AI in engineering: useful or not, and what's different?

The narrative around AI keeps bouncing from it's slop and it kills software engineering to it has allowed to clear 2 years of backlog in two weeks.

I think both are true, and ultimately the difference is made by how AI is deployed by Leadership. So what's different?

Durable Teams and Domain Context: while AI makes individual implementation much cheaper and faster, high-judgment individuals and agents still hit limits when they lack deep domain context. Durable, high-ownership teams remain the fundamental building block of engineering. AI can accelerate the writing of code, but teams must still possess the proprietary context to know what should be built—and what production metrics or instrumentation are actually required.

Structural Improvements Over Haphazard AI Adoption: clearing a multi-year backlog isn't just a matter of telling engineers to use AI; it requires leadership to focus on structural improvements and clear architectural patterns. When teams use AI to migrate configuration systems, unify mono-repos, or statically type legacy codebases, it is typically directed through disciplined reference architectures—such as having one engineer build a reference pattern that others follow systematically.

Managing the Pace of Work: the AI-tooling shift has changed the pace at which it’s possible to work. In hypergrowth or high-velocity environments, mistakes reveal themselves much faster because things break loudly when moving at high speeds. Leadership must revise its rules and guardrails to handle compressed timelines safely, ensuring that speed does not outpace system observability or code review standards.

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