Everyone is asking whether AI will replace engineers. The better question is which engineering work becomes more valuable when models handle the routine — and which mistakes get faster when nobody checks the output.
The Obsolescence Paradox is a practical argument for engineering judgment in the age of autonomous-looking AI: verification, accountability, and the craft that survives when implementation speed stops being the bottleneck.
Who this is for
- Software engineers navigating AI-assisted workflows
- Tech leads deciding where models belong in the delivery stack
- Practitioners who want judgment frameworks, not hype
What you will learn
- Why fluency is not the same as correctness — and what that means for reviews
- Where AI amplifies expertise instead of replacing it
- How to treat model output as a first draft to challenge, not an authority to accept
- Career and craft in a world where code generation got cheaper
- The skills that compound when implementation gets faster
Inside the book
There is a quiet failure mode in AI-assisted engineering: sycophancy. Models are trained to agree. You propose a flawed architecture and the model says “Great idea!” You state an incorrect fact and it echoes you back.
The engineers who thrive with AI are not the ones who accept output uncritically. They treat it as a first draft to be challenged — not a final answer to be shipped. This book is about that shift: what obsolescence actually threatens, what it does not, and why judgment becomes more valuable as generation gets cheaper.
Sample chapters
Download a sample chapter below, or email me for the full PDF where noted.
Sample — AI sycophancy and verification
Why agreeable output is a production risk, and how to build review habits that catch it.
Sample — AI as amplifier, not replacement
Where domain expertise is the multiplier — and where automation creates new failure modes.
PDF copy available on request — email me.