New capability brings clarity to the impact of AI transformation, helping engineering leaders report with confidence and drive smarter investment decisions.

Span, the developer intelligence platform, today 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.

Solving the Measurement Gap in Engineering

Today, 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 today,” 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."

Available in Public Preview

A public preview is now available at www.span.app/detector, enabling anyone to analyze code samples in seconds and share results. The capability is also available to Span customers in private beta, integrated as part of the company’s broader developer intelligence platform – which helps engineering leaders get a complete picture of developer productivity and impact.

Later this year, Span will add the ability to verify code quality by comparing defect rates between AI-assisted and human-written code, giving leaders even deeper insight into the impact of AI on software development outcomes.

About Span

Span is the AI-native developer intelligence platform bringing clarity to engineering organizations with a holistic, human-centered approach to developer productivity. World-class companies like Ramp, Vanta, Carvana, Writer, and Braze use Span to get a complete picture of engineering impact and health, drive high performance, and make smarter business decisions. Span is backed by Alt Capital, Craft Ventures, SV Angel, Bling, BoxGroup, and dozens of founders, CTOs and CPOs at places like Slack, Notion, Rippling, and Square.

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