Lenny's Podcast: Product | Career | Growth - The engineering mindset | Will Larson (Carta, Stripe, Uber, Calm, Digg)

Starting point is 00:00:00 I think that we often treat engineers a little bit like children instead of giving them like the responsibilities and ability to actually thrive as adults. And so like, oh, the engineers won't want to do that work. Well, that's actually not good for the engineers to kind of be sheltered from what is important. And so I actually like one of the, I think, highlights is that I think we're coming back this moment where we can actually treat engineers like our peers and put them into really senior leadership roles and not have this kind of baseline assumption of like, oh, we have to coddle them or hide them from the real problems.

Starting point is 00:00:30 and this is how they're going to get the opportunity to grow as well. Today, my guest is Will Larson, one of the most requested guests I've had on this podcast. Will is currently CTO at Carta. He's been a software engineering leader at Stripe, Uber, and Com. He's the author of two essential books for all engineers, an elegant puzzle and staff engineer, and he's releasing his newest book,

Starting point is 00:00:54 The Engineering Executives Primer, in February of next year. He also publishes regularly on his blog at lathean.com, which is a must read for every engineer and Engie leader. In her conversation, Will shares advice on developing your engineering strategy and strategy in general, how to improve the relationship between an Eng manager and a PM, how he finds time to write while also working an intense full-time job, how he recommends approaching measuring engineering productivity, how to develop your company values, an amazing story about his time at Dig,

Starting point is 00:01:26 and so much more, Will is such a gem of a human and leader, and I'm excited to bring you this episode. With that, I bring you Will Larson, after a short word from our sponsor. Today's episode is brought to you by DX, a platform for measuring and improving developer productivity. DX is designed by the researchers behind frameworks such as Dora, space, and DevX.

Starting point is 00:01:50 If you've tried measuring developer productivity, you know that there are a lot of basic metrics out there and a lot of ways to do this wrong. And getting that full view of productivity is still really hard. DX tackles this problem by combining qualitative and quantitative insights, giving you full clarity into how your developers are doing. DX is used by both startups and Fortune 500 companies, including companies like Twilio, Amplitude, eBay, Brex, Toast, Pfizer, and Procter & Gamble. To learn more about DX and get a demo of their product, visit their website at getdX.com slash Lenny.

Starting point is 00:02:23 That's getdX.com slash Lenny. Today's episode is brought to you by One Schema, the embeddable CSV importer for SAS. Customers always seem to want to give you their data in the messiest possible CSV file. And building a spreadsheet importer becomes a never-ending sync for your engineering and support resources. You keep adding features to your spreadsheet importer, but customers keep running into issues. Six months later, you're fixing yet another date conversion edge case bug. Most tools aren't built for handling messy data, but one schema is. Companies like Scale AI and Pave are using one schema to make it fast and easy to launch delightful spreadsheet import experiences,

Starting point is 00:03:02 from embeddable CSV import to importing CSVs from an SFTP folder on a recurring basis. Spreadsheet import is such an awful experience in so many products. Customers get frustrated by useless messages like error on line 53 and never end up getting started with your product. One schema intelligently corrects messy data so that your customers don't have to spend hours in Excel just to get started. with your product. For listeners of this podcast, OneSchema is offering a $1,000 discount. Learn more at OneSchema.co slash letty. Will, thank you so much for being here. Welcome to the podcast. Thank you so much. Super, super excited to be here. So many people have suggested that I bring you on this podcast.

Starting point is 00:03:45 You have a lot of fans out there. And I am excited to be digging into engineering topics, which we don't do enough of on this podcast. So thank you for making time for this. No, thanks. I hope to be a good early engineering guest before we pivot entirely to engineering at some point in the future. Wow, I love that. How cool would that be? I was an engineer, actually, when I started my career. How interesting would that be if we come full circle? Anyway, I thought it would be fun to start with just what is changing in engineering. It feels like there's been a lot that has changed over the past few years, especially from kind of the ZERP, zero interest rate era to today's market, which is very different. What have you seen most change from an engineer's perspective? And then just also what are you telling engine leaders about how to handle all this change? I think it's a pretty strange time in the market. So I think that, you know, I started working and, you know, right before

Starting point is 00:04:39 the 2008 kind of crash. And so the first two years there were not so good. When I joined Yahoo, there was a layoff basically every like four months. There was a layoff of some sort. It's pretty chaotic. But then we got into the last decade. And it was just smooth, right? And so, you know, numbers went up, revenue went up, headcount went up, and people started learning how to build really large teams. People started learning how to hire a lot. When I was at Uber, some days I would do like six, like interviews back to back. I would just be in a conference room. And at some point, you can't even remember who you're talking to because you talk to so many people, one after another after another. You just have some

Starting point is 00:05:15 scramble notes. You're trying to like decode afterwards. Pretty different now. Like a lot of engineering managers are spending half their time. or more hiring like 18 months ago. And now they're doing like two interviews a month or less, maybe zero interviews a month. So there's a real shift in just the amount of time people are putting into hiring. Instead, like all these different competencies that have kind of become more critical or a really great engineering director might have just been spending their time hiring, hiring really well. And that could be like a top performer.

Starting point is 00:05:47 Now that person can't actually demonstrate like what they're great at. And so that person might be perceived as a low performer if they're not. not also like figuring out how to lead the team, getting deeper into the details. And also like, you know, sometimes getting into the figuring out, like what is the right allocation? Like, what is the right sizing of engineering teams, right? Like, this is stuff that we weren't talking about much. Or maybe if you, uh, if you really pissed off the CEO, maybe in infrastructure, you just grew a little bit slower next year. But, but, but, you know, different, different ballgame at this point where teams are actually disappearing. Teams are getting cut down. Teams are getting

Starting point is 00:06:19 consolidated and that that's just something that we've kind of avoided for that serp era that now we become like a core part of a lot of the job it feels like also engineers of they're so they used to have a lot of leverage over companies inside companies I imagine that's also changed in a big way I think that's true I think this actually has been bad for engineers in some way like one of my hobby horses is that I think that we often treat engineers a little bit like children instead of giving them like the responsibilities and ability to actually thrive as adults. And so like, oh, the engineers won't want to do that work. Well, that's actually not good for the engineers to kind of be sheltered from what is important. And so I actually,

Starting point is 00:06:59 like, one of the, I think, highlights is that I think we're coming back this moment where we can actually treat engineers like our peers and put them into really senior leadership roles and not have this kind of baseline assumption of like, oh, we have to coddle them or hide them from the real problems. And this is how they're going to get the opportunity to grow as well. That's like a highlight for me in kind of the ship recently. I have definitely worked with that and experience that where you don't want to piss off engineers. And so are you saying that because that's changing leaders maybe don't have to worry as much about upsetting engineers, plus you just generally think we shouldn't treat engineers, though?

Starting point is 00:07:32 Well, yeah, I think a little bit of both. But I think there's been, you know, in this previous era where hiring and retention were kind of like one of the biggest ways you evaluated kind of middle management. Losing team members was a huge, huge issue, right? And so you started to coddle a little bit. which is actually bad, again, bad to the engineers, bad for the teams, bad for kind of everyone, bad for efficiency of the organization. But now, like, something that I love is we get to, like, give engineers real hard problems,

Starting point is 00:08:01 and we get to actually hold them accountable. And that means we can put them in senior roles. So one of the things that I've been pushing on, I wrote my last book, Staff Engineer, about, like, what is the career path for senior engineers? One of the challenges is, like, if we aren't comfortable holding engineers accountable because we just want to retain all the engineers, that we can't put them in senior roles. And so I think we're actually seeing a bit of the shift

Starting point is 00:08:23 where we can actually hold them accountable, which means you can put them in senior roles, which means the engineers can actually get what they've been trying to get the entire time, but we haven't been able to because we've been coddling them a little bit too much. Okay, so there's a few directions I want to go, and I'm just going to poke around and see where we go.

Starting point is 00:08:37 The first is your big advocate of systems thinking. We were chatting about this before. I think a lot of people have heard this term, systems thinking, and there's books about it. It's like sounds great. I want to be a systems thinker. What does that actually mean? How do you find you apply it in your work?

Starting point is 00:08:54 How do people get better at this way of thinking? A lot of the least successful but smartest people I've worked with were really strong systems thinking advocates. And so I do want to say like every kind of like framework has like a lot of downsides. There's no framework that people can apply consistently, universally and get good results. And so briefly on this one, what I see is like often. people will find a spot where their system and reality are in conflict. And they'll be like, reality is wrong.

Starting point is 00:09:26 And so what's a concrete example? At Stripe, we worked on incident management. And so Stripe, pretty important company where our API is available. If the API is down, you lose money. And if you lose a lot of money, you leave Stripe because you're pretty upset about that. The number one thing businesses need to do is to collect money successfully for the service that are selling, right? It's right. It's super important. And so we did a lot of analysis on incidents trying to understand why things weren't working, you know, what we could do better. But we got like so caught up in the analysis that we sort of lost track of whether we're actually improving things. It took us a while to figure that out because we were so stuck in the systems thinking model. And it's not like, oh, the team was wrong. It's like I was wrong. I was caught up in that model myself to realize like, hey, we weren't actually prioritizing improvements. We were just prioritizing measurement. And you can't you can't keep measuring.

Starting point is 00:10:18 You know, there's like measure twice, cut once. Like, sure, but you don't measure in infinite times and never, never get to cut. And you do have to cut at some point to actually make impact. But we just got caught a little bit there. And I think a lot of people who get too far in systems thinking make the same mistake, where they think reality is wrong and reality is never wrong. Reality is always right. Your model is always wrong if it's in conflict with reality.

Starting point is 00:10:44 But that conflict, that gap is really interesting. and that's where you can learn. And so I have this model. It's really clean. It represents your hiring pipeline that moving through different steps. It represents your incidents and how you remediate incidents. It can model almost anything pretty quickly when you get good at it. And then understanding how reality is in conflict with that, you start to understand

Starting point is 00:11:06 where your mental model is wrong. Then you can go educate yourself and improve the model and just keep doing that. And at some point, like the model is close enough and you can stop doing that and go, like, actually do the work. so that the biggest thing I tell people is this is a great way to learn, but you also have to do things. You can't just learn. That's not our entire job.

Starting point is 00:11:25 To make it a little more concrete, how would you best describe this idea of systems thinking? What's a good way to just like, okay, I get, I know what you're talking about? This is probably a better place to start, right, versus a rambling anecdote about using it. We can go backwards. It'll be great. Systems thinking is basically you try to think about stocks and flows.

Starting point is 00:11:44 So stocks are things that accumulate. and flows are kind of the movement from a stock to another thing. And so what's a simple example of a stock? A stock could be the number of fish in a lake. A stock could be the number of people fishing in a lake. And so a flow between those two could be the number of fish in a lake will decrease at some rate based on the number of people fishing in that lake. So if there's a ton of fishers, then the stock of fish will go down faster.

Starting point is 00:12:12 There's only a couple of fish. It will go down slower. But also then there's these flows, which kind of dictate that, where if, you know, we got much more efficient fishermen, the flow of a fish out might go down. But also, the fish do reproduce. So then there's another flow going back. So based on the current number of fish and the reproduction rate, the current number of

Starting point is 00:12:31 fishers and their fishing rate, you start to see how these can evolve over time. And a lot of this, so first, always recommend Thinking in Systems by Donatella Meadows, really phenomenal book. And a lot of her work is also kind of referencing the work in Silent Spring by Rachel Carson, which talks about how small amount of kind of carcinogens or something low in the ecosystem or the food chain, rather, as they get consumed by predators, further and further up get concentrated. And that that's like a kind of a classic system's thinking problem to think about where you wouldn't think a small amount of carcinogens and like a small

Starting point is 00:13:07 fish actually matter. But as they go up to the food chain, they start to concentrate it and an unexpected change can happen. So then going back to your example of the hiring pipeline, Let's come back to that to help connect this definition, which I've never heard, which is awesome, very clear to how you actually implemented, say, in hiring. The first thing to do is to get a model out there of any sort. And so you think about your stocks. And so in a hiring pipeline, you might have, you know, potential candidates. And this could be kind of basically infinite. And then you have a couple of inflows.

Starting point is 00:13:37 You could have like sourced. You could have like outreach. You could have referrals. And then you have like the, you know, candidates. and those, you know, how many people get sourced is probably a function of number of sources you have dedicated to a role. So you have another stock of like how many resources you have. And then that would impact the rate going from potential candidates to actual candidates you've sourced. And then from candidates that you have in that box, you'd have like a conversion rate

Starting point is 00:14:03 for people who pass the first recruiter screen. And that would move into step of two of your process. And then from step two, you have maybe like a hiring manager screen. And that would be another conversion rate. So you can see like over time how the candidates of the infinite potential candidates like wandering around LinkedIn, posting about their deep thoughts, kind of convert into actual people your first, your second, your third. But then as you get deeper into it, you start to actually see interesting things. And so for a lot of candidates or a lot of pipelines, the biggest issue is hiring managers don't want to extend any offers because the hiring managers can't get to confidence on any candidate. And you'll see in this pipeline,

Starting point is 00:14:41 You'll see a ton of candidates getting to offer stage, but almost none of them converting from the potential offer to actually offer. And then you can say, hey, here's the problem. You need to go work with the recruiter and the hiring managers on, like, getting conviction about who they should hire. Classic problem with early, early managers, right? Here's a second problem. Manager wants to extend a ton of offers.

Starting point is 00:15:02 They do extend them, and none of them actually accept. And so that focuses you on the second problem. But there's a third potential world where actually you're just like not getting enough candidates in, you're actually doing a great job of making decisions, great job of closing candidates. There's just not enough candidates coming in. And so by looking at this, and you can build this model, then you can go to your applicant tracking system, like greenhouse or whatever, and pull the historicals. And you can just see how the historicals work versus how you'd expect it to work, and you can see the drop-offs. And this helps you figure out where should you go try to fix things

Starting point is 00:15:36 first. I think we've all worked in companies where you roll out kind of like big changes with no data behind them. It's like, oh, it feels like we're not not hard enough in how we evaluate candidates or something. You go change a bunch of stuff. But often the real problem might be that the hiring manager is making offer extensions to people who never pass the loop anyway. It's just the managers are issuing too many offers because they're panicking. And, man, less true now, but a decade, or not a decade, like two years ago, like hiring managers panicking to get offers out, that was a real thing that happened a lot. And this just helps you take a complex, kind of abstract problem and turn it into something you can actually work in a

Starting point is 00:16:16 systematic way. I feel like product managers will either naturally do things this way already because a lot of them think in funnels. And it's interesting to hear this version of it, of this idea of just like following the stock through the flow of the different steps. Awesome. Another thing that I know that you are very passionate about and spend a lot of time thinking about is engineering strategy. I think you have this kind of feeling like engineers don't think enough about the end strategy. Every other function has a strategy and engineers often don't. Talk about what you find there and what your advice is around that. First, I start to question whether any function has a strategy at most companies.

Starting point is 00:16:56 My general experience is that there's very rarely like a written strategy for any company. Sometimes it's like a value statement. It's like, we build the highest quality products. And you're like, good. Okay. Like, what is, what, what do I do with that? You're like, build a high quality product. You're like, okay, I don't know what that means.

Starting point is 00:17:15 Engineering often has this problem where I think people will make comments like in their, kind of their culture AMP or their quarterly surveys or whatever. It's like, hey, the strategy is not clear or where's the engineering strategy? And the biggest thing I tell people when they complain, and then engineers complain about the product strategy, like the PMs don't have any strategy or the business has no strategy. And the reality is like product, and business always have a strategy. It's just often not risen down.

Starting point is 00:17:45 And so I really like the first thing I want to do is like I push people like not to get cut up on like the fact that there's no template out there, which is like product strategy that someone's like forked and like filled in doesn't mean you don't have a strategy. You do have a strategy. It's maybe like a little bit hard to like articulate and maybe it's like applied inconsistently across different like layers of the product like reporting chain because it's. not written down. But like, it's never true that there's like no product strategy. There's always a product strategy. Sometimes it's bad, but there's always one. And true, true for engineering as, well, there's always an engineering strategy. It's just sometimes it's bad. And the first rule of strategy is that if you write it down, then you can like improve it. If it's not written down,

Starting point is 00:18:26 it's hard to say like if this PM is just like not a good PM or if they're trying to apply the strategy that they've misunderstood or if they actually are correctly, applying the strategy from the head of product that's just not appropriate to the problems they're working on. How do you debug any of that? If you have a written document, even if it's not a super compelling strategy, at least you can start debugging. It's like, hey, the head of product should improve the clarity of this document. Hey, this PM actually isn't applying it correctly. Hey, the strategy actually isn't appropriate for this one business unit where it makes sense for the others. So that's like kind of the first thing I think about. But the second kind of big

Starting point is 00:19:06 theme on strategy I think about is that often good strategy is so boring. It's like hard to talk about. And so, for example, on the engineering side of thing, a common strategy that's really good, but very boring is we only use the tools we have today. So, you know, a lot of times you get engineers that want to introduce new programming languages, new databases, new cloud providers. and a really good strategy for almost all companies is like, we just use the standard kit we already have today. And at Carter, when I joined, one of the engineers, Eric Vogel wrote the standard kit, and that is our strategy of the tools we use.

Starting point is 00:19:48 And you know what? Some people are really frustrated by that. And I feel for them. Like it feels like they're losing control. But the power of these boring strategies is that it focuses people's energy on the problems that we value as a company. And so it is painful coming into alignment if you're kind of like slightly misaligned over time. But boring strategies that tell you what actually matters and aligns you with what the company actually cares about are really good for you, even if even if they're a little bit annoying at a time.

Starting point is 00:20:16 And I can expand on this idea a lot, but I won't ramble indefinitely on it. Well, maybe what might be helpful is what are some other examples of engineering strategies that you've seen just to give people even more just like, oh yeah, maybe this should be part of our strategy? So first, what is the definition of strategy? And the best one I've ever seen is from Richard Rimmelt. He wrote Good Strategy, Bad Strategy. He's coming on the podcast. Amazing. He also wrote The Crux, I think, came out this year sometime, which I also read, and I think

Starting point is 00:20:47 both great. And just like a phenomenal thinker who has so much depth. I think one of the challenges of writing about strategy is you're like, I've seen two things and I write the book. But I think that's impressive about Richard Rommel. as he's seen so many different scenarios that he's able to really operate from both like the particular, but also like the general and data set in a really interesting way. Another book with similar characteristics is how big things get done by,

Starting point is 00:21:14 I forget the authors, but really amazing data set of how mega projects kind of succeed and fail. But anyway, Richard Drumalt, definition of strategy is basically three components. There's a diagnosis, like what is the current status quo? Like, what are the things that are real today? there are guiding policies, which are basically based on the diagnoses, like, how do you want to address them? And there's actions. And actions are, how are we going to implement this guiding policies? And he talks a lot about actions because he's concerned about this idea of like inert strategy where you have like, like, we're going to deprecate our old product features we don't use, but no one deprecates any of them.

Starting point is 00:21:52 So he's really concerned about this like non-implementation, kind of like useless strategy that doesn't do anything. On engineering, I'm a little bit less worried about that. I think strategy is more interesting on engineering in terms of kind of clarifying how we make future decisions. And so what are a few examples of that? At Uber, we only used our own data centers. We didn't use the cloud. And this has changed since the era I was there. But in the 2014 era, no cloud.

Starting point is 00:22:18 And we had a strict no cloud policy. And this was annoying because we had to indent everything ourselves or run copies of everything ourselves. But it also meant that we were able to spin up in China in like literally three months. And some like surreal stories from that. We couldn't fit our racks into the data centers. So they had to like take the roof off the data center and like lift like the racks in with the crane. They're just like tons of stories. And like all this got down in three months.

Starting point is 00:22:47 And truly, truly phenomenal. And Uber wasn't in China for very long. So in some ways you're like, we did all that just to leave. But but they left with like a nice, a nice stake of D.D. Quiet. and not a bad outcome overall. But I think that strategy, we run everything in data centers. We don't use the cloud, meant we were able to move in and out of different geopolitical constraints. And companies that relied on cloud presence simply can't.

Starting point is 00:23:13 They're fully constrained by where AWS or Google Cloud or Azure have built out. So that's one good example. Another good example at Stripe was this idea of we run a Ruby monolith. And that's like, that's what we did. And that's evolved a bit since then. There's more, there's more Java and the stripe of 2023 than there was in the, the stripe of 2016 or the 2012 or whatnot. But that, that policy really focused the engineers on building innovative features for

Starting point is 00:23:46 our users rather than building kind of different tooling to support different programming languages. And so in both cases, both the Uber policy around like running our own data centers and the strike policy around, you know, Ruby Monoliths. A lot of engineers hated these. But the goal of good strategy is not to appease everyone. The goal of good strategy is to dictate how we invest the limited capacities we have or the limited capabilities we have into the problems we care about. And I think both of them were really effective towards that end. A common theme across all these examples is essentially constraint deciding we will constrain our options to move faster and

Starting point is 00:24:26 focus on the things that really matter. In solving the constraints is, to me, I think the most interesting thing that that strategy really does. And I think when we talk about bad strategy, it usually is because the diagnosis is bad. And it's usually because people are sort of exerting what they want to be true on, on constraints where it's like, hey, we can do all these projects at once. And often that's just not true. But it's hard to convince people that when they're the CEO or they are really.

Starting point is 00:24:56 be committed to believing it. But almost all bad strategies basically come down from a willful disbelief of what are an accurate diagnosis views, which means then your guiding policies are kind of incoherent to begin with. Awesome. I'm excited for that episode of the Richard where we're going to go real deep into strategy, but maybe just as a lasting topic around there, if someone listening wanted to get better at say N strategy specifically or a strategy in general, is there anything you recommend they do is that read these books, is there anything else? If people want to get good at strategy, there's a lot of different types of strategy, right? But here are some things I'd really recommend. First, I think the Richard Drew Melt book, I think good strategy, bad

Starting point is 00:25:37 strategy is probably the right starting point. I think the crux also quite good, but maybe I would read that one second. Great overview of how to think about strategy. I also think thinking in systems, I mentioned that before, related to systems thinking. The big part of strategy, is being able to model the reality so you can improve your diagnosis. And so I think that one's really quite good as well. If you get into the engineering side of things, there's a lot of interesting books here.

Starting point is 00:26:04 There's technology strategy patterns by Evan Hewitt. There's the value flywheel effect by Anderson, McCann, O'Reilly. The Phoenix Project by Kim Burr, Spafford, which is kind of a modern rewrite of the goal by Goldrat. But I think there's like still the missing. The missing canonical book is kind of missing on this one. So I took a stab at strategy and my upcoming book, which is coming out, the engineering executives primer, coming out early next year.

Starting point is 00:26:33 I also took a staff at it and staff engineer my previous book. But I still think there's like a missing book here. So I sort of am like dreaming of writing like an engineering strategy book for my next project, although we'll see, we'll see if that actually comes together. Well, let's actually follow that thread of writing, something I was definitely hoping to chat about you write a lot. You've written two, three, four-ish, you're writing a new book. How many books you've published two books and there's a third one coming out? So I have two books, the first one, the first one was Stripe Press, the second one self-published and the third one of the Riley

Starting point is 00:27:05 coming out in like two months effectively. Okay. And then also many, many block posts for many, many, many years. And I asked a few people what to ask you. And this came up a lot. Gerge Oros and Alex Zhu from Bite Bite Go both asked just this question of just how do you make time to write as much as you do. And then I also, I'll ask this too and just answer it either first or second, just what impact does writing have in your career? Why has it been, why do you keep doing it? I feel really strongly that you can write a lot more if you write what you want to write. And so this is one of the reasons I don't I don't write for financial gain. And I don't write, I don't write very much on like on schedule.

Starting point is 00:27:51 So I've done like a few pieces for, for magazines, et cetera. But I find that actually really draining to be, you have a topic. You have to agree on the topic. If the topic starts like missing like you're what you want to write about, you can't fix it a lot of the time. And you're also like on this deadline. You're like, I'm like, I'm screwing up. I need to ship this. It needs to be done tomorrow.

Starting point is 00:28:10 And I just find that really draining. Where conversely, like when I own this schedule, when I get to like write about, hey, like, I'm writing on something. So I started writing this infrastructure engineering book a couple years ago. And I just like, it just wasn't there. I just couldn't get it to come together. And so I just stopped. And I'm not writing it anymore. Maybe I'll come back to it at some point, but probably not.

Starting point is 00:28:33 To me, the biggest, the biggest strength of writing what you want is you get to write where there's energy. and you don't have to write where there's no energy, which takes you like really, really negative. And this also ties into how I write books, which is that I basically write the entire thing before I start working with the publisher. And if you are, I think, diligent and good at anticipating what their concerns are going to be, you can mostly reuse the content that you're trying to write. This is also easier in the sorts of books I write, I think, harder to do in like a really technical introduction to like my sequel or something,

Starting point is 00:29:11 you can't just like resequence those chapters and pretend it's going to work. Those chapters like built in like a different way than the sort of like business book that I write does. But yeah,

Starting point is 00:29:21 that writing the stuff that's energizing and just giving up on the stuff that's not energizing. That's how I write a lot and how I've been, you know, I've been writing for 16 some years. And the way I keep doing it

Starting point is 00:29:34 is just by writing what's energizing and what I'm thinking about now. And I don't write what, I'm not thinking about. And I don't write for any audience. Just write, write what is interesting to me. And, you know, that means some people don't like it. And that's great. Like, that's, that's totally fine. It's not, it's not really for them. It's, um, for people who want to follow the ride. And that, that's where I focus. This episode is brought to you by Vantta, helping you streamline

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Starting point is 00:30:41 For a limited time, Lenny's podcast listeners get $1,000 off Vanta. go to vanta.com slash Lenny. That's V-A-N-T-A.com slash Lenny to learn more and to claim your discounts. Get started today. Okay, there's a lot more I want to dig into here. How many posts have you written do you think over the 16 years? I would guess about 1,000 like that. That would roughly be my assumption.

Starting point is 00:31:04 I think there are a few years. years where I wrote, you know, hundreds of posts. And so if you do that, like three years, it's not that hard to get to a thousand from there. That's incredible, especially because you've had intense jobs for all of those years or most of those years, very high pressure, fast-growing hypergrowth companies. Somehow you find time to work. So first, let me just double click slash co-sign your advice here around paying attention to what gives you energy and working on things that you're actually curious about. This is exactly the advice of gift to people.

Starting point is 00:31:38 A lot of people start this like full-time writer, greater life. And they're like, what do people want? What do people want me to write about? What's popular? What's going to go viral? And that's easy to do like a couple of times. But then you end up creating this job for yourself that you don't want. I don't be spending all your days writing about AI if you're not that excited by

Starting point is 00:31:57 AI or whatever's hot these days. And I find that what I find is important is almost like 80, 90% of what you write. It has to be stuff. you're excited about and then maybe there's a bit of, here's what I know people really want. Here's what I know is going to do really well. Because otherwise you just burn out. You create a job for yourself that you don't want. Why would you do that?

Starting point is 00:32:13 Yeah, I just 100% agree with that. I think the other thing is it, like, everyone converges on the same thing that they think people want. So it's like, it's crypto two years ago. It's like AI right now or it's like counter AI. Like AI is going to like ruin the world. It's just like it's hard to say something very novel because one, like everyone's trying to like say something about it. too, like, it's almost certainly not what you're that knowledgeable about. Where if you just stick in your lane, I think the biggest risk to writers is quitting,

Starting point is 00:32:43 a little bit like the 40-year career idea, the biggest risk to content creation of any sort is quitting soon because you get burned out. The biggest risk is not that you grow too slow initially. There's always a sense that, like, you've missed the wave. Like, it's too late to join substack. There's already, the top writers are already there. You'll never be a top writer. It's too late to podcast.

Starting point is 00:33:04 There's too many podcasts. You'll never make it. It's too late to join medium. You'll never make it. There's too many medium writers. But it's just like not true. If you just like keep writing good stuff, you'll build an audience over time. And you can take that audience from platform to platform. What really matters is finding something can actually keep doing for the next decade. That's way harder than doing it for one year. We have the same exact advice on this. This is exactly the things I tell everyone. When I joined Substack, I thought it was too late. I was like, man, it's over.

Starting point is 00:33:33 And when I started this podcast, like, oh, man, there's a billion podcasts. How's there ever going to work? So I so agree. And I also so agree on the fact that this whole thing is such a, it's a long game. There's a lot of people. I always say it's easy to start at ease letter. Hard to keep it up. Nobody actually keeps it up.

Starting point is 00:33:50 There's people are going to come and go. The thing that really separates success from not, from failure is just people that can keep at it. And there's not like an end game to this, right? It's an infinite game. And it's about being able to sustain that over one. term. And you're not competing with other content creators. If you think of it as an infinite game, right? Like you're all, you're all working together. You can all like help each other grow. There's no like maximum number of product writers or thinkers who can like be doing something. Like you're

Starting point is 00:34:19 not less successful because Shreya exists or something like that. Like there's no there's no competition there. It's like a false, false dichotony. Yeah, I totally agree with that. Unless there's so many. and then all of a sudden, what happens is the bar just gets higher, which is good because then people get better stuff, and that's fine. And that's happening anyway. Just the bar continues to increase because there's more and more content out there. And to me, that's like the ultimate thing you've got to get right is just the bar. You just got to be at a high bar for anyone to care about anything you're writing about. And to your point, to do that well, you have to actually be excited about writing about it and have background and have something to contribute the way.

Starting point is 00:34:59 I'm just ranting here, but the way I think about this is you need to add something new to the conversation for anyone to pay attention because there's so much fluffy superficial stuff and to get anyone to care is you need to say something new that no one's heard before or share new information. They haven't seen anywhere else. I totally agree. I just, I could keep ranting about this for the entire time. I don't want to derail on this, but I totally agree. We'll control ourselves. So then getting very tactical, I think this is what a lot of people are always wondering, how do you make time? What's your work? How do you make time for writing so that you keep at it with knowing you have an intense full-time job? I used to do things differently before I had a kid. So I have a three and a half year old. And just your timing, your life just like really shifts a lot once you have kids. But the biggest thing that I found is finding things to write about that also directly relate to what I'm working on. And this is where I can do something that helps me at work and helps me write at the same time.

Starting point is 00:35:59 Because I think it's incredibly hard to find time to write about stuff that has nothing to do with your work. And it just distracts you from your work. Because these, you know, particularly if you're in like a senior role, like these can be like pretty demanding jobs. But they're not demanding because you're responding to an urgent DM on Slack like every, every five or six minutes. They're demanding because you need to make some really difficult decisions really well. And I think writing about like related topics is a great way to refine your thinking. and improve your performance. It's not like a conflict or you do one,

Starting point is 00:36:34 you either write or you do your job. Well, I think you can find a way to align. And so a lot of podcasts folks do interviews with folks who are related to what they're thinking about at work. And that's a great way for them to, like, learn, to build their network, to refine their thinking, to test their thinking against experts in the field. It's not like in conflict with the work.

Starting point is 00:36:52 It's an alignment. So that's like one piece. But, yeah, I've played around with my schedule a lot. Before I had a kid, often like Saturdays would be like the writing day. And so like, you know, morning and early afternoon would just be like writing. I can't do that anymore. So now I mostly write at night, which is tricky from an energy management perspective. But the biggest thing I would say is just if you're actually excited about something,

Starting point is 00:37:19 you will find time and energy for it. If you're not excited about it and it's 9 p.m., you're just going to get to sleep. And so, like, I really think that this is where you have to schedule a little bit deliberately, but the first thing we talked about, about energy management, like, they really come together. When your schedule gets tight, if you're not energized, you just won't get it done. And why would you? Like, just doesn't make sense. Just to close out this thread, for someone that wants to do more writing, knows that it's going to be valuable,

Starting point is 00:37:46 but just hasn't any one tip that you would leave them with to get on this train. It depends why people want to write. I tell people, if you just want to, like, write something that, is going to help advance your career. You should really focus on writing two or three really good things and spend a ton of time drafting, revising, getting feedback.

Starting point is 00:38:05 You just focus on making like one great artifact or two or three great artifacts. You don't need to create like a long-running blog where you publish every week. Like there's really no need to like do that if your goal is just like create some artifacts that show you're like a deep thinker that kind of like help position you in the industry.

Starting point is 00:38:21 Don't start a newsletter. If you just want to like advance yourself in the industry a little bit, just write like two or three really good. things. So that's the first thing I'd say. But if your goal is to write like a lot consistently over time, my biggest advice would be just like just publish. And so I, there's a lot of people out there with like stuff that, you know, hundreds of drafts. And they've not published anything. And my thing is like I publish almost everything I write. If there's something that I'm not going to publish, I like don't start writing it because I just, I have like a, you know,

Starting point is 00:38:52 a quick check in my head. Like, is this something I can write and publish? And if my answer is like, no, I just don't even start. And my accuracy has gotten higher, like, over time as I've written more. But I published pretty much everything I write. That's why some of it's, like, not that good. And like, and that's okay. Like, I'm like, again, like, I want to write. I want to get these ideas out.

Starting point is 00:39:11 I want to show, like, what I'm focused on. And my evolution as, like, a thinker, trying to learn how, like, operating these different roles and these different companies. I'm not writing trying to create, like, a polished, perfect thing. And also, like, I'm not writing to maximize their reader's experience of reading it. And some people don't like that. And I think that's like a totally reasonable thing not to like, but I think what I can bring you is like my experience as an operator who's like actively learning and thinking through. I think that's really valuable to other operators in

Starting point is 00:39:38 the industry in terms of like giving you the perfect writing. Like I try to do that closer to that. I don't know if I ever hit perfect writing. But that's where my books are. Like the books are taking like a collection of thoughts over like a couple of years, cleaning them up a little bit, packaging them. They're way higher quality than my typical writing. But yeah, I would just publish. Publish a lot. Don't worry about the quality. Sometimes people will send you like silly feedback.

Starting point is 00:40:02 And like, I just don't respond to that stuff anymore. And like, you know, you never know why people send you something like that. I think trying to debug people you don't know is like a bad use of time. It's just kind of like, thank you. Move on to the next. Like don't even, don't even spend time worrying about it. And like that term debug people. I think people way overestimate how much anyone cares about what you put out.

Starting point is 00:40:24 Most people are going to look at. for three seconds and be like, eh, that's the worst case scenario, basically. It's just like, I don't care about this. Not like, oh, Will is such a fool. What a dumb thing to say. Right. No one's, no one has time for that. And if they do, like, that's like, that's a them problem, right? Like, it's like there are, the internet's a big place with a lot of people. And there are people who are going to be having like a really bad day when they encounter something you do. And they're going to be, they're going to channel that anger at you or that frustration at you. But that's like, that's not about you. That's just like, you happen to be there.

Starting point is 00:40:56 when they engage with it. You don't have to take that on. That's like that's not your situation. Like it's okay. Also, when you're just starting out, that's going to be the least stressful time to write because nobody knows it or sees it. So that's when it's like take all the crazy shot,

Starting point is 00:41:11 just do stuff. Like it only gets more stressful as you build a audience over time. Yeah, absolutely, absolutely true. Okay. Shifting topics. There's a lot of product managers to listen to this. Something PMs often wonders how to have better relationships

Starting point is 00:41:25 with their engineers, their engine managers. What advice could you give to product managers to build more productive, happy relationships with their engineers and engine managers? So the core problem in most of the EMPM pairs that I've worked on, there's two core problems. So one, sometimes the incentives are misaligned. And that's hard to navigate.

Starting point is 00:41:49 But if you can just be honest with each other and understand the incentives, sometimes you can find a compromise. But sometimes the EMs and PMs will be misaligned because just their incentives are so far apart that there's no way to get to the bottom of it. And so this might be like timeline related or saying yes to sales related. Or the engineers like, hey, we definitely can't say yes to that. And the PMs are like, actually like we're going to say yes to it because it's really important for me getting promoted or something like that. And is it ever this simple?

Starting point is 00:42:20 It's really never that simple. people create like simplistic narratives to like find villains that they work with. There are no villains in the workplace. They're just people with like complex incentives that are doing complex things. But sometimes like I talk to EMs who think like, oh, the product manager is just saying yes, because they want to get promoted because the salesperson will review their promotion or something. The reality is never as simple.

Starting point is 00:42:42 The reality is like the business needs to sell stuff to remain functioning. You can't just like say no and like have the business succeed. That doesn't work either. So one, understanding the incentives. The other piece, though, and I think this is the more common case, or just that the EM or the PM just don't understand the other person's needs, and they start arguing before understanding. And so my biggest advice to both the EMs and the PMs,

Starting point is 00:43:07 it's before you try to solve the conflict. It's like pushing to ship this feature, pushing to change the approach, just make sure you actually understand what they care about. There is this idea that you have to make tradeoffs, and that there are tons of hard tradeoffs to be made in kind of the field. But my experience is if you really deeply understand what everyone wants, there's usually like a compromise solution that gives everyone exactly what they want that doesn't take more time,

Starting point is 00:43:36 just have to be willing to dig deeper into it and understand the true needs for each party, which is often not what they're saying, by the way, which is part of the confusion. On the incentives piece, is there anything you've seen work? to fix that problem. Because if PM performance reviews are based on impact engineering, performance reviews are based on interesting projects or uptime, do you just work to change those latter definitions? What actually can help that situation? So my biggest thing has been trying to force this idea that like EMP pairs are pairs and they generally have the same performance rating. And there's exceptions here, right?

Starting point is 00:44:16 Like it could be the EM is clearly not performance. And then it's not the PM's fault. The EM is like, you know, can't show up to work. Like the team, like, doesn't respect them. You know, sometimes there's a clear non-performance. But generally, like, hard situations are not situations or one person's, like, obviously terrible. Those are easy to diagnose.

Starting point is 00:44:39 Those are the easy ones. But in cases where there's two folks who seem to be, like, pretty good, but just like the overall execution is not working out, I think this idea that, like, same perp, same perf rating for both, drives like a level of one pain, but the right sort of perspective. And also, you know, something that I think Carter has experimented a little bit with over time.

Starting point is 00:45:01 Henry, our CEO, has a blog post about trifectas in doing that, but not just for EM and for PM, but also for the business leadership as well, where you all get graded the same score based on your ability to evaluate and solve for the entire set of constraints, not just your functional constraints. Wow. that's so interesting. So your recommendation, something you were doing, it sounds like, is the

Starting point is 00:45:26 engineer manager and the PM get the same performance review rating. And so they're discussed in the same, in the same music. So, yeah, the head of the, our chief product officer, Bruce Ali and I, like, spend a fair amount of time, like, calibrating together and making sure, like, again, there's cases where there's, like, an exception, right? Because there's, like, clear, clear issues happening for someone. But on average, that is what's happening. And I think people know that's what's happening because we told them that. And I think that that's pretty powerful. That is so interesting. I've never heard of that approach. That is definitely solving that problem of EMPM. Yeah, the incentives, the incentives are shared now, which doesn't, which isn't perfect. It's so hard to balance them.

Starting point is 00:46:08 They can still like make the wrong tradeoffs, but at least they understand the incentives are shared, which I think is a pretty powerful idea. That is really interesting. And I imagine some companies might even want include design managers in that take another step. You know, the role and the primacy of different functions and different companies like varies so much that it's hard to have like a one size. Like you could also imagine where you want like a staff engineer and that or not. And so I think very company specific. But but yeah, I think design could absolutely be involved, particularly for like a design like company like an Airbnb or something like that. Wow. So interesting. Maybe just as a final thought there, if a PM is having challenges with their,

Starting point is 00:46:49 EM, what do you think PMs maybe don't think, don't realize their engineering managers are finding important or maybe are stressed about that they're just like, oh, wow, I never thought about that. I think one of the biggest challenges I've historically seen, pretty in the last, like, decade is that the idea that engineering managers have the job of giving their team interesting work. And I think that that can put, you know, you often see this in like growth teams where the growth teams like, hey, we just need to do a ton of experiments. And the engineers, like, I want to build something brand new. And the engine managers in between those, try to figure out, like, need to ship 50 experiments that are pretty boring and they want to do something brand new.

Starting point is 00:47:28 Like, I don't know how to do it's all this. And so it's a tricky, a tricky moment. And good, good EMs kind of like find the way to balance. But that's like the biggest source of kind of ongoing friction where the EMs have been told by their teams they need to do something, that the PMs just have no visibility into. And it makes the EM seem like totally unreliable partners because they're trying to solve these, a little bit of these invisible constraints. And that's where I think pushing further to understand like,

Starting point is 00:47:56 hey, like you keep prioritizing this like rewrite into a new programming language. To me, that seems like completely idiotic thing to be doing. Like what's going on? And then once you'll understand, you might not agree with them, but at least you can have an honest conversation about how to navigate those constraints versus just like, man, you won't believe what my EM partner did today. Like this bozo did, like, blah, blah, blah.

Starting point is 00:48:19 And having sort of this like victim villain kind of mindset about your peers. An adjacent topic that I wanted to spend some time on is measuring engineering, velocity, productivity. I think it's probably one of the most common and also maybe the most annoying questions angiators get is just how do I know if my engineers are moving as quickly as they can? How do we help them move faster? What advice you give to engineers for an inch teams just for how to measure productivity? well. This is a question that's coming up even more, right, in a moment when we're kind of reducing a lot of the sides of teams, when the industry, when the venture capitalists that are

Starting point is 00:48:52 on the board for these like venture back companies are pushing on the efficiency of engineering. Engineers are trying to figure out, like, how do we like, how do we represent this? How do we prove that we're appropriately productive for the amount of headcount and funding that we have as an organization? And man, that's hard. And so the first way that people kind of focus on trying to answer these questions is just like benchmarking by like the amount of funding that you have. And that's pretty straightforward to do. It's a mechanical exercise. You look at like you get a data set from your your venture capital funds or whatnot. And you figure out like, okay, how much should we be spending an R&D, how much should we be spending

Starting point is 00:49:30 engineering, how much should we be sending on, you know, infrastructure engineering in R&D. And you can like benchmark this all out and figure out what the correct numbers are there. the problem is this is like a very mechanical and not very like insightful driven way. It will get you a defensible answer. It's like the old like no one gets fired for buying IBM, which definitely hasn't been true in my career ever. But you know, this idea that if you just have the right benchmarks, like DCs won't judge you for standing too much in engineering. But it doesn't actually help you get to the right place. It just helps you get your board to be less angry at you.

Starting point is 00:50:07 which is useful because it's hard to do good work when your board is angry at you, but it's not useful in the sense that doesn't actually help you run your organization effectively. So then there's like the much harder, immediate problem of how do you actually know if your R&D team or engineering team is like effective? And what I find is a couple of things. First, if you're a good leader and you talk the engineers, they will tell you. The engineers know if their teams are effective or not. And if they're not, they'll also tell you why not.

Starting point is 00:50:40 And their diagnosis can be wrong. But there's like a crumb you can start like picking up and you can trace the crumbs to figure out what's wrong. Often you'll have more experience to analyze the complaints to figure out what kind of the contributing causes are to them. But yeah, if you just go talk to the team on an ongoing basis, you will know if they're effective or not. and you can go work to solve those specific problems. But again, you can't tell like you're bored, oh, like, it's fine. I talk to the teams that they're good. My intuition's spot on.

Starting point is 00:51:14 Because how do they know if your intuition's good or not, right? They're dealing with like a huge portfolio and some of their leaders they're talking to are good. And some of them have terrible intuition. How do they actually assess? I think it's tricky. And what I've tried to do is basically two things. one aligning engineering evaluation to the business and product goals so i want us to be wholly

Starting point is 00:51:35 accountable with the product goals not a um well we did a good job products like screwing up over there obviously a lot of companies find comfort doing that but like really we're here to support the product to support our customers in doing like something interesting we're not here to like build novel systems unless it supports the customer and the product. And so first try to align heavily there. Second, I think just showing the roadmap of the valuable things we've done in the last six months is really powerful.

Starting point is 00:52:08 Because I think sometimes people are like, I don't have anything to put there. And you're like, yeah, that's a real issue. Or if you have the kind of stuff to put there, that's great. And I really find that if you just commit, show the number of meaningful,

Starting point is 00:52:21 meady things that have impact that you're doing. and you can explain the impact. People will kind of step back and give you space. If you can't populate that list, people will have concerns. And rightly so, like, they should be concerned about that. Is there any metrics or tools or anything like that that you find useful too? Because these are all awesome piece of advice. But I imagine everyone's always just like, give us this number we're tracking,

Starting point is 00:52:46 give us this dashboard, see what engineers are doing. So one of the most influential books in the last decade in kind of software engineering, leadership and kind of infrastructure is accelerate by Nicole Horsgren, Gene Kim, and I believe there's a third author on that one, but I'm forgetting right now. Really phenomenal book, and it kind of comes up with these like four metrics. It comes up with like lead time. It comes up with like incident remediation time. It comes up with failure rate and the fourth one of some sort. And there's like at least 50 different startups out there that are selling you dashboards that the kind of instrument these pieces of data, and they want you to just evaluate your team on them.

Starting point is 00:53:30 The challenge is these are really good diagnosis metrics. And so, hey, our deployments are slow. Why is that? How do we speed them up? But your deployments being slow doesn't make you a good company or a bad company. It just tells you where you should focus on improving. It doesn't actually change how you are. And similarly, if your lead time is quick or slow, tells you where you should invest.

Starting point is 00:53:53 or it doesn't actually tell you if you should fire your engineers or something like that. That's like way more detail-specific. So people do like to see these metrics, just like they see uptime metrics. A lot of engineers report on like sprint points or stuff like that to their board,

Starting point is 00:54:08 which are just like totally, totally fake thing to be like reporting on. But people get some comfort on it. So my biggest thing here is when people measure things, this isn't an engineering only problem. But when people measure, They take on the perspective and an expert, and they can tell you why not to measure everything. You can tell you why every measure is wrong or inaccurate, and they rule everything out.

Starting point is 00:54:32 So they measure nothing. And they go to someone who's not an expert and they're like, well, actually, there's no accurate measure to give. They're not an expert is like, you don't know what you're doing. And so you just have to get comfortable measuring something that's not perfect, but you can actually measure in reporting on it. And then the measure that's imperfect. As people ask questions, that's like an opportunity to educate. people on like why the measure isn't perfect. What are some things that misses or kind of elides from the conversation? Metrics are about educating the people consuming the metrics,

Starting point is 00:55:01 about the reality of the rich data underneath. They're not about this perfect data set that shows everything. Starting with something mediocre and the Dora metrics are really helpful for diagnosis. But if you have to, they can also be a good enough starting place to start reporting to your board or your CEO or to the other executives. And then you're like, oh, there's all these problems with them. Yes, there are all those problems with them, but that's this place you start. And you educate people up from there to help them understand the nuances. And that's how they become more sophisticated understanding engineering, not by refusing to give them anything they can possibly measure. Awesome. I'm glad that was your answer because we had Nicole on the

Starting point is 00:55:39 podcast and she talked through Dora and all the frameworks that she recommends. And she even actually shared some benchmarks that she points people to that give you some sense of just like, are you in a good place roughly or not? So we'll point people to that up. set it to dig deeper. Awesome. I'm glad that you're a fan. Okay, just a couple more questions before we get to our very exciting lightning round. One is around values, company values, org values. You have some really good advice for people for how to think about coming up with values. What do you share? What do you recommend to people that are trying to figure out what values they should define for their org in their company? I mean, values are really interesting,

Starting point is 00:56:15 right? And different companies talk about values in different ways. I once worked at a company where the execs went to visit the Facebook campus. They saw the values written up on the wall, and they took the Facebook values and wrote them up in our walls. And that didn't do a whole lot. It may be undermined people's confidence in the critical thinking of the executive team that just took these written up on Facebook walls and replicated it. But I think those values did work well for Facebook, and those values were meaningful for Facebook. And so it's, first, you can't do is just like steal values. I value cargo cullting.

Starting point is 00:56:49 Man, users first. Great Amazon value, right? A lot of companies aren't users first. And that's okay. But what's not okay is when you put, hey, we're users first, and then you actually show like the decisions you're making. And you clearly aren't users first. And so one of the things I think about is just like honesty.

Starting point is 00:57:08 And so good values have to be honest. And so any value can be honest or just, there's no universally honest values, right? Like you can say something like we're thrifty or we can say something like we spend as much as we need to get the best value. Those are totally different and good companies are run both ways. So the first rule I think about a lot is honesty. You actually do what you claim you do in the value. The second one is like applicability. Like you have to have values that you can actually figure out how to apply to your work.

Starting point is 00:57:38 And so one of stripes values was no longer a value, I believe, but it's like optimized globally. And so optimize globally is a really interesting problem because sometimes you'll have something you want to do or interesting value. Sometimes you want to do something and you're like, hey, I want to introduce a new programming language because that's better for my team. We're like for the organization overall, this is actually much worse for an organization. So I'm not going to do it. Uber didn't have this as a written value. But implicitly, Uber's value was do what's good for your team and ignore everyone else because that will slow us down. And so the two different companies had opposite values, but they're both very applicable.

Starting point is 00:58:17 It's like how should we navigate decisions? Should I optimize for my team or for the organization? And then so those are applicable to real problems and they were honest. Where Uber was just like, don't worry about other people. Like make it work for your team. And that's how they move so fast because they just didn't worry. Wasn't there value of toe stepping, encouraging toe stepping, something like that? Let builders build, toe stepping.

Starting point is 00:58:38 There were a number of values that could be interpreted in different ways. and sometimes they got weaponized in various ways as all values do. But these are both interesting in different ways. And so number one is honest and two is applicable. Three is I think the last thing for a good value is this idea of reversibility. So there's some values that aren't actually usable. And so here's a good example on we build good software. Like, okay, but like why would you ever not build good software?

Starting point is 00:59:11 Like that doesn't that doesn't make sense or um we solve customer problems that matter like good what who would ever say they're what what company doesn't think they're solving customer problems that that matter and so there's certain like values that just you can't apply and so like I think of these as like identity values like these are really just you describing like who you want to be like we care about our customers like great but like who would say they don't care about that. They're just like there's certain values that I think of it just like identity values. And they're not like they're not wrong to have identity values. You just aren't very useful. You can't actually use them for anything. And so I just always push people not to spend too much time on these.

Starting point is 00:59:57 Because they feel good when you're an executive team kind of debating like what are these identity values? It's like we're kind to other people. Or like, you know, sure, that that sounds good. Like we're a family. Like sure that that sounds good. That one I guess is. it's a little bit reversible. Because, you know, there's Netflix, which is like we're a team, like a sports team, we're not a family. And so a little bit reversible, but not perfectly. But yeah, but these are the three that I found really useful for any value.

Starting point is 01:00:23 Like, is it honest? Is it applicable? And can you reverse it? And if not, it's probably actually not helping the team make decisions. These are great. It reminds me a lot of, I was there during Airbnb's period of coming up with values. Something that I maybe add and maybe fits into one of these buckets is it needs to be clear who doesn't.

Starting point is 01:00:40 Like there needs to be a group that doesn't quite fit Because if everyone fits, you're not doing anything useful What's the point? Which feels weird to say like, right? Why would not everyone fit in our big group of awesome company? But it's clear like who is not a good fit? Who doesn't belong? It's kind of like a cult a little bit.

Starting point is 01:00:56 Like who's not in our cult? Who doesn't belong? But I agree. Like if it doesn't apply to anyone, then like why bother saying it? It's like it doesn't mean anything. And you could say it's actually a hiring filter where there are people who you've explicitly chosen not to hire because this wouldn't apply to them,

Starting point is 01:01:13 then I think it's useful because it helps you actually figure out who they bring in. But if it doesn't apply to anyone you're hiring or anyone that you have in the company, then it just isn't worth having because you already have too many values. You're already trying to get rid of values

Starting point is 01:01:25 who you have like 17, you need to get down to like four where people can remember them. So if it doesn't apply to anyone, like why bother having it at all? Yeah, like integrity is a common one. Integrity. Like everyone has it.

Starting point is 01:01:35 Nobody wouldn't want integrity. Like what is unique? Yeah. Or the non-integrity. company. Like, we were the company that, like, thinks integrity is bad. Like, that's, like, not a real thing. The other one I'll add to is honest.

Starting point is 01:01:48 So at Airbnb, we had six values initially. One of them was simplify. And a year or two later, everyone just realized we're not actually good at this. We want to simplify, but we're not great at this skill. And values should describe who you are, not who you want to be and aspire to be. So they cut two values, including that one. And there's like, let's just do these four, because this is actually who we are. Let's be honest with ourselves.

Starting point is 01:02:11 Okay. Final question. I wanted to visit Failure Corner, something that I've added recently to this podcast or people share a story of failure. And you have this amazing post about your experience with Dig and the rewrite that you all went through. I think it was the version 4 of Dig. Can you just tell that story and what happened and how much of a mess it ended up being? Yeah, Big V4 is, I mean, still something I have a lot of like fond memories. for. There's one picture that I, that I've kept, and there's a picture of a lot of the

Starting point is 01:02:45 engineers around this table, the middle of this giant office, and they're like serving like sushi. We had like waiters, caterers come in that day. They're serving like sushi. They have like, you know, plates with like champagne flutes on it. There was a full bar. And we're all around this table because the site's not up. And so dig before, essentially what Kevin Rose or the board some combination there realized is that dig was losing was losing to the social networks and that this idea of kind of aggregated news was going to be outcompeted by the twitter's the facebook's etc if we didn't find a way to move to have a social component for it even outcompeted by reddit long term was kind of the fear although at the time that that was far from obvious and so

Starting point is 01:03:32 we needed to move to support kind of social functionality and the previous version we simply couldn't get it to work. And so the decision that was done, like, two and a half years before I joined, and this shipped about six months after I joined, was they need to do a complete rewrite in order to get there. This is a decision that never works out for anyone. And so I think, like, as somewhat more experience, I could have predicted this wasn't going to work out. But I was earlier in my career, my PM counterpart at Yahoo, Dash Gopinoss. He went to the office. He went to Yahoo and he's like, come to dig. Worst case, you'll make a couple hundred thousand in a year.

Starting point is 01:04:13 Worst case, probably a really great outcome. Anyway, that's not what happened. The worst case was a little bit optimistic. But so we go and, you know, the CEO got fired two days before I joined. So the current CEO left and then Kevin Rose came back for about six, six months, something like that. And we're just on this death march trying to get this thing out. And so we we push really hard. This is before the cloud for the most part.

Starting point is 01:04:42 So we wiped pretty much all of our existing servers to re-image them to the new software. We try to bring the site up and just keeps crashing. And so it basically takes us a month to get it fully functional again. And so that day sitting around that table with like champagne and sushi, that's just like day one. And by, you know, 30 days in, most people, aren't even trying to get the site back up anymore. There's maybe like five of us who are still trying.

Starting point is 01:05:11 And, and, you know, we did. And I think, like, that was, like, a really powerful moment for me. And I think in the first two days, like myself and Rich Schumacher, like, one of the other engineers, we had to write, like, a caching system from scratch, which got us, like, half the way up. Really a terrible way to do soft run a side note. Like, I'm not recommending this to anyone. This was, like, a series of anti-patterns cludged into, like, a launch.

Starting point is 01:05:36 But we got it personally up, but we had to restart it every 12 months. Basically every server, sorry, every 12 hours, every server had to be restarted even with the caching mechanism. And then, you know, about three weeks after that, like, I finally figured out what the core bug was that was bringing us down every 12 hours. And it was this incredibly simple issue that had just been hard to debug, basically related to the way that Python initiates variables used as default parameters. and something like super, super silly. And we just had someone who hadn't written Python before who was working on the API code, so he didn't realize this gotcha,

Starting point is 01:06:15 and no one else caught it when it was reviewed. And it just took a long time to debug because it was like such a non-obvious, it didn't break anything. It was just doing a lot of extra load on the servers. And we finally figured it out. And it was just really remarkable experience pulling through. And you know what?

Starting point is 01:06:34 the company still went to zero. And so we had this dad launch. I think we did this heroic, heroic stretch to get it working. A couple weeks after that, like a new CEO came in, did a round of layoffs. This is back, I think, like 20, 2012. The team, nine months after I started, was down to like 30 people from about 100. And it just went, it went downhill from there, from a business perspective. But we launched a lot of functionality.

Starting point is 01:07:03 has really learned just a tremendous amount. And it kind of shaped like what I think about in terms of earlier career, getting learning and going into a company that is maybe having a rough time. Like I became a manager like two and a half years into my career, basically running the entire engineering team there because everyone who had a lick of sense quit or got laid off. And it was just like complete idiot me like trying to like be the manager for the engineering Oregon wasn't qualified and no one would have given me that job but I was the only one dumb

Starting point is 01:07:36 enough to take it at that point and and I learned so much and I really like that that's like the kernel that like turned into like my entire career was that opportunity even though at the time it was that it was pretty pretty grim that's an amazing story I feel like a lot of these experiences were in the moment it's just like what is going on this is so bad and hard end up being the most interesting in looking back into being the most biggest teaching experiences the ones you like bond over with people you work with, like Apple always comes to mind where it's just like Steve Jobs is jove people like crazy

Starting point is 01:08:08 and then they look back and that was the best moment in my career. You would never like voluntarily take on a lot of these really challenging things. But sometimes like when they show up, like you're with a group of people, you really respect you love working with and you like want to overcome together. And that's like, that's really powerful experience.

Starting point is 01:08:25 Even if Uber China was similar where like if someone had been like, hey, do you want to go work on this Uber China migration? would have been like absolutely not. But like no one asked. They're just like, get this done. And so we did. And I think these things are like pretty remarkable. And just to be clear, so Dig was down for a month basically during this period. So it basically didn't work properly for much of the month.

Starting point is 01:08:47 It was, it was like read only was back up in like about three days. But the vast majority of the actual user functionality just wasn't working properly for pretty much an entire month. And it was not not that good. that I mean, like not great. But, you know, that wasn't the biggest problem who dig had at that point. But it was one of the biggest problems that had at that point. And it wasn't a real sign of things likely to go well for us.

Starting point is 01:09:18 But, you know, like I said, you learn from those. And I'm really proud that like we and the team like got it working, got it, got it running. Even if like ultimately like we still went to zero and like ran out of money and kind of sold for parts. Do you think Dig could have made it? There was a world where Dig would have been a hugely successful business, or do you think it was just way too late and it was the wrong product?

Starting point is 01:09:39 The thing that really killed Dig is the change. It was an SEO driven. So monetization was from ads. Dig was the, well, many companies, including Dig, claimed that it was the first in kind of stream, in fee, like advertising company, like where Twitter has, like, ads within, like, the tweets or Facebook does. But Dig did that before Facebook or Twitter. really innovated kind of ad format.

Starting point is 01:10:04 But the vast majority of our monetization was on these, like, we called them Kermalink Pages, which is the page where you had then, like, article we crawled. And the vast majority traffic for that was driven by Google Search. And so there was an SEO change, which really is like the thing that started creating the urgency for us to launch this migration. SDO change, traffic started going down, monetization was driven by that. And so we were already on fire by the time we tried to launch this.

Starting point is 01:10:29 But I do think that I still want something like what Dig was trying to become today. Social news based on what my friends are actually reading and liking, merged with like a global index of kind of similar users who are interested in similar topics. It's still a product that I think Google Reader has some kind of similar components to it. These are both interesting products solving interesting problems that have not, for whatever reason, then successful as businesses. And I do think there's a gap there still, but there's a lot of people trying on success. to fill it. And there must be a reason why people struggle to fill it despite so many people

Starting point is 01:11:04 trying. Awesome. Will, is there anything else you want to share or leave people with before we get to our very fast lightning round? Because I know you have to run in about five minutes. I think we've covered a lot of it. New book coming out, new book coming out in February, engineering executives, Primer O'Reilly, but that's probably it. Awesome. Where do people find that? I know it's on O'Reilly. You can look at a preview of it even today, right? Yeah. O'Reilly can see that the early copy. You can order it on Amazon as well, but it won't be shipping until February. Okay. And then just to be clear, who's this for? It's for engineering executives by the sound of it. It's for engineering executives, but more so like anyone who wants to be one,

Starting point is 01:11:46 anyone who's trying to figure out how to work with the engineering executive. So I think if you are struggling to understand why your CTO keeps doing boneheaded things, or if you want to side manage them, you're the head of product and you can't get the CTO to stop complaining about that engineers need more interesting projects to work on, this might be useful for you too. Amazing. Okay. Ready for the lightning round? Let's do it. What are two or three books you recommended most to other people? So I talked about thinking systems of primer. I talked about good strategy, bad strategy, but I'll give you a third one, which is don't think of an elephant by George Lakoff. It's a really interesting book about framing things and conversations that has

Starting point is 01:12:22 really changed how I communicate. Amazing. Favorite recent movie or TV show you really enjoyed? I don't watch much TV or many movies anymore, but something I do still watch is Top Chef with my wife. She's a Top Chef super fan. And there's something just like very relaxing from like these formulaic structured shows where we kind of know what's going to happen. There's no real consequences that matter too much. And just kind of escaping from real life through these like formulas can be pretty pretty peaceful. Never one's ever mentioned Top Shop before. So that's fun.

Starting point is 01:12:53 Do you have a favorite interview question that you like to ask candidates that you're interviewing for? job. A lot of my interviews now are trying to help people decide if they actually want to join a company. And so my favorite question I ask now is like, hey, we've really loved you. You're going to come through. I think you're going to get a lot of offers from other companies to you. I bet you'll have three or four really compelling offers because you're a fantastic candidate. How are you going to figure out really specifically which of those options are right for you? And I think it forces people to tell you what they want. And then you tell them why you have that more than anyone else. And then you can actually pitch them on what matters versus pitching on things that don't.

Starting point is 01:13:31 Love that. Do you have a favorite life motto that you often come back to, share with friends, find useful either in work or in life? No, no mottos. I can think of like two things I thought about a lot. At Uber, something I talk to people a lot because it was a challenging time for much of it was there's no way around just through. And that was like, hey, we're not going to dodge around this. We're going to gut through it and we're going to get to the other side. and then we're going to be there.

Starting point is 01:13:58 What I think about a lot more now is, will anyone remember what we decided in six months? I think people stress out about a lot of decisions, but I increasingly believe, like, most decisions people stress that out about, just like aren't that important. So I'm like, well, anyone care in six months what we did here? And the answer is no, like, just do something reasonable and, like, let's move on the next more important thing.

Starting point is 01:14:17 I love that. You've done a lot of writing. Is there a piece that you've written that you feel like is underappreciated that no one really totally got and hasn't spread. And you're like, oh, I'm so proud of that one. Maybe the piece I'm most proud of from last year was like hard to work with. So hard to work with is basically, I see a lot of people who are incredibly talented, but they try to hold their peers to a high standard.

Starting point is 01:14:43 And then they're viewed as like combative or difficult to work with. And this one comes from, you know, a core struggle of my early career where I kept trying, I thought I was holding people accountable. People were just like, you suck to work with. And I was like, but I'm just trying to have a high standard. Isn't that what we want? And every, like, talk about like honest values. Every company is like, we have high standards.

Starting point is 01:15:05 And you're like, well, let's do it. And then they're like, we don't have high standards here. Like you suck. So that one, that one's one that I really is so transformational to me. And I think it really hits some people hard. Because I think a lot of people really go their entire career without figuring this one out. And there's some of the most talented, hardest working people you'll ever work with and can't quite land this one idea that's holding them back. And they care so much

Starting point is 01:15:29 and they are often despised because they care so much. And I think this is one that I, you know, hoped more people would, hope more people will read over times. And there's a really important lesson from you there. Well, we will link to it in the show notes and help more people discover it. Two last questions. Where can folks finding online if they want to reach out and maybe follow up on questions? And how can listeners be useful to you? So find me online, lathean.com. com, L-E-T-H-A-I-N-com. All my writing, my books, everything linked, linked there. And the biggest thing that I'm thinking about right now is just strategy.

Starting point is 01:16:03 So really curious for folks who are thinking about strategy, think they've done product, business, or engineering strategy well. We'd love to hear from folks what they're thinking about, what's actually worked. And maybe what are the lies that have not turned out to work that they thought might work earlier in their journey. Amazing. Will, thank you so much for being here. Thank you so much. This is really fantastic.

Starting point is 01:16:25 Same for me. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's Podcast.com. See you in the next episode.

Lenny's Podcast: Product | Career | Growth - The engineering mindset | Will Larson (Carta, Stripe, Uber, Calm, Digg)

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