A veteran software engineer with 10 years of experience in financial systems finds their expertise eroding as AI tools increasingly handle specialized domain knowledge. Once a key differentiator, their understanding of PCI compliance, payment lifecycles, and banking idempotency can now be replicated by advanced language models that connect technical concepts across documentation.

The second pillar to fall: debugging complex systems. What took days of troubleshooting race conditions and distributed system issues is now being resolved instantly by tools like Claude Code and GPT-5.5, especially when paired with monitoring platforms like DataDog. The engineer watched their unique problem-solving value disappear as AI systems one-shot bugs that previously required deep technical intuition.

Even the final pillar of code quality stands on shaky ground. While agents struggle with maintaining clean architecture principles, the industry is accepting lower-quality codebases as increasingly written for machines rather than human readability. With all expertise becoming promptable, engineers face commoditization in a market that values generalists over specialists.