Lenny's Podcast: Product | Career | Growth
How to measure AI developer productivity in 2025 | 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 not 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 kind of handed off, at least in part 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 and product team can do this week, next week, to get more depth? 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? And 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 in 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. If you enjoy this podcast, don't forget to subscribe and follow your favorite podcasting app or YouTube. It helps tremendously. Also, if you become an annual subscriber of my newsletter, you get a year-free of 15 incredible products, including lovable, replant, bold, and they ran linear superhuman descrip whisper flow, gamma perplexity, warp granola magic patterns, raycast, jeopardy, and mobin.
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