The article by Sahil Mohan Bansal critiques the growing trend of tracking the percentage of AI-generated code as a metric for business success, particularly among major companies like Google and Microsoft. It argues that such metrics are overly simplistic and may lead to misguided conclusions about productivity and cost savings, while potentially introducing more bugs and technical debt. The article highlights the disconnect between executives and developers regarding the benefits of AI tools, noting that many companies are adopting AI mandates tied to stock performance and cost-cutting, sometimes at the expense of quality and developer satisfaction. It suggests that more meaningful metrics should focus on the quality of AI-generated code, actual productivity gains, and developer feedback, rather than merely the volume of AI-generated content. The piece advocates for a holistic approach to AI adoption throughout all stages of the software development lifecycle, emphasizing that effective metrics should be collaboratively developed with engineering teams to reflect real business outcomes and productivity improvements.