How to measure AI developer productivity in 2025 | Nicole Forsgren

0:00 A lot of companies are trying to measure productivity. For their teams. Most productivity metrics are a lie. If the goal is more lines of code, I can prompt something to write the longest piece of code ever. It's just too easy to gain that system. How do I know if my edge team is moving fast enough if they can move faster, if they're just not performing as well as they can? Most teams can move faster, but faster for what? We can ship trash fast. Faster every single day. We need strategy and really smart decisions to know what to ship. One of the biggest issues we're gonna probably have with AI is learning how much to trust code. That it generates. We can't just put in a command and get something back and accept it. We really need to evaluate it. You know, are we seeing hallucinations? What's the reliability? Does it meet the style that we would typically write? So much of the time is now gonna be spent reviewing code versus writing code. There's some real opportunity there to not just rethink workflows, but rethink how we structure our days and how we structure our work. Now we can also make A 45 minute work block useful because getting into the flow is actually kind of handed off, at least in part to the machine, or the machine can help us get back into the flow by reminding us of context and generating diagrams of the system. What's just like one thing that you think an edge team, a product team can do this week, next week to get more done. Honestly, I think the best thing you can do.

1:12 Today my guest is Nicole Forsgren. With so much talk about how AI is increasing developer productivity. More and more people are asking. How do we measure this productivity gain? And are these AI tools actually helping us or hurting how our developers work?

1:27 Nicole has been at the forefront of this space longer than anyone. She created the most used frameworks for measuring developer experience. Called Dora and Space. She wrote the most important book in the space called Accelerate, and is about to publish her newest book called Frictionless, which gives you a guide to helping your team move faster and do more in this emerging AI world. Her core thesis is that AI indeed accelerates coding.

1:51 But developers aren't speeding up as much as you think, because they still have to deal with broken builds and unreliable tools and processes, and a bunch of new bottlenecks that are emerging. In our conversation, we chat about her current best and very specific advice for how to measure productivity gains from AI. Signs that your team could be moving faster. What companies get wrong when trying to measure engineering productivity, how AI tools are both helping and hurting engineers, including getting into flow states. Her seven step process for setting up a developer experience team in your company.

2:23 how to get buy-in and measure the impact of a team like this, and a ton more. This episode is for anyone looking to improve the performance of their engineering teams. If you enjoy this podcast, don't forget to subscribe and follow it on your favorite podcasting app. Or YouTube? It helps tremendously.

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5:05 Go to workost dot com to make your app Enterprise ready today. Nicole, thank you so much for being here and welcome to the podcast. Thank you. It's so good to be here. It's so good to have you back. I was just watching our first episode, which we did

5:23 Two and a half years ago, I was watching it and I was both shocked and not shocked that we barely talked about AI. If the episode was called How to measure and improve developer productivity. And we got to AI barely like an hour in and we're just like, Hmm, I wonder what's gonna happen with AI and productivity. Does that just blow your mind? Yeah, because I mean it was Just hitting the scene. It was

5:45 The topic of so much conversation and at the same time So many things Don't change, right? So many things are still important. So many things are the same. Um Yeah, it's also a little while but it's been two and a half years. Where's so good? Time is a social construct.

6:00 Yeah. Well how might this impact people? How will we change The way we build product. And now It basically was not It was barely a thing back then. Now it's the only thing that I imagine people want to talk about when they talk about engineering productivity. That's a right.

6:17 Spending a lot of her time focusing on today. The reason I'm excited about this conversation, it feels like there's been so much money poured into A I tools, increasing productivity, all the fastest growing companies in the world are these engineering AI tools and now More and more people are just asking this question of just like what gains are we getting out of this? How much is this actually?

6:36 Helping us be more productive, how do we become more productive? You've been at the center of this world for longer than anyone you uh invented so many of the frameworks that people rely on now. So I'm really excited to have you back and to talk about this stuff.

6:49 I wanna talk I wanna start with just like this term devices, which is uh uh something that comes up a lot in this in this whole space. So we're gonna and we're gonna hear this term a bunch in this conversation. Can you just explain what is DevEx, this term DevEx? So DevEx is Uh Developer experience.

7:04 And When we think about developer experience, it's we're really talking about What it's like to build software. day to day for a developer. Right. So The friction that they face.

7:15 the workflows that they have to go through, um any support that they have. And it's important because when DevEx is poor. Everything else. Just isn't gonna help. Right, the best processes, the best tools, the best

7:28 Whatever magic you have, right? If if the DevX is bad. Everything kinda takes. And so within DevEx is productivity and I think the key insight that you had and

7:38 other folks in the space about is not just like productivity, but there's also engineering happiness and And we're gonna get into a lot of these parts, but just maybe speak to if there's productivity and there's broader components to engineers being successful at a company. Yeah, and I love that point, right? Because Productivity, first of all, is hard to define anyway, but if if you're just looking at like

7:57 Output transcript: You can get there in a lot of different ways, but if you're getting there in ways that are High toil or high friction. then at some point a developer's gonna burn out or If it's

8:07 you know, super high cognitive load if it's hard to even think about what you're doing'cause you're concentrating on like the m the mechanics of, you know, the plumbing of something. Then you don't have the brain space left to come up with like really innovative solutions and and questions. And so I love that it's kind of the self reinforcing loop in terms of you do more work. You do better work.

8:28 And It it's better for people, it's better for, you know, the systems, it's better for our customers. Something I was gonna get to this later, but I wanna actually get to this right now. This idea of flow state for engineers. So I was an engineer actually early in my career, I went to school for computer science. I was an engineer for ten years. The best part of the job is for me was just this flow state you enter when you're coding and building and just things feel like so funny.

8:50 It feels like AI is making that harder in a lot of ways because there's all these agents you're working with now. There's all this Code that's kinda being written for you. Talk about just the importance of flow state to a developer. Happiness develop productivity and just what you've seen. AI.

9:04 Impacting, how you've seen AI impacting that. A lot of times well, there are lots of different ways to talk about DevEx, right? One way to talk about it is kind of three key things. That. Have

9:15 components that are important of themselves, they also kinda reinforce each other. So flow state is one of them. Cognitive load is another and then feedback loops are another. And so I think you know when you touch on this. Your question about full state is a really good one. And I'll I'll admit, you know, we're just a few years into this.

9:31 We're still Figuring out what the best flow state and and But cognitive. Uh

9:39 requirements are for people in this because to your point, sometimes we're getting interrupted all the time. Right? You don't just Get in the flow and lock down and write a whole bunch of code. And do like the typey of a whole bunch of code as much anymore. Instead You're kind of creating a prompt. Uh

9:54 getting some code back and reviewing the code, trying to integrate what's happening in the system. Um And that can really interrupt. At the same time though it can contribute to flow if and I've I've seen some senior engineers pull together see tool chains that are really incredible where

10:09 they figured out how to kind of keep the flow going, right? the fast feedback loops really, really w work well for them. They can kind of assign out different pieces uh to agents. It helps them keep in the flow in terms of instead of details and line by line writing. They're in the flow in terms of what's my goal. What are the pieces that I need to get there? How quickly can I get there so then I can step back and kind of evaluate everything and then dive back in.

10:31 And You know, fixing pieces. Is there anything more you could say about this engineer that figured out this really cool workflow about just what that looks like? So I've spoken with a handful of them and I've kind of watched them work. I haven't built it myself. Yet.

10:44 Um So they've been able to set up this click. Really incredible work space and workflow where Like right now a lot of us

10:52 you know, play around with tools and we'll like put in a prompt and we'll get a few lines back, or maybe we'll put in a prompt and we'll get like Whole programs back. Well what they can do is they can Many times I'll see them say Uh.

11:02 to kind of helped help Prime it, you know. This is What I want to build. It needs to have these basic architectural components. It needs to have this kind of a stack. It needs to follow like this.

11:13 general workflow. Help me think that through and it'll kind of design it for it. And then for each piece It'll assign an agent. to go work on each pace in parallel. And then it'll say, Oh, and up front, you know. These need to be able to work together, make sure it's architected correctly, make sure we use, you know, appropriate

11:28 uh APIs and conventions. Then at the end Uh And then they can like let it run for a few minutes and they can think through something else that's interesting or they anticipate it's gonna be hairy. And they come back to something that's

11:41 I mean probably a little better than vibe coded, right? Because like because they were so systematic about it up front. They're much closer to something that looks like production code. So what I'm hearing is spending a little more time up front planning

11:56 AI engineers are doing versus just like powering through and just figuring out as you go. Okay, cool. Let me get to this quite a core question that I think on is a lot of people's minds that Lot of companies are trying to measure Productivity.

12:10 For their teams. Is this improving our productivity? Is this hurting our productivity? So let me just start with this question. How are people doing this wrong currently? When they try to measure their

12:21 Productivity gains with AI. I will say most productivity metrics are in lie. Oh. You know, it's

12:29 It's really Tricky because Historically, now look, lines of code has always been a bad metric, right? But many folks still use lines of code. A some proxy. Yeah. A some proxy for

12:39 uh output or productivity or complexity or Something, right? Well now For many of the systems that they would sometimes like whisper and not super talk about that uses lines of code. It's just blown out of the water.

12:53 Because what do you mean by lines of code? Uh If the goal is more lines of code. I can prompt something to write. Yeah. the longest piece of code ever and add tons of comments and, you know, we know that agents and

13:06 And Lons tend to be very verbose. Uh by definition. And so It's just too easy to game that system and then introduce complexity and technical debt into all of the work that you're doing. I will say there are Some things that we can kind of

13:23 Watch and pay attention to because So lines of code is a productivity metric, is it great? Right. It's pretty bad. But now it's kind of more relevant if we can tease out which code came from people and which code came from AI because now we can answer downstream questions.

13:40 What is the code survivability rate? What is the quality of our code? Is our code being fed back into trained systems and for that code that's that's retraining systems later, especially if we're doing like fine tuning and local tuning. how much of that is machine generated, right? What types of loops is that creating and what types of

13:57 patterns or biases might it be inadvertently introducing. So On the one hand, like it's not good as a productivity metric, but it can be useful, right? And I'll I'll even say the same for Dora, right? So I have done Dora metrics, their speed metrics, their stability metrics. If that's all you're looking at. It's it's not gonna be sufficient anymore because AI

14:17 has now changed the way we think about feedback loops. Right. They need to be much faster. Now, what door is meant for uh, you know, kind of assessing the pipeline overall in terms of speed and stability. It's still that works. But we can't just blindly apply the existing metrics we've used before because we'll miss Super important.

14:34 Phenomenon. Changes in the way people work. Interesting. So so you you invented Dora, that was kind of the main framework people used for a long time to measure productivity. And then there's space

14:46 There's uh Core Four, there's probably others. So what I'm hearing here is all these are kind of out of date now, where AI is contributing large portions of code. I will say if it is a prescriptive metric. It needs to be used only in the way it was prescribed. So Dorafor.

15:02 Therefore Key metrics, there's Uh two speed metrics. uh deployment frequency and Lead time, so code commit to code deploy.

15:10 There's stability metrics, uh MTTR and change fail rate. If those are used to assess the speed of the pipeline. And the General performance of the pipeline. That's great.

15:21 If you're trying to use those to understand because implied in that is feedback loops, right? Because you used to kind of like to get feedback from customers. Um But we can't just use that blindly now when we're using AI as an example because We have feedback loops. much earlier and not even just at like the local build and test phase. We have feedback loops.

15:40 Throughout And even sometimes in the middle of some of the pipeline that we really want to leverage in ways T. Works. as useful before. I won't say they weren't possible, but like we just didn't really focus there.

15:53 So those are prescriptive metrics. When we think about space Space is a framework. It doesn't tell you what metric to use. So I'll say sometimes people get real frustrated because I didn't tell them what to measure, right? Uh But now I think that's the power of it. We're actually seeing that space.

16:10 applies fairly well in these new emerging contexts like AI. Because we still wanna look at so space is an acronym, right? So we still wanna look at satisfaction. We still wanna look at performance, what's the outcome? Um, we still want to look at activity. Yes, you know.

16:25 In some ways, lines of code and number PRs can be useful. For something, right? uh or number of alerts or number of Yeah, things activities or counts. Cs communication and collaboration.

16:35 This is also super important and useful because it's how our systems communicate with each other and also how our people do. Yeah, what proportion of work is being offloaded to a chat bot. versus talking to a senior engineer on the team. More isn't always better, less isn't always better. Depends. And then efficiency and flow. Can people get in the flow? How much time does it take to do things? What is the flow like through our system?

16:56 And here I would probably add a couple dimensions, right? So uh chatting with some of the early authors to say, you know, trust. Not to say trust wasn't important before, but now it is very, very front of mind, right? Before you you know, build your code, like if the crop if the compile comes back, you're fine and like that's the way it is. LLMs are non deterministic, right? Now we can't just put in a command and get something back and accept it. We really need to evaluate it. So

17:21 Yeah, are we seeing hallucinations? What's the reliability? Does it meet like the style that that we would typically write. And if it doesn't meet, is that fine? So That's my kind of kind of it depends on answer. Prescriptive. You gotta make sure you're using it.

17:37 Fit for purpose, right? We're gonna get to your do this stuff. You have a book coming out that Explains the

17:46 How to do this well. So we're gonna get to that. One thing I wanted to highlight in our last chat that we had. You uh You highlighted one of the biggest issues we're gonna ex probably have with AI is trust. Understanding

17:58 And learning how much to trust. code that it generates and also how much You said this two and a half years ago. that so much of the time is now gonna be spent reviewing code versus writing code. That's exactly what I'm hearing. I think it'll be interesting to see

18:12 how that impacts the way we structure work moving forward. You know, we were talking about flow state and cognitive load. Now that our attention has to focus on things at certain times and and it's broken up from how we used to do it. Um, I think there's some real opportunity there to Not just rethink workflows, but rethink how we structure our days and how we structure our work.

18:31 Can you say more about that? Just what is that? What do you what are you thinking will be happening? Where do you think things go? What are you seeing working? Uh so purely speculative. Um But for example, uh Gloria and Mark has done some really good work on attention and deep work and Humans can get a About four hours.

18:48 Uh. Good deep work a day. Like That's about it. Yeah, they kill that. And that that's like kind of the

18:55 Upper limit ish for the most part. And I'm sure people are gonna be like, Well, I am superhuman and I can do it. But if you take twenty grams of creatine. Fright. What's the microdose? Yeah, exactly. Yeah. So

19:07 in the context of knowing we have about four hours of good deep work. And I'm sure s many of us have probably hit this, right? We're like we have good period, like maybe it's morning, maybe it's afternoon for folks, and then you hit a time where you're like I'm gonna clean up my inbox because that is all I can do right now. Right. Like I can be functional, but I'm not gonna come up with my best Innovative problem solving

19:28 Authoring. Code writing work. A lot of times the way to do that and to get into it is to have Things. long chunks to get into flow and to get that deep work, right? And it's usually

19:41 This is then making I'm like hand waving, right? Two hours I minimum. Right. Like an hour can be tricky'cause it could take time to get into that state. Okay, well when we think about what it Used like Back in the olden days, three years ago, three and a half years ago.

19:54 We could block off four hours of time. And we could probably get two or three hours of really good work done. Now'cause we were just focused, right? There were no interruptions, minimal interruptions. Now The nature of writing code in systems itself is

20:09 Interrupt driven. or or full of interruptions at least, right? Because you start something and then it interjects. And so how do we think about that? Does that mean that a four hour Work block is still useful. I mean probably.

20:21 But does that mean that now we can also make A forty five minute work block useful. Because getting into the flow. is actually kind of handed off, at least in part to the machine, or the machine can help us get back into the flow by

20:34 Reminding us of context and generating diagrams of the system and You know, all the things and so I think that's a really, really interesting area that's just ripe for questions and opportunity. And and please, folks, do this research and and come back to me because it might not make my list, but it's such a great question. That is so interesting. Essentially everyone every engineer is turning into an

20:55 Yeah. Engineering manager. coordinating all of these junior AI engineers. And so your point is even if you have like a thirty hour block. You can't get deep into code, but you can unblock all these AI engineers that are running off doing tasks. Plus, your point is they give you they remind you of just like here's where you left off. Okay, you can just jump into this. I would maybe.

21:15 Make some tweaks. Yeah. So interesting. Let me zoom out a little bit and Before we get into your framework for how to

21:24 approach developer experience, the latest thinking you've got. Beyond just like obviously engineering engineers doing more is great. What What's your best pitch for why companies should really, really, really focus on developer experience? I hate to say return on investment, but like the business value is

21:41 The opportunity here is huge. Right, in general, we write software. Well For fun and for hobbies, right. But we also have software because it meets a business need.

21:51 Yeah. It helps us with market share. It helps us uh attract and retain customers. It helps us do all of these things and You know, I think DevEx is important because

22:00 It enables all of that software creation. It enables all of that problem solving. It enabled the super rapid experimentation with customers that Before you know you You'd need a while for a prototype and maybe a little bit longer to actually flight it through. An A B test on a production system.

22:18 I mean, you can do it in hours right now. Getting maybe the opposite end of the spectrum getting very tactical. Before we get into the larger. Framework. What's just like one thing that you think

22:29 an edge team, a product team can do this week, next week. to help their developer experience maybe get more done. Honestly, I think the best thing you can do is go talk to people who listen. And I love that, you know, the audience of this podcast is primarily PMs'cause they tend to be really good at this. And I would say start with listening and not with tools and automation.

22:48 So many times companies are like, Well, I'm just gonna build this tool or Yeah. I'm gonna build this thing. Often you build a thing that you yourself have had a challenge with or that Like is easy.

23:00 To do, easy to automate. And if you just go talk to people. And ask the developers. Like. Think of

23:08 Think of yesterday. What did you do yesterday? Walk me through it. What were the points that were just delightful? What were the points that were really difficult? Where did you get frustrated? Where did you get slowed down? Where was their friction?

23:21 And if you go talk to a handful of people. A lot of times you can Surface. A handful of things that are A relatively low lift does still have impact.

23:30 Um or you can identify a process that's Unnecessarily complex and slow. So the listening tour here almost is You want to help your teams move faster, be happier edge teams. Your advice is just before you do anything, just like go ask them what is bothering you. Go ask them. Yeah. And trust me, like

23:49 Most developers are gonna be more than happy to tell you what's broken and what's bad. And Yeah, I mean I'll I'll say there was one company that I had worked with, I remember they s they had a Process that was like Really difficult.

24:02 And it was on a old mainframe system and they were gonna have to like replatt the whole thing and so they never went. to work on it or talk about it. Uh everyone hated it'cause it was this huge delay. I mean All you had to do was change a process. Sometimes all you have to do is change a process, and they changed it so that instead of

24:18 I think it was someone had to like print it out and walk it down three or four. Flights. And then get approval and then someone else had to like lock it back up. And so it was just that interim. They didn't replot anything, they didn't redesign anything major. They just had it send an email. Let me push on that and

24:33 I'm curious, just what are the most common things people do. Like if you're just starting on, okay, we need to focus on engineering. experience. What do you think are the most like I don't know, two or three most common Improvements companies need to make.

24:45 I will say, you know, kinda echo that process. There's almost always a process that can be improved. And it can be approved. improved without a lot of engineering lift or a lot of engineering. Head count.

24:56 Right. Uh most large companies in particular. Have something that is Several, several steps.

25:04 It's the way it is because it's the way it is, but that's no longer the way it is, right? Um, and even small companies, sometimes it's just a little too YOLO and you don't know what it is and you're kind of chasing everyone around. So if you can create a very lightweight process, that can also be helpful. That can be one of the best places to start, especially if you have Limited. uh exposure to the whole rest of the org, right? Sometimes just a team process can help.

25:27 Um I will say from a business leaders' standpoint. A lot of what you can do is provide structure and support for this organizational change. communicate what you're doing, communicate what the priorities are, communicate why this is important, celebrate wins.

25:42 Because If folks try to do this just like a one off side fully isolated project. It's really challenging to Get some good momentum and get.

25:54 people to care to get them stay involved, right? Because it feels like Just another interim internal project that isn't gonna matter or th isn't gonna get celebrated, but it has these huge Uh upside.

26:08 uh potential returns for the business. It's interesting, what I'm hearing here is Nothing about tools or technologies. It's not like move to this Cloud. It's not like

26:17 Install this new deployment system, it's processes. And people and Oregon morale. Yeah. No.

26:25 There will be technical pieces that are very important, right? Uh, especially now with AI, right? We're we're rethinking how build and test systems work, we're rethinking feedback to users so that it's very, very customized in terms of what is shared and when it is shared. There are a lot of technical pieces that are involved.

26:44 But that's not the only thing. Right, it's necessary but not sufficient. And that doesn't have to be the place that you start. I'm gonna ask you, I have a hard question I wanna ask you that I thought of as you were talking. I feel like this is the question that most founders and Heads.

26:57 Think about. And the question is just like how do I know if my edge team is moving fast enough if they can move faster. If not. performing as well as they can.

27:07 What are just maybe smell, signs? That's how you yeah, my team should be moving faster versus like this just the way It works. This is as fast as they can move. Most teams can move faster. Right.

27:19 So And also Uh given what we know about cognitive float. Not All speed gains are necessarily good. Right.

27:28 Um or The upside is gonna be kind of limited, right? Once you hit kind of a certain point. Most people are not even near that point. Uh I don't know a single team, frankly. But how do you know?

27:40 Uh you know if You're always hearing about Bills. Breaking. Flaky tests.

27:49 Uh. Overly long. Processes. If you have to request a new system. Or if you need to provision a new environment.

28:00 Or if it's really, really hard to switch tasks or switch projects. Right. So if someone has an opportunity to go work another part of an org. And They don't for reasons that are unclear and like not political and Anyone says anything about the system?

28:15 That's Usually a pretty good Smell. uh that there's friction somewhere because once you finally figure out your system and you're able to get work done. You don't

28:25 The switching costs can often be really, really high to go anywhere else. And so sometimes people will do that. But you know, I've work with companies where

28:34 Switching. Orgs within the company. Uh you had to basically pay the same tax as a new hire. Because the systems were so different and they were

28:45 so full of friction and it was so difficult to do so many things. I love the first part of your answer especially, which is you can always move faster. I think every founder is gonna love hearing that. Uh to your point that there's diminishing returns over time. Yeah. And and you don't know about the quality, right? So like I think that's the other side is that you can always move faster, but faster for what? Are we making the right business decisions? I think you know that's especially where PMs come in. You could we can ship trash faster every single day.

29:12 We need strategy and really smart decisions to know what to ship. What to experiment. uh with what what features we want to do in what order and what roll out, right? The strategy is the core piece. And then think about speeding that up. If we don't have the other pieces in place.

29:28 I mean garbage in, garbage out. I'm gonna follow that thread, but before I do that, just to mirror back what you shared. So signs that your team There's a lot of low hanging fruit to improve this perfect the productivity of your team is

29:39 builds are always breaking, there's flaky tests that are constantly Incorrect, false positives. Yeah, it's hard to context switch. between different projects, the system you just hear people talking about the system is just really hard to work with. That roughly.

29:51 Right. Yep. Cool. Okay, so going back to the point you just made. There's a sense that AI is making teams so much faster because it's writing all this code for them. You're gonna have all these asynchronous agents, engineers working for you. Feels like a core part of your message is That's just a one part of engineering work.

30:09 There's so much more, including figuring out what to build, alignment internally. Maybe just speak to just like Up there is a lot of opportunity to improve engineering's performance, productivity, but there's so many other elements that are Not improved through AI. Yes.

30:23 Uh Or or could be in the future, right? Like I think there are a lot of ways that we can pull in AI tools to help us Refine our strategy, refine our message. Think about the experimentation methods or you know uh

30:36 targets of experimentation or think about our total addressable market, right? But we need to have that. Strategy and plan fairly well aligned, right? Or at least have like two or three alternatives that you want to test because now the engineering can go. or at least the prototyping especially can go much, much faster. Like right. We can throw out prototypes.

30:55 Uh we can run A B tests and experiments. There. Customer facing, right? Assuming that you know, we have the infrastructure in place. Which allows us to

31:06 learn and progress much faster before, right? Like it it Some places it used to take, you know, months to get something through production to do A B testing and get feedback. We can do this in a day or two, right? Definitely under a week. but we wanna make sure that we're building and testing the right things.

31:22 Are we partnering with the repo? Do we have the data that we need, right? And I will say AI. can actually be a pretty good partner there if you keep if you have like a good conversation with it and then also Check with U experts. Right. What type of data should I be looking at what type of instrumentation do I need, what type of analysis can I do. Uh, because then you can also go to your data science team.

31:41 And say, like I'm planning on doing this. I'd like to'cause let's not just yellow A B tests, right?'Cause That can be. It it's a shame to do a large test and end up disrupting users or disrupting customers or Breaking the privacy or security.

31:57 uh protocols. And also end up with data that's unusable. Right, because you just can't get the civil that you're looking for. But now I'm also seeing people kind of accelerate that into a few days versus A few weeks.

32:09 And so they can kind of start those key stakeholder discussions. From Uh Much more informed, kind of filled out space. Today's episode is brought to you by Koda. I personally use Coda every single day.

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33:27 To get started for free and get six months of the team plan. Coda.io slash Lenny. I love that you work with a bunch of different companies and a bunch of different Types of businesses. I think I'll

33:39 Very few people get to see inside a lot of different places. What kind of gains are you just seeing in terms of Increase productivity with AI. Like how real? Like how big of a gain have you seen? I'd say it's real. And I would also say we don't have great measures for it yet. We're still trying to figure out. what to measure and what that looks like. Uh one of the best is

33:59 Going to be velocity. Right, all the way through the system. How quickly can you get Uh A feature or a product or something through the system. so that you can then experiment and test, right? Either from like idea to like final end or even kind of a

34:13 uh feature and a piece through the system so we can test. That's Really good. Now that's also Hard. to tie back directly to

34:21 Like a particular AI tool in the hands of a particular developer. But there are some other things that That we can look at and we can see. Um

34:30 And and that I've seen is again this kind of rapid prototyping. Um I hate lines of code, but I'm gonna use lines of code. Um We do see I I know I worked with uh some folks who who had kind of a a whole set of companies they were looking at and they found that

34:47 Uh AI was generating like significantly more code for the people who were using it regularly. But then they also found that for folks who were like, you know, regular users of

35:00 AI coding environments, AI IDEs. The the tool kind of gave them more code, and then the engineers themselves The increase was double. What the uh coding agent given them. So one

35:15 I'd say probably s it' kind of a secondary or knock on or or just a smell, right? Is It can unblock you, it can speed up the work that you would already do. Right. I know sometimes when I work, it's like the first few minutes. It's hard for me to start. But once I get started, I'm there. And so they're really good at unblocking and unlocking that. Something I've seen people on Twitter sharing is how good

35:35 Uh open AI codex especially is at finding really gnarly bugs. And I think it was Kurpathy that shared he was like so stuck in a bug and no AI tool could figure it out. And then the latest version of codex spent like an hour or something looking into it and found it for him. Yeah, I'm I'm hearing incredible things like that. Right. Well and even also, you know, writing unit tests and spinning up unit tests and uh creating documentation and cleaning up documentation because I know now people are like, Oh well, we have agents. I don't need to I don't need to read the docs because there's the code there.

36:08 Turns out Uh agents Rely on good data. Right, because it's it's all about how they've been trained or how they've been grounded. Um, and better data gives you better outcomes. And some of that data includes

36:21 Documentation and comments. And the better documentation, the better comments you have. the better performance you're gonna get out of your AI tools. And AI can help you write that documentation. I've been working with Devon a little bit and it's really good at that stuff.

36:34 Yeah. Okay. Let's talk about this framework this book. So you're publishing a book called Frictionless, which sounds like a dream. How do you Tweeted. Uh a a dev team that's frictionless. So it's called frictionless seven steps to remove barriers along value and outpace your competition in the age of AI.

36:51 There's a seven step Process to this. Walk us through this, maybe give us just context on this book, what it who is meant for, what problem it solves, and then the seven steps. So I will say I uh also wrote this with Abi Nota, who has just uh of DX. He has incredible experience in the space, right? He's worked with hundreds of companies. And so it was kind of nice

37:10 bouncing ideas off of him and, you know, also thanks to all of the uh engineering leads and dev ex leads and CTOs and engineers that we talk to to kind of Make sure. That we our smells were right, right? And so

37:24 Who is this book for? Let me actually take a let me take a tangent on Abi N DX since you mentioned him. Uh, this is super interesting, and I think it connects so directly with this conversation. So I started this company called DX, which is such a great name for a company around developer experience. They Just hold the company for a billion dollars to Atlassian. Uh

37:42 It's a very high multiple on their AR. It to me shows exactly why this conversation is so valuable. Just how much value companies are putting into improving developer experience.

37:54 Atlassian would spend a billion dollars on this. It's like a early stage ish startup. That was doing really well, and people left it, but it was like early stage ish. A billion dollars. Uh and now and the idea is they have all these companies working using Jera and all their products. Uh, they're all trying to figure out how do we measure productivity. Uh it's worth a lot of money to them.

38:12 So and I know you were an early advisor to them too. So Yeah. It just shows us how important this is. Yeah. Well and I think it also shows us um How much value you can get out of this, right? Like There's so much low hanging fruit, there's so much unlocked potential, and it's hard to know where to start.

38:28 A lot of times even in I've been at large companies that have a lot of expertise and a lot of really, really smart people. But if you haven't kind of been in this space and thinking about it this way It's hard to know where to start or it's easy to make Simple mistakes up front that mean like you kinda need to start over later.

38:45 Um, so I guess which kinda also brings us back to, you know, who is this book for? It's for any one. That Definitely technology leaders, um.

38:57 Anyone who's trying to kick off a DevEx program? Or is working on a DevX? DevEx Improvement Program. Um I think it's particularly though

39:05 particularly relevant for PMs. Because If you're PMing something that involves software and building and creating software, improving DevX. Will only help your team. And also

39:17 You have Key skills and insights and instincts Тарасон то девекс та мані там. I will say I've seen engineering teams Just miss.

39:30 Mm. Okay. What's the framework? What are the steps? Where do people start? Uh so the book goes through uh seven step process and then also kind of provides some some key kind of uh principles at the end.

39:45 Step one is to start the journey. Right. So assuming you're kicking off, you can start the journey. And this involves what we have already talked about, right? Go talk to people, have a listening tour. Synthesize what you learn. uh visualize the workflow tools, right? Like get a handle on kind of

40:02 What the current state is. Uh step two is to get a quick win. Right. So start small, get a quick win. uh pick the right projects. Share out what you've done.

40:12 Um step three. is using data to optimize the work. Right. So kind of establish some of your data foundation, find the data that's there, start collecting new data. Um

40:23 You some surveys. For some really fast insights and we include uh example surveys. Uh step four. That is To decide strategy and priority. Once you have some data, then you need to know of all the things that are potentially broken and you've already gotten your quick win of all the things that are left.

40:39 What should I do next? And so we walk through some evaluation frameworks there. Step five is to sell your strategy. Once you've decided. Now you have to kind of convince everyone else, right?

40:49 So now you want to get feedback, you want to share why this is the right strategy right now. Uh. Step six is to drive change at your scale. So here we address folks that have local scope of control, right? If you're starting on just a deb team. Wanna do it yourself kind of grassroots effort.

41:05 uh or global scope of control, right? If you're You know, the the VP of developer experience or something like there are some things that you can leverage for a top down. Um, and then how do you drive change when you're kind of somewhere in the middle? Cause you can leverage both both types of strategies. Uh and then step seven is to evaluate

41:22 Your progress? And show value. And then kind of uh move back around. And I will say that we wrote this so that

41:30 You could kind of jump into any step. Where wherever you are right now, right? Like if you're kicking off a team, you'll or an initiative you'll probably want to start at step one. You should definitely start at step one. If you're joining an existing initiative, you could jump into uh picking the priority or

41:46 uh implementing the changes. So those are So there's a seven steps. Um there are a few practices that we also recommend. So Thinking about resourcing it, uh

41:58 Change management. Uh Making technology sustainable, and then also bringing a PM lens to this, right? How can we think about Developer experience as a product. And how do we think about the metrics that we have as a product?

42:13 Awesome. Okay, I have questions. Point people to the book real quick. What uh what's the URL? How do they get it? When does it come out? Yeah, developer experience book dot com. So right now you can sign up for the mailing list, we'll we'll let you know when it's out on pre order and we'll also be Sharing

42:28 pieces of the workbook. So we've got almost a hundred page workbook. That goes along with the book. Um, and then it should be out by end of year. Okay. So one piece of this is just this term developer experience feels very intentional.

42:41 in that it's not developer productivity, developer Uh Work. It's How do we make developer experiences better at our company? Which includes they get more done.

42:50 But also they're happier. Things like that. So I think that's an important element of this, right? Yeah. Yeah, absolutely. Because

42:58 Again, it's not just About productivity, right? We talked about this from from kind of the frame in the lens of we need to be building the right thing. And you want to be productive, but you also want to be thinking about and this is what engineers are also just really incredibly good at. Give them a problem and don't tell them how to solve it.

43:15 And then they can solve it better, right? They have uh the freedom, they have the innovation, they have the creativity so that they can solve this problem. If it's only about productivity, then it's just like lines of code or number of PRs or whatever, right? But we really want to talk about value and how do we unlock value and how do we get value faster. And that involves Yes.

43:33 Making them more productive and removing friction. Because then they have the flow and the cognitive load and the things that we kinda talked about. Awesome. Okay. And then say someone wants to start this team.

43:45 What does it usually look like? At Airbnb, I remember this team for me and it was just like an engineer or two. Getting it started and taking charge. What do you recommend as the the pilot team and then what does it look like as it grows? So there are there are a few ways to do this, right? So if you're doing it yourself. You could do it with a couple of engineers.

44:03 Uh maybe a a P M or a Or a PGM or a T PM. Um to kind of help communicate because really Calm's plans are just So

44:12 Important here. Uh on a small On a small scale. Right. What we want to do is

44:19 Look for those. quick wins, look for things that you can do at small scale. Are there, you know, some folks call them things like paper cuts. Are there small things that you can do? Help people see the value and feel the benefit themselves.

44:32 Right. How can a developers work? Get better. How can their day to day work get better? kind of build momentum from there. If you're working from a top down structure,

44:41 And you have the remit. You still want Uh some Some quick wins, but those quick wins can look a little more global in scale because you have the

44:51 The infrastructure or the backing to make you know, different types of changes that aren't only local. So you know, an example of a small local change could be just cleaning up your tests. Yes suites. Right. Any team could do that.

45:02 Any two could do that. Uh more more global scale might be changing uh organization like process that is just overly cumbersome or throwing some resourcing into, you know, cleaning up the provisioning environment.

45:15 What kind of impact have you seen from teams like this forming? On The engineering teams at their companies. I I'll say I've seen a huge impact. Right. Uh

45:24 For for smaller companies, hundreds of thousands of dollars for large companies. Yeah. In the billions. Cut well also. We need to learn how to communicate that, right? Like what does the math look like? Are we many times we can look at saving time.

45:37 We can look at saving costs. Um we can look at a lot of different things. We can look at speed to value and speed to market. We can look at risk reduction. Um But the gains really are there. I I will mention that it tends to follow something like J curve, right? So like you'll have a couple of quick wins and it'll look like a big

45:53 Big win. And then you'll hit it. kind of a little divot where suddenly The really obvious projects, the low hanging fruit are handled. So now we need to do a little bit of work.

46:03 Right. We n we might need to build out a little bit more infrastructure. We might need to build out a little more telemetry so that we can capture the things we want to capture. And then once we get that done. Uh then we start to see those benefits really compound. So going back to that measurement number, what do you recommend? How do people

46:20 Find these numbers'cause I think that's So much of the power of this is like we saved a million dollars doing this. What do you look at to do to figure that out? You know, I think there are A few different things to keep in mind, right? Who who is our key audience? And

46:34 We usually have a few key audiences, right? We really want to be able to speak to developers because they're the ones that are going to be using the systems. They'll be partnering with you on either building them or at least providing feedback about what you're doing. Um and so for them. We often want to frame

46:49 Tess in terms of things they care about. So time savings. Right. If something gets faster, they can save time. They can Yeah.

46:56 They don't spend time doing setup when they don't need to anymore. really just that is reduced toil. Right. Compliance and security are super important. Also Many times it requires several, several s manual steps that

47:10 I don't say they're not value add. They are not value add from an individual human perspective. Right, if we can automate as much as possible. That's great. Um and

47:20 You know, improved focus time. So that's from the developer side of view. Leadership often cares about. They care about those things, right? But they always care more about other things. So We could talk about

47:32 Uh Usually costs in dollars. Right, so can we accelerate revenue? What does our uh time to value look like? Uh

47:40 What is our velocity? How quickly can we get pe feedback from customers? Um, and for folks and organizations that are in really competitive environments, that can be really compelling. Because it's all about speed. We could talk about saving money. Right. So here we can look at maybe quantifying savings. So you know, one example is

47:58 Uh test and build. If we can clean up a test and build suite. To a developer. They really want to hear about Yeah. Time saved.

48:06 And more reliable systems, right? There's less toil because they don't have to keep rerunning tests or kind of go clean up test suites. From the business perspective, cleaning up a test in a build suite can be Uh Cloud cost savings.

48:19 because all of those tests are running somewhere on a cloud. And if they always fail or if they're if it's just kind of a waste of spend. That can be useful, right? Uh recovering some capacity, right? Uh we can always talk about time. And productivity gains. Right. So how much Uh

48:36 Equivalent developer time are we losing? on things that are not necessarily value add. Right. And then sometimes we can correlate to business outcomes and correlate is usually the best we can we can do here, but There can be some pretty compelling correlations in terms of

48:50 speeding up time to value and increase market share, for example. So let me fold that thread and come back to this. What I think is the biggest question people have right now with AI and productivity. Which and I don't

49:01 I don't think anyone has the answer yet, but I'm curious to get your take of just What should people do? Today, what's the best approach? Two understanding what impact AI

49:12 tools are having on their productivity. because they're spending lots of money on there. Like I don't know, what are we getting out of this? And I guess things are moving faster, but I don't know. So if someone had to just like Okay, here's what I should probably try to do. What would be your best advice here for measuring the impact of AI tools on productivity?

49:28 I would say it depends. Um And in part it depends on What your leadership chain really cares about.

49:36 Right. Like we c we're usually pretty good at like figuring out what matters to developers. We could communicate that to them, but if we're trying to just identify two or three data points to really kind of focus on because when we're first starting with data, sometimes it can be challenging. What do they care about? Think about the messaging you've been hearing. Have they been talking about market share.

49:55 Right, losing market share can Or competitiveness in the marketplace. If that's it. Focus on sped. Think about ways that you can capture metrics for speed for from like feature to production or feature to customer or feature to experiment.

50:09 And what that feedback. loop looks like. Yes. They're talking about Uh

50:16 Profit margin. All the time. Right. No. We always talk about money, right?'Cause this is business. But if that seems to be an overarching narrative. Look for ways that you can save money and then translate that into

50:28 Uh recovered and recouped. Uh headcount cost. Right. Or sometimes you'll kind of like reinvent, change a process, and then you no longer need as many vendors, right? So so reductions in vendor spend can also help there.

50:40 Um And I I say also it depends because sometimes Something will be does say something, right? Like leadership will say something and it it kind of comes up as a theme. If you could solve a problem That they have

50:53 Or it's like Something that they're focused on. If you can slightly reframe it even. Right. Like if they're calling everything developer productivity, go ahead and call it productivity. If they're calling it velocity, and velocity is what matters to them. Think about how to frame this in terms of velocity.

51:06 If they're talking about transformation or disruption, right, how does this help with the disruption? Because then it will resonate with them. We don't want to make them work to understand what it is that we're doing and the value that we provide. That is such good advice. So just to reflect back the advice here is

51:25 If you're companies trying to figure out what sort of impact our AI tools having on our company. What does the company care about most? What do leaders care about most? Could be market share, could be profit margin, could be Velocity. We need higher velocity.

51:38 Or we need a transform, transformation. So your advice there is like Figure that out. Based on Words and phrases you're hearing.

51:46 Then figure out Ways to measure that. Ways to measure market share growing. uh profit margin increasing. So it could be Uh I love these examples like time from feature.

51:57 idea to production or to experiments. So maybe start tracking that. If it's margin, it's like money saved by Fewer tests failing or Some vendor you don't have to pay for, things like that. And then velocity.

52:09 And velocity, I imagine that's where things like Dora come in of just like speed of engineering shipping, or what would you think about there for velocity? I would say it's actually, you know, one of those uh I would pick as as broad a swath as you can. So if you can go from idea to customer or idea to experiment. How long does that take? How long does it typically take and how long

52:27 Can it take and and does it take now with uh improved. Use of AI tooling and reduction in friction, right? And that's where I will say we talk about this a little bit in the book, uh You know, how do we deal with attribution?

52:39 Challenges. What was responsible for this? Was it the DevEx or was it the AI? Um Go ahead and disclose that. Right, say yes, we rolled out AI tools. We also had this effort in DevEx.

52:49 They partnered very closely together. Both of them probably contributed to this, right? Like if we had AI tools without the DevX improvements. We probably would have had some improvements, but not nearly as much. Right. If people were starting to do this today, say they're just like, I wanna start measuring developer experience.

53:06 Are there like a two or three metrics everybody basically needs they should just start measuring ASAP? If you're just starting today and if you have nothing at all. Uh talk to people obviously after that. I would do surveys. Uh because surveys

53:18 Can give you a nice kind of overall view of the landscape quickly. So that you know where the the big kind of challenges are. And I say that because if if you're just starting, you you might not have instrumentation through your system. All the metrics.

53:33 And if you do already It might not be what you think you want, right? Metrics that were designed Without purpose. Questionable. Metrics that were designed for another purpose.

53:43 They might work for what you want, but they might not. So we can't just assume we have them. So that's one reason I like surveys and Uh we include an example in the book. You can just ask a few questions, right? How satisfied are you? Uh

53:55 What are the biggest barriers? To your productivity, or what are the biggest challenges to getting work done? Um and let them pick, you know, maybe either from a set of tools or maybe like a set of uh Processes.

54:08 And then say And like let them pick three. Just three. Of those three, how often? Does this affect you?

54:15 Right, is this hourly? Is this daily, is this weekly, is this quarterly? Right. Because sometimes it hits you every single day and you're just mad about it. Sometimes it only hits you once a quarter because it's end of quarter. But it's so onerous. Right. And then kind of open text, right? Like is there anything else we should no

54:32 Um That That can give you incredible signal. Because by Making folks

54:39 Prioritize the top three things. Let them pick everything. Like it it makes the the data super, super messy. But three things And how often? You can just come up with a score or a weighted score if you want. And then go kind of dig into where should that dial?

54:54 Where should that data be? What data do we need? But also then you've got at least some kind of baseline. baseline, right? It'll be a subjective baseline. But now you'll know what the biggest challenges are. I love how all this just comes back to starting by talking to people, asking them these things, which is very similar to product management and just building great products is Have you talked to your customers and everyone thinks they're doing this, but most people are not doing this enough.

55:16 Yeah, and I will say like one thing that's challenging when you're start When you think about getting data. Right, so interviews your data and that's important. Surveys are little more quantified, right? Because we can we can turn it to counts, but that's where we also want to be careful, right?

55:31 A lot of folks go to write a survey question and they'll say Something like Were the build and test systems slow or complicated? In the last week. You're asking four different questions there.

55:41 If someone answers, was it the build? Was it the test? Was it slow? Or was it like flaky or complicated or something, right? So

55:49 It can be really difficult to Untangle. what the signal is you're actually getting there. And so it is worth time Uh Chatting with

55:59 With someone who's familiar with survey design. Um having a conversation with Claude. Or Gemini or Chat GPT around Uh here are the survey questions or can you propose some and then make sure you take a couple of routes. Is this a good survey question?

56:13 What s what questions can I answer? From the data that I get. What problems could I solve? If you can't answer a question with data, don't get it. Mm. And you have example surveys in your book for folks that wanna just copy and paste and not have to think about those. Example surveys, um, a lot of example questions. We even recommend like what the format

56:32 Like how how what the flow should look like. How long it should be, how long it should not be. One thing that I was reading, uh Is that you don't love happiness surveys specifically asking engineers how happy they are? Is that true? If so, why I don't No.

56:46 I will I don't say I I don't love a happiness survey. Because There are too many things that contribute to happiness. Happiness is a lot. So happiness.

56:57 Is work. Happiness is family, happiness is hobbies, happiness is weekends, happiness There's so many things that contribute to happiness. No, that doesn't mean I don't care about happiness. I think happiness surveys are Not

57:09 Particularly useful here. What can be helpful is satisfaction. And people are like, What's the same thing? It's not. Because you can ask Are you satisfied with this tool? Right. And then ask some follow up questions. Now those two are related.

57:23 Because the more satisfied you are with your job and your tools and the work and your team It contributes to happiness. And I used to joke, remember the old commercials like happy cows make happy cheese? That was the best. Um Happy dance.

57:38 Make happy code. They they write better programs, they do better work. Um They're they're better team members and collaborators. But

57:47 But capturing trying to directly influence happiness, like that's That's that's not what we're here for, right? And it's just it's too challenging, it's too all encompassing. Satisfaction can give us subscribed. In a totally different direction.

58:01 Uh In terms of just tools you see people using, are there any that just like, Oh yeah, this one's really commonly uh great for people. This is just like a tool people are finding a lot of success with. Like there's the common ones copy cursor Uh, I don't know. Is there anything that stands out that you You want to share just like hey, you should check this tool out. People seem to love it.

58:20 I I think The huge, right? Co pilot, cursor, Gemini. Click out. Yep, claw code.

58:27 I love cloud code. I've been I have a whole post coming on. Waste use clo clod code for non engineering use cases. It's so Nice. So interesting. For example. Cloud code. Uh

58:39 Find ways to s clean up storage on my laptop. And it just tells you here's a bunch of files. It's just like chat GPT running on your computer. And he could do all kinds of crazy stuff on your computer for you, like a little m mini mini god. Well, I'm gonna do that now. This is great. It's so good. It's yeah, that's why I'm writing this. Uh I had a Dan Shipper's on the podcast and he said Clock out is the most underrated

59:02 AI tool out there because people don't realize what it's capable of. It's not just for coding. And that's um trying to explore more and more. Okay. Is there anything else that you think would be valuable for people to hear? for to help people improve their developer experience.

59:17 Help them. Adapt to this new world of AI. uh and engineering that we haven't covered. I think something that's important to think about in general is

59:26 To bring a product mindset. Two. any type of DevX improvements that are happening. And also the metrics that we kinda collect and capture. And by that I mean

59:37 We want to identify a problem. Right, make sure we're solving a problem for a set of users. We want to think about creating MVPs and experiments. And get.

59:48 fast feedback, you know, some do some like rapid iteration. We want to have a strategy. We wanna know who are addressable market is. We wanna know what success is. We wanna basically have a go to market. function, right? We we need to have calms. We need to get continuous feedback from our customers. We want to keep improving.

1:00:05 Um and at some point we want to think about Yeah. Sun setting something. Right. Is it in maintenance mode? Is it sunsetting? And I think that's important in general. But I think it's extra important now because

1:00:17 When we have AI tools. We're using AI tools, we're embedding AI into our products. Things are changing so rapidly. But it It can be really important to take like half a beat and say

1:00:29 Okay, what's the problem I'm trying to solve right here? Is this metric that we've had for the last 10 years still important, or should this be sunset because it's it's not really important anymore? It's not driving the types of decisions and actions that I need. Before we get to our exciting lightning round, I wanna take us to AI Corner. Which is a recurring segment on this podcast. Is there some way that you

1:00:47 Found a a use for an AI tool. in your life in your work that you think might be fun to share, that you think might be useful to other people? So I Uh Have been

1:00:59 kind of working on some home design and like like redecorating rooms and stuff. Um, I'm working with a designer because I know what I like, but I don't know how to get there. I'm not good at this. But I've really been loving Chat GPT and Gemini especially. To render pictures for me.

1:01:14 Right. So I can give it the floor plan, I can give it one shot of the room. That's like definitely not what it's supposed to look like. And then I can Give it a c pictures of a couple different things. And I can just tell it change the walls or change the furniture layout or change something. And it helps me. And it's

1:01:29 Relatively quick. Um, it helps me kind of visualize the things. Again, I know what I like, but I don't know how to get there. So I know if I like it or not. Which is probably a very random use, but it's Fun for now. My wife does exactly the same thing. She's sending me constantly here's what this rug will look like in her living room, here's this water feature.

1:01:47 Yeah, it's so good and it keeps getting better. It's just like that's exactly our house with this new rug. Yeah. Uh and all you do is just upload these two photos and just like cool, how would this look? And I'm I've been impressed a couple of times. I mean, definitely the machines are listening to us. Um it's given me a mock up of of a room or something, and then it throws in a dog bed. 'Cause I have I have dogs and I'm like, I did not tell you to do that, but Yeah, that's probably the color and style of dogbed that I should have in this room. Speaking of that, have you tried this

1:02:14 Use case. asked Chat G BT uh generate an image of what you think my house looks like based on everything you know about me. I have it. 'Cause it m is memory and mem remembers everything you've talked about and it's

1:02:27 Hilarious. You gotta do it. Okay, that's that's on my to do list. There we go. Uh uh bonus use case. Nicole, with that, we've reached our very exciting lightning round. I've got five questions for you. You ready? Awesome. Let's go. What are two or three books that you find yourself recommending most to other people?

1:02:43 Outlive. By uh Peter Tia is fantastic. Another one that's I guess maybe related. I hurt my back. Um so like it's not great. Back mechanic.

1:02:53 By Stuart McGill. Yes. Incredible. So Shout out to anyone who has uh heart lower back. Um it's for a lay person to read through and like figure out how to

1:03:03 Explor backgrounds. Kind of a random one. I will say I love how big things get done. I can't pronounce the name. I think one's They're Scandinavian one is. Um but it's it kind of dissects

1:03:15 really large projects. through recent ish history and Where they failed and why. And I think it's really interesting for us to think about, especially now in this AI moment where Basically all of our

1:03:28 at least software systems are gonna be changing. So how do we think about approaching What is essentially going to be a very large project? Um, and then sorry, I'm gonna throw on a bonus one. The undoing project. By Michael Lewis.

1:03:39 Uh Matt Veloso recommended it to me and it's so good. Yes. Uh I'm gasped at the last sentence. Oh the book. Yeah. I read that and I I do not remember that last sentence. So man. Yeah.

1:03:52 Okay, cool. Uh next question. Do you have a favorite movie or T V show you recently watched and enjoyed? I'll say I watch Love is Blind. I if I gotta like shut down the other day, love was blind as fun. There's a new season out. Yeah, very exactly.

1:04:04 Um shrinking. Shrinking. Have you seen shrinking? No, I I I think I started the therapists and Yeah, I gave it a shot. Okay, okay. Sweet.

1:04:15 Is there a product you've recently discovered that you really love? Could be an app. Could be some kitchen gadget, some clothing. Uh yeah. The Ninja Creamy. Yes.

1:04:25 Did you say this last time? I don't know. Somebody said this and I still remember it. It's like uh you make ice cream and stuff with it, right? Yeah, and you can basically freeze a protein shake and then it turns it into ice cream. Oh man. Which is delicious. Um

1:04:39 Uhhuh. Another one is a Jura. Coffee maker. I'd love good coffee and I'm not great at making it. So I can just push the button and it'll give me Anything I want, including like lattes.

1:04:49 cappuccinos or anything. So that's kinda funny. Sweet. Okay. Uh You have to sugar and caffeine. I just need a power through the day. There's the there's the engineering productivity one on one. Yeah.

1:05:00 Mm. Oh man. Okay, two more questions. Do you have a favorite life motto? They often Find useful in work or life and come back to in various ways.

1:05:09 Yeah, I think one that's come up. A couple of times. It's not Like a verbatim thing. It's I think it's more the vibe, but like hindsight. It's twenty twenty, but it's also really dumb. Right.

1:05:18 I think if we made The best decision we could. At the time. With the information that we had available. Like

1:05:26 Then it is what it is. Right. If if you make A bad decision because you made a bad decision and you you knew better, you had the information, not great. But I I don't think we give ourselves or other people enough grace because

1:05:40 We always end up finding more information out later. Here. Final question. I was gonna ask you something else, but uh as we were preparing for this You sure that you have a new role at Google? Maybe just talk about that. What you're up to there, why you join Google, anything functioning?

1:05:53 Sure. So I am senior director of developer intelligence in core developer. So It's Super exciting and super fun because of all of these things we've been talking about, right? It's like focused on Google and all their properties and and their kind of underlying infrastructure. How can we improve?

1:06:11 Developer experience, developer productivity, velocity, all of these things we've been talking about. And 'Cause I'm kind of the numbers person, right? How do we want to think about measuring it? How does measurement change? How do feedback loops change? How can we improve the experience throughout? and then kind of drive that change through an organization in ways that are meaningful and impactful and

1:06:31 Faster than they've been before. Nice job, Google getting Nicole. What a win. I need to get some more Google stock ASAP. Okay, to follow questions, where can folks find you online if they wanna and find your book online if they wanna Dig deeper.

1:06:44 And how can listeners be useful to you? So online you can find the book at Developer Experience Book dot com. Um, I'm at ecolef.com and LinkedIn. Sometimes it's a mess. I'll I try to

1:06:57 Wade we through all of those. Uh noise I get there. Um To be useful, um Sign up.

1:07:05 For the book and the workbooks. The workbooks are free. I'd love to get you know, any kind of feedback on What works, what doesn't, uh I always love hearing those kind of stories.

1:07:15 Nicole, thank you so much for being here. Thanks for having me, Lenny. My pleasure. Thanks again. 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.

1:07:31 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 dot com. See you in the next episode.

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