AI tooling is shifting the pressure point from raw model access to the systems around it. The valuable work is increasingly in orchestration, latency control, observability, and keeping inference paths predictable under load.

That changes how I think about infrastructure. The stack needs to be small where it matters, debuggable when things fail, and explicit about tradeoffs instead of hiding them behind abstractions that look elegant but age poorly in production.

That is the lens behind this post: where AI fits into real engineering systems, what the market is rewarding now, and how to keep the underlying platform disciplined enough to absorb change without losing performance.