The article argues that design systems remain essential but their scope is too narrow in an AI-driven world. Instead of just components and tokens, teams must capture and operationalize product context—decision rules, voice, governance and historical exceptions—to keep AI outputs coherent at scale.

• Design systems fail AI at scale because the actual decision logic lives in Slack threads and tribal knowledge, not component libraries.
• When engineers translated designs into code, they implicitly filled context gaps; AI removes that translation layer, exposing the missing rules.
• Small AI outputs that ignore invisible constraints compound into structural product drift rather than staying as isolated errors.
• The fix isn't bigger component libraries but formalizing "product context" as machine-readable rules covering governance, voice, and risk tolerance alongside human docs.