The Bottom Line: The challenge is not to choose a side, but to create feedback loops that mediate between the pace of AI-accelerated development and the requirements for reliability and maintainability.
Charity Majors identifies a central organizational problem: AI enthusiasts and AI skeptics often work on the same team to build better software, but have no natural feedback mechanism. Both positions are factually correct, yet they lead to conflicting requirements for development and maintenance.
Majors describes the dilemma with precise clarity: AI enthusiasts observe real, discontinuous performance leaps in teams that work intensively with AI tools. For CTOs and engineering leadership, this is an existential question — teams that forego AI tools risk being overtaken by competitors who ship code to production faster.
At the same time, the skeptics are right: code written faster than engineers can understand it, in domains without complete contextual knowledge, creates losses in reliability and institutional knowledge. System complexity grows, context fragments, and on-call rotations become a burnout accelerator. This too is a genuine existential threat.
Majors’ solution addresses this as a joint challenge for leadership and engineering: the critical problem is that no natural feedback mechanism exists between the two groups. CTOs leading teams working in parallel within this tension field must explicitly design feedback mechanisms — structures that bridge “the gap in shared reality” between enthusiasts and skeptics. This is an organizational design problem, not a technical one.
Source: simonwillison.net · Published June 5, 2026
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