Show full transcript 0:28 where you think the models will be in a year, both outcomes are equally wrong. 0:31 Only way to build is two to three months. 0:34 What if you had to most adapt to and adjust in how you operate as a PM in this world? 0:38 Being prolific and empirical is way more important than being academic or theoretical. 0:44 Rather than writing out some long reasoning doc instead, it's like, 0:47 how do I get to something I can try out and test with users as fast as possible? 0:51 It feels like not only are we able to be more ambitious, 0:54 We almost need to be more ambitious, which is not natural for a lot of people. 0:58 Elevating others' ambitions or reminding them of what's possible here is a huge part of the product management role. 1:03 I'm curious what's most surprised you about what it's actually like to work at OpenAI. 1:07 I came into the company expecting that there was a treasure trove of OpenAI secret strategy. 1:13 And actually, OpenAI is open. 1:17 Today my guest is Tara Sation. 1:19 Tara leads product for both Codex and Jatchi BT work at OpenAI. 1:23 I believe this is the fastest growing and arguably most important AI product for knowledge 1:28 workers today. 1:29 Tara works alongside Andrew Ambrosino, who was a recent podcast guest. 1:33 He's her Eng Manager. 1:35 Prior to open AI, Tara spent six years at Stripe, where she joined as one of the first 1:39 five product managers. 1:40 And for many of those years, she was named one of the top three Stripes across the 1:44 entire organization of Stripe. 1:46 She also led product at Watershed, was a founder and a Teal Fellow. 1:49 And most importantly of all, Tara was one of the three Lennie's newsletter fellows, 1:54 which is a program that I ran a few years ago to highlight some of the most amazing up 1:59 and coming product leaders. 2:00 I am so excited to see Tara in this new incredibly important and impactful role. 2:05 Before we get into it, don't forget to check out Lennie'sProductPass.com for a free 2:09 year of the hottest and most beautifully crafted AI products in the world, available 2:14 exclusively to Lennie's newsletter subscribers. 2:16 With that, I bring you Tara, station. 2:21 Tara, thank you so much for being here and welcome to the podcast. 2:25 Thank you, Lenny. 2:26 I'm so glad to be here. 2:27 It's so nice to see you. 2:28 I'm even more glad. 2:30 So you've been at OpenAI for just about a year now, which in most places would be a 2:35 very short amount of time. 2:37 In AI time, that's like a lifetime. 2:39 Yes. 2:40 I imagine when you joined OpenAI, you had a sense of what it was going to be like 2:44 to work at a friend to your lab. 2:47 I'm curious what's most surprised you about what it's actually like to work at 2:50 open AI and ideally both good and bad stuff. 2:53 So many things about working at open AI felt familiar to me because I had worked at other 2:58 places that were, you know, high growth, high talent, high intensity, hyper scaling mode 3:03 places before. 3:04 And so some of the things like, oh, my colleagues are so awesome, or the urgency is really 3:09 high, felt very familiar. 3:11 The part to me that actually felt the most surprising is that many companies I've 3:17 worked for. In fact, all the companies I've worked for in the past have been founder-led, 3:22 and OpenAI is actually founders-led, which is that everyone inside the company, especially 3:29 in their area, is in essence kind of a founder to some extent. The level of like top-down 3:36 direction at OpenAI is extremely limited relative to places I worked for prior. 3:43 And so, I think when I first got to the company that was both delightful and that I had come 3:51 from like a founding journey before and I was like, yes, I can continue to feel like the founder 3:54 of this product area or this team. And the distance between me and the market is very, 4:01 very thin. And sometimes at a larger company, you feel insulated from what users want or feel 4:06 insulated from, like what the market demands. But actually it opened the eye that you do 4:10 not at all, you are doing everything it takes to get product market fit for your product 4:15 akin to how a founder might. 4:18 But the counter to this is that, or maybe the more surprising side of this is I came 4:24 into the company expecting that there was a treasure trove of like open AI secret strategy 4:31 that I would be able to understand akin to how, you know, at past companies you 4:34 come in and you're like, oh yes, this is like the payments Bible and this is how 4:37 how we think about payments and operations. 4:40 And actually OpenAI is open. 4:43 Like every sort of thought that exists in terms of 4:47 this is how the world should look like 4:49 or this is how products should be built 4:51 or this is how the model should operate 4:54 very, very quickly becomes a part of the public product 4:57 or a part of like the public messaging. 4:59 And so that to me was incredibly, 5:04 both positively surprising 5:06 and just like a change in my operating mode for sure. 5:09 Tell us there's not like the secret room with AGI running there with the master plan that has all the answers. 5:14 Or at least I'm not in that room for sure. 5:16 But I think the piece that is really inspiring to me is that so much of what OpenAI does 5:24 immediately becomes something that users can touch and feel in the product 5:29 and that cycle is faster than anywhere else I've seen. 5:32 This episode is brought to you by our season's presenting sponsor, WorkOS. 5:38 What do OpenAI, Anthropic, Cursor, Replet, Sierra, Clay, and hundreds of other winning 5:43 companies all have in common? 5:45 They are all powered by WorkOS. 5:47 If you're building a product for the enterprise, you've felt the pain of integrating single 5:51 sign-on, skim, RBAC, audit logs, and other features required by large companies. 5:57 WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built 6:02 specifically for B2B SaaS. Literally every startup that I'm an investor in that starts to expand up 6:08 market ends up working with WorkOS. And that's because they are the best. Whether you are a 6:13 seed stage startup trying to land your first enterprise customer or a unicorn expanding 6:17 globally, WorkOS is the fastest path to becoming enterprise ready and unblocking road. It's 6:23 essentially striped for enterprise features. Visit workos.com to get started or just hit 6:28 Get up their Slack where they have actual engineers waiting to answer your questions. 6:32 WorkOS allows you to build faster with delightful APIs, comprehensive docs, and a smooth developer 6:37 experience. 6:38 Go to workOS.com to make your app enterprise ready today. 6:42 You've been a PM at a lot of different places, a long time PM leader. 6:47 What do you lose in this new world? 6:49 When a market is more static or a market is more slow moving, you have the chance 6:52 to actually do some grand strategy-esque work because it's more predictable, or you can at least 6:59 understand all the pieces. As an example, payments is certainly a dynamic market to some extent, 7:05 but it's also an established market, and you're able to say, yes, if I take this bet, 7:09 my competitor might take this other bet or a reason from first principles very rigorously 7:14 through what all the next actions might be. And in fact, the nature of that market 7:20 it mandates that you do that. 7:23 Like winners will think more rigorously than everybody else. 7:25 And if you aren't thinking rigorously, 7:28 it shows up as sort of like carelessness 7:30 because a lot of those decisions that you made 7:31 could have been predicted. 7:32 But in this market, it's so hard to understand 7:36 like what is going to emerge in the future. 7:41 It's very emergent, it's very fast changing, 7:43 it's really dynamic. 7:43 And most importantly, it's like very, very important 7:46 to stay tied to the research. 7:48 And so actually being prolific and being more like empirical is way more important than 7:56 being like maybe more academic or theoretical. 7:59 And I think lots of the past companies I've worked at have been very academic and theoretical 8:05 places and it was a real switch to go from rather than writing out some like long reasoning 8:12 doc almost like a PhD thesis of what I think should be the plan for the next like 8:16 end amount of time. Instead, it's like, how do I get to something I can try out and test with 8:21 users as fast as possible? And so yeah, that switch from theoretical to empirical felt very 8:26 jarring at first. I was like, oh, am I not doing my due diligence here? Am I not being thoughtful 8:31 enough? Shouldn't I be thinking through all of this in a ton of rigor? But actually, 8:36 you've got to try stuff and learn as much as possible. And what that means is the thinking 8:41 you need to do is being as pointed as possible about what your core hypothesis is 8:46 And that hypothesis definition is the most important thing, like what is actually to 8:51 use the Shashir Mahal Trofe phrase, like the Eigen question, what is like that specific 8:57 most important thing to test and everything else, like any other grand strategy concoct 9:02 is not relevant. 9:03 I'd love to hear more about that because that's really interesting as almost like 9:06 here's the thing of the PM role that is not changing. 9:09 So much is changing. 9:10 The world is changing, but like there's still this piece that is even more important. 9:14 And speak more to that of what specifically that you think people need to focus more on. 9:19 Yeah. 9:19 There were so many trappings around the PM role of like, you know, running, like execution 9:24 on time and writing all these specific docs and presentations, et cetera. 9:29 But the core of it has always been about what is like the most essential question 9:33 you need to ask about your product? 9:34 Like what is the thing that will determine whether your product works or doesn't work? 9:39 How do you test that? 9:40 How do you look at the results? 9:41 and how do you feed that back into a loop of like refining your hypothesis and running it again? 9:47 Like that truly has always been the PM job and that involves of course like trying to understand 9:52 users, trying to understand the market, trying to understand the actual technology you're building, 9:55 pulling those three things together to make the most sharp hypothesis you can and then 10:02 making the test as fast and effective as possible. And I think that is not only not 10:08 changed, but it's become the most important thing at the company to be able to do, like 10:16 EMs are thinking this way, engineers are thinking this way, like data scientists are 10:19 thinking this way, designers are thinking this way, like everyone has sort of moved 10:23 to focus their efforts on this really, really important problem definition and testing 10:29 loop, like what are we actually doing and how do we know if it's working thing. And from a 10:34 PM standpoint, it's great because PMs have always been really focused on trying to get 10:39 that stuff right. 10:40 That has always been the core of the job. 10:42 And actually, many of the other just trappings of the job have fallen away. 10:46 And that remains the key thing to get right every time. 10:50 You mentioned this idea of a loop. 10:52 And there's a lot of talk these days. 10:55 Loops were so hot, I don't know, a few weeks ago on Twitter, and it feels like 10:58 it continues to be a topic of discussion for knowledge work broadly and the way I understand 11:05 a loop, essentially AI, here's what success looks like. Go off and build and figure it 11:09 out until you achieve success. How do you think about just this idea of loops expanding 11:16 from just software engineering to product management to all knowledge work? Do you 11:21 think that's going to be a thing? 11:22 I do think that increasingly the future of work will look more like steering than 11:27 rowing in the sense that there will be agents that you'll be able to work with that do a lot 11:33 of the rowing. And your role increasingly becomes steering the ship in the right direction and 11:38 pointing it in the right direction. And to that point, I think that that steering might grow 11:44 higher and higher and higher level. The steering used to be at the level of, I wrote this line 11:48 of code, press tab to, oh wait, now I'm directing something a little bit more comprehensive 11:54 to maybe the goal level, maybe to like an even higher level. 11:57 I think the steering will continue to maybe go up layers of abstraction. 12:02 But ultimately, I think it's still not a person to be able to find like, 12:05 which direction are we pointing this in? 12:07 And given feedback and additional data, 12:10 where do I want to take this thing next? 12:12 Some of steering, I think, is about certainly like what the data tells you. 12:17 But a lot of it is about making a pinionated call. 12:20 I think sometimes that we underrate that power of intuition or even positive determinism 12:30 of what we want the future to be like, like picturing, hey, I would like the product to 12:33 look this way, not because the converse is not an equally valuable strategy, but because 12:38 I would like the world to look like the direction that I'm pushing it in. 12:43 And that, I think, will always remain an opinion, at least right now, that is required 12:48 from a person. 12:49 And so I think that loops are awesome, running agents in increasingly, increasingly larger 12:57 loops where they're doing more and more of that rowing for you is great. 13:00 But right now, you really still need to steer. 13:04 And I think work will also look like steering with other people over a group of agents 13:10 that you guys work with together, bringing in other teammates into that interaction 13:15 interaction between you and the agent where it's growing in your steering feels also incredibly 13:19 valuable. 13:20 That's such an interesting way of describing it. 13:22 There's also, like there's two thoughts here that come up, one is if everybody has access 13:27 to the same tools, the thing that will separate you is the human, the person, basically. 13:33 Otherwise, we're all just going to be building the same thing. 13:35 You could, everyone could be asking, how do we win? 13:38 What do we do? 13:39 And then the thing that almost, the unfair advantage almost is the human brain. 13:42 Yeah. 13:42 I think it reminds me a lot of fashion, actually, in some ways. 13:46 There are certainly functional clothes that everybody can wear and gets the job done. 13:52 But so much about what you wear, at least, or how I think about what I wear is about 13:55 what statement I want to make about my individuality or how I want to reflect to the rest 14:01 of the world. 14:01 And a lot of what makes that compelling is how it contrasts with other people's 14:05 expression. 14:06 The shirt I make makes a statement only because it is maybe different than what 14:11 everybody else is doing or different than some cohort of people are doing or make a statement 14:15 about my group membership or something of that kind. And I think a lot of the products that we 14:19 build feel similarly opinionated and artistic. Like Patrick Halzen has a really nice statement, 14:25 or maybe it's John Halzen has a really nice statement about software, which is that software 14:29 is not like real estate, you don't like put money in and get value out. It is a little bit 14:33 more like filmmaking, where you can put a lot of money into a film, but that doesn't guarantee 14:38 that the film is successful or good, there is some like auteur statement or is some opinionation 14:44 and artistry that goes along with it. And I think that relies on you having something 14:49 interesting to say or your team having something interesting to say about your product. 14:53 There's something Marty Kagan is big on, which is this idea that 14:58 when you have an idea for a product or a feature, rarely is that idea the thing that 15:03 ends up being there's this whole process you go through to kind of figure out what the 15:06 actually it should be. And it feels like that's kind of what you're saying here is like you need 15:10 to go through that process as a human to understand what it really is and what people actually want. 15:15 It's never going to be like, okay, got it. Go build this thing. I got it from the beginning. 15:19 Yeah, for sure, for sure. And those loops are moving faster and faster and faster. And so your 15:24 ability to like form those intuitions, get the information you need to form those intuitions 15:29 and then use that with people and agents to put that into action is the key. 15:35 I'm curious what you think the next shift will be in how we work just broadly as knowledge workers 15:40 It feels like not only do you have access to the most advanced tools that'll some people that other people don't yet 15:47 Also, you work around the most AI pill AI forward people in the world 15:52 How are people working internally that you think will become kind of a more normal way? 15:58 We all work using these AI tools in the next I don't know three to six months 16:01 Yeah, I think there's two aspects to this. One is continuing to work with agents at higher 16:10 and higher levels of abstraction. So letting the agent do more and more for you independently, 16:15 coming in, providing that steering, and then letting the agent continue to cook. Like 16:20 let the agent cook and provide details that higher orders of abstraction feels like 16:25 the way. People are increasingly thinking about agents that are persistent, that 16:29 feel like teammates, that feel like co-workers, where you can work with them the way I might 16:34 work with someone on my team, which is they do a whole bunch of work, I provide input, 16:37 and then they do work again. 16:39 We sync up at different cadences, look at each other's in-progress work, and provide 16:44 more and more feedback. 16:45 It feels like that co-worker model is the way that things are certainly going, 16:51 feels like a much more natural interface for us to be able to work with agents, 16:55 so we already see a lot of that internally as well. 16:57 The second is that a lot of my work with agents thus far has been one-on-one. 17:05 I work with my agent, maybe it spawns some sub-agents to get some tasks done, 17:09 but it's me and my agent together. 17:11 And that is potentially divorced from what my colleagues are doing with their agents. 17:16 And so there was a time where everyone internally was just like sending their codex threads, 17:22 screenshots of their codex threads to each other on Slack. 17:24 And we're like, okay, well, I wanted to share with you how I got to this number. 17:28 Here's how I got to this number. 17:29 Here's a screenshot of what I did. 17:31 But that's also not quite the most natural way for someone to collaborate together. 17:37 And so as more and more work gets done with our agents, shouldn't we be able to get work 17:42 done with our agents together? 17:45 And what is the most natural interface to make that happen? 17:48 And those are some of the things that we're thinking about. 17:50 Chat is what I'm picturing. 17:52 That makes so much sense. 17:53 It's like, okay, Tara's agent, here's my agent, she did some work on some analysis. 17:57 I'd be, hey, my agent, Lenny's agent, go check, make sure this is legit, 18:01 and connects to the way I think about the world. 18:03 Ideally, work feels like a multiplayer game where all of us together 18:07 are getting stuff done, steering our agents as our agents continue to take care of more and more 18:11 of those rowing tactical tasks. 18:14 It's interesting how it's just been this slow progression of trust 18:19 and just like awareness that this can be how we work. 18:22 Just this like, okay, go work for longer, 18:24 you can take on more. 18:25 It's just like you feel, 18:27 there's been this talk of like the slow takeoff, 18:29 the fast takeoff scenarios, 18:30 and everyone's afraid of this fast AI takeoff 18:32 where it's like way too smart and now we're in big trouble. 18:35 It feels very much like 18:36 we're on the slow takeoff scenario, 18:37 which is good, where it's just like slowly iterating. 18:40 It doesn't feel that slow, but in a sense, 18:43 you know, we're not like to some 300 IQ AI, 18:46 I mean, the models are incredibly smart, but I think a lot of the things that have enabled 18:51 us to then work with our agents together or have the agents take care of higher and higher 18:56 order abstraction things certainly are about the intelligence, their ability to perform 19:01 long running tasks and how long they can stay on tasks, but also actually there are very 19:05 meat and potatoes, tactical things that make this possible, like agents working locally 19:10 are really convenient because they have access to all the data that's on your machine 19:14 to make an agent successful in the cloud. 19:16 There is a ton of cloud infrastructure 19:18 that you have to build to make that possible 19:19 and just like access to your systems. 19:21 Like how can agents talk to all these third party systems 19:23 that have all of your data? 19:25 Just like a colleague who you hire, 19:27 who you like lock into a room, 19:29 never give them access to like Google Docs and Slack 19:31 and the company database would not be that useful to you. 19:34 Similarly, like a cloud agent that is similarly isolated 19:37 will not be that effective. 19:39 And so a huge part of making these agents useful 19:43 and achieving some of these futures are on the intelligence side, certainly, but a lot 19:47 of it is also just really tactical data access, like cloud infrastructure and reliability pieces 19:55 that feel much more prosaic than some of the broader intelligence questions, but matter 20:02 in some ways just as much for the ineffectiveness. 20:05 This touches on something else that has been coming up a bunch on this podcast, this 20:09 word ambition. I know you think a lot about this too. It feels like not only are we able 20:16 to be more ambitious because of these AI tools, we almost need to be more ambitious, which 20:21 is not natural for a lot of people, because everybody can now do all these easy things 20:26 really easily. Like the easy stuff is super easy. The hard stuff is easy. And the thing 20:31 that separates people now and companies now is just how ambitious they can be. 20:35 Talk about what comes up when I talk about the need and the kind of the emergence of this need for ambition. 20:41 Yeah, I think the people that we see who are most effective at using AI tools don't simply use it to automate wrote tasks, 20:49 but use it to expand the set of things that they are capable of doing. 20:52 Like, back in the day, you know, before all this, before all this AI stuff, 20:57 the like unicorn person was someone who was a really thoughtful product sense person 21:03 who also happened to be an engineer, 21:05 who may also have been a designer. 21:06 That person was always the like unicorn hire 21:09 because they were able to really flatten the layers 21:13 of translation needed between all these functions 21:15 and were able to like build something 21:17 or ideate something really quickly and easily themselves 21:21 and get it up and running. 21:22 And then we're able to like work with a team 21:24 and collaborate with a team on it. 21:25 And I think the most compelling thing I found 21:30 that certainly I try to be able to do with these tools 21:32 tools, and I've seen some of my most successful colleagues be able to do these tools, is really 21:37 expand the set of things that are, quote, unquote, within their range of possibilities 21:42 so that they can start realizing more and more of what's in their head into the reality. 21:48 The way that someone who was previously like Jack of all trades was able to do, we kind 21:52 of all have that superpower now that I can like spin up a set of designs on something 21:56 and I can like go build an initial prototype of it, and I can, you know, figure out 22:00 the right pricing model for it and model out all the scenarios, like really the set 22:04 of possibilities have widened dramatically. 22:07 And actually what that means in so many ways is that I have the ability to like be a, to 22:12 that point earlier about film, like be more of an auteur as I try to get something done 22:17 and like realize my vision maybe to higher fidelity. 22:20 And that to me is part of what can elevate your ambitions while pursuing new ideas 22:27 and new products that because all of these things are now within reach, because this new set of 22:32 capabilities is now within your reach to be able to try and access, you're not really limited, 22:37 your ambitions are no longer limited by like what you're capable of executing yourself, 22:42 what you're capable of communicating. It can be so much, so much wider. I think the hardest 22:47 part about doing this is simply just expanding your thinking. Actually, the capabilities have 22:51 expanded so dramatically. It is really expanding your thinking of what's possible in an 22:56 unreasonably short time frame. 22:58 And to me, the best way of trying to do that 23:00 is Patrick Halzen has like on his website, 23:04 patrickhalzen.com slash fast, I think, 23:06 which is all of these projects that were unreasonably 23:08 ambitious that were executed in a really, really 23:10 short time period. 23:11 And what for me is now remarkable 23:13 about that list of projects is that they all existed 23:15 before these tools made it possible for you 23:18 to learn how to build something almost instantly 23:20 or ask it with one question, 23:22 hey, can you summarize this very complicated text 23:24 or this very complicated book for me, immediately. 23:27 Or can I try to do all of these things 23:29 were previously impossible to me, 23:30 but now I'm able to do, like can I spin up a, 23:33 can you make for me like a CAD model 23:35 of this idea that I might have? 23:37 The really capabilities that are truly beyond my reach 23:39 are now in my reach. 23:41 And so if those fast projects were possible before 23:46 with the capabilities we used to have, 23:48 shouldn't we just see an exponential increase 23:50 of the number of those types of unreasonably quickly 23:53 and effectively executed things with what AI has given us. 23:57 To your point, the hardest part is just remembering to even to try just to be like, 24:02 oh yeah, well, let me see if Codex can do this for me. 24:04 It's just like a new habit and you like thing we have to build in our brain. 24:08 Tyler Cowan has this statement on his site, which is that you, 24:12 most people underrate the impact of going to someone else and saying, 24:16 hey, couldn't you, what is like a more ambitious version of what you're doing? 24:19 Or couldn't you try this faster? 24:21 or couldn't you try this at a 10x bigger scale? 24:24 And in some ways, again, when I think of like, 24:25 what do PMs do that is incredibly effective now, 24:28 or what can they do that is incredibly effective now? 24:30 I think elevating other's ambitions 24:32 or reminding them of what's possible here 24:34 is a huge part of the product management role. 24:37 Like when folks say, hey, I think we can get this done 24:39 in this way, or we can get this done by this timeline, 24:42 or maybe this is the first version of it. 24:44 Part of your job now is to elevate everyone's ambitions 24:47 and say, actually, isn't the possibility ceiling 24:50 meaningfully higher. Like, shouldn't we be more ambitious about what we're attempting here? Or 24:54 like, could we try this faster? And I think that's a, yeah, it's a great place to be. It's a great 25:00 place to be in terms of what you can build, what's possible, and in terms of, yeah, how exciting the 25:07 job becomes. That is so interesting. I remember Nick Turley was on the podcast who was maybe 25:13 had the role before you. I think he's working at Enterprise stuff now. He had this meme 25:17 internally is this maximally accelerated? Yes. There's like an emoji, I think, inside the 25:22 slack. Is this maximally accelerated? Is this maximally accelerated is totally a Open AI meme. 25:28 The other Open AI meme that Andrew and Brazilo and I love to ask the team is like, are you 25:34 mainlining it yet? Which is like, are you using this product all day every day to get your 25:38 thing done? And I think that in combination with, are we being as ambitious as possible, 25:45 which is about like the scope and the scale 25:47 of what you're trying to do. 25:49 Are, is this maximally accelerated? 25:51 Are we moving as fast as possible on it? 25:53 And then are you mainlining it yet? 25:55 Are you using it? 25:56 And are you bringing all your tastes to bear 25:57 on whether this thing works 25:58 and it's something that people really want 25:59 and tightening that feedback loop as much as possible. 26:02 Those to me are like the three memes 26:04 of product development that we, 26:05 we just have to spread as much as possible now. 26:08 I love that. 26:09 That's like the new dock footing. 26:11 Instead of dock footing, you got to mainline it. 26:14 Yeah, exactly. 26:14 And that shows so deeply in the tweets. 26:19 This is mostly how I see your team communicate 26:21 of just like how obsessed they are with the product 26:23 and are just constantly asking what can we do better? 26:27 What's bugging you now? Here's the thing we're building. 26:29 It's like, it's very clear how to your point earlier 26:32 that everyone is just the founder of their product 26:34 and it's very clear how they act as an external observer. 26:39 Are there any other memes internally? 26:41 Those are so interesting. 26:42 Any others? Yeah, I'm trying to think of there's other good cultural means. 26:48 Certainly a really important one is like feeling the AGI or just being conscious of AGI coming. 26:56 There are so many outcomes for what it could look like or how one thinks about it, but a huge 27:03 part of what puts most people at this company is believing in that mission of AGI being 27:09 beneficial, and trying to do whatever it takes to make that 27:12 possible, both realization of AGI and ensuring that it is 27:15 beneficial for for humanity. And in building products, another 27:20 just constant refrain I have to keep in the back of my mind is, 27:23 are we building for where the models are going to be in two to 27:26 three months? You fail if you build for where the models 27:29 are now you fail if you build for where you think the 27:32 models will be in a year, like both outcomes are equally 27:34 wrong. And I'm sure many people have talked about this, 27:37 but both outcomes are really equally wrong. 27:39 If you're too early, you're wrong. 27:41 If you build something that was overly focused 27:43 on a past model's capabilities, you're entirely wrong. 27:47 The only way to build is two to three months. 27:51 And having this beam of like models 27:52 are gonna get way better. 27:54 I need to think about model capability 27:55 as the center of this product. 27:57 I need to get out of the way of the model 27:58 in terms of the product constructs that I create. 28:02 How do I ensure that this is right 28:03 for the model in two to three months time? 28:04 How do you know what two or three months is like? It's like a challenging understanding, 28:10 especially while we're on this exponential. Is it just a gut feeling? Is there anything? 28:15 Do the researchers give you a sense? How does that work? 28:17 Yeah, certainly communicating really tightly with research on where they think things are going is 28:23 incredibly important. These things aren't entirely like a black box in that you kind 28:29 of know, hey, we're focused on these particular things that we would like 28:32 models to be better at coding in these specific ways 28:35 or better at writing in these specific ways. 28:38 So we certainly have focused efforts 28:40 on making the model better at specific capabilities. 28:43 And so knowing where that is 28:45 and ensuring that product development is as tied 28:48 as possible to what research has as its agenda 28:52 and its roadmap is really important. 28:54 A quote that I'll never forget 28:55 is when Kevin Wheel was on the podcast. 28:57 He was chief product officer at that time. 29:00 He said that this is the worst 29:02 the models will ever be. And it sounds so simple, but it's just like, it's hard to just 29:08 like wrap your head around that, that this is the worst they will ever be. Like, it's 29:12 such a cliche almost now to say that, but it's, it's true. It's absurd. This is like, 29:16 it's, it's absurd. It's really absurd. 29:20 Oh man. Okay. I want to talk about ChatGPT the app briefly. Okay. So I have it open 29:27 right now. Yes. Okay, so here's what I see in it. ChatGPT and then there's a 29:33 drop-down and there's ChatGPT and Codex. And then there's this toggle, chat and 29:37 work. Tara, what is going on? What are all these things? Help us understand what 29:42 each of these things are for and where does this, where do you think this goes? 29:46 Is it gonna stay like this? What is there like a next step that you're 29:48 imagining already? Our north star here is that users do not need to make 29:52 decisions between picking between all these different options. Ideally, there 29:56 there's no toggle here, that you go to the box, 30:01 you type in your task, I would like to build 30:03 a really awesome app that, I don't know, 30:06 helps my podcast guests do research before episodes 30:09 or something like that. 30:10 And it will just pick the right harness, 30:15 it'll pick the right model for you 30:16 to be able to get that thing done. 30:18 Ideally the choice here is not on our users 30:21 to have to pick between all these different concepts 30:23 and understand not only what are they trying to do, 30:26 but understand the limitations and capabilities of our products. 30:29 So that is certainly where we want to go. 30:32 In the near term, picking between chat GPT and Codex 30:36 is really a choice for are you a, 30:40 do you want to stay in sort of like more development oriented UI 30:43 or do you want to have the same power and capabilities in the chat GPT mode? 30:49 And so if you're a Codex user, keep using Codex. 30:51 You're not missing out on anything like continue using it as much as possible. 30:54 But if you're a chat GPT user who is like, 30:57 what are these new agenda capabilities? 30:59 You should probably be in chat GPT mode. 31:01 And then when you're in chat GPT, 31:03 if you wanna have conversations, if you wanna search, 31:06 that's where chat mode is the right thing. 31:07 It's the same chat mode you know and love 31:09 with better and better models 31:11 and newer and newer capabilities every time. 31:13 But in work mode, that's where under the covers, 31:17 this is Codex. 31:18 We've removed some of like the coding UI, 31:21 like you're not gonna see a work tree pop up 31:23 all of a sudden in work mode, but it is the same power to get things done to, for example, 31:30 generate a really complex financial model that's all possible in work mode. And we see people, 31:37 especially, I mentioned our corporate finance team, use work mode to do incredible, incredible 31:45 things that were previously either manual or required deep expertise from one person on the 31:49 team, become things that the whole team can be able to execute or just elevate the ambitions 31:53 of everyone on the team in terms of timeline or capabilities or frontier of what they can 31:57 get done. 31:58 Okay, that's really helpful. 31:59 So there's kind of like these three modes currently, there's like the engineering mode, 32:03 the chat mode, and then the do knowledge work mode. 32:06 And the knowledge work mode, it's actually codex doing all that work, but people may 32:10 not know what codex is, maybe not, they may be afraid of it. 32:13 Is there anything in that work mode that's not just codex, because that's actually 32:16 really interesting? 32:17 Is there like additional harness tweaks to make it feel a little different or is it just 32:21 the same thing with a little different UI? 32:23 It's really at the UI level. 32:26 So work mode and codex mode, if you go to codex and ask it to generate an amazing financial 32:31 model to price your product or something like that, or like, tell me, predict my revenue 32:36 for the next six months or something like that, codex will do as good a job as work 32:38 mode. 32:39 It's like really about, whilst it's doing so, what kind of UI do you want to see 32:43 in the chain of thought? 32:44 What kind of technical detail do you want exposed to you? 32:47 So it's like incredibly, it's similarly powerful. 32:50 And so Codex users aren't missing out on anything 32:52 by not switching modes. 32:53 In fact, we do not want them to stay in Codex 32:55 and do all the stuff you wanna do in Codex. 32:57 And we will show you the appropriate UI 32:59 based on the things you asked for. 33:01 Truly our North Star is to like merge all these things 33:03 so that users don't have to make any of these decisions. 33:07 The separation is really more about 33:09 how can we meet people where they are 33:12 as much as possible in terms of the products 33:14 that they use, in terms of their familiarity 33:15 with concepts and make sure that we are enabling everyone 33:20 to take advantage of working with agents, 33:23 which has transformed entirely the way 33:25 every single developer works. 33:27 We should do the same thing with knowledge work. 33:29 It makes sense. 33:31 There's this like, because the things move so fast, 33:34 like it would, I imagine somebody's like, 33:36 let's try Codex, this is gonna be awesome. 33:38 And then it takes off and there's 10 million 33:39 monthly active users. 33:41 And then they're like, wait, what are we doing here? 33:42 We got ChatGPT, we got Codex, how do we, 33:44 So it makes sense why these things, you know, like, it's not going to feel obvious and perfect 33:49 for a while because you have to kind of adjust as things work and things don't work. 33:53 And there's these kind of transition periods of like, okay, cool, now let's get people 33:56 moving towards this vision of the super app, let's say, okay, I imagine one of the hardest 34:04 parts of your job is balancing this hundred billion MAU product, chat GPT, maybe the 34:12 most successful consumer product in history with Codex, which is this new thing and other 34:17 new things that you guys want to try. 34:20 How do you think about that? 34:22 Just, I don't know, just balancing these very innovative fast-moving teams and products 34:26 with this like, okay, there's a billion people using this, we can't change this dramatically. 34:30 Yeah. 34:30 I think one of the most interesting things here is that we, one of the goals of launching 34:37 work in ChatGBT web and launching it in the desktop app and bringing these things together 34:42 was to look at those billion people who are using ChatGBT and bring them more and more 34:49 of the agent's power. 34:51 Like if you think about the first era of AI products as chat, the second era of these 34:57 products is clearly working with agents and primarily has been coding agents. 35:04 we'd like to bring it to more domains, certainly, like knowledge work, and that is part of the 35:09 goal of giving all these billion chat users the power of work. Certainly, the product challenge 35:15 that's on us is how do we not only bring it to them, but make it natural and easy to adopt, 35:21 make it not a decision they have to explicitly make. We can just help them do the right thing. 35:25 How do we take, decomplexify it so they don't need to think about 35:28 things like harnesses, which feel like crazy concepts for a billion consumers to understand. 35:33 So that is primarily the challenge. 35:37 And then of course, like that third era that might come soon 35:39 is how do you work with a persistent coworker 35:44 who is able to get things done with you, 35:47 maybe collaboratively with other people? 35:49 And so part of this challenge in the near term 35:53 is we're introducing agents to a billion people 35:56 who may not have experienced them yet. 35:59 How do we do so in the easiest, most natural 36:01 and most usable way possible. 36:04 Certainly there's a lot more for us to do 36:05 to make that happen. 36:07 But part of this is also a lesson I've had, 36:11 maybe contrasting like pre-AI era 36:14 or past product experience with this one, 36:16 which is at previous companies like Polish was king, 36:21 getting every UI interaction 36:23 or getting every little thing completely right 36:25 was way more important than shipping something early 36:28 because time didn't make as much of a difference 36:32 in terms of the outcome. 36:34 And so as such, like if every corner 36:36 wasn't like perfectly polished 36:38 and everything wasn't exactly correct, 36:41 you might as well not ship it. 36:43 But I think what's been really compelling 36:46 and interesting about this era 36:48 and this product experience has been getting the product 36:51 in the hands of users 36:52 when you have so much conviction 36:53 that hey, it's transformative, 36:55 like it's way better than perfect. 36:57 And that urgency and that introduction of that product is so important. 37:03 So we have a lot to do to make it more usable and easier for chat users, 37:07 certainly especially for folks who are not maybe even using it for productivity, 37:11 but using it for, like, consumer tasks. 37:15 But, yeah, done is better than perfect. 37:17 And we have so much more to do. 37:19 Yeah, I remember when the SAP first launched, there was a lot of comments about the confusion 37:24 and seeing how quickly the team iterated in response to the feedback is exactly what I'm 37:29 hearing here is get it out, figure out what the hell's, what's not working, how people are using 37:33 it, iterate quickly. It feels like that's the model now. And of course, there are things 37:37 that you can continue to iterate and get that feedback prior to launching. And there's a lot 37:41 that we can and should always do better. But iterating as quickly as possible and listening 37:47 to the right signals is regardless of whether that's pre-launch, post-launch, 37:51 which ideally pre-launch is the key thing. 37:55 This episode is brought to you by Mercury, 37:57 radically different banking, now with Spend. 38:00 I've been a Mercury customer for so many years now. 38:03 I switched all my business banking to Mercury, 38:05 and honestly, I could not be happier. 38:07 It's what online banking feels like 38:09 when it's built by product people, not by bankers. 38:13 And now with Spend, you can give your team 38:15 individual cards, set spending limits per person 38:18 or per team, and have expense receipts 38:20 automatically pulled in from Gmail or over text. You can even give your AI agents 38:25 their own cards with their own limits and policies. Most founders start out the 38:29 same way. One card used by everybody at the company. It works until it stops 38:33 working. Someone goes over, a receipt disappears, you spend two days trying to 38:38 figure out who spent what and why. Spend is expense management built 38:42 directly into Mercury. All your team's cards, budgets, and reimbursements 38:46 all live in the same place as your business banking. 38:49 No chasing, no manual reviews, no end of month scramble. 38:53 The result is a team that can move fast 38:55 and a founder who is no longer the bottleneck. 38:57 Learn more and get signed up at mercury.com. 39:01 Mercury is a fintech company, not an FDIC-insured bank. 39:04 Banking services provided the choice 39:05 financial group and column in any member's FDIC. 39:07 The IOCART is issued by Patriot Bank 39:09 and a member FDIC pursuant to a license 39:11 for MasterCard International Incorporated. 39:14 Something I've noticed on Twitter 39:15 Twitter is there's definitely been this vibe shift from Claude code to codex in the past 39:21 few months used to be everyone was Claude code this Claude code that more recently it 39:26 just feels like people are leaning now towards codex, at least on Twitter, which is a bubble, 39:31 but it's where a lot of tech people are. I'm curious what's shifted internally in the 39:36 past I don't know three to six months other than Tara joining and shaping up the 39:40 ship. Is there anything that you can share that's just like okay we figured this 39:44 thing out, we shifted this, we cut this thing, what helped shift the vibes and help Codex 39:51 become as successful as it is becoming. 39:53 You know, I think there's like this phrase, which is before enlightenment, carry wood or 40:00 carry water, chop wood, post enlightenment, carry water, chop wood sort of thing. 40:05 And actually with the Codex app, the team who initially got it up and running and 40:10 We're working on it. 40:12 We're super, again, user-focused, tight iteration loop. 40:16 Really dog-fooded the thing, mainlined the app 40:19 as much as possible to get everything right. 40:22 Folks started to realize that was happening externally 40:25 and on Twitter, and users started to really notice. 40:28 But the team was always really focused on users, 40:33 really focused on that iteration. 40:35 And it was merely the, to some extent, 40:38 like the market catching up. That was the change. And that process has not changed internally. 40:44 Everyone still constantly uses the app. Everyone who's building it obviously is a developer using 40:50 it for development and is constantly fixing not only their own problems, but trying to listen 40:56 to other people in the company's problems and user problems. Actually, what's sort of remarkable 41:00 is that the mode of operating hasn't changed. It's always been the same thing I had mentioned 41:05 earlier like are we like are we elevating our ambition sufficiently are we maximally accelerating 41:10 progress and are we mainlining it as much as possible and I think it's great that users and 41:18 folks on Twitter have noticed but that that operation like the full credit to the team like 41:23 that hasn't changed. What's really interesting about this answer is it's it's the very human 41:28 part of it. It's you, it's Andrew, it's Tebow, it's the team just like being obsessed 41:33 with the customer, the product. 41:36 And it's not like AI was the answer. 41:37 It's the humans that made the difference. 41:39 Yeah, I'll give the team deserves like full credit here. 41:43 The everyone on the team is incredibly thoughtful 41:46 and independent and to the point of like, 41:49 there are many founders at OpenAI, 41:52 like almost everyone on that team, 41:54 like the desktop team especially, like acts like founders 41:57 and cares about every piece and every detail. 42:00 And when they notice an area that should be better, 42:02 They go build it very independently and get the thing up and running. 42:07 And if it doesn't test well internally, like people aren't using it. 42:10 If people don't find it useful, they'll iterate on it. 42:12 And then finally, like ship it externally. 42:14 But that loop is full credit to like people on the team and individuals for making that happen. 42:20 Something you touched on is this idea of roles overlapping. 42:23 This idea of like, you know, engineers are doing a PME work. 42:26 You're doing probably shipping prototypes and building maybe shipping to production. 42:30 I don't know just it feels like that also creates a lot of challenges and hear from a lot of people like 42:35 What is my job now as a as a designer? What am I one of my responsible for what am I not responsible for as a marketer? 42:40 What am I what am I doing? Is that something you notice? Is that something that you're dealing with? 42:46 Just any thoughts along those lines. I think the thing I've always liked the most about working at startups and 42:53 Sometimes I've started at a startup that actually grew into a large company 42:56 but largely primarily working at startups is that there are very few boundaries around your role 43:01 that like everything and nothing is your responsibility, ultimately you're accountable 43:05 for success. Actually like Stripe was very, very much this way where there are no boundaries 43:11 around what a engineer could do versus a product manager could do versus a designer could do, 43:15 everyone could do anything. And so actually it kind of feels like I've always really 43:20 loves that mentality and now finally capability is catching up to that. 43:25 But the thing I really care about is that someone needs to look after the or have core 43:33 accountability for is this product being used by users, is it something that people want, 43:39 is it high quality, is it effective? 43:43 And whether that person is like an engineer or a designer or a PM or whomever like someone 43:47 is the DRI and then whatever work needs to be done to make that possible, you know, certainly 43:53 people can pick it up based on their affinity, based on their capability, but I like a team that 43:58 doesn't really mind what the boundaries are between individual roles, but everyone's just 44:03 sort of focused on making the outcome happen. The converse of this is I also really love 44:08 like the craft aspects of like being a PM. Like there are so many aspects to PM craft 44:14 to that. I know folks like Shreyas or maybe Marty Kagan or Shashir, like all these people 44:20 have really espoused that I think are wonderful. And sometimes maybe some of these questions 44:26 or come from, wait, I so love the craft of my domain by taking this more fluid approach to 44:34 teamwork and collaboration to get something done. Do I lose out on getting better and 44:38 polishing my craft? And I truly don't have an answer for that question. I think it's 44:42 something we're all experiencing together, which is some pieces of our craft are actually getting 44:47 abstracted by models being able to do it really effectively, maybe better than individuals can, 44:54 and your craft moves from being able to do that very specific task you did in the past to 45:00 now applying it to some other part of the product or the discipline. But yeah, that is still a 45:05 question I'm thinking about, which is how do I balance my desire to be part of a team and 45:11 use these tools and feel so compelled by how effective one can be now with all these products, 45:18 with my love of like the, yeah, it's really fun handwriting code for an engineer all the 45:25 time and one doesn't really do that anymore. 45:28 Yeah, that's where I was going to go. 45:29 It's just like unbelievable how different the engineering role is now. 45:34 Yeah. 45:35 It's like you used to write code all day, that was your job and it was no longer 45:38 your job. 45:39 Yeah. 45:39 And that happens so quickly. 45:41 I see people mourn the flow state of writing code manually yourself versus now and what 45:47 one does. 45:49 But I think it is a, yeah, it's a tough transition. 45:54 Yeah. 45:55 And some people love it, some people don't. 45:58 And that's a whole other topic. 46:00 Kind of along those lines, something I'd like to ask people at the frontier of AIs, 46:03 where do you think human brains will continue to be valuable in the future? 46:08 It's impossible to predict long-term what we need humans, hopefully. 46:13 But I'd say in the next couple of years, where do you think human brains will continue 46:17 to be most valuable? 46:18 I think humans will continue to be the most valuable, certainly as an entity of accountability. 46:27 So who ultimately owns the outcome here? 46:29 In some ways, you can think of your agent that you're working with as your report. 46:35 Ultimately, who owns, what was the end product, was it high quality, was it the thing that 46:40 you wanted it to do and say, that will certainly remain a person, at least for now, and especially 46:47 in industries and places that are highly regulated or require a direct human interface. 46:54 That makes a ton of sense to me. 46:56 I think the human brain is also really valuable for expression. 46:59 I had mentioned earlier that analogy of software is not like real estate. 47:05 It is more like a film where you could put money and a great film does not come out. 47:10 The greatest films are not the ones with the biggest budgets. 47:13 And given that there's a certain artistry and opinionation and expression in building 47:19 software where you feel like there is some authorship by a person or a group of people, 47:23 and that part remains to me so human. 47:27 Like what you choose to build and how it feels, 47:29 feels like such a human question. 47:33 I also think the human brain continues to be valuable 47:36 and like how we care for each other 47:38 and relate to one another. 47:40 That piece of my work has remained so human 47:44 and remained actually become more important than ever. 47:47 The part where you talk to other people on your team 47:50 and collectively figure out how you can be enthusiastic about an area, how you learn and 47:57 work together, how you elevate each other's ambitions. All of that feels and remains such 48:03 a human thing to do. Yeah, I think the human brain will continue to be so valuable in that 48:09 regard. That said, I can't predict what will happen with the models, but those pieces feel 48:15 to me to be incredibly incredibly human. I love that answer. There's this idea that you talked 48:21 about this idea of the this overhang of what AI is capable of and what we're actually doing with it. 48:28 People are always like it feels like one of the biggest gaps is like okay what should I do with 48:31 it. I'm curious what are some ways that you use AI in your work that may inspire people like oh 48:38 wow I didn't think about using it that like there's kind of two buckets here one is just 48:42 like, what's like the most, how your PM job has changed most 48:46 things to AI that you're just like, okay, now I use AI for 48:48 the stuff. And then what's like, is there any like super 48:51 interesting creative uses of AI recently that you're like, oh, 48:54 yeah, you should try this. 48:55 One of the most exciting ways that I use AI in work is 49:00 actually build sites all the time now. I don't know if 49:03 you've tried building sites. I haven't talked about sites. 49:06 Sites is a really fun, amazing product. You can basically 49:11 build a site. Certainly in work as a presentational artifact, but I also build sites for literally 49:16 anything. I built a site for the team as a game where we all played a game together using a site 49:23 because sites have a database. I actually built a site because I went on a backpacking trip 49:28 recently. I built a site of the route that tracked the elevation of everywhere we were going. 49:33 Everyone on our trip inputted all their food. It was super fast and effective. Sites kind 49:39 of realize the dream of like malleable personal software that Alan Kay, you know, flagged in 49:44 the in the 60s of like the true personal computer is one that has personal software in some 49:48 way sites are like the tangible way to make that possible. We'd all once dreamed of like 49:54 making personal software and certainly people with tools like notion, etc. Try with all these 49:58 blocks to configure what that could be. But with the site, it is literally a prompt. 50:03 I literally with a prompt say like build me this exact tool that I need to get 50:07 this thing done and it just does it. 50:11 They're shareable, they can use, they can auto update, 50:15 you can use like internal data to build a dashboard, 50:18 for example, with lots of metrics. 50:20 And rather than like painstakingly laboring over 50:24 some sort of like slide deck, 50:25 a site is just a way more dynamic surface 50:27 for presentation. 50:29 How do you use a site? 50:30 Do you have to do anything special? 50:31 Or do you tell it? 50:32 Let me create a site. 50:32 Codex be like create a site that is a I know isn't mafia game for my team and it will just do it 50:40 And like like I'm thinking capital S site, but it doesn't matter. I imagine it just knows what sites are 50:45 So it's yeah because it used to be here's a here's some source code go figure out where to deploy it 50:50 Yeah, and what you're saying here is it just hosted for you and host it for you can use it 50:54 Choose whether it's public you can choose whether it's with your team or choose whether it's private to you 50:58 So they're great, the easy reach of building a site all the time has changed what my day-to-day 51:06 looks like, which often in previous worlds used to look like creating lots of artifacts 51:11 like doxons, cheats, and whatever it might be. 51:14 Now I just make sites all the time. 51:16 And you could do that through, I imagine, work, or can you do it through all the 51:19 surfaces? 51:20 Codex, WorkChat, GPT Chat? 51:21 Can you do it through work? 51:22 You can do it through Codex. 51:23 You can do it in the web. 51:24 You can do it on mobile. 51:26 You can do it anywhere. 51:27 Okay, I just kicked off a creative site by Tara Sashon. 51:30 Great. 51:31 Is that your pronouncer last name, by the way? 51:32 I haven't asked you. 51:34 Tara Sashon, like station. 51:36 Sashon, okay, cool, okay, cool. 51:40 Sites, okay, any other quick tips 51:42 while we're on this topic for P? 51:43 Cause that was a great tip, 51:44 cause I don't think a lot of people know about sites, 51:46 so it's very useful. 51:47 Yeah, sites are awesome. 51:48 The other thing I really love 51:50 is using visualize in Codex. 51:52 Have you used slash visualize? 51:53 No. 51:53 Oh, slash visualize is incredibly exciting, ask you can just do slash visualize visualize 51:59 my chat to BT usage until now or something like that. 52:02 And it will pull in like all the things that you've done and create like an amazing visualization 52:06 for it. 52:07 The number of times that I've been thinking about how do I not only pull in a bunch of 52:11 charts and data but present them in a way that is understandable and useful for the 52:17 story I'm trying to tell has been infinite and visualize makes that incredibly simple. 52:21 It is like surprisingly delightful to use Visualize. 52:25 It's just like it's just these are such good examples of there's so much power here we don't even 52:29 know about or understand and that's the challenge you have here. 52:33 Help us know all these things. That's why podcasts like this are also useful. 52:37 Can't put it all in the product. I'm going to go in a totally different direction. 52:41 I want to talk about writing. I asked Brie Wolfson who knows you well. 52:45 What to ask you? Funny enough she suggested questions for the previous podcast 52:49 just a conversation I did with Adam Ward from Gerser. 52:53 So she said, okay, you should ask her 52:55 about writing slash thinking. 52:57 A tarot brief is iconic. 53:00 Help us understand just what makes 53:03 your writing your briefs iconic 53:06 and any tips that might be helpful for people 53:08 that are maybe trying to get better at writing 53:10 and writing documents. 53:12 I really strongly believe 53:14 that I do two types of writing at work. 53:16 One is writing as thinking 53:17 and the other is writing is reporting. 53:20 Writing is thinking is me writing a brief about 53:23 why we should build certain product 53:24 or why we should take a certain strategy 53:26 or why maybe a spicy take. 53:30 But writing is reporting is things like, 53:31 oh, I'm summarizing the status of 53:33 what our team has been up to this week 53:35 and I'm sending over a report about it 53:37 or this is our plan for this particular 53:39 launch or announcement or something like that. 53:42 Writing is reporting. I happily automate 53:44 or I use I use the models all the time to make that as as simple as it can be. 53:49 But writing is thinking is something I never will automate. 53:54 I really strongly believe that the at least for me the act of going through 53:58 and outlining something, turning it into some level of prose, cutting it and editing it, 54:05 continuing to iterate on it is one of the most important steps for me to get my ideas in line. 54:12 I think most people actually will paint with a really broad brush. 54:18 Like I will never use the use the models for writing or I always use the models for writing. 54:22 And actually, to me, like that brought both those broad brushes are wrong. 54:25 I think you should use the models as much as possible for writing as reporting and in as much as you think with writing as I really do. 54:33 And I think a lot of people do. 54:35 You should not use it. 54:36 Yeah, you shouldn't replace your thinking with it. 54:37 But my briefs in the past, because I write so much as a way of thinking, is that I will write a... 54:45 I will go into a whole, write a brief for a new idea or a product, spend a ton of time refining that particular idea, 54:55 shop it around with people and have them attack the ideas in it as much as possible and poke holes, 55:00 make it stronger and then take it to the next person and do the same thing. 55:04 And so at Stripe, this is something I did many, many, many, many times over, whether 55:10 that was like to kick off a new product area or to suggest a big change in direction or 55:15 to analyze a problem and suggest like a path forward. 55:19 And Stripe is incredibly oriented as a writing culture. 55:23 And there are many people like Jeff Weinstein who are also very into writing and sharing 55:30 briefs at Stripe. 55:31 I'm sure it was one of the few places where like a brief will go viral inside the company. 55:35 And so writing is thinking there is really prized and that's where I did like the majority 55:39 of that writing work. 55:44 At OpenAI, I think I still write as thinking all the time. 55:50 But the shareable artifact here is not really a long doc or a sort of proof of work in 55:59 that way. Partially because times have changed and a long doc is not a signal 56:06 that you thought through something because you can easily produce a long 56:08 doc that indicates that you haven't. And so actually the point maybe one of the 56:13 biggest changes I've experienced personally in my day-to-day which has 56:17 been a big maybe jarring change is that I used to think in a document and 56:23 then do some translation of that into a presentation artifact and that would 56:27 would be my indication that I thought through a problem 56:29 and this is what we're gonna do 56:30 and the team moves in that direction. 56:31 And now I am way more on mocks, not docs 56:35 or prototypes, not docs. 56:37 And if I have something that people can try 56:39 and interact with or even better, I have like results 56:42 where we try this, we ran an AB, here's like the results. 56:45 This is why I think we should like go in this direction. 56:47 That is a way better communication tool 56:50 than like the doc itself. 56:52 And so I still write hundreds of docs all the time 56:56 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 57:02 To talk and communicate that is probably the biggest change. I've experienced personally in 57:08 This era versus the previous era and it's so interesting. I really liked your tape of 57:14 getting tons of feedback on a doc like you know, it sounds obvious, but 57:18 You know, you can get to an iconic doc slash brief by just 57:21 Cheating almost and getting lots of feedback on it as you're iterating. Yes to make it stronger and stronger stronger 57:26 versus like, cool, here it is, first time, and it's really going to be amazing. 57:29 I previously had a manager who told me that the right thing to always do is write a doc to 70% 57:34 completion and then take it to the people that you need buying from and get it from 70% to 100%. 57:40 And that still is like a thing that I do all the time, because very few great people want 57:46 to interact with like a perfectly polished finished idea, like a perfectly polished idea, 57:51 their new ideas just like bounce off of it versus something that has more crags and more 57:56 rough edges that they too can polish with you together. 58:00 And I think that by bringing people into the process that way, 58:03 where like a dog is like an underlying artifact for that, 58:06 is one of the best ways to collaborate that I found. 58:08 How do you think about AI brain rot 58:11 and starting to over-reline AI? 58:14 That's just the challenge everybody's gonna have. 58:16 Why not use this magic to help look at something 58:20 and then we start to lose our ability 58:22 to write read-long documents? 58:24 Is there anything you do that you are trying to avoid that? 58:28 Yeah, I think this writing is thinking discipline is one of the main pieces that I employ in 58:33 my day-to-day to make sure I'm not overly atrophying my thinking abilities. 58:39 I think I will, again, outsource all writing as reporting as much as possible to the model, 58:43 but writing is thinking I have to do myself. 58:46 And I have this personal belief that if I'm going to make someone read my document, 58:53 document, I have to at least read it first that number of times. 58:57 Or I think about this in meetings, too, that if I'm going to call a meeting with a set 59:01 of people, I need to have prepped the collective amount of time that people are going to spend 59:06 in that meeting before the meeting. 59:08 And so when it comes to keeping my thinking sharp, I do that writing for the document 59:13 myself first and make sure I've invested the collective amount of time. 59:18 I expect people to read it, at least in writing it and producing it. 59:20 And I don't really rely on the model either for polishing my pros, which I don't think 59:25 it really does or especially not in like generating the first version. 59:32 But I do of course have the model help me a lot when it's like summarization or like 59:37 translation of content from one format to the other all the time. 59:41 So what I'm hearing is write the idea, the brief, the plan yourself as a human, 59:48 write it yourself, don't start with AI, and even don't use it to improve on the writing. 59:55 Just keep that all human. Yeah, at least for me, I start myself and I end myself. I might use AI 60:02 in the middle to research specific elements or drop in some data or go pull some data or help me. 60:07 Or push back on some ideas. Yeah, push back on some ideas, but start yourself and yourself 60:12 with a piece of writing and that doesn't deteriorate your thinking. 60:15 Okay. One last question. I want to ask about Sutter Hill. You had this very unusual career 60:21 step. You went to your PMPM founder person and then just like, okay, EIR at Sutter Hill Ventures, 60:26 which is an iconic VC. People can look it up. A lot of amazing companies came out of Sutter Hill. 60:33 It has a very unique way of approaching founding where basically they incubate companies, 60:38 Snowflake as an example. What was that about? What did you learn from that experience? 60:44 Sutter Hill is an iconic firm and is intentionally a very 60:48 illegible firm. Like if you go to the Sutter Hill website, you will see nothing on the website. 60:53 It is a firm that doesn't operate loudly. It tries to operate as under the radar as possible, 60:59 as modestly as possible, yet is somehow responsible for some of the most iconic 61:04 successes that Silicon Valley has seen. And they have this very unusual incubation model, 61:10 which Mike Spicer, who is one of the amazing partners there, started and has rolled out 61:18 success after success. I think the thing that was most iconic to me about Sutter Hill is that 61:23 people look at finding product markets fit as a dark art or building like a tens of billion 61:28 dollar company as a dark art. Like, oh, it's luck. Oh, it's chance. Oh, it's all these 61:33 like things that must come together. Yet Mike Spicer has done it multiple times. 61:38 And so there is, there's like clearly a way to do it. 61:42 There's clearly a roadmap for making that possible. 61:44 There is like a set of things one can do 61:46 to get this repeatedly. 61:47 It's not just luck. 61:48 It's not just a dark arch. 61:50 There is a playbook as it were, 61:53 and that playbook lives inside of the firm Sutter Hill. 61:57 And they have figured out how to be right a lot 62:00 in terms of like calling shots and making bets. 62:02 And they've learned how to be right a lot 62:05 in terms of the like daily compounding things 62:08 that one does to create a successful company, whether that's how you set up your enterprise sales team, 62:13 how you position your product, how you like build the initial founding team. The recruiting at 62:19 Sutter Hill is like an unparalleled, excellent thing. They have a secret tool called Retical, 62:25 where they have a map of, you know, everyone in that they've interacted with and the 10 62:30 best people that those people have interacted with that helps them be so, so effective at 62:34 this. So I went to Sutter Hill because in some way my career has been about how do I 62:40 try to find product market fit as many times as possible, whether that was as a 62:44 founder or in starting new products at Stripe or in like joining a startup like 62:48 Watershed. And so Sutter Hill is the place where they've figured out how to 62:52 find product market fit on B2B products and I wanted to learn what I could 62:56 from them. Would you learn what's one thing you took away from that 62:58 experience other than they know how to do it? They definitely know how to 63:01 do it. I think one thing that was very surprising to me that I learned there is that product 63:07 market fit is sure important, but actually I really underrated product marketing fit. 63:12 The idea that the way you talk about the product and the way you market it can proceed actually 63:17 even building the product. It should probably come from some sort of bringing together 63:23 of understanding the technology deeply and then understanding like the enterprise sales 63:27 process and then that product marketing fit that narrative that positioning is actually even 63:32 before you build the product experience the right thing to test so you should go like pitch 100 63:37 people figure out how to refine that pitch as much as possible get the marketing narrative of why 63:42 this thing is transformative right and then and only then go commit the okay this is exactly 63:48 the product shape and my spiser is like unbeatable at this art previously I'd always kind of 63:54 underrated PMM work. I was like, that's whatever. Like it's the glue between these functions. It's 63:59 fine. And then I realized how transformative that work done excellently is to a company's outcome. 64:07 And in fact, can be the element that makes a company successful. 64:11 Amazing. I so agree with that positioning. We talk a lot about that on this podcast. 64:15 Okay, I'm going to show you what sites got created real quick. 64:19 It was running while we were talking. Check this out. Look at this. 64:22 Oh man, as like as like make it more awesome and they made it more awesome multi-product 64:29 Beautiful look at this. This is like a legit design 64:32 Look at you got quotes big convictions small teams start the buyer 64:37 How do you feel about this being your website your new website? 64:40 I do think that the picture of me at maybe 19 years old at the top is really funny 64:45 But yeah, otherwise I love love the site 64:48 Good job. 64:49 It's looking good. 64:50 I think that was my badge photo from Stripe. 64:53 Wow. 64:54 Amazing. 64:55 I love that it built. 64:56 I already unshared it, but I love that it built the whole little thing around your head. 65:00 So cute. 65:02 Tara, is there anything else that you wanted to share, anything else you want to touch 65:06 on before we get to a very exciting lightning round? 65:08 Yeah. 65:09 One thing that we've been thinking about a lot in product building, especially with 65:12 chat GBT work in this new era, is how knowledge work and coding are actually fundamentally 65:16 different. 65:17 And one of the surprising things we learned as a part of that is that coding is so output oriented that when you 65:25 Ask it to do a coding task you can verify whether it did the task correctly or well via tests 65:30 You can try it out and see if it works like there is a way to validate it based on the output 65:36 But knowledge work is different in that I can't simply look at the deck in the end and see the numbers 65:43 Oh, it's like 90% success or whatever in the deck and actually believe that I really need 65:47 to think about the process and the inputs and the reasoning and how it went along the way. 65:52 And so in terms of how that looks in the product, like a lot of work that we have done and 65:59 have to continue to do is continue to adapt the product to knowledge work, which means 66:03 way more focus on making chat GBT your collaborator, allowing you to see all the in progress 66:10 work, see its citations and inputs, help you go on the journey with the model to 66:16 get to that end output such that you know in the end oh wait this thing is 66:20 right, this thing is good, this thing is useful and that shows up certainly in the 66:24 UX of the product quite a bit but also should show up in things like the 66:29 reasoning in the chain of thought like should I should you see more 66:31 citations along the way for example of how it got to that end state in that 66:34 data is the surface of a thread which is so suited to coding the right place for you to 66:41 see all of that for knowledge work as well. 66:43 There's so many big important product questions. 66:47 And so as we think of maybe bringing in human collaborators into your work, we also need 66:52 to think about how we can make the model more of a collaborator with you as you 66:56 get things done together. 66:57 That is such a good point. 66:59 I'm imagining an exec meeting where you're trying to pitch the exec on, here's what the 67:05 plan is, here's what I think we should be doing. 67:08 So much of that is helping them see, here's the work I did to get there. 67:12 Here's all the steps. 67:15 And so it makes sense that you need the AI to show you that same sort of work that 67:21 the proof of work essentially versus engineering where like, okay, I don't need to know 67:25 all of the little architectural decisions you made. 67:27 just what does it look like? Is it passing all the tests that we have? So that is a really 67:31 good point, just how different those two models are. And also, there's like the context. Does 67:36 it have the context it needs to do the thing that you want it to do? Does it know? Does 67:40 it can it see your email? Can it see all your Notion docs? Such a good point. So 67:45 I see the challenge in your job. If you call this work as one product, tricky, 67:48 tricky. Amazing. Anything else before you get to a very exciting lightning round? 67:53 Yeah, let's jump into it. 67:55 With that, we've reached our very exciting lightning round. 67:58 I've got five questions for you. 67:59 Are you ready? 68:00 Yes. 68:01 What are two or three books that you find yourself recommending most to other people? 68:06 One book I really recommend to people is Barbarian Days by William Finnegan. 68:10 I don't know if you've read it. 68:11 It's about a life of a man who is a New Yorker reporter, but how he fell in love 68:16 with surfing as his passion. 68:18 The thing I took away from the book is that one can be deeply passionate and 68:22 dedicated and have something to your life purpose without you being good at it. 68:26 And it is about the art of like falling in love with surfing and his striving for excellence 68:31 and perfection whilst knowing that he will like never reach it. 68:35 It is such a compelling and transformative story for how I think one should continue 68:42 to live our lives. 68:44 I really, really love that book. 68:47 book. Another book that I might recommend as a book that people should read. I really 68:56 love Anna Karenina. I've been rereading the classics lately. And I love Anna Karenina 69:02 because it's like a book of layers. And I think that a huge part of what we're going 69:07 to have to do in this new era is like transform ourselves or like take ourselves on a journey 69:11 to do different things than what we were used to. And when I think about that book, I think about 69:17 when I was 13 and I read it, I understood basically the plot. When I read it at like 17, I understood 69:24 the European history dynamics and like the class warfare. And then when I read it at 30, 69:28 I was like, Oh, this is like a story about like a woman and humans. And it just reminds me of 69:34 like growth and that it is possible to look at the same thing through multiple different 69:39 lenses as you continue to grow, which I think is kind of the challenge that's ahead of all of us, 69:44 ahead for all of us as we consider our careers as well. 69:46 It's interesting how both these, like, I could connect to AI in the time we're living in now too. 69:51 I also recently read Anne or Corinne in it. 69:54 What did you think? 69:55 Earlier this year. Amazing. I've never read it before. I saw it on a book list of, like, 69:59 here's what the smartest people in the world have read and it's like a whole list of books 70:02 and that was one that I hadn't read, so I got to read that. Yeah. It was amazing. 70:07 Someone gave away the ending which kind of made it less like surprising and I want to give anything right no spoilers 70:13 And I also felt like it was very long and but now I'm reading the power broker 70:16 Which is like set the set the new president for a long 70:20 Been reading it for half my life at this point 70:22 I love the I love the power broker and I think I highly recommend to people is if if anyone follows the substack 70:27 Like Simon Hazel's substack where he does a slow read of important books 70:33 So he did one of war and peace and he's doing one of wolf haul 70:36 I think or he did one of wolf haul which is the Hilary Mantel book like take it chapter by chapter 70:41 And that's like the only way to read something like the power broker or war of peace or even an accredited 70:45 It's like chapter by chapter speaking of that. There's a someone I forget who told me this there's a 70:51 99% visible book club breakdown of the power broker 70:56 Where it's 13 episodes an hour to each and they go through a couple chapters of the book one at a time and 71:02 talk about it and they have special guests like Pete Buttigieg and AOC and 71:06 folks that lived in that area and they talk about every you know the story and 71:10 it was so it's so fun to read and listen to their analysis of it and then they 71:14 have Robert Caro come on a couple times. Whoa, that's amazing. Yeah, hot tip. Okay 71:19 we'll keep going with our very lightning round. Favorite recent movie or TV 71:23 show you've really enjoyed if you've had time to watch anything. Of course I 71:26 watched The Odyssey. I found it to be an incredible, incredible film. It is 71:32 about AI as, or my hot take is that it's about AI or Christopher Nolan's view on how AI transforms 71:39 society, which I loved and I highly recommend watching The Odyssey. He is like the, he's just 71:45 incredible director and has bridged artistry and commercial success in a way that I think no other 71:52 modern director has like done. I also recently watched the film Rashomon, which is the Akira 72:01 time did that technique of telling a story through multiple people's perspectives where 72:06 you never know what was true in the end. Like that technique in film was pioneered by Kurosawa 72:11 and it reminds me what one can do under constraints. That film was made in like the 50s, 72:17 it was black and white. You know there's a guy holding the camera and yet it is so perfect 72:22 And it is such a tasteful, innovative, amazing example of creativity. 72:31 And what I'm reminded of watching that film is like, I have a hundred times the power and 72:36 tools that he had making that film in my iPhone. 72:38 And like, what's my excuse for not elevating my ambitions and making better stuff? 72:42 All comes back ambition. 72:45 On the Odyssey, I'm still trying to get tickets. 72:47 It's so hard. 72:48 I slept on it and now it's like impossible for like a month. 72:52 There's no seats anywhere Kevin got us tickets at 10 p.m. At the Metri on earlier this week. It was so good 72:58 Next time call me 73:01 Man, I have like bots running on it. I have a person working on 73:07 It's amazing you're gonna love it and I can't wait to hear what you think after you see it if you agree with me that it is about 73:12 AI and the collapse of morality. Okay, no spoilers 73:16 Hopefully by the time this comes out I have seen it 73:18 But if not, if anyone has hookups, please tell me. 73:21 And I'm trying to do like the IMAX, 73:23 full-power matron sort of thing. 73:24 Yeah. 73:25 Okay, next question. 73:27 Favorite or interesting AI product 73:29 that you've recently discovered 73:31 if ideally not open AI product, 73:33 but you know, you can also go there if you want. 73:36 Ooh, I mean, of course, 73:38 my favorite AI product is China GPT 73:41 and using cool sites and visualized stuff in Codex, 73:44 which is amazing. 73:45 but outside of open AI products, my favorite AI products are products that my friends make for me 73:52 because now actually people can do that. I think that's so cool. I'm such a huge fan of like the 73:56 cozy software movement where you like make software tools for like five of your friends and 74:00 you guys use it together. And so I have a friend named Sebastian who made a really cool AI app 74:08 that turns anything into a podcast and puts it in your like a little podcast app for you. 74:12 And he also made a really great private social network for our friends, and it's called Gats. 74:19 It is exactly what I think the future should be, which is people should make software that 74:24 exactly meets their and their friends' needs. 74:26 What does Gats stand for? 74:27 Is that some inside joke? 74:29 It is not, or at least if it is an inside joke, I don't know it. 74:34 But it is the place that I, it's like private Twitter maybe for like a group of, a small 74:39 group of friends. 74:39 and I learned the most interesting things on that product. 74:42 It's like a what's happened, but not. 74:44 Yes, exactly, exactly. 74:46 The podcast app is interesting, but I feel like the version 74:49 that I would love is it's actually like podcasts in your feed of podcasts 74:52 and then just new episodes get added of things you want to read or whatever. 74:56 Yeah, that's what it does. 74:57 It drops it in your Apple podcast feed or whatever you want. 75:01 Oh, amazing, I want it. 75:02 It's great. 75:03 Help me subscribe to this app. 75:05 For sure. 75:05 Okay, amazing. 75:06 Okay, two more questions. 75:07 Do you have a favorite life motto that you find yourself coming back to often in work or in life? 75:13 Ooh, my life motto that I come back to all the time in work is actually Toni Morrison's 75:17 three takes on work. Let me like pull it up really quickly. 75:20 Amazing. 75:22 Okay, it's four things. It's from her essay, The Work You Do, The Person You Are. The first 75:27 one is whatever the work is, do it well, not for the boss, but for yourself. The second is 75:33 is you make the job, it doesn't make you. 75:37 The third is your real life is with your family. 75:40 And the fourth is you are not the work you do, 75:42 you are the person that you are. 75:45 I got tingles. 75:48 Wow, so good. 75:49 And I think that's what you have pinned 75:50 to your Twitter profile. 75:52 Yes. 75:53 I remember seeing that. 75:54 So cool, okay. 75:55 Maybe we'll show that on screen 75:56 as you're talking about that. 75:57 I love that. 75:58 I love that that's a great way to remember something. 76:00 Just stick it to the top of your Twitter 76:01 because then every time I go to Twitter, 76:03 or other it is again. Okay, final question. You were a Teal 76:07 fellow back in the day. Teal fellow? Teal or Teal? 76:10 Teal. Teal. Yeah. What an alumni group. Holy moly. Just 76:14 like, so it's such a great idea and program. Any story from 76:19 that time that might be fun to share something that's like, 76:22 oh, that was crazy. I don't know. Any other Teal fellow 76:25 that you're proud of? Any other, what was the interview 76:27 like? I don't know anything along those lines. 76:30 Yeah. The Teal Fellowship was an inflection point in my life. I wouldn't be where I am without it. 76:37 Maybe to the point of like, there are key moments where you can tell people to elevate their 76:40 ambitions and they do and that changes them. Like, that was a moment where someone came to me and 76:44 elevated my ambitions and said, no, you can do this. You don't have to take the path that you 76:48 were on. And truly, I'm eternally grateful for them being able to do that. One of the Teal 76:55 fellows that I get to work with all the time now is Ari Weinstein, who founded a company 77:01 called Sky that was acquired by OpenAI. And prior to this, he worked at Apple for a while 77:07 because they acquired his previous company. Ari is just one of the most creative thinkers 77:12 I've ever seen and is truly the expert on what are all the cool things you can do 77:19 on a Mac. And so Ari leads a lot of our computer use stuff at OpenAI and he's like shipped a whole 77:26 bunch of great things for computer use. But yeah, his creativity and his like joy in what he does 77:32 and his love of his craft really inspires me. And like Ari is a cool guy. But I'm trying to think 77:39 what is like a good story from that time that feels... As you think about it, I'll explain 77:45 the teal fellowship for people that don't know this, and correct me if I'm wrong. Basically, 77:50 Peter Teal is like, hey, people shouldn't go to college. Instead, they should just try building 77:55 something that they want. And you get $100,000 to not do college and instead just go fall here 78:01 ambition. Is that roughly correct? Yeah, that is, that is exactly right. And you're with 19 78:07 other people at the time. There's like 20 people every year, because it's 20 under 20. 78:11 And how many years did it go on for? Is it still? I think it's still going, 78:14 But I think it was like that constrained at the 20 number for like the first four or five years or something like that 78:20 Yeah, I think a really crazy thing that happened my year is that I was the second every year of the fellowship 78:26 They decided to make it all a documentary on CNBC and so our whole 78:32 My pitch for the fellowship 78:35 Getting up on stage and presenting the idea 78:36 I was going to do all of that is unfortunately live on YouTube 78:39 So, if you really want to see me as a 19-year-old doing something embarrassing, it's there. 78:46 Of course, one of the most amazing and successful people who came out of that batch of the fellowship 78:51 is Dylan Field, who is not only an incredible talent, but also like a very kind person. 78:58 And yeah, I feel very lucky to be able to work with those folks. 79:02 Amazing. 79:03 Yeah. 79:04 It's interesting that Dylan's like the guy I think everyone thinks of when they 79:06 think of Teal Fellows. Yeah, yeah. What a brand. Okay, Tara, this was incredible. Is there anything 79:14 you want to plug? Anything you want to point people to? And how can listeners be useful to you? 79:18 Anything I want to plug and point people to, maybe they should use the ChatGBT desktop app. 79:22 They should use ChatGBT on the web and try work. It's like, unfortunately, a little toggle. 79:27 They can toggle over to it and try out work. Ask it to do some cool thing. Ask it to 79:36 make a little visualize a block of your chat GPT usage. It's a really cool way to 79:42 start like experiencing the power of this stuff very intimately and the list of use cases they can do 79:49 from that you know are infinite and I'm happy. Here's a better idea. Here's a better idea. 79:55 Ask it to build a site to tell you what you could do with work. Great. That will that will 80:00 work. 80:02 Solve all the problems. 80:03 Okay. 80:04 I interrupted you. 80:05 I apologize. 80:06 What else are you going to add? 80:07 Yeah. 80:08 My main plug is, yeah, go down with the Tritubity app, go use it on web. 80:12 Even more transformatively, go try it on mobile, then take like a long subway ride 80:17 or something like that or a Muni ride. 80:19 And when you pop out after having no service, the thing is done for you. 80:22 That's the part that feels super duper magical. 80:25 You're not like wandering around with your laptop open the entire time you've 80:28 Finally got these things running in the cloud doing real work. Yeah that last piece 80:32 I was gonna bring up, but that's I think it really underappreciated 80:36 Element of the product today on mobile and it's most it's that that's just a mobile only feature the cloud 80:42 No, it's everywhere. It's everywhere. Okay, so amazing 80:45 So in your mobile app, you can go to JGPT toggle work ask it to do some work 80:50 And you don't need to actually have the it's not running locally. It's already in the cloud 80:53 It'll go keep doing work until it's done and then you could chat to it 80:57 So like, it feels like really simple, 80:59 but that's a massively powerful thing. 81:04 Okay, anything else Tara, before we let you go? 81:07 Nope, that's it. 81:08 Okay, thanks. 81:08 Tara, this was awesome. 81:09 Thank you so much for doing this. 81:11 Such a pleasure. 81:11 What a journey since the fellowship. 81:14 Back in the day, I'll talk about that more in the intro. 81:16 Yeah. 81:16 All right, well, thanks for being here. 81:18 Thank you. 81:19 Bye everyone. 81:21 Thank you so much for listening. 81:22 If you found this valuable, 81:24 you can subscribe to the show on Apple Podcasts, 81:26 Spotify or your favorite podcast app. Also please consider giving us a rating or leaving a review 81:32 as that really helps other listeners find the podcast. You can find all past episodes or 81:37 learn more about the show at LenniesPodcast.com. See you in the next episode.
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