AI’s third era: the rise of persistent AI coworkers | Tara Seshan (OpenAI’s product lead)

If you think about the first era of AI products as chat, the second era of these products working with agents, that third era that might come soon is how do you work with a persistent coworker who is able to get things done with you?

There's this idea of the overhang of what AI's capable of and what we're actually doing with it.

So hard to understand what is going to emerge in the future. You fail if you build for where the models are now. You fail if you build for where you think the models will be in a year. Both outcomes are equally wrong. The only way to build is 2 to 3 months.

What have you had to most adapt to and adjust in how you operate as a PM in this world?

Being prolific and empirical is way more important than being academic or theoretical. Rather than writing out some long reasoning doc, instead it's like, how do I get to something I can try out and test with users as fast as possible?

It feels like not only are we able to be more ambitious, we almost need to be more ambitious, which is not natural for a lot of people.

Elevating others' ambitions or reminding them of what's possible here is a huge part of the product management role.

I'm curious what's most surprised you about what it's actually like to work at OpenAI.

I came into the company expecting that there was a treasure trove of OpenAI secret strategy, and actually OpenAI is open.

Today my guest is Tara Seshan. Tara leads product for both Codex and ChatGPT Work at OpenAI. I believe this is the fastest growing and arguably most important AI product for knowledge workers today. Tara works alongside Andrew Ambrosino, who was a recent podcast guest. He's her eng manager. Prior to OpenAI, Tara spent 6 years at Stripe, where she joined as one of the first 5 product managers. And for many of those years, she was named one of the top 3 Stripes across the entire organization of Stripe. She also led product at Watershed, was a founder and a Thiel Fellow. And most importantly of all, Tara was one of the 3 Lenny's Newsletter fellows. which is a program that I ran a few years ago to highlight some of the most amazing up-and-coming product leaders. I am so excited to see Tara in this new, incredibly important and impactful role. Before we get into it, don't forget to check out lennysproductpass.com for a free year of the hottest and most beautifully crafted AI products in the world, available exclusively to Lenny's newsletter subscribers. With that, I bring you Tara Seychian. Tara, thank you so much for being here and welcome to the podcast. Thank you.

Thank you, Lenny. I'm so glad to be here. It's so nice to see you.

I'm even more glad. So you've been at OpenAI for just about a year now, which in most places would be a very short amount of time. In AI time, that's like a lifetime.

Yes.

I imagine when you joined OpenAI, you had a sense of what it was going to be like to work at a frontier lab. I'm curious what's most surprised you about what it's actually like to work at OpenAI. And ideally both good and bad stuff.

So many things about working at OpenAI felt familiar to me because I had worked at other places that were, you know, high growth, high talent, high intensity, hyperscaling mode places before. And so some of the things like, oh, my colleagues are so awesome, or the urgency is really high, felt very familiar. The part to me that actually felt the most surprising is that Many companies I've worked for, in fact, all the companies I've worked for in the past have been founder-led. And OpenAI is actually founders-led, which is that everyone inside the company, especially in their area, is in essence kind of a founder to some extent. The level of top-down direction at OpenAI is extremely limited relative to places I've worked for prior. And so I think when I first got to the company, that was both delightful in that I had come from like a founding journey before, and I was like, yes, I can continue to feel like the founder of this product area or this, or this, uh, team. And, you know, the, the distance between me and the market is very, very thin, you know, and sometimes at a larger company, you feel insulated from what users want or feel insulated from like what the market demands. But actually at OpenAI, that You do not at all. You are doing everything it takes to get product-market fit for your product, akin to how a founder might. But the counter to this is that, or maybe the more surprising side of this is I came into the company expecting that there was a treasure trove of like OpenAI secret strategy that I would be able to understand, akin to how, you know, at past companies you come in and you're like, ah, yes, this is like the payments Bible and this is how we think about payments and operations. And actually OpenAI is open. Like every sort of thought that exists in terms of this is how the world should look like, or this is how products should be built, or this is how the model should operate, very, very quickly becomes a part of the public product or a part of like the public messaging. And so that to me was incredibly Both positively surprising and just like a change in my operating mode for sure.

Telling us there's not like the secret room with AGI running there with the master plan that has all the answers.

Or at least I'm not in that room for sure. But, um, I, I think the, the piece that is really inspiring to me is that so much of what OpenAI does immediately becomes something that users can touch and feel in the product, and that cycle is faster than anywhere else. I've seen.

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When a market is more static or a market is more slow moving, you have the chance to actually like do some grand strategy-esque work. because it's more predictable, or you can at least like understand all of the pieces. Like as an example, payments is a certainly a dynamic market to some extent, but it's also an established market. And you're able to say, ah, yes, if like I take this bet, my competitor might take this other bet, or reason from first principles very rigorously through what all the next actions might be. And in fact, um, the nature of that market mandates that you do that. Like winners will think more rigorously than everybody else. And if you aren't thinking rigorously, it shows up as sort of like carelessness, 'cause a lot of those decisions that you made could have been predicted. But in this market, it's so hard to understand like what is going to emerge in the future. It's very emergent, it's very fast-changing, it's really dynamic. And most importantly, it's like very, very important to stay tied to the research. And so actually being prolific and being more like empirical is way more important than being, um, like maybe more academic or theoretical. And I think lots of the past companies I've worked at have been very academic and theoretical places. And it was a real switch to go from, rather than writing out some like long reasoning doc, almost like a PhD thesis of what I think should be the plan for the next like N amount of time. Instead, it's like, how do I get to something I can try out and test with users as fast as possible? And so yeah, that switch from theoretical to empirical felt very jarring at first. I was like, oh, am I not doing my due diligence here? Am I not being thoughtful enough? Shouldn't I be thinking through all of this in a ton of rigor? But actually, you've got to try stuff and learn as much as possible. And what that means is the thinking you need to do is being as pointed as possible about what your core hypothesis is. And that hypothesis definition is the most important thing. What is actually to use the Shishir Mahultra phrase, like the eigenquestion, what is that specific most important thing to test? And everything else, like any other grand strategy you concoct, is not relevant.

I'd love to hear more about that because that's really interesting as almost like, here's the thing of the PM role that is not changing. So much is changing, the world is changing, but there's still this piece that is even more important. Speak more to that of what specifically that You think people need to focus more on?

Yeah, there were so many, there's so many trappings around the PM role of like, you know, running, um, like execution on time and writing all these specific docs and presentations, et cetera. But the core of it has always been about what is like the most essential question you need to ask about your product? Like what is the thing that will determine whether your product works or doesn't work? How do you test that? How do you look at the results and how do you feed that back into a loop? of refining your hypothesis and running it again. That truly has always been the PM job. And that involves, of course, trying to understand users, trying to understand the market, trying to understand the actual technology you're building, pulling those 3 things together to make the most sharp hypothesis you can, and then making the test as fast and effective as possible. And I think that is not only not changed, but it's become the most important thing at the company to be able to do. Like, EMs are like thinking this way, engineers are thinking this way, like data scientists are thinking this way, designers are thinking this way. Like everyone has sort of moved to focus their efforts on this really, really important like problem definition and testing loop. Like what are we actually doing and how do we know if it's working thing? And from a PM standpoint, like it's great because PMs have always been really focused on trying to get that stuff right. Um, that has always been the core of the job. And actually many of the other just trappings of the job have like fallen away. And that remains like the key thing to get right every time.

You mentioned this idea of a loop and there's a lot of talk these days, like loops were so hot, I don't know, a few weeks ago on Twitter. And it feels like it continues to be a topic of discussion for knowledge work broadly. And the way I understand a loop, you essentially AI, here's what success looks like. Go off and build and figure it out until you, this is what this, you achieve success. How do you think about just this idea of loops expanding from just software engineering to product management to all knowledge work? Do you think that's gonna be a, a thing?

I do think that increasingly the future of work will look more like steering than rowing in the sense that there will be agents that You'll, you'll be able to work with that do a lot of the rowing, and your role increasingly becomes steering the ship in the right direction and pointing it in the right direction. And to that point, I think that that steering might grow higher and higher and higher level. The steering used to be at the level of, I wrote this line of code, press tab, to, oh wait, now I'm directing something a little bit more comprehensive to maybe the goal level, maybe to like an even higher level. I think the steering will continue to— maybe go up layers of abstraction. But ultimately, I think it's still on a person to be able to find, like, which direction are we pointing this in? And given feedback and additional data, where do I want to take this thing next? Some of steering, I think, is about certainly, like, what the data tells you, but a lot of it is about making an opinionated call. I think sometimes that we underrate, like, that power of, like, intuition or even sort of like positive determinism of what we want the future to be like, like picturing, hey, I would like the product to look this way, not because the converse is not an equally viable strategy, but because I would like the world to look like the direction that I'm pushing it in. And that I think will always remain an opinion, at least right now, that is required from a person. And so I think that Loops are awesome. Running, like running agents in increasingly, increasingly larger loops where they're doing more and more of that rowing for you is great. But right now, like, you really still need to, to steer. And I think work will also look like steering with other people over a group of agents that you guys work with together. Um, bringing in other teammates into that, like, interaction between you and the agent where it's rowing and you're steering feels also incredibly valuable.

That's such an interesting way of describing it. There's also, like, there's 2 thoughts here that come up. One is, if everybody has access to the same tools, the thing that will separate you is this, is the human, the person, basically. Otherwise, we're all just going to be building the same thing. You could use it. You could, everyone could be asking, how do we win? What do we do? And then the thing that almost, the unfair advantage almost is the human brain.

Yeah, I think it reminds me a lot of fashion, actually, in some ways. Like, There are certainly functional clothes that everybody can wear and gets the job done. But so much about what you wear, at least, or how I think about what I wear, is about what statement I want to make about my individuality or how I want to reflect to the rest of the world. And a lot of what makes that compelling is how it contrasts with other people's expression. Like the shirt I make makes a statement only because it is maybe different than what everybody else is doing or different than some cohort of people are doing or makes a statement about my group membership or something of that kind. And I think of the products that we build feel similarly opinionated and artistic. Like Patrick Collison has this really nice statement, or maybe it was John Collison, has this really nice statement about software, which is that software is not like real estate. You don't like put money in and get value out. It is a little bit more like filmmaking where you can put a lot of money into a film, but that doesn't guarantee that the film is successful or good. There is some like auteur statement or is some opinionation and artistry that goes along with it. And I think that relies on you having something interesting to say or your team having something interesting to say about your product.

There's something Marty Cagan is big on, which is this idea that when you have an idea for a product or a feature, rarely is that idea the thing that ends up being. There's this whole process you go through to kind of figure out what the hell actually it should be. And it feels like that's kind of what you're saying here is like you need to go through that process as a human. to understand what it really is and what people actually want. It's never gonna be like, okay, got it, go build this thing. I got it from the beginning.

Yeah, for sure. For sure. And that loop, those loops are moving faster and faster and faster. And so your ability to like form those intuitions, get the information you need to form those intuitions, and then use that with people and agents to put that into action is, is the key.

I'm curious what you think the next shift will be in how we work just broadly as knowledge workers. It feels like not only do you have access to the most advanced tools that some people, that other people don't get, also you work around the most AI-pilled, AI-forward people in the world. How are people working internally that you think will become kind of a more normal way we all work using these AI tools in the next, I don't know, 3 to 6 months?

Yeah, I think there's, there's 2 aspects to this. One is continuing to work with agents at higher and higher levels of abstraction. So letting the agent do more and more for you independently, coming in, providing that steering, and then letting the agent continue to cook. Like, let the agent cook and provide details at higher orders of abstraction feels like the way. People are increasingly thinking about agents that are persistent, that feel like teammates, that feel like coworkers, where you can work with them the way I might work with someone on my team, which is they do a whole bunch of work, I provide input, and then they— Do work again, and we sync up at different cadences, look at each other's in-progress work, and provide more and more feedback. It feels like that, that coworker model is the way that things are certainly going. Feels like a much more natural interface for us to be able to work with agents. And we already see a lot of that internally as well. The second is that a lot of my work with agents thus far has been one-on-one. That I work with my agent, maybe it spawns some subagents to get some tasks done, but it's me and my agent together. And that is potentially divorced from what my colleagues are doing with their agents. And so there was a time where everyone internally was just like sending their Codex threads, screenshots of their Codex threads to each other on Slack. And we're like, okay, well, I wanted to share with you how I got to this number. Here's how I got to this number. Here's a screenshot of, of what I did. But that's also not quite the most natural way for someone to collaborate together. And so as more and more work gets done with our agents, shouldn't we be able to get work done with our agents together? Um, and what is the most natural interface to make that happen? And those are some of the things that we are, we're thinking about.

Chat is what I'm picturing. Uh, that makes so much sense. It's like, okay, here's Tara's agent, here's my agent. She did some work on some analysis. I'd be Hey, my agent, Lenny's agent, go check, make sure this is legit and connects to the way I think about the world.

Ideally, work feels like a multiplayer game where all of us together are getting stuff done, steering our agents as our agents continue to take care of more and more of those like rowing tactical tasks.

It's interesting how it's just been this like slow progression of trust and, um, just like awareness that this can be how we work. Just this like, okay, go work for longer. You can take on more. It's just like, you feel there's been this talk of like the slow takeoff, the fast takeoff scenarios, and everyone was afraid of this fast AI takeoff where it's like way too smart and now we're in big trouble. It feels very much like we're on the slow takeoff scenario, which is good, where it's just like slowly iterating. It doesn't feel that slow, but in a sense, you know, we're not like to some 300 IQ AI, like, you know.

I mean, the models are, the models are incredibly smart, but I think a lot of the things that have enabled us to then work with our agents together or have the agents take care of higher and higher order abstraction things certainly are about the intelligence, their ability to perform long-running tasks and how long they can stay on tasks. But also actually, there are very meat and potatoes tactical things that make this possible. Like agents working locally are really convenient because they have access to all the data that's on your machine. To make an agent successful in the cloud, there is a ton of cloud infrastructure that you have to build to make that possible. And just like access to your systems, like how can agents talk to all these third-party systems that have all of your data, just like a colleague who you hire, who you like lock into a room, never give them access to like Google Docs and Slack and I don't know, the company database would not be that useful to you. Similarly, like a cloud agent that is similarly isolated will, will not be that effective. And so a huge part of making these agents useful and achieving some of these futures are, are on the intelligence side, certainly, but a lot of it is also just really tactical. like data access, like cloud infrastructure and reliability pieces that feel, um, yeah, they feel much more prosaic than some of the, the broader intelligence questions, but matter in some ways just as much for the end effectiveness.

This touches on something else that has been coming up a bunch on this podcast, this word ambition. I know you think a lot about this too. It feels like not only are we able to be more ambitious, Because of these AI tools, we almost need to be more ambitious, which is not natural for a lot of people because everybody can now do all these easy things really easily. Like, easy stuff is super easy. The hard stuff is easy. And the thing that separates people now and companies now is just how ambitious they can be. Talk about what comes up when I talk about the, the need and the kind of the emergence of this need for ambition.

Yeah, I think the people that we see who are most effective at using AI tools don't simply use it to automate rote tasks, but use it to expand the set of things that they are capable of doing. Like back in the day, you know, before all this AI stuff, the like unicorn person was someone who was a really thoughtful product sense person who also happened to be an engineer who may also have been a designer. That person was always the like unicorn hire because they were able to really flatten the layers of translation needed between all these functions and were able to like build something or ideate something really quickly and easily. themselves and get it up and running. And then we're able to like work with a team and collaborate with a team on it. And I think the most compelling thing I found that certainly I try to be able to do with these tools, and I've seen some of my like most successful like colleagues be able to do with these tools, is really expand the set of things that are quote unquote within their range of possibilities so that they can start realizing more and more of what's in their head.

Mm-hmm.

into the reality the way that someone who was previously like jack of all trades was able to do. We kind of all have that superpower now that I can like spin up a set of designs on something and I can like go build an initial prototype of it and I can, you know, figure out the right pricing model for it and model out all the scenarios. Like really the set of possibilities have widened dramatically. And actually what that means in so many ways is that I have the ability to like be a to that point earlier about film, like be more of an auteur as I try to get something done and like realize my vision maybe to higher fidelity. And that to me is part of what can elevate your ambitions while pursuing new ideas and new products that because all of these things are now within reach, because this new set of capabilities is now within your reach to be able to try and access, you're not really limited. Your ambitions are no longer limited by like what you're capable of executing yourself, what you're capable of communicating. It can be so much, so much wider. I think the hardest part about doing this is simply just expanding your thinking. Actually, the capabilities have expanded so dramatically. It is really expanding your thinking of what's possible in an unreasonably short timeframe. And to me, the best way of trying to do that is Patrick Collison has on his website, patrickcollison.com/fast, I think, which is all of these projects that were unreasonably ambitious that were executed in a really, really short time period. And what for me is now remarkable about that list of projects is that they all existed before these tools made it possible for you to learn how to build something almost instantly or ask it with one question, hey, can you summarize this very complicated text or this very complicated book for me immediately? Or can I try to do all of these things that were previously impossible to me, but now I'm able to do? Like, can I spin up a— can you make for me like a CAD model of this idea that I might have? Like really capabilities that were truly beyond my reach are now in my reach. And so if those fast projects were possible before with the capabilities we used to have, shouldn't we just see an exponential increase of the number of those types of unreasonably quickly and effectively executed things, um, with what AI has given us?

To your point, the hardest part is just remembering to even to try, just to be like, oh yeah, well, let me see if Codex can do this for me. It's just like a new habit, a new thing we have to build in our brain.

Tyler Cowen has this statement on his site, which is that most people underrate the impact of going to someone else and saying, hey, couldn't you, what is the more ambitious version of what you're doing? Or couldn't you try this faster? Or couldn't you try this at a 10x bigger scale? And in some ways, again, when I think of what do PMs do that is incredibly effective now, or what can they do that is incredibly effective now? I think elevating others' ambitions or reminding them of what's possible here is a huge part of the product management role. When folks say, hey, I think that we can get this done in this way, or we can get this done by this timeline, or maybe this is the first version of it, part of your job now is to elevate everyone's ambitions and say, actually, isn't the possibility ceiling meaningfully higher? Shouldn't we be more ambitious about what we're attempting here? Or couldn't we try this faster? And I think that's a, yeah, it's a, it's a great place to be. It's a great place to be in terms of what you can build, what's possible, and in terms of, um, yeah, how exciting the job becomes.

That is so interesting. I remember, uh, Nick Turley was on the podcast who was maybe had the role before you. Uh, I think he's working on enterprise stuff now. He had this, uh, meme internally. Is this maximally accelerated?

Yes.

There's like an emoji, I think, inside the Slack. Is this maximally accelerated?

Is this maximally accelerated? Is totally an OpenAI meme.

Mm-hmm.

The other OpenAI meme that, uh, Andrew Ambrosino and I love to ask the team is like, are you mainlining it yet? Which is like, are you using this product all day, every day to get your thing done? And I think that in combination with, are we being as ambitious as possible, which is about like the scope and the scale of what you're trying to do, are, are, is this maximally accelerated? Are we moving as fast as possible on it? And then are you mainlining it yet? Are you using it? And are you bringing all your taste to bear on whether this thing works and is something that people really want and tightening that feedback loop as much as possible? Those to me are like the 3 memes of product development, uh, that we, we just have to spread as much as possible now.

I love that. It's like the new dogfooding. Instead of dogfooding, you gotta mainline it.

Yeah, exactly.

And that shows so deeply in the, the tweets, mostly how I see your team communicate, of just like how obsessed they are with the product and are just constantly asking, what can we do better? What's bugging you now? Here's the thing we're building. It's like, it's very clear how, to your point earlier, that everyone is just the founder of their product. And it's very clear how they act as an external observer. Are there any other memes internally? Those are so interesting. Any others? I don't know.

Yeah, I'm trying to think if there's other good cultural memes. Certainly a really important one is feeling the AGI or just being conscious of AGI coming. There are so many outcomes for what it could look like or how one thinks about it, but a huge part of what puts most people at this company is believing in that mission of AGI being beneficial and trying to do whatever it takes to make that possible, both realisation of AGI and ensuring that it is beneficial for humanity. And in building products, another just constant refrain I have to keep in the back of my mind is, are we building for where the models are going to be in 2 to 3 months? You fail if you build for where the models are now. You fail if you build for where you think the models will be in a year. Like, both outcomes are equally wrong. And I'm sure many people have talked about this, but both outcomes are really equally wrong. If you're too early, you're wrong. If you build something that was overly focused on a past model's capabilities, you're entirely wrong. The only way to build is 2 to 3 months. And having this meme of, like, models are going to get way better. I need to think about the model capability as the center of this product. I need to get out of the way of the model in terms of the product constructs that I create. How do I ensure that this is right for the model in 2 to 3 months' time?

How do you know what 2 or 3 months is like? It's like a challenging understanding, especially while we're on this exponential. Is it like just a gut feeling? Is there anything the researchers give you a sense? How does that work?

Yeah, certainly communicating really tightly with research on where they think things are going is incredibly important. Like these things aren't entirely a, like a black box in that you kind of know, hey, we're focused on these particular things. Like we would like models be better at coding in these specific ways or better at writing in these specific ways. So we certainly have focused efforts on making the model better at specific capabilities. And so knowing where that is and ensuring that product development is as tied as possible to what research has as its agenda and its roadmap is really important.

A quote that I'll never forget is when Kevin Weil was on the podcast. He was Chief Product Officer at that time. Uh, he said that this is the worst the models will ever be. And it sounds so simple, but it's just like, it's hard to just like wrap your head around that, that this is the worst they will ever be. Like, it's such a cliché almost now to say that, but it's, it's true. It's absurd. This is like—

Yeah, it's, it's absurd. It's truly absurd.

Oh man. Okay. Um, I want to talk about ChatGPT the app briefly. Uh, okay, so I have it open right now.

Yes.

Uh, okay, so here's what I see in it. ChatGPT, and then there's a dropdown and there's ChatGPT and Codex, and then there's this toggle, chat and work. Tara, what is going on? What are all these things? Help us understand what each of these things are for and where does this, where do you think this goes? Is it gonna stay like this? What is there like a next step that you're imagining already?

Our north star here is that users do not need to make decisions between picking between all these different options. Ideally, there is no toggle here. That you go to the box, you type in your task, like, I would like to build a really awesome app that, I don't know, helps my podcast guests do research before episodes or something like that. Yes, I like that. And it will just pick the right harness, it'll pick the right model for you to be able to get that thing done. Ideally, the The choice here is like not on our users to have to pick between all these different concepts and understand not only what are they trying to do, but understand the limitations and capabilities of our products. So that is certainly where we want to go. In the, in the near term, picking between ChatGPT and Codex is really a choice for, are you a, do you want to stay in sort of like more development-oriented UI, or do you want to have the same power and capabilities in the ChatGPT mode? And so if you're a Codex user, keep using Codex. You're not missing out on anything. Like, continue using it as much as possible. But if you're a ChatGPT user who is like, what are these new Agenta capabilities? You should probably be in ChatGPT mode. And then when you're in ChatGPT, if you want to have conversations, if you want to search, that's where chat mode is the right thing. It's the, you know, same chat mode you know and love with better and better models and newer and newer capabilities every time. But in work mode, that's where under the covers, This is Codex. We've removed some of like the coding UI. Like you don't, you're not gonna see a work tree pop up all of a sudden in, in work mode, but it is the same power to get things done to, for example, generate like a really complex financial model. That's all possible in work mode. And we see people, especially I, I mentioned our corporate finance team, use work mode to do incredible incredible things that were previously either manual or required deep expertise from one person on the team become things that the whole team can be able to execute or just elevate the ambitions of everyone on the team in terms of timeline or capabilities or frontier of what they can get done.

Okay. That's really helpful. So there's kind of like these 3 modes currently. There's like the engineering mode, the chat mode, and then the do knowledge work mode. And the knowledge work mode, it's actually Codex doing all that work, but people may not know what Codex is, maybe not, may, may be afraid of it. Is there anything in that work mode that's not just Codex? Because that's actually really interesting. Is it like, is there like additional harness tweaks to make it feel a little different, or is it just the same thing with a little different UI?

It's, it's really at the UI level. Um, so work mode and Codex mode, if you go to Codex and ask it to generate, uh, an amazing financial model to price your product or something like that, or like, tell me, predict my revenue for the next 6 months or something like that, Codex will do as good a job as work mode. It's like really about whilst it's doing so, what kind of UI do you wanna see in the chain of thought? What kind of technical detail do you want exposed to you? The, it's like incredibly, it's similarly powerful. And so Codex users aren't missing out on anything by not switching modes. In fact, we do not want them to like stay in Codex and do all the stuff you wanna do in Codex, and we will show you the appropriate UI based on the things you asked for. Truly our north star is to like merge all these things so that users don't have to make any of these decisions. The separation is really more about how can we meet people where they are as much as possible in terms of the products that they use, in terms of their familiarity with concepts, and make sure that we are enabling everyone to take advantage of working with agents, which has transformed entirely the way every single developer works. We should do the same thing with knowledge work.

It makes sense. Uh, there's this like, because things move so fast, Like it would, I imagine somebody's like, let's try Codex. This is going to be awesome. And then it takes off and there's 10 million monthly active users. And then they're like, wait, what are we doing here? We got ChatGPT, we got Codex. How do we— so it makes sense why these things, you know, like it's not going to feel obvious and perfect for a while because you have to kind of adjust as things work and things don't work. And there's these kinds of transition periods of like, okay, cool. Now let's get people moving towards this vision of the super app, let's say. Um, okay. I imagine one of the hardest parts of your job is balancing this $100 billion MAU product, ChatGPT, maybe the most successful consumer product in history, with Codex, which is this new thing, and other new things that you guys want to try. How do you think about that? Just, I don't know, just balancing these very innovative, fast-moving teams and products with this, like, okay, there's a billion people using this. We can't change this dramatically.

Yeah, I think one of the most interesting things here is that we, we, one of the goals of launching work in ChatGPT Web and launching it in the desktop app and bringing these things together was to look at those billion people who are using ChatGPT and bring them more and more of the agent's power. Like, if you think about the first era of AI products as chat, The second era of these products is clearly working with agents and, you know, primarily has been coding agents. We'd like to bring it to more, more domains, certainly like knowledge work. And that is part of the goal of giving all these billion chat users the power of work. Certainly the product challenge that is, that's on us is how do we not only bring it to them, but make it natural and easy to adopt, make it not a decision they have to explicitly make. We can just help them do the right thing. How do we take— decomplexify it so they don't need to think about things like harnesses, which feel like crazy concepts for a billion consumers to understand? So that, that is primarily the, the challenge. And then of course, like that third era that, that might come soon is how do you work with a like persistent coworker who is able to get things done with you? maybe collaboratively with other people. And so part of this challenge in the near term is we're introducing agents to a billion people who may not have experienced them yet. How do we do so in the easiest, most natural, and most usable way possible? Certainly there's a lot more for us to do to, to make that happen. But part of this is also a lesson I've had, maybe contrasting like pre-AI era or past product experience with this one. Which is at previous companies, like polish was king. Uh, getting every UI interaction or getting every little thing completely right was way more important than shipping something early because time didn't make as much of a difference in terms of the outcome. And so as such, like if every corner wasn't like perfectly polished and everything was, wasn't exactly correct, you might as well not ship it. But I think what's been really compelling and interesting about this era and this product experience has been getting the product in the hands of users when you have so much conviction that, hey, it's transformative, like, is way better than perfect. And that urgency and that introduction of that product is so important. So we have a lot to do to make it more usable and easier for chat users, certainly, especially for folks who are not maybe even using it for productivity, but using it for like consumer tasks. But yeah, done is better than perfect. And then we have so much more to do.

Yeah, I remember when this app first launched, there was a lot of comments about the confusion and seeing how quickly the team iterated in response to the feedback is exactly what I'm hearing here is get it out, figure out what the hell's— what's not working, how people are using it, iterate quickly. Feels like that's the model now.

And of course, there are things that you can continue to iterate and get that feedback prior to launching. And there's a lot that we can and should always do better. But iterating as quickly as possible and listening to the right signals is, regardless of whether that's pre-launch, post-launch, ideally pre-launch, is the key thing.

This episode is brought to you by Mercury, radically different banking, now with spend. I've been a Mercury customer for so many years now. I switched all my business banking to Mercury, and honestly, I could not be happier. It's what online banking feels like when it's built by product people, not by bankers. And now with Spend, you can give your team individual cards, set spending limits per person or per team, and have expense receipts automatically pulled in from Gmail or over text. You can even give your AI agents their own cards with their own limits and policies. Most founders start out same way. One card used by everybody at the company. It works until it stops working. Someone goes over, a receipt disappears, you spend 2 days trying to figure out who spent what and why. Spend is expense management built directly into Mercury. All your team's cards, budgets, and reimbursements all live in the same place as your business banking. No chasing, no manual reviews, no end-of-month scramble. The result is a team that can move fast, And a founder who is no longer the bottleneck. Learn more and get signed up at mercury.com. Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column A members FDIC. The IO card is issued by Patriot Bank and a member FDIC pursuant to a license from Mastercard International Incorporated. Something I've noticed on Twitter is there's definitely been this vibe shift from Claude Code to Codex in the past few months. It used to be everyone was cloud code this, cloud code that. More recently, it just feels like people are leaning now towards Codex, at least on Twitter, which is a bubble, but it's where a lot of tech people are. I'm curious what's shifted internally in the past, I don't know, 3 to 6 months, other than Tara joining and shaping up the ship. Uh, is there anything that you can share that's just like, okay, we figured this thing out, we shifted this, we cut this thing, what, what helped? shift the vibes and, uh, help Codex become as successful as it is becoming.

You know, I think there's like this phrase, which is, uh, before enlightenment, uh, carry wood or carry water, chop wood. Post-enlightenment, uh, carry water, chop wood sort of thing. And actually with the Codex app, um, the team who initially got it up and running and were working on it, uh, were super, again, user-focused. Tight iteration loop, really dogfooded the thing, like mainlined the app as much as possible to get everything right. Folks started to realize that was happening externally and on Twitter, and users started to really notice. But the team was always really focused on users, really focused on that iteration. And it was merely the, to some extent, like the market catching up that was the change. And that process has not changed internally. Everyone still constantly uses the app. Everyone in the— who's building it obviously is a developer using it for development and is constantly fixing not only their own problems, but trying to listen to other people in the company's problems and user problems. Actually, what's sort of remarkable is that the mode of operating hasn't changed. It's always been the same thing I had mentioned earlier, like Are we elevating our ambitions sufficiently? Are we maximally accelerating progress? And are we mainlining it as much as possible? And I think it's great that users and folks on Twitter have noticed, but that operation, full credit to the team, that hasn't changed.

What's really interesting about this answer is the very human part of it. It's you, it's Andrew, it's Thibault, it's the team. just like being obsessed with the customer or the product. And it's not like AI was the answer. It's the humans that made the difference.

Yeah, I'll give the team deserves like full credit here. Everyone on the team is incredibly thoughtful and independent. And to the point of like, there are, you know, many founders at OpenAI, like almost everyone on that team, like the desktop team especially, like acts like founders and cares about every piece and every detail. And when they notice an area that should be better, they go build it very independently and get the thing up and running. And if it doesn't test well internally, like people aren't using it, if people don't find it useful, they'll iterate on it. And then finally, like ship it externally. But that loop is full credit to like people on the team and individuals for making that happen.

Something you touched on is this idea of roles overlapping, this idea of like, you know, engineers are doing PME work, you're doing probably shipping prototypes and building, maybe shipping to production. I don't know. Just, it feels like that also creates a lot of challenges. I hear from a lot of people like, what is my job now as a designer? What am I responsible for? What am I not responsible for? As a marketer, what am I doing? Is that something you notice? Is that something that you're dealing with? Just any thoughts along those lines?

I think the thing I've always liked the most about working at startups and Sometimes I've started at a startup that accidentally grew into a large company, but largely primarily working at startups is that there are very few boundaries around your role, that like everything and nothing is your responsibility. Ultimately, you're accountable for success. Actually, like Stripe was very, very much this way where there are no boundaries around what an engineer could do versus a product manager could do versus a designer could do. Everyone could do anything. And so actually it kind of feels like I've always really loved that mentality, and now finally capability is catching up to that. But the thing I really care about is that someone needs to look after the, or have core accountability for, is this product being used by users? Is it something that people want? Is it high quality? Is it effective? And whether that person is like an engineer or a designer or a PM or whomever, like someone is the DRI. And then whatever work needs to be done to make that possible, you know, certainly people can pick it up based on their affinity, based on their capability, but I like a team that doesn't really mind what the boundaries are between individual roles, but everyone's just sort of focused on making the outcome happen. The converse of this is I also really love like the craft aspects of like being a PM. Like there are so many aspects to PM craft that I know folks like Shreyas or Maybe Marty Cagan or Shishir, all these people have really espoused that I think are wonderful. And sometimes maybe some of these questions come from, wait, I so love the craft of my domain. By taking this more fluid approach to teamwork and collaboration to get something done, do I lose out on getting better and polishing my craft? And I truly don't have an answer for that question. I think it's something we're all experiencing together. Which is some pieces of our craft are actually getting abstracted by models being able to do it really effectively, maybe better than individuals can. And your craft moves from being able to do that very specific task you did in the past to now applying it to some other part of the product or the discipline. But yeah, that is still a question I'm thinking about, which is how do I balance my desire to be part of a team and use these tools and feel so, um, compelled by how effective one can be now with all these products, with my love of like the, yeah, it's, it's really fun handwriting code for an engineer all the time and one doesn't really do that anymore.

Yeah, that's where I was gonna go. It's just like unbelievable how different the engineering role is now.

Yeah.

It's like you used to write code all day. That was your job and that is no longer your job.

Yeah.

And that happens so quickly.

Like, you're not writing— I, I see people mourn like the flow state of writing code.

Yeah.

Manually yourself versus now what, what one does. But I think it is a, um, yeah, it's a, it's a, it's a tough transition. Yeah.

And you know, some people love it, some people don't, and that's a whole other topic. Kind of along those lines, something I'd like to ask people at the frontier of AI is where do you think human brains will continue to be? valuable in the future? It's impossible to predict long-term. Well, we need humans, hopefully. But I'd say in the next couple years, just like, where do you think human brains will continue to be most valuable?

I think humans will continue to be most valuable as a, certainly as a, like an entity of accountability. So who ultimately owns the outcome here? In some ways you can think of your agent that you're working with as like your report. Ultimately, who owns, like what was the end product? Was it high quality? Was it the thing that you wanted it to do and say? Like that will certainly remain a person, at least for now. And especially in industries and places that are highly regulated or require like a direct human interface, like that makes a ton of sense to me. I think the human brain is also really valuable for expression. I had mentioned earlier that analogy of software is not like real estate. It is more like a film where you could put money and a great film does not come out. The greatest films are not the ones with the biggest budgets. And given that, there's a certain artistry and opinionation and expression in building software where you feel like there is some authorship by a person or a group of people. And that part remains, to me, so human. Like, what you choose to build and how it feels feels like such a human question. I also think the human brain continues to be valuable in how we care for each other and relate to one another. That piece of my work has remained so human and has actually become more important than ever. The part where you talk to other people on your team and collectively figure out how you can be enthusiastic about an area, how you learn and work together, how you elevate each other's ambitions. All of that feels and remains such a human thing to do. Yeah, I think the human brain will continue to be so valuable in that regard. That said, I can't predict what'll happen with the models, but those pieces feel to me to be incredibly, incredibly human.

I love that answer. There's this idea that you talked about, this idea of the, this overhang of what AI is capable of and what we're actually doing with it. People are always like, it feels like one of the biggest gaps is like, okay, what should I do with it? I'm curious, what are some ways that you use AI in your work that may inspire people? Like, oh wow, I didn't think about using it that, like, there's kind of 2 buckets here. One is just like, what's like the most how your PM job has changed most thanks to AI that you're just like, okay, now I use AI for this stuff. And then what's like, is there any like super interesting creative uses of AI recently that you're like, oh yeah, you should try this?

One of the most exciting ways that I use AI in work is, um, I actually build sites all the time now. I don't know if you've tried building sites in Canva.

I haven't. Talk about sites.

Sites is a really fun, amazing product. Um, you can basically build a site, certainly in work as a presentational artifact, But I also build sites for literally anything. I built a site for the team as like a, as a game where we all played a game together using a site, 'cause sites have a database. You can build a site. I actually built a site 'cause I went on a backpacking trip recently. I built a site of the route that like tracked the elevation of everywhere we were going. Everyone on our trip like inputted all their food. It was like super fast and effective. Sites kind of realized the dream of like malleable personal software that Alan Kay, you know, flagged in the '60s of like the true personal computer is one that has personal software. In some ways, sites are like the tangible way to make that possible. We'd all once dreamed of like making personal software, and certainly people with tools like Notion, et cetera, try with all these blocks to configure what that could be. But with a site, it is literally a prompt. I literally, with a prompt, say like, build me this exact tool that I need to get this thing done, and it just does it.

They're shareable.

Uh, they can use, they can auto-update. You can use like internal data to build like a dashboard, for example, with lots of metrics. And rather than like painstakingly laboring over some sort of like slide deck, a site is just a way more dynamic surface for presentation.

How do you use a site? Do you have to do anything special or you tell it, make, create a site?

In Codex, be like, create a site that, um, is a, I don't know, is a mafia game for my team and it will just do it.

And like, like I'm thinking capital S site, but it doesn't matter. I imagine it just knows what sites are. So it's, yeah, 'cause it used to be, here's a, here's some source code, go figure out where to deploy it. And what you're saying here is it just hosts it for you and you can use it.

Hosts it for you and you can choose whether it's public, you can choose whether it's with your team or choose whether it's private to you. Um, they're great. They've, the easy reach of building a site all the time has changed what my day-to-day looks like, which often in previous worlds used to look like creating lots of artifacts like docs and sheets and whatever it might be. Now I just make sites all the time.

And you could do that through, I imagine, Work, or can you do it through all the surfaces? Codex, Work, ChatGPT? You can do it through Work.

You can do it through Codex. You can do it in the web. You can do it on mobile. You can do it anywhere.

Okay. I just kicked off a create a site about Tara Seishon.

Great.

Is that how you pronounce your last name, by the way?

I haven't asked you. Uh, Tara, say station like station.

Station. Okay, cool. Okay, cool. Um, sites. Okay. Any other quick tips, uh, while we're on this topic for people? Because that was a great tip because I don't think a lot of people know about sites. That's, um, very useful.

Yeah, sites are awesome. Um, the other thing I really love is using visualize in Codex. Have you used /visualize?

No.

Oh, /visualize is incredibly exciting. Ask, you can just do /visualize, visualize my ChatGPT usage until now or something like that. And it will pull in like all the things that you've done and create like an amazing visualization for it. The number of times that I've been thinking about how do I not only pull in a bunch of charts and data, but present them in a way that is understandable and useful for the story I'm trying to tell has been infinite. And Visualize makes that incredibly simple. It is like surprisingly delightful to use Visualize.

It's just like, it's just, these are such good examples of there's so much power here. We don't even. know about or understand. And that's the challenge you have here. For sure.

For sure.

Help us know all these things. That's why podcasts like this are also useful. Can't put it all in the product. Uh, I'm gonna go in a totally different direction. I wanna talk about writing. I asked, uh, Brie Wolfson, who knows you well, what to ask you. Uh, funny enough, she suggested questions for the previous podcast, uh, conversation I did with Adam Ward from Cursor. Uh, so she said, okay, you should ask her about writing. A Tara brief is iconic. Help us understand just, uh, what makes your writing, your briefs iconic, and any tips that might be helpful for people that are maybe trying to get better at writing and writing documents.

I really strongly believe that I do 2 types of writing at work. One is writing as thinking, and the other is writing as reporting. Writing as thinking is me writing a, like a brief about why we should build a certain product or why we should take a certain strategy or why Like maybe a spicy take. But writing as reporting is things like, oh, I'm summarizing the status of what our team has been up to this week and I'm sending over a report about it, or this is our plan for this particular launch or announcement or something like that. Writing as reporting, I happily automate, or I use, I use the models all the time to make that as simple as it can be. But writing as thinking is something I never will automate. I really strongly believe that the, at least for me, the act of going through and outlining something, turning it into some level of prose, cutting it and editing it, continuing to iterate on it is one of the most important steps for me to get my ideas in line. I think most people actually will paint with a really broad brush. Like, I will never use the models for writing, or I always use the models for writing. And actually to me, both those broad brushes are wrong. I think you should use the models as much as possible for writing as reporting. And inasmuch as you think with writing, as I really do, and I think a lot of people do, you should not use it.

Yeah.

You shouldn't replace your thinking with it. But my briefs in the past, because I write so much as a way of thinking, is that I will write a— I will go into a hole, write a brief for a new idea or a product, spend a ton of time refining that particular idea, shop it around with people and have them attack the ideas in it as much as possible and poke holes, make it stronger, and then take it to the next person and do the same thing. And so at Stripe, this is something I did many, many, many, many times over, whether that was like to kick off a new product area or to suggest a big change in direction or to analyze like problem and suggests a path forward. And Stripe is incredibly oriented as a writing culture. And there are many people like Jeff Weinstein who are also very into writing and sharing briefs at Stripe. Stripe is one of the few places where a brief will go viral inside the company. And so writing is thinking there is really prized. And that's where I did the majority of that writing work. At OpenAI, I think I still write as thinking all the time, but the shareable artefact here is not really a long doc or a sort of proof of work in that way, partially because times have changed and a long doc is not a signal that you thought through something because you can easily produce a long doc That indicates that you haven't. And so actually, the point, maybe one of the biggest changes I've experienced personally in my day-to-day, which has been a big, maybe jarring change, is that I used to think in a document and then do some translation of that into a presentational artifact. And that would be my indication that I had thought through a problem and this is what we're going to do, and the team moves in that direction. And now I am way more on mocks, not docs. or prototypes, not docs. And if I have something that people can try and interact with, or even better, I have results where we tried this, we ran an A/B, here's the results, this is why I think we should go in this direction, that is a way better communication tool than the doc itself. And so I still write hundreds of docs all the time, but I do it for me and I no longer do it for other people. Really, like that no longer is the best way to, to talk and communicate. That is probably the biggest change I've experienced personally in, um, this era versus the previous era.

That is so interesting. I really liked your tip of getting tons of feedback on a doc. Like, you know, it sounds obvious, but you know, you can get to an iconic doc slash brief by just cheating almost and getting lots of feedback on it as you're iterating.

Yes.

To make it stronger and stronger and stronger. Versus like, cool, here it is first time and it's rarely going to be—

I previously had a manager who told me that the right thing to always do is write a doc to 70% completion and then take it to the people that you need buy-in from and get it from 70% to 100%. And that still is a thing that I do all the time because very few great people want to interact with a perfectly polished finished idea, a perfectly polished idea, their new ideas just bounce off of it. Versus something that has more crags and more, um, rough edges that they too can polish with you together. And I think that, um, like bringing people into the process that way where like a doc is like an underlying artifact for that is one of the best ways to collaborate that I found.

How do you think about, uh, AI brain rot and starting to over-rely on AI? That's just the challenge everybody's gonna have. Why not use this magic to help look at something and then we start to lose our ability to write, read long documents. Is there anything you do that you are trying to avoid that?

Yeah, I think this writing is thinking discipline is one of the main pieces that I employ in my day-to-day to make sure I'm not overly atrophying my, my thinking abilities. I think I will again outsource all writing is reporting as much as possible to the model, but Writing is thinking I have to do myself. And I have this personal belief that if I'm going to make someone read my document, I have to at least read it first that number of times. Or I think about this in meetings too, that if I'm going to call a meeting with a set of people, I need to have prepped the collective amount of time that people are going to spend in that meeting before the meeting. And so when it comes to keeping my thinking sharp, I do that writing for the document myself first and make sure I've invested like the collective amount of time I expect people to read it, at least in writing it and producing it. And I don't really rely on the model either for polishing my prose, which I don't think it really, it really does, or, um, especially not in like generating the, the first version. But I do, I do of course have the model help me a lot when it's like summarization or like translation of content from one format to the other all the time.

So what I'm hearing is, uh, write the idea, the brief, the plan yourself as a human. Write it yourself. Don't start with AI. Don't, and, and even don't use it to improve on the writing. Just keep that all human.

Yeah. At least, at least for me, I start myself and I end myself. I might use AI in the middle to research specific elements or drop in some data or go pull some data or help me.

Or push back on some ideas. Yeah.

Push back on some ideas, but Start yourself, end yourself with a piece of writing, and that doesn't deteriorate your thinking.

Okay. One last, uh, question I wanna ask about Sutter Hill. You had this very unusual career step. You went to, you're a PM, PM founder person, and then just like, okay, EIR at Sutter Hill Ventures, which is an iconic VC. Uh, oh, people can look it up. A lot of amazing companies came out of Sutter Hill. It was, it has a very unique way of approaching founding where basically they incubate companies. Snowflake as an example. Um, what was that? What was that about? What'd you learn from that experience?

Sutter Hill is an iconic firm and is intentionally a very illegible firm. Like, if you go to the Sutter Hill website, you will see nothing on the website. It is a firm that doesn't operate loudly. It tries to operate as under the radar as possible, as modestly as possible, yet is somehow responsible for some of the most iconic successes that Silicon Valley has seen. And they have this very unusual incubation model, which Mike Spicer, who is one of the amazing partners there, started and has rolled out success after success. I think the thing that was most iconic to me about Sutter Hill is that people look at finding product-market fit as a dark art, or building a tens of billion dollar company as a dark art. Like, oh, it's luck. Oh, it's chance. Oh, it's All these things that must come together. Yet Mike Spicer has done it multiple times. And so there is clearly a way to do it. There's clearly a roadmap for making that possible. There is a set of things one can do to get this repeatedly. It's not just luck. It's not just a dark art. There is a playbook, as it were. And that playbook lives inside of the firm Sutter Hill. And they have figured out how to be right a lot in terms of calling shots and making bets. And they've learned how to be right a lot in terms of the daily compounding things that one does to create a successful company, whether that's how you set up your enterprise sales team, how you position your product, how you build the initial founding team. The recruiting at Sutter Hill is an unparalleled excellent thing. They have a secret tool called Reticle where they have a map of everyone in that they've interacted with and the 10 best people that those people have interacted with that helps them be so, so effective at this. So I went to Sutter Hill because in some way my career has been about how do I try to find product market fit as many times as possible, whether that was as a founder or in starting new products at Stripe or in like joining a startup like Watershed. And so Sutter Hill is the place where they've figured out how to find product market fit on B2B products. And I wanted to learn what I could from them.

What'd you learn? What's one thing you took away from that experience other than they know how to do it?

They definitely know how to do it. I think one thing that was very surprising to me that I learned there is that product market fit is sure important, but actually I really underrated product marketing fit. The idea that the way you talk about the product and the way you market it can precede actually even building the product. It should probably come from some sort of bringing together of understanding the technology deeply and then understanding like the enterprise sales process. And then that product marketing fit, that narrative, that positioning is actually even before you build a product experience, the right thing to test. So you should go like pitch 100 people, figure out how to refine that pitch as much as possible, get the marketing narrative of why this thing is transformative right. And then and only then go commit the, okay, this is exactly the product shape. And Mike Spicer is like unbeatable at this art. Previously, I'd always kind of underrated PMM work. I was like, oh, it's whatever. Like, it's the glue between these functions. It's fine. And then I realized how transformative that work done excellently is to a company's outcome, and in fact can be the element that makes a company successful.

Amazing. Uh, I so agree with that positioning. We talk a lot about that on this podcast. Mm-hmm. Okay. I'm gonna show you what, uh, sites got created real quick. It was running while we were talking. Check this out. Look at this.

Oh man.

I was like, I was like, make it more awesome. And it made it more awesome. Multi-product. Beautiful. Look at this. This is like a legit design. Look at it. You got quotes. Big conviction, small teams. Let's start with the buyer. How do you feel about this being your website, your new website?

I do think that the picture of me at maybe 19 years old at the top is really funny. But yeah, otherwise I love, love the site. It's looking good. I think that was, that's my, that was my badge photo from Stripe.

Oh, wow. Amazing. I love that it built, I already unshared it, but I love that it built a whole like little thing around your head. So cute. Uh, Tara, is there anything else that you wanted to share? Anything else you want to, uh, touch on before we get to a very exciting lightning round?

Yeah. One thing that we've been thinking about a lot in product building, especially with ChatGPT work in this new era, is how knowledge work and coding are actually fundamentally different. And one of the surprising things we learned as a part of that is that coding is so output-oriented that when you ask it to do a coding task, you can verify whether it did the task correctly or well via tests. You can try it out and see if it works. Like, there is a way to validate it based on the output. But knowledge work is different in that I can't simply look at the deck in the end and see the numbers, like, oh, it's like 90% success or whatever in the deck, and actually believe that. I You really need to think about the process and the inputs and the reasoning and how it went along the way. And so in terms of how that looks in the product, like a lot of work that we have done and have to continue to do is continue to adapt the product to knowledge work, which means way more focus on making ChatGPT your collaborator, allowing you to see all the in-progress work, see its citations and inputs. Help you go on the journey with the model to get to that end output, such that you know in the end that, oh wait, this thing is right, this thing is good, this thing is useful. And that shows up certainly in the UX of the product quite a bit, but also should show up in things like the reasoning and the chain of thought. Like, should I— should you see more citations along the way, for example, of how it got to that end state in that data? Is the surface of a thread, which is so suited to coding, the right place for you to see all of that for knowledge work as well. There's so many big, important product questions. And so as we think of, um, maybe bringing in human collaborators into your work, we also need to think about how we can make the model more of a collaborator with you as you get things done together.

That is such a good point. I'm imagining an exec meeting where you're trying to pitch the exec on here's what the plan is, here's The success, here's what I think we should be doing. So much of that is helping them see, here's the work I did to get there. Here's all the steps. And so it makes sense that you need the AI to show you that same sort of work that it did, the proof of work essentially, versus engineering where like, okay, I don't need to know all of the little architectural decisions you made. Just what does it look like? Is it passing all the tests that we have? So that is a really good point, just how different those two are. models are. Um, and also there's like the context. Does it have the context it needs to do the thing that you want it to do? Does it know, does it, can it see your email? Can it see all your Notion docs? Mm-hmm. Uh, such a good point. So I see the challenge in your job. Make all this work as one product. Tricky, tricky. Uh, amazing. Anything else before we get to our very exciting lightning round?

Yeah, let's jump into it.

With that, we've reached our very exciting lightning round. I've got 5 questions for you. Are you ready?

Yes.

What are 2 or 3 books that you find yourself recommending most to other people?

One book I really recommend to people is Barbarian Days by William Finnegan. I don't know if you've read it. It's about a life of a man who is a New Yorker reporter, but how he fell in love with surfing as his passion. The thing I took away from the book is that one can be deeply passionate and dedicated and have something be your life purpose without you being good at it. And it is about the art of falling in love with surfing and his striving for excellence and perfection whilst knowing that he will never reach it. It is such a compelling and transformative story for how I think one should continue to live our lives. I really, really love that book. Another book that I might recommend as a book that people should read I really love Anna Karenina. I've been rereading the classics lately, and I love Anna Karenina because it's like a book of layers. And I think that a huge part of what we're going to have to do in this new era is transform ourselves or take ourselves on a journey to do different things than what we were used to. And when I think about that book, I think about when I was 13 and I read it, I understood Basically the plot. When I read it at like 17, I understood the European history dynamics and like the class warfare. And then when I read it at 30, I was like, oh, this is like a story about like a woman and humans. And it just reminds me of like growth and that it is possible to look at the same thing through multiple different lenses as you continue to grow, which I think is kind of the challenge that's ahead all of us, ahead for all of us as we consider our careers as well.

It's interesting how both these, like, I could connect to AI in the time we're living in now too. Uh, I also read, recently read Anna Karenina.

What did you think?

Earlier this year. Amazing. I've never read it before. I saw it on a book list of like, here's what the smartest people in the world have read. And it's like a whole list of books and that was one that I hadn't read. So I'm like, I gotta read that. Yeah. Uh, it was amazing. Uh, someone gave away the ending, which kind of made it less like surprising. I don't wanna give anything away. No spoilers. And I also felt like it was very long, but now I'm reading The Power Broker, which has set the new precedent for a long— been reading it for half my life at this point.

I love The Power Broker. Another thing I highly recommend to people is if anyone follows the Substack, like Simon Hazel's Substack, where he does a slow read of important books. So he did one of War and Peace and he's doing one of Wolf Hall, I think, or he did one of Wolf Hall, which is the Hilary Mantel book. Like, take it chapter by chapter. That's like the only way to read something like The Power Broker or War and Peace or even Anna Karenina. It's like chapter by chapter.

Speaking of that, there's, uh, someone— I forget who told me this— there's a 99% Invisible book club breakdown of The Power Broker where it's 13 episodes, an hour or 2 each, and they go through a couple chapters of the book one at a time and talk about it. And they have special guests like Pete Buttigieg and AOC and Folks that lived in that area and they talk about every, you know, the story. And it was so, it's so fun to read and listen to their analysis of it. And then they have Robert Caro come on a couple times.

Whoa. That's amazing.

Yeah. Hot tip. Okay. We'll keep going with our very lightning round. Uh, favorite recent movie or TV show you've really enjoyed, if you've had time to watch anything.

Of course I watched The Odyssey. I found it to be an incredible, incredible film. It is about AI, or my hot take is that it's about AI, or Christopher Nolan's view on how AI transforms society, which I loved, and I highly recommend watching The Odyssey. He is like the— he's just an incredible director and has bridged artistry and commercial success in a way that I think no other modern director has done. I also recently watched the film Rashomon, Which is the Akira Kurosawa film that for the first time did that technique of telling a story through multiple people's perspectives where you never know what was true in the end. That technique in film was pioneered by Kurosawa, and it reminds me what one can do under constraints. That film was made in the '50s. It was black and white. You know there's a guy holding the camera, and yet it is so perfect. And it is such a tasteful, innovative, amazing example of creativity. And what I'm reminded of watching that film is like, I have 100 times the power and tools that he had making that film in my iPhone. And like, what's my excuse for not elevating my ambitions and making better stuff?

All comes back, ambition. Uh, on the Odyssey, I'm still trying to get tickets. It's so hard. I slept on it and now it's like impossible. For like a month, there's no seats anywhere.

Kevin Kwok got us tickets at 10 PM at the Metreon earlier this week. It was so good.

Next time, call me.

I'm in.

Yes, for sure. Oh man, I have like bots running on it. I have a person working on it. I have a friend. We're all trying to find a seat.

It's amazing. You're going to love it. And I can't wait to hear what you think after you see it. And if you agree with me that it is about AI and the collapse of morality.

Okay. No spoilers. Uh, hopefully by the time this comes out, I have seen it, but if not, If anyone has hookups, please tell me. Uh, and I'm trying to do like the IMAX full power Matron sort of thing. Yeah. Okay. Next question. Uh, favorite or interesting AI product that you've recently discovered? If ideally not an OpenAI product, but you know, you can also go there if you want. Ooh.

Um, I mean, of course my favorite AI product is ChatGPT and using cool sites and visualize stuff in, in Codex, which is amazing, but outside of OpenAI products. Um, my favorite AI products are products that my friends make for me because now actually people can do that. I think that's so cool. I'm such a huge fan of like the cozy software movement where you like make software tools for like 5 of your friends and you guys use it together. And so I have a friend named Sebastian who, um, made a really cool, uh, AI app that turns anything into a podcast and puts it in your like a little podcast app for you. And he also made a really great private social network for our friends, and it's called Gats. It is exactly what I think the future should be, which is people should make software that exactly meets their and their friends' needs.

What does Gats stand for? Is that some inside joke?

It is not, or at least if it is an inside joke, I don't know it. But it is the place that I— it's like private Twitter maybe for a small group of friends. And I I learned the most interesting things on that product.

It's like a WhatsApp, but not.

Yes, exactly. Exactly.

Uh, the podcast app is so interesting, but I'm like, I feel like the version that I would love is it's actually like podcasts in your feed of podcasts. And then it just new episodes get added of things you want to read or whatever.

Yeah, that's, that's what it does. It's a, it drops it in your Apple Podcast feed.

Oh, amazing. I want this.

It's great.

Help me, help me subscribe to this.

For sure.

Okay. Amazing. Okay. 2 more questions. Do you have a favorite life motto that you find yourself coming back to often in work or in life?

Ooh, my life motto that I come back to all the time in work is actually Toni Morrison's 3 takes on work. Let me like pull it up really quickly.

Amazing.

Um, okay. It's 4 things. It's from her essay, The Work You Do, The Person You Are. The first one is, whatever the work is, do it well, not for the boss, but for yourself. The second is you make the job, it doesn't make you. The third is your real life is with your family. And the fourth is you are not the work you do, you are the person that you are.

I got tingles. Wow. So good. And I think that's what you have pinned to your, um, your Twitter profile.

Yes.

Because I, I remember seeing that. So cool. Okay. Uh, maybe we'll show that on screen as we're talking about that. I love that. I love that. That's a great way to remember something. Just stick it to the top of your Twitter because every time I go to Twitter, oh, there it is again. Okay. Final question. You were a Thiel Fellow back in the day. Thiel Fellow? Thiel or Thiel?

Thiel.

Thiel. Yeah. What an alumni group. Holy moly. Just like, so it's such a great idea and program. Any story from that time that might be fun to share? Something that's like, oh wow, that was crazy. I don't know. Any other Thiel Fellow that you're proud of? What was the interview like? I don't know, anything along those lines.

Yeah, the Thiel Fellowship was an inflection point in my life. I wouldn't be where I am without it. Maybe to the point of there are key moments where you can tell people to elevate their ambitions and they do and that changes them. That was a moment where someone came to me and elevated my ambitions and said, no, you can do this. You don't have to take the path that you were on. And truly, I'm eternally grateful for them being able to do that. One of the Thiel Fellows that I get to work with all the time now is Ari Weinstein, who founded a company called Sky that was acquired by OpenAI. And prior to this, he founded and worked at Apple for a while because they acquired his previous company. Ari is just one of the most creative thinkers I've ever seen and is truly the expert on what are all the cool things you can do on a Mac. Um, and so Ari leads a lot of our computer use stuff at OpenAI, and he's like shipped a whole bunch of great things for, for computer use. But yeah, his, his creativity and his like joy in what he does and his love of his craft really inspires me. And like, Ari, Ari's a cool guy.

Um, but I'm trying to think what is like a good story from that time that feels As you think about it, I'll explain the Thiel Fellowship for people that don't know this, and correct me if I'm wrong. Basically, Peter Thiel's like, hey, people shouldn't go to college. Instead, they should just try building something that they want. And you get $100,000 to not do college and instead just go follow your ambition. Is that roughly correct?

Yeah, that is exactly right. And you're with 19 other people at the time. It was like 20 people every year because it's 20 Under 20.

And how many years did it go on for? Is it still going?

I think it's still going, but I think it was like constrained at the 20 number for like the first 4 or 5 years or something like that. Um, yeah, I think a really crazy thing that happened my year is that I was the second ever year of the fellowship. They decided to make it all a documentary on CNBC. And so our whole, uh, my pitch for the fellowship, uh, getting up on stage and presenting the idea I was going to do, all of that is unfortunately live on YouTube. So if you really want to see Me as a 19-year-old doing something embarrassing, it's there. Of course, one of the most amazing and successful people who came out of that batch of the fellowship is Dylan Field, who is not only an incredible talent, but also a very kind person. And yeah, I feel very lucky to be able to work with those folks.

Amazing. Yeah. It's interesting that Dylan's the guy I think everyone thinks of when they think of Thiel Fellows. Yeah.

Yeah.

What a, what a brand. Okay, Tara, this was incredible. Uh, is there anything you wanna plug, anything you wanna point people to, and how can listeners be useful to you?

Anything I wanna plug and point people to, maybe they should use the ChatGPT desktop app. Um, they should use ChatGPT in the web and try Work. It's like, unfortunately, a little toggle. Um, they can toggle over to it and try out Work. Ask it to do some cool thing. Ask it to build a site about you. Um, maybe to start or ask it to make a little visualize, uh, block of your ChatGPT usage. It's a really cool way to start like experiencing the power of this stuff very intimately. And the list of use cases they can do from that, you know, are infinite. And I'm happy to—

Here's a better idea. Here's a better idea. Ask it to build a site to tell you what you could do with work.

Great. That will, that will work.

Solve all the problems. Okay. Uh, I interrupted you. I apologize. Uh, what else were you gonna add or say?

Yeah, my main plug is, yeah, go, go download the ChatGPT app, go use it on web. Even more transformatively, go try it on mobile, then take like a long subway ride or something like that, or a Muni ride. And when you pop out after having no service, the thing is done for you. That's the part that feels super duper magical. You're not like wandering around with your laptop open the entire time. You've Finally got these things running in the cloud doing real work.

Yeah, that last piece I was gonna bring up, but that's, I think, a really underappreciated element of the product today on mobile. And it's most— that's just a mobile-only feature, the cloud piece?

No, it's everywhere.

It's everywhere. Okay, so amazing. So on your mobile app, you can go to ChatGPT, toggle work, ask it to do some work, and you don't need to actually have the— it's not running locally, it's running in the cloud. It'll go keep doing work until it's done and then you could chat to it. So like, it feels like really simple, but that's a massively powerful thing. Okay. Uh, anything else, Tara, before we let you go?

Nope. That's it.

Okay.

Thanks, Tara.

This was awesome. Thank you so much for doing this.

Such a pleasure.

Uh, what a journey since the fellowship back in, back in the day. I'll talk about that more in the intro.

Yeah.

Uh, all right. Well, thanks for being here.

Thank you.

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