Responsible AI is dying. Long live responsible AI (www.treycausey.com)

Trey Causey argues that “Responsible AI” teams are shrinking even as companies go AI‑first, a paradox driven by three core problems: insufficient technical proficiency, epistemic overreach/lack of product‑market fit, and an academic posture that emphasizes critique over practical solutions. The result is visible failures and invisible successes—RAI groups producing checklists and frameworks that aren’t implemented or operationalized, and too few engineers able to ship production tools. That matters to the AI/ML community because weak or sidelined RAI capabilities increase the risk that fairness, accountability, and safety won’t be embedded into high‑impact systems built with deep learning, LLMs, and RL techniques. Causey’s prescription is pragmatic: make RAI technical and product‑focused. Hire and evaluate RAI staff by the same standards as engineering teams, “build tools not decks,” reserve company time for continuous learning, and produce short “position memos” to translate research into prototyping plans. He also counsels realistic scope—prioritize operationalizable areas (e.g., compliance, auditability, deployable fairness tools) and develop a clear theory of change so RAI work is adoptable and impactful. In short, responsible AI must move from an isolated ethics function to embedded, engineering‑led practices that ship and scale inside production ML pipelines.