Transcription
I'm going to take you back. If you are like, gray-bearded, like me, you remember before Google, when we were using the internet and we depended on hyperlinks. You remember that sense of joy when you, like, find something and you couldn't have found it by search, because search didn't exist. And there was this little tip, and then you went to the hyperlink, and you found a page. That's what AI is like right now; that's what using AI is like.
And look, I don't want another Google of AI, although we might have one with OpenAI, to be honest. But we do need something that enables us to more effectively understand what we have with artificial intelligence. And if you look at it as a problem of understanding—if you look at it that way—you have so many different dimensions of this problem that crystallize into a single core issue. And that's what I want to talk about.
I would argue that, effectively, we don't know how to navigate latent space. We don't understand what latent space in large language models is like. We haven't visualized it well; haven't understood it properly. And everything stems from that. As an example, all of these annoying prompting tips—and prompt this, and prompt this way, and like, tell it you want it to be amazing at its job, and tell it you're on vacation in France, and tell it this, and tell it that—I kid you not, every time I turn around, there's another one of these things. They're all about trying to nudge the LLM through latent space, but we don't really understand it.
Another example that's the same core issue: all of these building tips that basically are like, "This is how you tell Cursor or Builder or Lovable or Windsurf—or pick your tool of choice—how to build something." Tell it like this; give it a full dev plan; break it into chunks; do this; do that. It's all about basically telling a large language model how to navigate latent space to produce tokens of code.
And if you go farther afield—if you're talking about how to make marketing posts, about how to produce content for customer success emails, about how to write sales emails or sales scripts—again, you run into the same issue, where effectively companies are monetizing the inability to navigate latent space. A lot of the solutions being shared, or built, or monetized, or built through YC or whatever you have, are basically ways to take these large, unwieldy models and productize them into something that is a lot more stable and a lot more consistent. And that's not bad; I don't object to companies doing that. Being able to basically take an intelligence and package it is a totally legitimate service. But I do think it raises the fundamental question; it highlights the fundamental question, if you're watching properly, because it underlines the fact that nobody really has a good grasp on latent space, and we certainly do not have a good grasp on how to talk about latent space.
I saw a digital representation of a single Chain of Thought running through an LLM, and it looks like a rat's nest—like it's like running all through this, like, visualized, uh, latent space. And of course, that's not how latent space actually looks. So it's like this colored string running through, and I was like, "Wow, this looks complicated." And that's how I walked away. And at best, that's where people are at. Like, I'm the fluent one. The people who don't even know what latent space are are scratching their head saying, "How did the LLM come up with a sentence?" And we are so lost on communicating how this works that we don't have good answers to people who are at that level. We certainly don't have good answers to how to use the technology for people. We've given people a chat screen and said, "Here's a chat," but people are used to talking with other human beings; they're not used to talking with a hyperdimensional intelligence that navigates through latent space to answer their queries.
And a lot of the issues come from the fact that they treat the chatbot as if it was a person. In some cases, they treat it as if it was a person with the expectations of a computer, and we've talked about that, where you sort of expect the computer to be perfect, and so you expect the AI to never make a mistake. But by and large, they treat it like a person: "Make sure you answer me in this way," or "Write this email to Bonnie in this style," or "Hey, I'm having a bad day today." Those companion apps definitely make money. Um, and so I think that we can get farther if we are more honest about—about how weird these things are. LLMs are really weird. It's weird that they work; it's not necessarily intuitive. And we kind of got farther on the internet by just acknowledging it was a new thing—like, "Hey, here's the internet. It's not like the newspaper; you can click on links now, and you can go new places. And oh, there's a search engine, so you can search for anything." It's not like a newspaper or a book; imagine a card catalog, but you can search the entire world. Those were the kinds of things we talked about.
We need that kind of language for large language models. We need it to be like, "Imagine a world where you have an intern that has read every book ever written, but it's still kind of dumb." Or imagine a world where you have a very specialized professor who knows everything there is to know about biochemistry, but you'd never trust him out at dinner because he doesn't know a whole lot. Or imagine a world where you need to get an answer to something, and you're going to get six answers, and none of them are right, but all of them are interesting, and they will help you get to the right answer. We're not doing enough of that kind of communicating, and we're not demystifying it. When we portray it as a secret, when we portray it as, "Here's a tip from an expert," it's not helping; it makes people think it's hard. And I don't think it does any of us a service who know AI well if we keep portraying it as something that is difficult to practice, something that is difficult to try, something that it is difficult to execute on at a high level, because honestly, it's not.
Let me give you a specific example here. We'll talk about building for AI, which is one of those things that people—to—like, if you're an engineer, you sort of know how to build, but then you have to learn how to build with AI. And if you've never built or never coded, you just sort of scratch your head and like stare at the wall, and you don't know what to do. Let me try something on with you—like, we're actually going to try and solve this one as an example of how to solve this stuff better. I have eight steps that I think you can walk through with anyone, even someone who hasn't built an application before, and say, "Look, this is kind of how you work with an AI on this."
Number one: figure out what you want to make. You can use an AI to brainstorm, and that's as hard as it needs to get, right? Brainstorm; come up with some solutions; brainstorm some ideas for features. Um, and then you kind of want to think about what you don't want to have in, right? Now, in a product management sense, that's scoping. I don't need to use the word scoping for that; I can just say what you don't want to have in. Now, I will say, as I go through these eight, this doesn't mean that if I can explain this well, everyone in the world is going to become an AI builder, just like I can explain cooking, but not everyone's going to become a chef. But we can still communicate clearly. So let's go to number two: architecture. You want to outline how the AI is going to work, because as you can imagine, the AI has to use information. Where does that information live? Does it live on the web page? Do you want to change it much? Does it live in the library behind the web page? Are you going to have payment? You want to understand those pieces, and you probably want to work with an AI to figure out what all those pieces are, and then start to brainstorm what you need to build. It might have some fancy words like API; it might have a—a word like a database in it, which is really a fancy word for a data library. But at the end of the day, you're going to come out of an architecture and technical planning conversation, and you're going to say to yourself, "I understand where the data goes," because that's really all it is. And AI can help with that.
Once you understand where the data goes, you have to understand what the data looks like. We would call that data schema—how you actually structure the data. AI can help a ton with that, because if you know what the data is, AI is pretty good with arranging it. Number four: setting up your building world. Now, if you're building something simple, it comes preset up. You can go to Lovable, you can go to Bolt, or you can go to Cursor and Windsurf, and you can just set it up yourself with some simple rules. Either way, it's like clearing the table and getting set up to build a model. You want to make sure that you're set up correctly. You see how I'm using these analogies all the way through? I'm not just doing this because I think you don't know how to build; I'm doing this because I want to practice communicating well and show how important it is to communicate well with a real example that I run into all the time.
Backend and database implementation is what you start with after you've decided to start to build. And you could say that really simply by saying, "You know what, if you're building, you want to start with a foundation." The foundation is the library of information. You want to make sure the library of information is solid, so you can pull information in and out of it. If you build the front of the website first and just build the page, you'll have a nice-looking website with no library of information behind it; you'll be in trouble. After you build the library of information, you're going to want to build the web page. You look at that; that's the part you're probably really excited about. But you see, it's six; we've had to be really patient to get here. Just like building a great model airplane, you may want to put the wings on and make it look really pretty at the end, but you have to wait. And by the way, if—if this is all feeling child-level, I have in the back of my head my own kid who I have to explain this stuff to, and so if I can explain it to her, I can explain it to anybody. Okay, number seven: you're going to have to test to see if it works. So make sure when you're building, you include testing. Test if the data runs into the library of data; test if it runs back. That's really important. Finally, you want to put it somewhere where other people can get it. We call that deployment. There are apps for that. You can see where I'm going. That's the eight steps to build.
Now, I ran through them really quickly. I'm sure you can find better ways to communicate that. It doesn't have to be at the level of a 9-year-old, which is basically what I did. But we do need to find simpler ways to explain how LLMs work and what they do for us. And that's the heart of the point that I want you to take away. It's really important to be clear about that. So give me your best takes: How can we get better at explaining these weird large language model intelligences so that it's easy to understand, and other people can understand what we're trying to talk about and share, and why it's so cool?