Firstly I think this is neat, but the dam has burst.
This might be great for educational institutions but the idea of people needing to know what everyline does as output feels mute to me in the face of agentic AI.
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I’m Henry, cofounder and CTO at Span (https://span.app/). Today we’re launching AI Code Detector, an AI code detection tool you can try in your browser.
The explosion of AI generated code has created some weird problems for engineering orgs. Tools like Cursor and Copilot are used by virtually every org on the planet – but each codegen tool has its own idiosyncratic way of reporting usage. Some don’t report usage at all.
Our view is that token spend will start competing with payroll spend as AI becomes more deeply ingrained in how we build software, so understanding how to drive proficiency, improve ROI, and allocate resources relating to AI tools will become at least as important as parallel processes on the talent side.
Getting true visibility into AI-generated code is incredibly difficult. And yet it’s the number one thing customers ask us for.
So we built a new approach from the ground up.
Our AI Code Detector is powered by span-detect-1, a state-of-the-art model trained on millions of AI- and human-written code samples. It detects AI-generated code with 95% accuracy, and ties it to specific lines shipped into production. Within the Span platform, it’ll give teams a clear view into AI’s real impact on velocity, quality, and ROI.
It does have some limitations. Most notably, it only works for TypeScript and Python code. We are adding support for more languages: Java, Ruby, and C# are next. Its accuracy is around 95% today, and we’re working on improving that, too.
If you’d like to take it for a spin, you can run a code snippet here (https://code-detector.ai/) and get results in about five seconds. We also have a more narrative-driven microsite (https://www.span.app/detector) that my marketing team says I have to share.
Would love your thoughts, both on the tool itself and your own experiences. I’ll be hanging out in the comments to answer questions, too.
Getting more to the heart of your question: the main use-case for this (and the reason Span developed it) is to understand the impact of AI coding assistants in aggregate for their customers. The explosion of AI-generated code is creating some strange issues that engineering teams need to take into account, but visibility is super low right now.
The main idea is that – with some resolution around which code is AI-authored and human-authored – engineering teams can better understand when and how to deploy AI-generated code (and when not to).