Span Launches Universal AI Code Detector to Help Technology Leaders Measure the Adoption and Impact of AI-assisted Coding

Span, the developer intelligence platform, announced the launch of its AI code detector, the industry’s first tool to identify AI-assisted vs. human-written code with over 95% accuracy across all AI coding tools. As AI-assisted coding adoption skyrockets across engineering organizations, Span’s new innovation addresses a critical gap in the market by providing objective, verifiable metrics on AI usage and impact.

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Span’s AI code detector provides the evidence leaders need to make better business decisions at scale.

Solving the Measurement Gap in Engineering

boards and executives are increasingly demanding credible metrics to evaluate the ROI and quality implications of AI-assisted coding. However, CTOs and engineering leaders are flying blind, relying on self-reported metrics or internal surveys to answer a critical question: How much of our code is really written by AI—and is it paying off? Span’s AI code detector provides the evidence leaders need to make better business decisions at scale.

“Engineering leaders are facing immense pressure to demonstrate the value of AI investments, but they’re making decisions based on anecdotal evidence and inflated claims,” said Henry Liu, Co-founder and CTO of Span. “Our AI code detector changes the game by providing the objective ground truth that executives need to successfully lead through AI transformation.”

Powered by Proprietary Machine Learning

At the core of this capability is span-detect-1, Span’s proprietary machine learning model trained on millions of examples of AI and human-written code. The model can accurately estimate the percentage of AI-written code within code samples by analyzing latent features, for example, by detecting patterns in token sequences, syntax quirks, and stylistic regularities.

Initially supporting Python, TypeScript, and JavaScript with additional languages planned, the detector works universally across all AI coding tools, providing tool-agnostic insights that go beyond narrow telemetry or self-reported usage metrics.

“Span is the only solution we’ve found that helps us measure all the AI coding tools we use at Vanta ,” said David Ko, Senior Director, Head of Product Engineering at Vanta. “Having visibility into AI usage at the code level will help us answer questions around productivity and quality, and inform key business decisions.”

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