In this episode of Technical Debt: Design, Risk and Beyond, hosts Maxim Silaev and Nikita Golovko explore whether artificial intelligence can really spot technical debt more effectively than human architects and engineers.

Drawing on real-world projects: from investor due diligence to scaling SaaS platforms, they share stories of how AI has surfaced invisible hotspots, misread healthy churn as risk, and mapped sprawling dependencies. Together, they examine three critical signals of hidden debt:

• Bug Density: how AI clusters recurring defects and predicts hotspots, versus how humans add testing relevance and guardrails.
• Frequent Changes (Churn): distinguishing between harmful rework and healthy iteration using AI-driven churn analysis, with human context to prevent false alarms.
• Dependency Sprawl: where graph-based models and SBOM scans reveal fragile chains, but human judgment decides when not to "clean up" aggressively.

Maxim and Nikita also reflect on their consulting and startup experience, where AI tools accelerated discovery but human intuition and business context made the final call. The discussion closes with practical guardrails for blending AI insights with architectural judgment, so teams can make technical debt visible, manageable, and tied to real business outcomes.

If you have experimented with AI to uncover hidden debt, or wondered how to balance automation with experience, this episode gives you practical frameworks, war stories, and pitfalls to avoid.