I am a programmer, not a rubber-stamp that approves Copilot generated code (prahladyeri.github.io)

A Reddit poster described a rapid loss of faith in their software career after managers began mandating the use of AI assistants (Copilot, ChatGPT and other LLMs), monitoring that usage, and folding it into performance reviews. What started as optional productivity tooling has shifted into enforced policy where engineers are expected to rely on LLM-generated code for everyday tasks—yet remain fully accountable when bugs or security issues surface. The post warns this creates a dynamic in which programmers become “rubber stamps” approving AI output rather than crafting solutions, and where traditional metrics (bug counts, code reviews, function point analysis) are being displaced by opaque AI-usage KPIs. This trend matters for the AI/ML community because it raises technical and governance challenges: forced LLM adoption can accelerate latent technical debt, propagate hallucinations and insecure code at scale, and erode critical developer skills. It also highlights the need for rigorous evaluation, auditing and observability of AI-assisted code generation—metrics should measure product outcomes, code quality, and reproducibility, not just AI usage. The situation underscores urgent questions about accountability, testing pipelines, explainability of LLM outputs, and institutional policies to ensure responsible, evidence-driven integration of AI tools rather than blanket mandates.