Lenny's Podcast: Product | Career | Growth
00:43:12 · Keith Rabois, Lenny Rachitsky
[00:43:12] S0: Well, let me give you a couple of concrete examples. So at Shopify, the way they develop, they've been doing this for over two years now. So this may seem normal, but they have not let PMs provide PowerPoint or keynote presentations on product for two years. Every presentation on product has to be a workable demo, and they just expect the PMs to create the products. And the execs just refuse to look at static, "We're going to have this feature." No, I want everything working. This is for two years. So I think that, again, everything's just merging together. [00:43:51] S1: That's so interesting. You would think though, because there are so many products launching every single day, there's just endless things to pay attention to, you would think design would be a differentiator more and more. [00:44:02] S0: I do agree with that. I do agree with that actually. I think the alpha is in design just like in marketing. It's not the tools, it's not the channels, not the metrics. It's the storytelling. It's how do you cut through the clutter in the snappiest, most compelling, possible way? It's an NP problem. There's so many different words you can use to express the same concept, but the person who can say, "This is the way to frame it," the proverbial 1,000 songs in your market, is worth all the tools in the world.
00:44:02 · Keith Rabois, Lenny Rachitsky
[00:44:02] S0: I do agree with that. I do agree with that actually. I think the alpha is in design just like in marketing. It's not the tools, it's not the channels, not the metrics. It's the storytelling. It's how do you cut through the clutter in the snappiest, most compelling, possible way? It's an NP problem. There's so many different words you can use to express the same concept, but the person who can say, "This is the way to frame it," the proverbial 1,000 songs in your market, is worth all the tools in the world. [00:44:31] S1: Yeah. So that's the other part of my insight recently is just as AI makes it easier to build, it's kind of expanding from the middle out. And what remains is figuring out what to build and making iterating on the idea. And then it's at the other end of the spectrum, which is distribution, getting anyone to pay any attention to what the heck you've done, because again, there's so much happening every single day.
00:44:31 · Lenny Rachitsky, Keith Rabois
[00:44:31] S1: Yeah. So that's the other part of my insight recently is just as AI makes it easier to build, it's kind of expanding from the middle out. And what remains is figuring out what to build and making iterating on the idea. And then it's at the other end of the spectrum, which is distribution, getting anyone to pay any attention to what the heck you've done, because again, there's so much happening every single day. [00:44:48] S0: Yeah. Cutting through the clutter. I mean, that's always been critical. I mean, when people are pitching me as an investor, it's one of the things I'm dialing into immediately is how the hell is this going to cut through the clutter? It's one of the reasons why I don't like to take customer feedback into account because by definition, when you put something in front of a customer, that's not a proxy for the real world. In the real world, you have to cut through the clutter while they're going to their Barry's bootcamp, while they're doing their job, while they're raising their kids, et cetera, et cetera, while they're walking, while they're on the subway. An isolated fake experiment doesn't give you actionable insights and often is directionally wrong. [00:45:26] S1: Interestingly on that line, there's a few companies that are launching, Simile is one, that is simulating humans. I don't know if you heard about this company. They basically are building AI models of the actual people so that you can simulate your marketing launch and your product experience with people before you launch.
00:45:26 · Lenny Rachitsky, Keith Rabois
[00:45:26] S1: Interestingly on that line, there's a few companies that are launching, Simile is one, that is simulating humans. I don't know if you heard about this company. They basically are building AI models of the actual people so that you can simulate your marketing launch and your product experience with people before you launch. [00:45:44] S0: I don't know if I've seen that specific company. I have seen one or two pitches. My general question as a refrain on those type of companies is what are they training on? Because again, if they're not training on the right data, it's dangerous to say you're simulating humans. [00:46:02] S1: Yeah. Yeah. That is the question. Oh, man. What a weird world. Along these lines, actually, you have this, I don't know if you call it a hot take that AI content is going to surpass human content and that's just the future. [00:46:13] S0: Oh, that's inevitable. [00:46:15] S1: I was talking to someone and then she was saying in the Chinese TikTok app, it's just all AI videos now and it's very good. She actually enjoys watching these videos and these stories.
00:46:15 · Lenny Rachitsky, Keith Rabois
[00:46:15] S1: I was talking to someone and then she was saying in the Chinese TikTok app, it's just all AI videos now and it's very good. She actually enjoys watching these videos and these stories. [00:46:26] S0: I think there'd be a binary sort maybe. I could see products and content thriving that is clearly still human-generated and that there's some desire for authenticity. Just like, for example, this piece of art is a Warhol. Anybody could create this now, high fidelity, but there's still an appreciation for this was created by Andy Warhol. I think there's going to be a curated experience with a premium of provenance that you know is human-created. And then there's just going to be a rank filtering of what's the best content and whether it's AI-generated or not. It's a little bit like the Ben Thompson strategy thing of curation versus algorithm. There's going to be two polls and the curation may be for human created stuff. And then there's going to be the algorithm, which is just what's the best? [00:47:18] S1: I 100% believe that. That makes so much sense. I was just thinking the other day, it's so interesting that AI is getting very good at video. Videos are actually fun to watch that are AI-generated. And then images are getting really good, but writing is still really bad that is AI-generated. And it's ironic that it's called a large language model. It's all context, text, text trained. And it's just like that's the thing it's least good at, which is really weird.
00:47:18 · Lenny Rachitsky, Keith Rabois
[00:47:18] S1: I 100% believe that. That makes so much sense. I was just thinking the other day, it's so interesting that AI is getting very good at video. Videos are actually fun to watch that are AI-generated. And then images are getting really good, but writing is still really bad that is AI-generated. And it's ironic that it's called a large language model. It's all context, text, text trained. And it's just like that's the thing it's least good at, which is really weird. [00:47:44] S0: Well, I think part of that's an economic decision of token rationing by the LLMs. They basically are, when you prompt, they're trying to make their economics work within a bell curve distribution. And so for example, if you prompt an LLM to write something that's very short, the quality is significantly better than if it's a page to chapter to book. I think it's tokening ratioing versus actual quality. [00:48:17] S1: You would think though, I don't know if it was Hemingway of just like, "If I had more time, I would've made it shorter." It's more work. Yeah. [00:48:23] S0: But then you have ... I think the writing, right now what works best is short, many short examples and then- [00:48:32] S1: Tweets, basically.