https://idiallo.com/blog/is-30-percent-of-microsoft-code-ai-generated Ibrahim Diallo Main Menu * [?] [ ] * Home * Blog * Book * Byte-Size * JavaScript * Video * Photography * About Is 30% of Microsoft's Code Really AI-Generated? What percentage is copy-pasted from Stack Overflow? By Ibrahim Diallo Published 2 days ago ~ 4 minutes read Fund this Blog A few months back, news outlets were reporting that Satya Nadella claimed 30% of the code in Microsoft's repositories was AI-generated. This fueled the hype around tools like Copilot and Cursor. If Microsoft's developers were now "vibe coding," everyone should embrace the method. I have to admit, for a moment I felt like I was being left behind. When it comes to adopting new technology, I typically choose the slow and careful approach. But suddenly, it seemed like the world was moving on without me. Here's the thing though, I use Copilot. I use Cursor at work as well. But I can't honestly claim that 30% of my code is AI-generated. For every function an AI generates for me, I spend enough time tweaking and adapting it to our specific use case that I might as well claim authorship. Is that what Microsoft employees are doing? Or are they simply writing prompts or a set of instructions, then letting the LLM write the code, generate the tests, and make the commits entirely on its own? So I went back to reread what Satya actually said: I'd say maybe 20%, 30% of the code that is inside of our repos today and some of our projects are probably all written by software. Fair enough. But then I watched the video where he actually said it. Interestingly, it was Zuckerberg who asked the question. What you hear in the interview is a whole lot of "maybe," "probably," "something like". Not the confidence portrayed in the written headlines. But here's what I really want to know. How are they tracking this? Are developers labeling all AI-generated code as such? Is there some distinct signature that marks it? How can you even tell when code is AI-generated? Unlike a written article where we can identify clear patterns, telltale phrasing, word choices that deviate from an author's typical style, code doesn't come with obvious fingerprints. For example, there's no way to tell when a senior developer on my team uses AI. Why? Because they don't commit code they haven't thoroughly reviewed and understood. They treat AI suggestions like rough drafts, useful starting points that require human judgment and refinement. With junior developers, you might occasionally see a utility function defined for absolutely no reason, or overly generic variable names, or unnecessarily verbose implementations that scream "AI-generated." But these issues rarely make it past the code review process, where more experienced eyes catch and correct them before they reach production. Before LLMs entered the picture, what we worried about was developers copying and pasting code from Stack Overflow without understanding or modifying it. These snippets weren't easy to identify either, unless they broke the logic or introduced bugs that revealed their origin. You couldn't reliably identify copy-pasted code back then, so what makes it any easier to identify AI-generated code now? Both scenarios involve code that works (at least initially) and follows conventional patterns, making attribution nearly impossible without explicit tracking mechanisms. The line between "AI-generated" and "human-written" code has become blurrier than the headlines suggest. And maybe that's the point. When AI becomes just another tool in the development workflow, like syntax highlighting or auto-complete, measuring its contribution as a simple percentage might not be meaningful at all. --------------------------------------------------------------------- Did you like this article? You can buy me a coffee. Share your insightful comments here. Join my newsletter [ ] [ ] Subscribe JavaScript is required to combat spammers. Follow me on Twitter, Spotify, or RSS Feed Next: How Do You Send an Email? Previous: The App Developer's Attachment Issues On a related note, here are some interesting articles. Vibe Coding Vibe Coding Describe what you want and AI generates the entire codebase. That's vibe coding. Imagine you have an LLM that can build a car for you. All you have to say is "Build a car for me, here are the specs." And it gets started right away. Before you know it, a two ton machine drops in front of you and bounces as the suspension recoils. You open the door, press on the ignition button and nothing happens. Unless you understand how cars work internally, which you don't, your option will be limited to "Rebuild the car, but with a working power button". When Solutions Get Fixed When Solutions Get Fixed As a company grows, so do its processes. At first, it's all about building fast and solving problems. But once you hit a certain size, things like SOC 2, GDPR, and compliance become part of everyday operations. These changes are well-intentioned. They're meant to protect the company and its customers, but they often overreach. And when they do, they don't just add structure. They reduce efficiency. The Great AI Filter The Great AI Filter Google just released the future of their Chrome browser. To put it simply, it's AI everything. Meta also released their new smart glasses, complete with a "neural" wristband for input. It too is AI everything. The more I watched these product launches, with their proclamations about the future, the more I was reminded of this observation from Catch-22: View all articles Comments There are no comments added yet. Let's hear your thoughts Comment [ ] Your Name (Required) [ ] Your Email (Required) [ ] For my eyes only Your Website [ ] Would you like to sign up to the news letter? 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