKRs are still the industry. You're probably talking to a lot more folks about who are making those decisions. I'm curious, is that something that you're seeing from folks? Is it still common for people to do OKRs? Yeah, absolutely. Many companies use OKRs, love OKRs. Many companies hate OKRs. [38:59] I am not surprised that OpenAI is not an OKR-driven company. Along those lines, I don't know how much you can share about all this stuff, but how do you measure success for things that you launch? I know there's this ultimate goal, AGI. [39:09] Is there some way to track? We're getting closer. What else do you guys look at when you launch? [39:14] say, GPT store or systems or anything, that's like, cool, that was exactly what we're hoping for. Is it just adoption? Yeah, adoption is a great one. I think there's a bunch of metrics around revenue, number of developers that are building on our platform, all those things. And a lot of these, and I don't want to dive, I'll let Sam or someone else on our leadership team go more into details. But I think a lot of these are actual abstractions towards something else. Even if revenue is a goal,

[39:44] is a proxy for getting more compute, which is then actually what helps us get towards getting more GPUs so that we can train better models and actually get to the goal. So there's all these intermediate layers where even if we say something is the goal, and you hear that in a vacuum, and you're like, oh, well, OpenAI just wants to make money. And it's like, well, really, money is the mechanism to get... [40:04] better models so that we can achieve our mission. And I think there's a bunch of interesting... [40:10] Interesting angles like that as well. [40:12] I don't know if I've heard of a more ambitious vision for a company to build artificial general intelligence. [40:18] I love that. I imagine many companies are like, [40:21] What's our version of that? [40:22] Before we leave this topic, is there anything else that you've seen OpenAI do really well that allows it to move this fast and be this successful? You talked about hiring people with higher expectations. [40:33] agency and high urgency. Is there anything else that's just like, oh, that's a really good way of operating? [40:39] Imagine part of it is just hiring incredibly smart people. I think that's probably it. [40:42] unsaid thing, but yeah, anything else. [40:45] I think there's a non-trivial benefit to using Slack. And I think maybe that's controversial, and maybe some people don't like Slack, but opening up such a Slack-heavy culture, and really the instantaneous real-time communication on Slack is so crucial. And I just love being able to tag in different people from different teams and get everybody coalesced. So everybody is always on Slack. So even if you're remote or you're on a different

[41:15] company culture is ingrained in Slack. And it allows us to really quickly coordinate where it's actually faster to send someone a Slack message sometimes than it would be to walk over to their desk because they're on Slack and they're going to be using it. And I saw, if you saw the recent Sam and Bill Gates interview, but Sam was talking about how Slack is his number one most used app on his phone. I'm like, I don't even look at the time thing on my phone frameworks. I don't want to know how long I'm using Slack, but I'm sure the Salesforce people [41:45] So... [41:46] I also love Slack. I'm a big promoter of Slack. I think there's a lot of Slack hate, but it's such a good product. I've tried so many alternatives and... [41:52] Nothing compares. I think what's interesting about Slack for you guys is one of the, like, you don't know if someone in there is just an AGI that is not actually a person that's just there working at the company. I know they're real people. There is no, no AGI's yet. But I think, like, yeah, even Slack is building a bunch of, like, really cool AI tools, which, like, I'm excited to. And that's why, like, there's so much cool AI progress. And, like, at the end of the day, [42:16] It's so exciting from being a consumer of all these new AI products. Google is a great example. I'm so happy that Google is doing really cool AI stuff because I'm a Google Docs customer. I love using Google Docs and a bunch of their other products. It's awesome that people are building such useful things around these models. [42:33] How big is the OpenAI team at this point, whatever you can share, just to give people a sense of the scale? [42:37] Yeah, I think the last public number was something around like 750 near the end of last year, 780 or something like that near the end of last year. And we're growing. We're still growing so quickly. So I don't want to I won't be the messenger to share the specific update numbers. Like the team is growing like crazy. And we're also hiring like across all of our engineering teams. So folks are and PM teams. So folks are interested. We'd love to we'd love to hear from folks who are who are curious about joining.

[43:03] Maybe one last question here. So you're growing, maybe getting to 1000 people, clearly still very innovative and moving incredibly fast. [43:11] Is there anything you've seen about what OpenAI does well to enable innovation and not [43:16] kind of slow down [43:17] new big ideas. [43:19] Yeah, there's a couple of things. One of which is the actual research team who sort of seeds most of the innovation that happens at OpenAI is intentionally small. Most of the growth that OpenAI has seen is around our customer-facing roles, our engineering roles to provide the infrastructure to protect ABT and things like that. The research team is, again, intentionally kept small. And there's all of this talk. And it's really interesting. [43:49] world where you're constrained by the amount of GPU capacity that you have as a researcher, which is the case for [43:56] open AI researchers, but also researchers everywhere else, like each new researcher that you add is actually like a net productivity loss for the research group, unless that person is like, [44:07] up-leveling everyone else in such a profound way that it increases the efficiency. If you just add somebody who's going to go and tackle some completely different research direction, you now have to share your GPUs with that person and everyone else is now slower on their experiments. So the really interesting... [44:24] trade-off that research folks have that I don't think product folks... If I add another engineer to our API team or to some of the ChatGPT teams, you can actually write more code and do more. That's actually a net...

[44:38] beneficial improvement for everybody. And that's always not the case in the case of researchers, which is interesting in a GPU constrained world, which hopefully we won't always be in. [44:47] I want to zoom out a bit, and then there's going to be a couple follow-up questions here. [44:51] Where are things heading with OpenAI? What's in the near future of what people should expect from [44:56] the tools that you guys are going to have in launch. [44:58] Yeah, new modalities. I think ChatGPT continuing to push... [45:03] all of the different experiences that are going to be possible. Today, ChatGPT is really just text in, text out. Or I guess three months ago, it was just text in, text out. We started to change that with now you can do the voice mode and now you can generate images and now you can take pictures. So I think continuing to expand the way in which you interface with AI through ChatGPT is coming. I think GPTs is our first step towards the agent future. Again, today when you use a GPT, [45:33] right away. And that's kind of the end of your interaction. I think as GBTs continue to get more robust, you'll actually be able to say, hey, go and do this thing and just let me know when you're done. I don't need the answer right now. I want you to really spend time and be thoughtful about this. And again, that's [45:50] If you think back to all these human analogies, that's what we do as humans. I don't expect somebody when I ask them to do something meaningful for me to do it right away and give me the answer back right away. So I think pushing more towards those experiences is what is going to unlock so much more value for people. And I think the last thing is...

[46:08] GPTs as this mechanism to get the next few hundred million people into chat GPT and into AI. So I think if you have conversations with people who aren't close to the AI space, [46:20] Oftentimes you talk about, even if they've heard of Chat2PT, a lot of people haven't heard of Chat2PT, but if they have, they show up in Chat2PT and they're like, [46:27] I don't really know what I'm supposed to do with this. This blank slate [46:30] I can kind of do anything. It's not super clear how this solves my specific problem. But I think the cool thing about GPTs is you can package down, here's this one very specific problem that AI can solve for you and do it really well. And I can share that experience with you. And now you can go and try that GPT, have it actually solve the problem and be like, wow, it did this thing for me. I should probably spend the time to investigate these five other problems that I have to see if AI can also be a solution to those. [47:00] because... [47:02] Very narrow vertical tools are what's going to be a huge unlock for them. So in the last case, a classic horizontal product problem where it does so many things and people don't know what exactly it should do for them. [47:14] So that makes a ton of sense, just being a lot more... [47:17] template-oriented, use case-specific, helping people onboard, makes tons of sense. A common problem for so many SaaS products out there. The other ones you mentioned, which are really interesting, basically more interfaces to more easily interact with OpenAI voice, you mentioned, [47:34] audio and things like that. That makes tons of sense. [47:36] And then this agents piece where the idea is, instead of just it's a chat, it's like, hey, go do this thing for me. Kind of along those lines.

[47:44] GPT-5, we touched on this a bit. [47:46] There's a lot of speculation about the much better version. People just have these wild expectations, I think, for where GPT is going. GPT-5 is going to... [47:54] solve all the world's problems. I know you're not going to tell me when it's launching and what it's going to do, [47:59] But I... [48:00] heard from a friend that there's kind of this tip that when you're building products today, you should build [48:04] towards a GPT-5 future, not based on limitations of GPT-4 today. [48:09] So to help people do that, what should people think about that might be better in a world of GPT-5? Is it just like it's faster? [48:17] It's just smarter. Is there anything else that might be like, oh, wow, I should really rethink how I'm approaching my product. [48:22] If folks have looked through the GPT-4 technical report that we released back in March when GPT-4 came out, GPT-4 was the first model that we trained where we could reliably predict the capabilities of that model beforehand based on the amount of compute that we were going to put into it. And you could actually, we did like a scientific study to show like, hey, this is what we predicted and here is what the actual outcome was. So it'll be one, I think. [48:47] just as somebody who's interested in technology, but interested to see, like, does that continue to hold for GPT-5? And hopefully we'll share some of that information whenever that model comes out. I also think you can probably draw a few observations. One of them, which is... [49:02] GPT-4 came out, the consensus from the world is everything is different. [49:08] Like all of a sudden, everything is different. This changes the world. This changes everything. And then slowly but surely, we come back to reality of like, this is a really effective tool and it's going to help solve my problems more effectively. And I think that is like the...

[49:23] undoubtedly the lens in which people should look at all of these model advancements like gbt5 is like surely going to be extremely useful and like solve some whole new echelon of problems hopefully they'll be faster hopefully they'll be better in all these ways but like [49:36] fundamentally the same problem that exists in the world are still going to be the same problems. You now just have a better tool to solve those problems. And I think going back to vertical use cases, I think people who are solving very specific use cases are just now going to be able to do that much more effectively. I don't think that's going to... People have these unrealistic expectations that GBT5 is going to be doing backflips in the background in my bedroom while it also writes all my code for me and talks on the phone with my mom or something like that. [50:06] the case like it is just going to be this like very effective tool very similar to gpt4 and it's also going to become like [50:13] very normal very quickly. And I think that is actually a really interesting piece. If you can [50:21] people become very, very used to these tools very quickly. I actually think that's like an edge. And like assuming that this thing is going to like absolutely change everything. And in many ways, I think it's actually like a... [50:34] a downside is like the wrong mental framing to have of these tools as they come out. [50:38] Kind of along these lines, you guys are investing a lot into B2B offerings. I think half the revenue last I heard was B2B, and then half is B2C. I don't know if that's true, but that's something I heard. [50:50] What is it that you get if you work with OpenAI as a company, as a business? What does it unlock? Is it just called OpenAI Enterprise?

[50:57] What's it called? [50:58] And what do you get as a part of that? Yeah, so I think a lot of our B2B customers are using the API to build stuff. So I think that's one angle of it. I think if you're a ChatGPT B2B customer, we sell Teams, which is the ability to get multiple subscriptions of ChatGPT, package it together. We also have an enterprise version of ChatGPT. There's a bunch of enterprise-y things that enterprise companies want around SSO and stuff like that related to ChatGPT Enterprise. [51:28] like prompt templates and GPTs internally. So again, you can make like custom things that work really well for your company with like all of the information that's relevant to solving problems at your company and like share those internally. And to me, that's like, [51:41] You want to be able to collaborate with your teammates on the cool things you create using AI. So that's a huge unlock for companies. I think those are the two biggest value adds. There's higher limits and stuff like that on some of those models. But I think being able to share your very domain-specific applications is the most useful thing. [51:59] And, [52:00] I think if you're a company listening and you think a lot of employees are using ChatGPT, [52:05] Basically, the simplest thing you could do is just roll it up into a business account with single sign-on, and that probably saves you money and makes it easier to coordinate and administer. [52:14] There's also a bunch of security stuff too. If you want to control, you don't want people to use certain GPTs from the GPT store because you're worried about security or privacy and stuff like that. You don't want your private data going in places. It makes a lot of sense to sign up for that so that you have a little bit more control over what's happening.

[52:29] Okay, got it. [52:30] There's a launch happening tomorrow, I think, after we're recording this. [52:34] Can you talk about what [52:36] is new, what's coming out. I think this is going to come out a couple weeks after recording, but just what should people know that's new, that's coming out from OpenAI tomorrow in our time, in our world? Yeah, update it. So there's a few different things. A couple of quick ones are updated GPT-4 Turbo model, update the preview model that we released at DevDay. There's an updated version of that. It fixes this, if folks have seen online people talking about this sort of laziness phenomenon in the model. We improve on that, and it fixes a lot of the cases where [53:06] Sleazy. [53:06] And [53:07] The big thing is the third generation embeddings model. So we were talking off camera before recording about all of the cool use cases for embeddings. So if folks have used embeddings before, it's essentially the technology that powers many of these question and answering with your own documentation or your own corpus of knowledge. And Lenny, you were saying you actually have a website where people can ask questions about recordings of the podcast. LennyBot.com. Check it out. Yeah, LennyBot.com. [53:37] assumption was that moneybot.com is actually powered by embedding. So you take all of the corpus of knowledge, you take all the recordings, your blog posts, you embed them. And then when people ask questions, you can actually go in and see the similarity between the question and the corpus of knowledge and then provide an answer to somebody's question and reference an empirical fact, something that's true from your knowledge base. And this is super useful and people are doing a ton of this is trying to ground these models in reality in what they know to be true.

[54:07] Like we know all the things from your podcast to be at least something that you've said before and to be true in that sense. And we can bring them into the, the answer that the model is actually generating in response to a question. So that'll be super cool. And these new V3 embeddings models, [54:22] again, you know, state-of-the-art performance. The cool thing is actually the non-English performance has increased super significantly. I think historically people really were only using embeddings for like, it only worked really well for, for English. And I think now you can, you can use it across like so many new languages because it's, it's just so much more performant across those, uh, across those languages. And it's like, [54:45] five times cheaper as well, which is wonderful. There's no better feeling than making things cheaper for people. I love it. I think now it's like you can embed [54:54] I'm pretty sure it was like 62,000 pages of text. [54:58] for $1, which is very, very cheap. So lots of really cool things you can do with embeddings and excited to see people invent more stuff. [55:07] What a deal. [55:09] Final question before we get to our very exciting lightning round. Say you're a product manager at a big company or even a founder. What do you think are the biggest opportunities for them to leverage the... [55:22] tech that you guys are building, GPT-4, all the other APIs. How should people be thinking about, "Here's how we should really think about leveraging this power in our existing product." Or, [55:31] new product, whichever direction you want to go. [55:34] Yeah, I think going back to this theme of new experiences is really exciting to me. I think consumers are...

[55:42] just going to be like, you're going to have an edge on other people if you're providing AI that's not accessible in a chatbot. People are using a ton of chat and it's a really valuable service area. It's clearly valuable because people are using it, but I think products that move beyond this chat interface, [55:59] really are going to have such an advantage. And also thinking about how to take your use case to the next level. I've tried a ton of chat examples that are very, very basic and providing a little bit of value to me. But I'm like, really, this should go much further and actually build your core experience from the ground up. I've used this product that allows you to essentially manage or view the conversations that are happening online around certain topics and stuff like that. [56:29] like go and look online, like what are people saying about GPT-4? And like that, what I just said out loud, what are people saying about GPT-4 is like the actual question that I have. And like in a normal product experience, I mean, like I have to go into a bunch of dashboards and like change a bunch of filters and stuff like that. And what I really want is just like, [56:47] ask my question. What are people doing? What are people saying about GPT-4? Like get an answer to that question in like a very data grounded way. And I've seen people like, [56:57] It's all part of this problem where like, oh, let me like, oh, here's a, here's a few examples of what people are saying. And like, well, that's not really what I want. Like I want this like, [57:05] summary of what's happening. And I think it just takes a little bit more engineering effort to make that happen. But I think it's like, that is the magical unlock of like, wow, this is an incredible product that I'm going to continue to use instead of like, yeah, this is kind of useful, but like, I really want more.

[57:20] Awesome. I'll give a shout out to a product. I'm not an investor, but I know the founder. [57:24] called visualelectric.com, which I think is doing exactly this. It's basically [57:29] tools specifically built for creatives, I think specifically graphic design. [57:33] to help them create imagery. [57:36] So there's like Dolly, obviously. [57:38] But this takes it to a whole new level where it's kind of this canvas, infinite canvas, that you could just generate images, edit, tweak them, and continue to array until you have the thing that you need. [57:47] Is it similar to Canva? [57:50] It's even more niche, I think, for more sophisticated graphic design, I think, is the use case. [57:56] But I'm not a designer, so... [57:58] I'm not the target customer, but I will say my wife is a graphic designer. She'd never used AI tools. [58:04] I showed her this and she got hooked on it. She paid for it without even telling me that she was going to become a paid customer. [58:09] And she just started, she created imagery of her dog. [58:12] and all this art, and now it's like on our TV, the art she created is now sitting. It's like we have a frame TV. [58:18] and that's the image I don't see. [58:20] I love that. What was it called again? [58:24] Anyway, anything else you wanted to touch on? [58:28] or share before we get to our very exciting lightning round. [58:31] I've made this statement a few times online and other places, but like for people who are, have cool ideas that they should build with AI, like this is the moment. Like there are so many cool things that need to be built for the world using AI and like, [58:45] Again, if I or other folks on the team at OpenAI can be helpful in like getting you over the hump of like starting that journey of building something really cool, like please reach out. Like there's just the world needs more cool solutions using these tools and would love to hear about like the awesome stuff that people are building.

[59:01] I would have asked you this at the end, but how would people reach out? What's the best way to actually do that? [59:06] Twitter, LinkedIn, my email should be findable somewhere. I don't want to say it. And I get stand with a bunch of emails like you should be able to find my email if you need it online somewhere. But yeah, Twitter and LinkedIn is usually like the easiest place. And how do they find you on Twitter? [59:20] It's just Logan Kilpatrick, or I think my name shows up as Logan.gpt or at official Logan K. Yeah, awesome. Okay, and we'll link to it in the show notes. Amazing. Well, Logan, with that, we've reached a very exciting lightning round. Are you ready? [59:34] I'm ready. First question, what are two or three books that you've recommended most to other people? [59:39] I think the first one, and it's one that I read a long time ago and came back to recently, is The One Room Schoolhouse by Sal Khan. [59:47] incredible yeah i i don't want to it's a lightning round so i won't say too much but like incredible story and ai is what is going to enable style con's vision of like a teacher per student to actually happen so i'm really excited about that and the other one is uh that i always come back to is why we sleep um i yeah sleep sleep and sleep science are so cool um if you don't care about your sleep like it's one of the the biggest up levels that you can do for yourself [1:00:14] What is a favorite recent movie or TV show that you really enjoyed? [1:00:18] I'm a sucker for a good, inspirational human story. So I watched with my family recently over the holidays this Gran Turismo movie. And it's a story about a kid from London who grew up doing sim racing, which is a virtual race car, and did this competition. Ended up becoming a real professional race car driver through some competition.

[1:00:48] competing in the 24-hour Le Mans and all that stuff. [1:00:50] I used to play that game, and it was a lot of fun, but... [1:00:52] I don't think I have any clue how to drive a real car. [1:00:55] Race car. So that's inspiring. [1:00:58] Do you have a favorite interview question that you'd like to ask candidates that you're interviewing? [1:01:02] Yeah, I'm always curious to hear what people's like, the thing that they so strongly believe that people disagree with them on. [1:01:11] What do you look for in an answer that seems like, wow, that's a really... [1:01:15] Good signal. I'm oftentimes, it's just an entertaining question to ask in some sense, but it's also, it's interesting to see like what somebody's like deeply held strong belief is. I think that's it. And, you know, not to like judge whether or not I believe in that, but like just curious to like see why people feel that way. [1:01:35] What is a favorite product that you've recently discovered that you really like? [1:01:39] On the narrative of sleep, I have this really nice sleep mask from this company called, not being paid, I have to say this, but it's called Manta Sleep or something like that. It's a weighted sleep mask. And it feels incredible when I, I don't know, maybe I just have a heavy head or something like that, but it feels good to wear a weighted sleep mask at night. I really appreciate it. I have a competing sleep mask that I highly recommend. I'm trying to [1:02:04] find it. I've emailed people about it a couple times in my newsletter. [1:02:08] For gift cards? Okay, my favorite is called the Wawa Sleep Mask. [1:02:13] What do you like about it? [1:02:16] W-A-O-A-W. I'll link to it in the show notes. It makes a lot of room. It's very large, and there's space for your eyes, so your eyelashes and whatever eyes are impressed on.

[1:02:27] And it just fits really nicely around the head. And my wife, we both wear masks at night. It just... [1:02:32] Speaking of sleep, it really helps us sleep. [1:02:34] Yeah, same here. I love it. It doesn't have the weight in this. [1:02:38] piece so it might be worth trying but uh everyone i've recommended this to is like that changed my life thank you for helping me sleep better [1:02:46] And so we'll link to book limit. Look at that. Sleep mask. Look at us. Adult. Two more questions. Do you have a favorite life motto that you often come back to share with friends or family, either in work or in life? [1:02:59] Yeah, I've got it. It's on a post-it note that I write behind my camera and it's measure in hundreds. I love this idea of measuring things in hundreds and it's, [1:03:09] For folks who are at the beginning of some journey, I talk to people all the time. They're like, yeah, I've tried this thing and it hasn't worked. And if your mental model is to measure in hundreds, I measure in hundreds the five times that you failed at something, you failed and tried zero times. And I love that. It's such a great... [1:03:27] reminder that [1:03:29] Everything in life is built on compounding and multiple attempts at stuff. And if you don't try enough times, you're never going to be successful at it. [1:03:38] I love that. I could see why you are successful at OpenAI and why you're a good fit there. [1:03:42] Final question. So I asked ChatGPT, [1:03:47] For a very silly question, give me a bunch of silly questions to ask Logan Kilpatrick, head of developer relations at OpenAI. And I went through a bunch. I have three here, but I'm going to pick one.

[1:03:58] Iffident AI started doing stand-up comedy. [1:04:01] What do you think would be its go-to joke or funny observation about humans? [1:04:06] I think today, I think if you were to do this, like I think the go-to question would be something along like the, so an AI walks into a bar and likely because, again, it's trained on some distribution of training data. And like, that's like the most common joke that comes up. And that's probably like, I wonder if you came up with a joke right now, whether or not that would show up in one of the examples. I love it. What would be the joke, though? We need the joke. We need the punchline. I'm just joking. [1:04:36] come up with amazing... That's where we have chat-chip-y-tee for. That's right. We're ready and relevant. [1:04:40] Amazing. Logan, thank you so much for being here. [1:04:44] Two final questions, even though you've already shared this information, but just for folks. [1:04:47] to remind them, where can folks find you if they want to reach out and [1:04:50] ask you more questions. [1:04:52] And how can listeners be useful to you? [1:04:55] Yeah, Twitter and LinkedIn, Logan Kilpatrick or Logan.gbt on Twitter. Please shoot me messages. I get a ton of DMs from people and it's always really, really interesting stuff. I think the thing that I can... [1:05:07] that I would love to have help on is like if people find bugs and things that don't work well in chat gbt like I oftentimes like see people be like this thing didn't work really well and the the key and I think we as open ai need to do a better job of like messaging this to people but having like shared chats or like actual like tangible reproducible examples are like the two things that we need in order to like actually fix the problems that people have like the model

[1:05:37] figure out what was going on because people would be like, oh, the model's lazier, but like, it's hard to figure out like, what were the prompts they were using? What was the examples, all that stuff. So send those examples as you come up on things that don't work well, and we'll, we'll make stuff better for you. [1:05:49] Amazing. And I'll also just remind people, if you're listening to this and you're like, oh, okay, cool. A lot of cool ideas for OpenAI and ChatGPT. [1:05:58] What you need to do is actually just go to chat.openai.com and try... [1:06:02] stuff out. There's a lot of just like theorizing [1:06:05] But I think once you actually start doing it, [1:06:07] you start to see things a little differently and [1:06:09] At this point, every day I'm in there doing something, like asking for ideas for questions, [1:06:14] doing research on a newsletter post and it's just like a tab I'm always [1:06:17] coming back to. And I know there's a lot of people just like talking about this sort of thing. And I just want to remind people just like go sign in. [1:06:24] play with it, ask it questions on something you're working on, and just see how it goes and keep [1:06:28] Keep coming back to it. Is there anything else you want to share along those lines to inspire people to [1:06:32] Give this a shot. [1:06:33] I love it. I think that the phrase of like, you know, people being worried about humans being replaced by AI. And I've seen this narrative online that it's like, it's not AI that's going to replace humans. It's like other humans that are being augmented and like using AI tools that are like going to be more competitive in a job market and stuff like that. So go and try these AI tools like this is the best time to learn, like you're going to be more productive and like empowered in your job and the things that you're excited about. So yeah, excited to see what people use ChatGPT for.

[1:07:03] bucks a month. A lot of companies are paying for this for you. So ask your boss if you can just have it expensed and make sure you use the latest version. [1:07:11] Anyway, Logan, thank you again so much for being here. [1:07:15] This is awesome, buddy. Thanks for having me. And thoughtful questions. Hopefully those weren't all from ChatGVT. Nope. Only the last one. I did have a bunch of others. I had in the belt or in the pocket. I don't know if the metaphor is. In the back pocket, that's the metaphor. But I did not get to them because we had enough great stuff. [1:07:32] No, that was all me. [1:07:34] Human AI. Thank you. Thanks, Logan. Lenny.ai. I love it. Lennybot.com. Check it out. [1:07:40] Okay, thanks, Logan. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.