Lenny's Podcast: Product | Career | Growth
00:17:23 · Lenny Rachitsky
[00:17:23] S1: So what I'm hearing here is you almost don't know what will be possible with every model release. And so the important things to focus on is being adaptable as things emerge. To your point, the product itself has to catch up to what is possible. To your point again, just like it can do so much, but people may not understand how to do it and may not be able to do it. So the product making it easy and even just telling you, here's something you could do feels like an important part. Is that roughly what you're describing?
00:17:53 · Dianne Penn, Lenny Rachitsky
[00:17:53] S0: I think so. I think there's some really interesting graphs in the original scaling law papers. And I think folks are very familiar with the scaling loss in the lens of as you add in more compute and data, what's called loss, AKA the loss from next token prediction goes down. And so it's a very smooth linear curve of the models get more intelligent as you scale them up. What's actually also interesting in that paper is there are these very different emerging capability graphs. And so for example, as you add in more data and you train the models with more compute, you essentially see these actually discontinuous emerging capabilities jump. So the models go from one plus one being a thing that it can't calculate to a thing that it could reliably calculate. And so these emerging capabilities, this some nature of predictability is not necessarily everyone knows the exact moment. You need the evals to be able to assess that has actually always been a part of how this technology works. And also what makes things like safety harder. Because unless you have the evals, unless you have the systems to test, these jumps might actually happen and you don't know. [00:19:16] S1: That's so interesting that you may have developed this AI brain that can do something you're not even aware of. And so part of the job is just uncovering, "Wow, we just got really good at this thing. What can we do with that?"
00:19:16 · Lenny Rachitsky, Dianne Penn
[00:19:16] S1: That's so interesting that you may have developed this AI brain that can do something you're not even aware of. And so part of the job is just uncovering, "Wow, we just got really good at this thing. What can we do with that?" [00:19:27] S0: I think there's product overhang and user overhang, to maybe put it in our PM language, even on today's models. And I think there's a lot that we could be exploring on our current opuses and definitely with Fable, for example. And that discovery is actually another part of what's been in the early days of Anthropic's DNA. And I think it's also continuing to be a big part of how we operate in product, in Labs and across research. [00:20:03] S1: This makes me think about something Garry Tan's been talking about, president of YC, I don't know what his title is. He had this interesting point that if you are willing to spend $100,000 a year right now in tokens, you are living the way somebody in 2028 is going to live. Because by then it'll be really cheap. Everyone can work this way. But there's this alpha opportunity right now to just live in the future, go crazy on token spend. And so there's a big opportunity for people to learn what the future's like and also just build much faster. Thoughts on this idea and the value of token maxing, let's call it.
00:20:37 · Dianne Penn
[00:20:37] S0: Yeah. I think I take more of a almost product lens. It's almost like token spin is more the input. And really the output is what you described of experimentation. And I think if we were orienting goals around experimentation, I feel like that might be the better framing of the outcomes. And therefore there might be different ways of achieving that outcome. I will say internally, some of the most creative thinkers, the best prototypers do spend a lot of time with Claude, with every new version of a research model that we have. And so there is something around you have to be using the models to then come up with good, then great, then better ideas. And there's no substitute for that. It's very hard to come up with a perfect strategy without touching the technology when it's moving this quickly. At the same time, I think there's other things that we could be doing. So one thing that we do a lot is actually working in public internally within Anthropic. And so in the early days when we had less product surfaces, there was a Slack channel where everyone, almost the entire company was testing early versions of Claude and trying different use cases. People were not calling them use cases, but you might be asking it to edit an essay or to come up with the right way to send this email. They were all different use cases, but we all worked in public. And then what you would see magically is different users or different folks on the team coming up with an idea and then other people trying different variations of that idea. And then within maybe 10 or so requests, there was something magical or potentially in a use case that emerges. And I think there's a lot in not just individuals figuring out by themselves how to use this technology. I think we could be doing more to actually bring that communal discovery when we do experimentation. Experimentation is not always necessarily a individual sport.
00:22:55 · Lenny Rachitsky, Dianne Penn
[00:22:55] S1: It's so interesting. Yeah, this idea that we're not sure what this is capable of or what we could do with it. And it takes all this poking around and people trying things, hearing what other people are trying to figure out what's possible. Such an interesting, I don't know, technology. We're just like, okay, here's what... Oh, I figured out it could do this thing. What are you going to do with that? [00:23:14] S0: I think in a broad theme we know. We know that the models could write great essays or could write long form writing, but individual pain points of what can you actually solve with that and bring it to a user level that people can use, I think is something that is more exploration or experimentation based. [00:23:33] S1: So following this thread, you oversee product for the Labs team, which is extremely cool. We've had Ben Mann on the podcast, Mike Krieger, whom both work on Labs now. Talk about Labs. What is Labs? What's come out of Labs? Many people have heard of these things. And how do they work that enables them to create such innovative ideas outside of even the core Anthropic product team? [00:23:58] S0: The thesis of Labs in many ways is identifying and pulling the thread on... ... in many ways is identifying and pulling the thread on the thread of discontinuous large bets that might not be in the core roadmap, and figuring out is there a there there? And also, what is the 10X, 100X, 1000X of the there there? And so for example, things like Claude Code, I think- [00:24:26] S1: I've heard of it.
00:24:26 · Lenny Rachitsky, Dianne Penn
[00:24:26] S1: I've heard of it. [00:24:29] S0: Things like Claude Code, things like skills, and most recently Claude Design, MCP. The thing that we really try to emphasize within the teams is, especially right now, there are so many things that could be built. What does it mean then to have a discontinuous bet? And I think one approach that we're taking this year is you can be very strongly held opinion about the theme or the area, and then more weakly held about the exact prototype. And so there's a culture of experimentation. There's a lot of the bottoms up engineers on the team are very self-enabled, self-driven to test out different ideas. And sometimes, we have a thesis, and it might not work yet, and so we then might revisit it in one to two model generations. And so this idea of these prototypes that actually end up just helping us learn, that's also valuable even if it doesn't lead to something immediately shipping. And so I think that allows the incubation and the charter of Labs to really accelerate and see around corners more broadly for Anthropic. [00:25:44] S1: It's so funny to think about Labs within an Anthropic, which was already so innovative and creative and just shipping like crazy, that there's value to still creating a Labs team within Anthropic. What enables Labs to work as well as it has? Because you listed all these products and it's like, what else has Anthropic shipped? It feels like all the biggest wins almost. I'm sure there are many that I'm not thinking about right now. What's core to creating a successful Labs org within a larger company?