Lenny's Podcast: Product | Career | Growth
00:12:34 · Lenny Rachitsky, Tulsee Doshi
[00:12:34] S1: There's actually a conversation on All In this last week about this whole issue that now that AI is getting really good at cyber, people are going to be investing a lot more in cyber defense and AI for protecting. And so there's going to be a lot of money being spent on protecting their systems from this ever smarter AI. So it's really cool that you guys are investing there. So I guess just coming back to the question of, do you feel like we'll figure out this concern and this issue we feel, a lot of people feel that AI is going to Take over the world, cause a lot of damage. Do you feel like this is a solvable problem? Are you optimistic or is it something you think a lot about and are kind of worried about? [00:13:12] S0: Maybe the answer to your question is both in the sense that I think we need to be consistently thinking about it. Right. I think the way to optimism here, for lack of a better way of putting it, like the path to optimism is through thinking about it and being like very careful and very thoughtful and very intentional on the path to get there. And so that is like my source of optimism comes more from Do I feel like we have people who are being intentional and thoughtful about what the safety story looks like? And I think that is the critical threat is you need a number of people to be worried and to be having conversations and to be actually interrogating how we're building our models. And that's the step by step process we have to take.
00:13:12 · Tulsee Doshi, Lenny Rachitsky
[00:13:12] S0: Maybe the answer to your question is both in the sense that I think we need to be consistently thinking about it. Right. I think the way to optimism here, for lack of a better way of putting it, like the path to optimism is through thinking about it and being like very careful and very thoughtful and very intentional on the path to get there. And so that is like my source of optimism comes more from Do I feel like we have people who are being intentional and thoughtful about what the safety story looks like? And I think that is the critical threat is you need a number of people to be worried and to be having conversations and to be actually interrogating how we're building our models. And that's the step by step process we have to take. [00:13:50] S1: Awesome. And what's interesting is I don't think I've read a story of Google's models hacking into anyone, which I think shows that this is something you all have been thinking about for a long time. I don't know, for better or worse. But it feels like it's working, whatever y'all are doing. [00:14:05] S0: We like stories about fixing vulnerabilities. That's a good place for us to be.
00:14:05 · Tulsee Doshi, Lenny Rachitsky
[00:14:05] S0: We like stories about fixing vulnerabilities. That's a good place for us to be. [00:14:09] S1: Okay. So one of the interesting things that's happening kind of from a product perspective with AI is Products are getting very complicated. For example, Google gets a lot of flack for it being hard to find all the magical things you all do. Like a lot of people don't know AI Studio even exists. A lot of people can't find VIA or Genie and all these things. Similarly, Codex, ChatGPT recently launched Dots and now you go to the interface of Codex and it's like tabs and dropdowns and Dots and it's just like out of control. And Claude Code is the same thing. It's just getting very complicated. That code work and then they drop code work and And interestingly, I'm in this WhatsApp group with a bunch of PMs, and there's a PM from one of the labs. [00:14:51] S0: Well, I won't be in this WhatsApp group. Sorry. Okay. [00:14:53] S1: Let's talk. Let's talk. And he showed that there's this kind of urgency to get frontier power of AI out to users as fast as possible, partly to stay competitive, partly because they don't know exactly what it's used for yet. So let's get it to people, figure out what they're using for it, and then make the UX better. So there's this kind of intentional, let's just get it out there, figure it out later how to make it a great UX. Thoughts on this kind of balance of making the product really easy to use versus just getting things out as quickly as possible. And is that something you deal with?
00:15:26 · Tulsee Doshi
[00:15:26] S0: Two thoughts, maybe, maybe three. The first one I'll say is I do think we do need to be playing with new paradigms and new UX approaches, if you will, to how people will engage with models. Right. So you mentioned, for example, Genie or Veo, which is which we're now building Omni. Like if you look at, for example, Flow. Flow is a product that we've been designing, thinking about video editing in generation, music editing in generation. And so I think we are trying to play with like NotebookLM is a good example of a different form factor. I think our labs teams are actively thinking about like, how do we try different form factors in different UX for different individuals to engage with these models? And so I do think we need to keep innovating and playing around with what UX looks like. I think at the same time, Your point is also right, which is like, I think depending on who your user is, you want a different form factor too, right? So like, I think, for example, with AI mode, we're talking about billions of users coming to search, asking their questions. I think you do need to have a certain UX there that is accessible to every user, right? At the same time, on the other end of the spectrum, when you have something like anti-gravity, you are trying to build for a power user. And you are trying to build for users who know how to use slash commands and can actively engage across both the terminal and the IDE. And you want to be able to support kind of a wide range of what a user can do. And so I think depending on who your user is, you have a certain level of flexibility with how you think about the UX, right?
00:16:58 · Lenny Rachitsky, Tulsee Doshi
[00:16:58] S1: That makes sense. Basically, there's different services for different levels of how radical you can change things. [00:17:03] S0: Yeah. And I think the other part, too, is like, I think it's also important to think about the model as having a UX also. Right. So one of the things I think about a lot is like a PM for the model is like, what is the UX of actually using the model? Right. So how transparent is the model? How well does it communicate with you? What questions does it come back and ask you? Right. These are all actually this is all UX in its own way. And so I think it's also really interesting to think about, like, how do we bring conversations about UX into the actual model development process itself? [00:17:31] S1: That is really interesting. Basically, the personality of the model is the UX of the model. How do you, as a PM, inform that work with the researchers? Is it like, here's feedback on the way it's talking, and it should be a little more verbose here, and here's ways it could format things differently?
00:17:31 · Lenny Rachitsky, Tulsee Doshi
[00:17:31] S1: That is really interesting. Basically, the personality of the model is the UX of the model. How do you, as a PM, inform that work with the researchers? Is it like, here's feedback on the way it's talking, and it should be a little more verbose here, and here's ways it could format things differently? [00:17:46] S0: I think the role of the PM here is actually really awesome because you are actually getting to shape what is the direction of where we want to go with the model's behavior, right? And so we have a team that works on model behavior here. And a lot of what we've been thinking about together is, A, looking at customer feedback and user feedback for how they engage. But also thinking through what does good look like for Gemini specifically, right? And how do we develop kind of a spec, if you will, for what does that look like? And then being able to translate that to rubrics, right, that we can then evaluate the model on to be able to translate that into data that we can actually leverage for the model to be able to translate that into our reward modeling. And so it's been actually like a really nice collaboration between PM and the research team. [00:18:30] S1: Something I hear a lot is that even if we stopped development on the model, there's so much it can do that we are not yet doing. There's this overhang. Is that something you think a lot about? I feel like that's a big part of this job is exposing the powers of the AI model to users.