Lenny's Podcast: Product | Career | Growth
00:53:09 · Brendan Foody
[00:53:09] S0: Yeah, so there's been like a dozen, but I'll choose my favorite two. So when I was in eighth grade, I started Doughnut Dynasty, where I saw that Safeway donuts were selling for $5 a dozen. And I was amazed because I felt like as an eighth grader, this was such an incredible deal. And so I started to bike down to Safeway, buy Safeway donuts for $5 a dozen, and then, go back to my middle school and then sell them for $2 each. Running really good, margins of course. It sold out super quickly and so then I need to scale up. So I would pay my mom $20 to drive me in her minivan, down to Safeway, buy 10 dozen donuts, go to my middle school, sell them all out. And then the school tried to shut me down and so because I was selling like food on school campus, which they didn't like, so they had me in the principal's office asking me to not do that. And then I moved my donut stand over 50 feet so it was off school campus saying that they could no longer, you know, police me. I remember we had competitors pop up where the competitors were charging. They bought these Chuck's donuts, which if anyone in the Bay Area knows are, you know, higher end donuts than Safeway donuts, but they have a higher cost basis. They cost a dollar per. And so I dropped my prices to $1 for two weeks to run them out of business before I, before I knew, what anti competitive practices were. I'd, I'd hire all my friends, paying my friends in donuts because, you know, they perceived the donuts as, as $2 each where they could sell them throughout the school, and I could have a lower cost basis on them. So I had all of these like fun experiences in selling donuts. And then I could talk more about my high school business as well, which was, more significant scale. But I think the takeaway from that was just like, you can just do things. Like so many people have ideas, but the barrier to more companies being built, I think is just initiative and taking the steps to build the product or experience that customers want and investing the time and the ambition to scale that up. And so I think it was really getting reps of that that enabled me to realize that I should do it later on at a much larger scale.
00:55:23 · Lenny Rachitsky, Brendan Foody
[00:55:23] S1: Amazing story. I love how wholesome that is versus like drugs. Then [00:55:27] S0: my mom was very worried. She was like, Oh, is there any pod of these donuts? I was like, No, mom, I assure you. These are pure donuts. [00:55:36] S1: I love that you paid your mom $20 to drive. [00:55:38] S0: Yeah, it was. She was adamant. It couldn't be a handout that she was taking her time to drive these, so she needed to make a little bit of money off of it. We haggled over her title where eventually she wanted to be head of global operations, which we found very entertaining. [00:55:54] S1: I hope that's on her LinkedIn. [00:55:56] S0: Not yet. Maybe she'll have to add it. And so you [00:55:59] S1: said that you've started a dozen companies. Yeah. Wow. Okay. [00:56:02] S0: Well, a dozen projects, but I think there were... It was that and then my AWS company, were the two that I sort of scaled up. [00:56:10] S1: What's the story behind Merkor as the name? [00:56:13] S0: Merkor means marketplace in Latin or to buy, sell, trade. And we want to build the largest marketplace in the world, the marketplace for how everyone finds jobs, and that was really the draw to it. [00:56:27] S1: Okay, maybe a last question. This is going back to earlier in discussion, because it's something I've been thinking about as we're talking. There's been this shift from data as kind of the fuel for models, and now it's experts. Do you think there's a next step, or is this just like, will take us to AGI superintelligence?
00:56:27 · Lenny Rachitsky, Brendan Foody
[00:56:27] S1: Okay, maybe a last question. This is going back to earlier in discussion, because it's something I've been thinking about as we're talking. There's been this shift from data as kind of the fuel for models, and now it's experts. Do you think there's a next step, or is this just like, will take us to AGI superintelligence? [00:56:46] S0: I don't think it's necessarily changing from data to experts. It's more just the paradigm of realizing that labs need this close collaboration with experts to help understand what are the evals that they're building and how can they push the frontier. But I think it's very clear that evals are evergreen, that so long as we want to improve models, we'll need experts to create evals for them and to create the post training data for them to learn those capabilities. And of course, there might be changes in the exact way that people do training with RL or otherwise, but they will always need an eval to measure what does success look like across every domain that they want to build. [00:57:33] S1: Okay. So then building on that, a question that comes up a lot these days is, and I know we're talking about fun stuff, but I'm getting to serious stuff again, scaling laws and just like progression of model intelligence. A lot of people are feeling like, I don't know. It's slowing down. We're not gonna really get to super intelligence at this rate. What is your sense?
00:57:33 · Lenny Rachitsky, Brendan Foody
[00:57:33] S1: Okay. So then building on that, a question that comes up a lot these days is, and I know we're talking about fun stuff, but I'm getting to serious stuff again, scaling laws and just like progression of model intelligence. A lot of people are feeling like, I don't know. It's slowing down. We're not gonna really get to super intelligence at this rate. What is your sense? [00:57:52] S0: I totally agree with that. Like, don't think it's I know there's been some executives at big labs that say we'll have super intelligence in three years, but I think the truth is that it's a longer road. And that's not to diminish from how extraordinary the models are. Like, I think we'll be able to automate a majority of knowledge work tasks the next ten years for sure. But that long road is paved with all of the evals that help to make those capabilities possible. It's not going to be 10x more pre training data that gets those capabilities. It's much more going to be all of the post training data sets that are far more data efficient and thoughtful that help us get there. [00:58:36] S1: David Sacks tweeted this interesting point that, like, the situation we're in now is almost the best case scenario where AI is not in this fast takeoff to superintelligence. There's a lot of competitors kinda keeping each other in check. Models are already very valuable and only getting valuable... More valuable, but there's not just this like winner super intelligence taking over the world situation.
00:58:36 · Lenny Rachitsky, Brendan Foody
[00:58:36] S1: David Sacks tweeted this interesting point that, like, the situation we're in now is almost the best case scenario where AI is not in this fast takeoff to superintelligence. There's a lot of competitors kinda keeping each other in check. Models are already very valuable and only getting valuable... More valuable, but there's not just this like winner super intelligence taking over the world situation. [00:58:57] S0: Yeah, I think that's true. I think a lot of the super intelligence fear mongering is probably overrated, But at the same time, a lot of people's framing around that is even if there is a five to 10% chance of this p doom, then we should be careful, which seems logical. But I think that it's going to be an extraordinary ten years for all of Silicon Valley and all of the world as this technology is able to create abundance and giving everyone better medical treatment, you know, the best access to legal recommendations and the ability to build great products more than we've ever seen before. [00:59:37] S1: And education feels like is transforming. [00:59:39] S0: Absolutely, right? Like I even have felt bits of this over the last ten years, where like I remember ever, my parents would give me a hard time for not going to classes in college, and I'd be like, well, there's way better lectures on YouTube, why not just listen there? But I can only imagine as the models get extremely good at conveying information better than the best professor, what that'll mean, right, and access to all sorts of information to better forward humanity and upskill everyone.
00:59:39 · Brendan Foody, Lenny Rachitsky
[00:59:39] S0: Absolutely, right? Like I even have felt bits of this over the last ten years, where like I remember ever, my parents would give me a hard time for not going to classes in college, and I'd be like, well, there's way better lectures on YouTube, why not just listen there? But I can only imagine as the models get extremely good at conveying information better than the best professor, what that'll mean, right, and access to all sorts of information to better forward humanity and upskill everyone. [01:00:13] S1: So I'll use that as a segue to a final question. I'm going to take us to AI Corner, which is a recurring segment on the podcast. What's some way that you personally use AI to do better work, to help you in life? [01:00:24] S0: Well, let's see. I use it a lot to write documents, as you would expect. I also talk to get advice on problems. I find it helpful to just reason through almost as a thought partner. I don't know. I think better sometimes when I'm talking something through, but I can't talk through everything, with colleagues or people around me. [01:00:47] S1: And so this is like ChatGPT voice mode mostly or [01:00:50] S0: something Yeah. I like ChatGPT voice mode a lot. There's stuff Cool. Room for improvement, but I I am very excited about the future of voice.