How to measure AI developer productivity in 2025 with Nicole Forsgren

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>> 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 faster every single day.

>> We need strategy and really smart decisions to know what to ship.

>> One of the biggest issues we're going to 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.

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 going to 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 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 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.

>> 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?"

Are these AI tools actually helping us or hurting how our developers work?

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.

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 at your company, 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.

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With that, I bring you Nicole Forescreen.

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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 two and a half years ago.

I was watching it, and I was both shocked and not shocked that we barely talked about AI.

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 going to happen with AI and productivity."

Does that just blow your mind?

Yeah, because it was just hitting the scene.

It was the topic of so much conversation, and at the same time, so many things don't change.

So many things are still important.

So many things are the same.

Yeah, it's also a little wild that it's been two and a half years.

Where does this sound good?

Time is a social construct.

Yeah, it was like most of our conversation was just like questions like, "Well, how might this impact people?

How will we change the way we build product?"

And now, it basically 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 already spending a lot of our time focusing on today.

The reason I'm excited about this conversation, it feels like there's been so much money poured into AI tools, increasing productivity.

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 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've invented so many of the frameworks that people rely on now.

So I'm really excited to have you back in to talk about this stuff.

I want to start with just like this term "devx," which is something that comes up a lot in this whole space.

So we're going to hear this term a bunch in this conversation.

Can you just explain what is "devx," this term "devx"?

So devx is a developer experience.

And when we think about developer experience, we're really talking about what it's like to build software day to day for a developer.

So the friction that they face, the workloads that they have to go through, any support that they have.

And it's important because when devx is poor, everything else just isn't going to help.

The best processes, the best tools, the best whatever magic you have.

If the devx is bad, everything kind of takes.

And so within devx is productivity.

And I think the key insight that you had and other folks in the space about it is not just like productivity, but there's also engineering happiness.

And we're going to get into a lot of these parts, but just maybe speak to 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 you're just looking at output, you can get there 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 is going to burn out.

Or if it's super high cognitive load, if it's hard to even think about what you're doing because you're concentrating on the mechanics of the plumbing of something, then you don't have the brain space left to come up with really innovative solutions and questions.

And so I love that it's kind of this self reinforcing loop in terms of you do more work, you do better work.

And it's better for people, it's better for the systems, it's better for our customers.

Something I was going to get this later, but I want to 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 10 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 fun.

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 kind of being written for you.

Talk about just the importance of flow state to a developer, happiness, developer productivity, and just what you've seen AI impacting, how you've seen AI impacting that.

A lot of times, well, there are lots of different ways to talk about DevEx.

One way to talk about it is three key things that have components that are important up themselves, they also kind of reinforce each other.

So flow state is one of them, cognitive load is another, and then feedback loops are another.

And so I think when you touch on this, your question about flow state is a really good one.

And I'll admit, we're just a few years into this, we're still figuring out what the best flow state and cognitive requirements are for people in this because to your point, sometimes we're getting interrupted all the time.

You don't just get in the flow and lock down and write a whole bunch of code and do the type of a whole bunch of code as much anymore.

Instead, you're kind of creating a prompt, getting some code back, and reviewing the code, trying to integrate what's happening in the system.

And that can really interrupt.

At the same time, though, it can contribute to flow.

And I've seen some senior chairs pull together some tool chains that are really incredible where they figured out how to kind of keep the flow going.

The fast feedback loops really, really work well for them.

They can kind of assign out different pieces 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 and fix some 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.

It's on my list.

So they've been able to set up this really incredible workspace and workflow where like right now a lot of us play around with tools and we'll put in a prompt and we'll get a few lines back or maybe we'll put in a prompt and we'll get whole programs back.

Well, what they can do is they can many times I'll see them say to kind of help prime it, 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 this 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 piece in parallel.

And then it'll say, "Oh, and upfront, these need to be able to work together, make sure it's architected correctly, make sure we use appropriate APIs and conventions."

Then at the end, and then they can let it run for a few minutes and they can think through something else that's interesting or they anticipate it's going to be hairy.

And they come back to something that's probably a little better than vibe coded because they were so systematic about it upfront, they're much closer to something that looks like production code.

>> So what I'm hearing is spending a little more time upfront planning what all these AIA engineers are doing versus just powering through and just figuring out as you go.

>> Yeah.

>> Okay, cool.

Let me get to this quite a core question that I think that is a lot of people's minds.

A lot of companies are trying to measure productivity for their teams.

Is this improving our productivity?

Is this hurting your productivity?

So let me just start with this question.

How are people doing this wrong currently when they try to measure their productivity gains with AI?

>> I will say most productivity metrics are a lie.

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 as some proxy for output or productivity or complexity or something, right?

Well, now, for many of the systems that they would sometimes whisper and not super talk about that uses lines of code, it's just blown out of the water because what do you mean by lines of code?

If the goal is more lines of code, I can prompt something to write the longest piece of code ever and add tons of comments.

And we know that agents and LLMs tend to be very verbose by definition.

And so it's just too easy to gain 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 watch and pay attention to because so lines of code as a productivity metric isn't 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.

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 retraining systems later, especially if we're doing fine tuning and local tuning, how much of that is machine generated, right?

What types of loops is that creating and what types of patterns or biases might it be in infirmly introducing?

So on the one hand, it's not good as a productivity metric, but it can be useful, right?

And 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 not going to be sufficient anymore because AI has now changed the way we think about feedback loops, right?

They need to be much faster.

Now, what Dora is meant for, you know, kind of assessing the pipeline overall and 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 phenomenon and changes in the way people work.

Interesting.

So you invented Dora, that was kind of the main framework people used for a long time to measure productivity.

And then there's space, there's core four, there's probably others.

So what I'm hearing here is all these are kind of at a 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 Dora four, they are four key metrics, there's two speed metrics, deployment frequency and lead time, so code commit to good deploy, their stability metrics, 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.

If you're trying to use those to understand, because applied to that is feedback loops, right?

Because you used to kind of like to get feedback from customers.

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 throughout, and even sometimes in the middle of some of the pipeline that we really want to leverage in ways that weren't as useful before, I won't say they weren't possible, but like we just didn't really focus there.

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.

But now I think that's the power of it.

We're actually seeing that space applies fairly well in these new emerging contexts like AI, because we still want to look at so space is an acronym, right?

So we still want to look at satisfaction.

We still want to look at performance, what's the outcome?

We still want to look at activity.

Yes, in some ways lines of code and number of PRs can be useful for something, right?

Or number of alerts or number of things, activities or counts.

Seeing as communication and collaboration, this is also super important and useful because it's how our systems communicate with each other and also how our people do.

What proportion of work is being offloaded to a chatbot versus talking to a senior junior 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?

And here I would probably add a couple of dimensions, right?

So chatting with some of the early authors to say, trust, not to say trust wasn't important before, but now it is very, very front of line, right?

Before you build your code, if the compile comes back, you're fine.

And 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 are we seeing hallucinations?

What's the reliability?

Does it meet the style that we would typically write?

And if it doesn't meet, is that fine?

So that's my kind of, it depends answer, prescriptive, you got to make sure you're using it fit for purpose, right?

We're going to get to your current thinking on the best way to do this stuff.

You have a book coming out that explains the how to do this well.

So we're going to get to that.

One thing I wanted to highlight in our last chat that we had, you, you highlighted one of the biggest issues we're going to probably have with AI is trust, understanding 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 going to be spent reviewing code versus writing code.

That's exactly what I'm hearing.

I think it'll be interesting to see 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 it's broken up from how we used to do it.

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.

Can you say more about that?

Just what is that?

What are you thinking will be happening?

Where do you think things go?

What are you seeing working?

So purely speculative.

But for example, Gloria Mark has done some really good work on attention and deep work, and humans can get about four hours of good deep work a day.

Like, that's about it.

Yeah, I feel that.

And that that's like kind of the upper limit ish for the most part.

And I'm sure people are going to be like, well, I am superhuman.

I can do this.

What if you take 20 grams of creatine?

Right.

What if we micro dose?

Yeah.

So in the context of knowing we have about four hours of good deep work, and I'm sure many of us have probably had this right, we're like, we have good periods, like maybe it's morning, maybe it's afternoon for folks, and then you hit a time where you're like, I'm going to clean up my inbox, because that is all I can do right now, right?

Like I can be functional, but I'm not going to come up with my best, innovative, problem solving, authoring, code writing work.

A lot of times the way to do that and to get into it is to have these long chunks to get into flow and to get that deep work, right?

And it's usually, this is making, I'm like hand waving, right?

Two hours, right?

Like an hour can be tricky, because it could take time to get into that state.

Okay, well, when we think about what it used to be like back in the old days, three years ago, three years ago, we could block off four hours of time, and we could probably get two or three hours of really good work done.

Now, because we were just focused, right?

There were no interruptions, minimal interruptions.

Now, the nature of writing code and systems itself is interrupt driven, 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.

But does that mean that 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, and all the things.

And so I think that's a really, really interesting area that's just right for questions and opportunity.

And please folks, do this research 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 EM, engineering manager, coordinating all of these junior AI engineers.

And so your point is, even if you have like a 30 hour block, you can 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 the remind you of just like, here's where you left off, okay, you can just jump into this code, maybe make some tweaks.

Yeah.

So interesting.

Let me zoom out a little bit.

And before we get into your framework for how to approach developer experience, the latest thinking you've got, beyond just like, obviously, engineering, engineers doing more is great.

What's, what's your best pitch for why companies should really, really, really focus on developer experience?

I hate to say return of investment, but like the business value is 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.

It helps us with market share, it helps us attract and retain customers.

That helps us do all of these things.

And, you know, I think DevEx is important because it enables all of that software creation, it enables all of that problem solving, it enables the super rapid experimentation with customers that before, you know, 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.

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 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 and listen.

And I love that, you know, the audience of this podcast is primarily PMs because they tend to be really good at this.

And I would say start with listening and not with tools and automation.

So many times companies are like, "Well, I'm just going to build this tool or I'm going to build this thing."

Often you build a thing that you yourself have had a challenge with or that like is easy to do, easy to automate.

And if you just go talk to people and ask the developers, like, "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 there friction?"

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 and still have impact.

Or you can identify a process that's unnecessarily complex and slow.

So the listening to her here most is you want to help your teams move faster, be happier, and 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 most developers are going to be more than happy to tell you what's broken and what's bad.

And I mean, I'll say there was one company that I had worked with.

I remember they had a process that was really difficult and it was on an old mainframe system and they were going to have to like replat the whole thing.

And so they never went to work on it or talk about it.

Everyone hated it because it was this huge delay.

I mean, all they had to do was change a process.

Sometimes all you have to do is change a process and they changed it.

So instead of, I think it was someone had to print it out and walk it down three or four flights and then get approval.

And then someone else had to walk it back up.

And so it was just that interim.

They didn't replat anything.

They didn't redesign anything major.

They just had to send an email.

Let me push on that.

And I'm curious, just what are the most common things people do?

Like if you were just starting on, okay, we need to focus on engineering experience.

What do you find are the most like, I don't know, two or three most common improvements companies need to make?

I will say, you know, kind of echo that process.

There's almost always a process that can be improved and that can be approved, improved without a lot of engineering lift or a lot of engineering headcount.

Most large companies in particular have something that is several, several steps.

It's the way it is because it's the way it is, but that's no longer the way it is.

And even small companies, sometimes it's just a little too yellow 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 exposure to the whole rest of the org, right?

Sometimes just a team process can help.

I will say from a business leader 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.

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 people to care and to get them stay involved, right?

Because it feels like just another internal project that isn't going to matter or that isn't going to get celebrated, but it has these huge upside 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 install this new deployment system.

It's processes and people and Oregon morale.

Yeah.

Now there will be technical pieces that are very important, right?

Especially now with AI, right?

We're rethinking how built-in 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, 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 going to ask you, I have a hard question I want to ask you that I thought of as you were talking.

I feel like this is the question that most founders and heads 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 they're just not performing as well as they can?

What are just maybe smells, signs that tell you, yeah, my team should be moving faster versus like this is just the way it works.

This is as fast as they can move.

Most teams can move faster, right?

And also, given what we know about cognitive load, not all speed gains are necessarily good, right?

Or the upside is going to be kind of limited, right?

Once you hit kind of a certain point, most people are not even near that point.

I don't know a single team frankly.

But how do you know?

You know if you're always hearing about bills breaking, flaky tests, overly long processes, if you have to request a new system, or if you need to provision a new environment, or if it's really, really hard to switch tasks or switch projects, right?

So if someone has an opportunity to go work in 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, that's usually a pretty good smell that there's friction somewhere.

Because once you finally figure out your system, and you're able to get work done, 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 worked with companies where switching orgs within the company, you had to basically pay the same tax as a new hire.

Because the systems were so different, and they were 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.

To your point, though, there's diminishing returns over time.

Yeah, 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 can we can ship trash faster every single day.

We need strategy and really smart decisions to know what to ship, what to experiment with, what, what features we want to do in what order and what rollout, right?

The strategy is the core piece.

And then think about speeding that up.

If we don't have the other pieces in place, I mean, garbage in garbage out.

I'm going to 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, the productivity of your team is builds are always breaking.

There's flaky tests that are constantly incorrect false positives.

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.

Is that roughly right?

Yeah.

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 one part of engineering work.

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

Yes.

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 targets of experimentation or think about our total address of the market, but we need to have that strategy and plan fairly well aligned, 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.

We can throw out prototypes, we can run any tests and experiments that our customer are facing, assuming that we have the infrastructure in place, which allows us to learn and progress much faster before.

Like some places that used to take months to get something through production to do A/B testing and get feedback, we can do this in a day or two, definitely under a week.

But we want to make sure that we're building and testing the right things.

Are we partnering with the repo?

Do we have the data that we need?

And I will say AI can actually be a pretty good partner there if you have a good conversation with it and then also check with your experts.

What type of data should I be looking at?

What type of instrumentation do I need?

What type of analysis can I do?

Because then you can also go to your data science team and say, "I'm planning on doing this.

I'd like to..."

Because let's not just yellow A/B test, right?

Because that can be...

It's a shame to do a large test and end up disrupting users or disrupting customers or breaking privacy or security protocols and also end up with data that's unusable, right?

Because you just can't get the signal that you're looking for.

But now I'm also seeing people accelerate that into a few days versus a few weeks.

And so they can start those key stakeholder discussions from a much more informed, filled out space.

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coda.io/leni I love that you work with a bunch of different companies and a bunch of different types of businesses.

I think very few people get to see inside a lot of different places.

What kind of gains are you just seeing in terms of increased productivity with AI?

How real?

How big of a gain have you seen?

I'd say it's real.

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.

One of the best is going to be velocity all the way through the system.

How quickly can you get a feature or a product or something through the system so that you can then experiment and test, either from idea to final end or even a 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 a particular AI tool in the hands of a particular developer.

There are some other things that we can look at and we can see.

That I've seen is, again, this kind of rapid prototyping.

I hate lines of code, but I'm going to use lines of code.

We do see, I know I worked with some folks who had a whole set of companies they were looking at and they found that AI was generating significantly more code for the people who were using it regularly.

But then they also found that for folks who were regular users of AI coding environments, AI, IDEs, the tool gave them more code and then the engineers themselves, the increase was double what the coding agent had given them.

One, I'd say probably a secondary or knock on or just a smell is it can unblock you.

It can speed up the work that you would already do.

I know sometimes my work is like the first few minutes.

It's hard for me to start, but once I get started, I'm there.

They're really good at unblocking and unlocking that.

Something I've seen people on Twitter sharing is how good OpenAI Codex, especially, is at finding really gnarly bugs.

I think it was Karpathy that shared he was so stuck on a bug and no AI tool could figure it out.

Then the latest version of Codex, it's been like an hour or something, looking into it and found it for him.

Yeah, I'm hearing incredible things like that.

Even also writing unit tests, spinning up unit tests and creating documentation and cleaning up documentation because I know now people are like, "Oh, well, we have agents.

I don't need to read the docs because there's the code there."

Turns out agents rely on good data because it's all about how they've been trained or how they've been grounded and better data gives you better outcomes.

Some of that data includes documentation and comments.

The better documentation, the better comments you have, the better performance you're going to get out of your AI tools.

AI can help you write that documentation.

I've been working with Dev in a little bit and it's really good at that stuff.

Yeah.

Okay.

Let's talk about this framework, this book.

You're publishing a book called Frictionless, which sounds like a dream.

How do you create a Dev team that's frictionless?

It's called Frictionless, seven steps to move barriers along value and outpace your competition in the age of AI.

There's a seven step process to this.

Walk us through this.

Maybe give us just context on this book, what it was meant for, what problem it solves, and then the seven steps.

I will say I also wrote this with Abhinoda who has just of DX, he has incredible experience in the space.

He's worked with hundreds of companies and so it was nice bouncing ideas off of him.

Also, thanks to all of the engineering leads and dev actually, the CTOs and engineers that we talked to to make sure that our smells were right.

Who is this book for?

Let me actually take a tangent on Abhi and DX since you mentioned him.

This is super interesting and I think it connects so directly with this conversation.

Abhi started this company called DX, which is such a great name for a company around developer experience.

They just sold the company for a billion dollars to Atlassian.

It's a very high multiple on their ARR.

It to me shows exactly why this conversation is so valuable.

Just how much value companies are putting into improving developer experience.

Atlassian would spend a billion dollars on this.

It's like an early stages startup.

It was doing really well and people left it, but it was like early stages.

A billion dollars.

And now, and the idea is they have all these companies working using Jira and all their products.

They're all trying to figure out how do we measure productivity.

It's worth a lot of money to them.

I know you were an early advisor to them too.

It just shows us how important this is.

Yeah.

Well, I think it also shows us how much value you can get out of this.

There's so much low hanging fruit.

There's so much unlocked potential and it's hard to know where to start a lot of times.

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 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 you need to start over later.

So I guess, which also brings us back to who's this book for.

It's for anyone that cares about DevX.

So definitely technology leaders.

Anyone who's trying to kick off a DevX program or is working on a DevX improvement program, I think it's particularly relevant for PMs because if you're PMing something that involves software, building and creating software, improving DevX will only help your team.

And also, you have key skills and insights and instincts that are so important to DevX that many times I will say I've seen engineering teams just miss.

Okay.

What's the framework?

What are the steps?

Where do people start?

So the book goes through a seven step process and then also provide some key principles at the end.

Step one is to start the journey.

So assuming you're kicking off, you can start the journey.

And this involves what we have already talked about.

Go talk to people, have a listening tour, synthesize what you learn, visualize the workflow and tools, get a handle on what the current state is.

Step two is to get a quick win.

So start small, get a quick win, pick the right projects, share out what you've done.

Step three is using data to optimize the work.

So establish some of your data foundation, find the data that's there, start collecting new data, use some surveys for some really fast insights and may include example surveys.

Step four, then 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, 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 convince everyone else.

Right?

So now you want to get feedback, you want to share why this is the right strategy right now.

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 dev team, you want to do it yourself, kind of grassroots effort or global scope of control, right?

If you're the VP of developer experience or something like there are some things that you can leverage for a top down.

And then how do you drive change when you're kind of somewhere in the middle?

Because you can leverage both types of strategies.

And then step seven is to evaluate your progress and show value and then kind of loop back around.

And I will say that we wrote this so that you could kind of jump into any step wherever you are right now, right?

Like if you're kicking off a team 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 picking the priority or implementing the changes.

So those are seven steps.

There are a few practices that we also recommend.

So thinking about resourcing it, change management, 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?

Awesome.

Okay, I have questions.

Point people to the book real quick with what's the URL, how did they get it?

When does it come out?

Yeah, developer experience book.com.

So right now you can sign up for the mailing list.

We'll let you know when it's out on pre-order and we'll also be sharing pieces of the workbook.

So we've got almost 100 page workbook that goes along with the book.

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 in that it's not developer productivity, developer work.

It's how do we make developer experiences better at our company, which includes they get more done, but also they're happier, things like that.

So I think that's an important element of this, right?

Yeah, yeah, absolutely.

Because again, it's not just about productivity, right?

We talked about this from kind of the frame and 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.

And then they can solve it better, right?

They have the freedom, they have the innovation, they have the creativity so 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, making them more productive and removing friction, because then they have the flow and the cognitive load and the things that we kind of talked about.

Awesome.

Okay.

And then say someone wants to start this team.

What does it usually look like?

At Airbnb, I remember this team forming, it was just like an engineer or two, getting it started and taking charge.

What do you recommend as the pilot team?

And then what does it look like as it grows?

So there are a few ways to do this, right?

So if you're doing it yourself, you could do it with a couple engineers, maybe a PM or a PGM or a TPM to kind of help communicate because really comms plans are just so important here.

On a small scale, what we want to do is look for those quick wins, look for things that you can do at small scale.

Some folks call them things like paper cuts, but they're small things that you can do to help people see the value and feel the benefit themselves, right?

How can a developer's 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 and you have the remit, you still want some quick wins, but those quick wins can look a little more global in scale because you have the infrastructure or the backing to make different types of changes that aren't only local.

So an example of a small local change could be just cleaning up your tests in your test suites, right?

Any team could do that.

Any team could do that.

More global scale might be changing organization-like process that is just overly cumbersome or throwing some resourcing into cleaning up the provisioning environment.

What kind of impact have you seen from teams like this forming on the engineering teams at their companies?

I'll say I've seen a huge impact for smaller companies, hundreds of thousands of dollars for large companies in the billions.

Well, also, we need to learn how to communicate that.

What does the math look like?

Many times, we can look at saving time.

We can look at saving costs.

We can look at a lot of different things.

We can look at speed to value as speed to market.

We can look at risk reduction.

But the gains really are there.

I will mention that it tends to follow something like J-curve.

You'll have a couple of quick wins and it'll look like a big win.

Then you'll hit a little divot where suddenly the really obvious projects, the low-hanging fruit are handled.

Now we need to do a little bit of work.

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.

Then once we get that done, then we start to see those benefits really compound.

Going back to that measurement number, what do you recommend?

How do people find these numbers?

Because 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 figure that out?

I think there are a few different things to keep in mind.

Who is our key audience?

We usually have a few key audiences.

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.

For them, we often want to frame this in terms of things they care about.

Time savings, if something gets faster, they can save time.

They don't spend time doing setup when they don't need to anymore related to that is reduced toil.

Compliance and security are super important.

Also, many times it requires several, several manual steps that I don't say they're not value add.

They are not value add from an individual human perspective.

If we can automate as much as possible, that's great and improve focus time.

That's from the developer side of you.

Leadership often cares about they care about those things, but they often care more about other things.

We could talk about usually costs in dollars.

Can we accelerate revenue?

What does our time to value look like?

What is our velocity?

How quickly can we get feedback from customers?

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.

Here, we can look at maybe quantifying savings.

One example is test and build.

If we can clean up a test and build suite to a developer, they really want to hear about time saved and more reliable systems.

There's less toil because they don't have to keep rerunning tests or go clean up test suites.

From the business perspective, cleaning up a test and build suite can be cloud cost savings because all of those tests are running somewhere on a cloud.

If they always fail or if it's just a waste of spend, that can be useful.

Recoverying some capacity.

We can always talk about time and productivity gains.

How much equivalent developer time are we losing on things that are not necessarily value add?

And then sometimes we can correlate to business outcomes.

Correlate is usually the best we can do here, but there can be some pretty compelling correlations in terms of speeding up time to value and increased market share, for example.

So let me follow that thread and come back to this.

What I think is the biggest question people have right now with AI and productivity, which 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 to understanding what impact AI tools are having on their productivity?

Because they're spending all this 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?

I would say it depends.

And in part, it depends on what your leadership chain really cares about.

Right?

Like, we're usually pretty good at like figuring out what matters to developers, and 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, right?

Losing market share or competitiveness in the marketplace.

If that's it, focus on speed.

Think about ways that you can capture metrics for speed from like feature to production or feature to customer or feature to experiment and what that feedback loop looks like.

If they're talking about profit margin all the time, right?

Now, we always talk about money, right?

Because 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 recovered and recouped 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 reductions in vendors spent can also help there.

And I say also it depends because sometimes something will, they'll say something, right?

Like leadership will say something and it kind of comes up as a theme.

If you can solve a problem that they have 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.

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 if your company is trying to figure out what sort of impact our AI tools are having on our company versus just like 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 or we need to transform, transformation.

So your advice there is like figure that out based on words and phrases you're hearing, then figure out ways to measure that, ways to measure market share growing, profit margin increasing.

So it could be, I love these examples like time from feature idea to production or to experiment.

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, and velocity, I imagine that's where things like Dora command of just like speed of engineering shipping.

Or what would you think about there for velocity?

I would say it's actually one of those, I would pick as broad as small 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 can it take?

And does it take now with improved use of AI tooling and reduction in friction?

And that's where I will say, we talk about this a little bit in the book, how do we deal with attribution challenges?

So like, what was responsible for this?

Was it the DevEx or was it AI?

Go ahead and disclose that, right?

Say, yes, we rolled out AI tools.

We also had this effort in DevEx.

They partnered very closely together.

Both of them probably contributed to this, right?

Like if we had AI tools without the DevEx 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 want to start measuring developer experience, are there like a two or three metrics, everybody basically needs that you should just start measuring ASAP.

If you're just starting today, and if you have nothing at all, talk to people, obviously, after that, I would do surveys.

Because surveys can give you a nice kind of overall view of the landscape quickly, so that you know where the big kind of challenges are.

And I say that because if you're just starting, you might not have instrumentation through your system, all the metrics.

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, 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 we include an example in the book, you can just ask a few questions, right?

How satisfied are you?

What are the biggest barriers to your productivity?

Or what are the biggest challenges to getting work done?

And let them pick, you know, either from a set of tools or maybe like a set of processes, and then say, let them pick three, just three.

Of those three, how often does this affect you?

Is this hourly?

Is this daily?

Is this weekly?

Is this quarterly?

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.

And then kind of open text, right?

Like, is there anything else we should know?

That can give you incredible signal.

Because by making folks prioritize the top three things, let them pick everything.

Like it makes 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 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.

Yeah, and I will say like one thing that's challenging when you start, when you think about getting data, right?

So interviews are data, and that's important.

Surveys are a little more quantified, right?

Because we can turn into counts.

But that's where we also want to be careful, right?

A lot of folks go to write a survey question, and they'll say something like, "Were the build and test systems slower or complicated in the last week?"

You're asking four different questions there.

If someone answers yes, was it the build?

Was it the test?

Was it slow?

Or was it like flaky or complicated or something, right?

So it can be really difficult to untangle what the signal is you're actually getting there.

And so it is worth time chatting with someone who's familiar with survey design, having a conversation with Claude or Gemini or chat GPT around here the survey questions or can you propose them?

And then make sure you take a couple of rounds.

Is this a good survey question?

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.

And you have example surveys in your book for folks that want to just copy and paste and not have to think about that much.

Example surveys, a lot of example questions, we even recommend like what the format, like how what the flow should look like, how long it should be, how long it should not be.

One thing that I was reading is that you don't love happiness surveys, specifically asking engineers how happy they are.

Is that true?

If so, why is that?

I don't.

Now I will say I don't love a happiness survey because there are too many things that contribute to happiness.

Happiness is a lot, right?

So happiness is work, happiness is family, happiness is hobbies, happiness is weekends, happiness.

There are so many things that contribute to happiness.

Now that doesn't mean I don't care about happiness.

I think happiness surveys are not particularly useful here.

What can be helpful is satisfaction and people are like, "What's the same fate?"

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 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?

How to California, that was the best.

Happy devs make happy code.

They write better programs, they do better work.

They're better team members and collaborators.

But capturing and trying to directly influence happiness, that's not what we're here for.

And it's too challenging, it's too uncompensating.

Satisfaction can give us some signal.

In a totally different direction, in terms of just tools you see people using, are there any that just like, "Oh yeah, this one's really commonly great for people."

This is just a tool people are finding a lot of success with.

There's the common ones, CoPilot, Cursor, I don't know.

Is there anything that stands out that you want to share just like, "Hey, you should check this tool out."

People seem to love it.

I think the use, right?

CoPilot, Cursor, Gemini.

Cloud code.

Yep, cloud code.

I love cloud code.

I have a whole post coming on waste use cloud code for non-engineering use cases.

Ooh, nice.

It's so interesting.

For example, cloud code, find ways to 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 you could do all kinds of crazy stuff on your computer for you, like a little mini God.

Well, I'm going to do that now.

This is great.

It's so good.

Yeah, that's why I'm writing this.

I had a Dan Shippers on the podcast and he said, "Cloud code is the most underrated AI tool out there because people don't realize what it's capable of.

It's not just for coding."

And that's what I'm trying to explore more and more.

Okay.

Is there anything else that you think would be valuable for people to hear, to help people improve their developer experience, help them adapt to this new world of AI and engineering that we haven't covered?

I think something that's important to think about in general is to bring a product mindset to any type of DevX improvements that are happening.

And also the metrics that we collect and capture.

And by that, I mean we want to identify a problem, make sure we're solving a problem for a set of users.

We want to think about creating MVPs and experiments and get fast feedback, do some rapid iteration.

We want to have a strategy.

We want to know who our addressable market is.

We want to know what success is.

We want to basically have a go to market function.

We need to have comms.

We need to get continuous feedback from our customers.

We want to keep improving.

And at some point, we want to think about sunsetting something.

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 when we have AI tools, we're using AI tools, we're embedding AI into our products.

Things are changing so rapidly that it can be really important to take half a beat and say, "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 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 want to take us to AI Corner, which is a recurring segment on this podcast.

Is there some way that you found 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 have been working on some home design and like redecorating rooms and stuff.

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.

So I can give it the floor plan, I can give it one shot of the room that's definitely not what it's supposed to look like, and then I can give it 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 relatively quick.

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.

It's so good.

And it keeps getting better.

It's just like, that's exactly our house with this new rug.

And all you do is just upload these two photos and just like, how does this look?

Yeah, I've been impressed a couple times.

I mean, definitely the machines are listening to us.

It's given me a mock up of a room or something and then it throws in a dog bed.

Because I have dogs, I'm like, I did not tell you to do that.

But yeah, that's probably the color and style of dog bed that I should have in this room.

Speaking of that, have you tried this use case?

Ask chat JBT, generate an image of what you think my house looks like based on everything you know about me.

I have it.

Because it is memory and remembers everything you've talked about.

And it's hilarious.

You got to do it.

Okay, that's on my to do list.

There we go.

Bonus use case.

Nicole, with that we've reached our very exciting lightning round.

I've got five questions for you.

Awesome.

Let's go.

What are two or three books that you find yourself recommending most to other people?

Outlive by Peter Attia is fantastic.

Another one that's maybe related.

I hurt my back.

So like, it's not great.

Back mechanic by Stuart McGill is incredible.

So shout out to anyone who has hurt lower back.

It's for a layperson to read through and like figure out it.

Dicks lower back problems.

Kind of a random one.

I will say I love how big things get done.

I can't pronounce the names.

I think once there's Scandinavian one is, but it's it kind of dissects 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 you know, in this AI moment where basically all of our deadly software systems are going to be changing.

So how do we think about approaching what is essentially going to be a very large project.

And then sorry, I'm going to throw on a bonus one.

The Undoing Project by Michael Lewis.

Matt will also recommended it to me and it's so good.

Yes.

I read that at the last sentence.

Oh, the book.

Yeah, I read that.

And I do not remember that last sentence.

Oh, man.

Okay, cool.

Next question.

Do you have a favorite movie or TV show you recently watched and enjoyed?

I will say I watched Love is Blind.

If I got a like shutdown the other day, Love is Blind is Fun.

There's a new season out.

Yeah, very excited.

And shrinking, shrinking.

Have you seen shrinking?

No, I think I started the therapists and yeah, I gave it a shot.

Okay, sweet.

Is there a product you've recently discovered that you really love could be an app could be some kitchen gadgets and clothing?

Yeah, the Ninja Creamy is.

Did you say this last time?

I don't know.

I mean, I still remember it.

It's like, you make ice cream and stuff with it, right?

Yeah, you can basically freeze a protein shake, and then it turns it into ice cream.

Oh, man, which is delicious.

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, cappuccinos or anything.

So that's kind of sweet.

Okay.

Do you have fish or like?

For sugar and caffeine, I just need to power through the day.

There's the engineering productivity 101.

Yeah.

Oh, man.

Okay, two more questions.

Do you have a favorite like motto that you often find useful in work or life and come back to in various ways?

Yeah, I think one that's come up a couple times.

It's not like a verbatim thing.

I think it's more levi, but like hindsight is 2020, but it's also really dumb, right?

I think if we made the best decision we could at the time with the information that we had available, like that it is what it is, right?

If you make a bad decision because you made a bad decision and you knew better, you had the information, not great.

But I don't think we give ourselves or other people enough grace because we always end up finding more information out later.

Here, here.

Final question.

I was gonna ask you something else.

But as we were preparing for this, you share that you have a new role at Google.

Maybe just talk about that.

What you're up to there, why you joined Google, anything folks should know.

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 their kind of underlying infrastructure.

How can we improve developer experience, developer productivity, velocity, all of these things we've been talking about?

And because 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 faster than they've been before?

Nice job, Google, getting Nicole.

What a win.

I need to get some more Google stock ASAP.

Okay, two follow up questions.

Where can folks find you online if they want to and find your book online if they want to dig deeper and how can listeners be useful to you?

So online, you can find the book at developerexperiencedbook.com.

I'm at nicolefv.com and LinkedIn, occasionally sometimes it's a mess.

I try to wade through all of the noise I get there.

To be useful, sign up for the book and the workbooks.

The workbooks are free.

I'd love to get any kind of feedback on what works, what doesn't.

I always love hearing those kinds of stories.

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.

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