Charity Majors’ latest Substack post takes aim at a growing split inside engineering teams: AI enthusiasts who see fast gains and skeptics who worry those gains may be hiding a mountainton of cleanup work. Majors argues that both camps are reacting to real risks, not just talking past each other.
The post opens with a warning about overly selective success stories, especially when conference talks celebrate speedy AI-assisted wins without much attention to the downstream mess. Majors suggests that this gap in the narrative can deepen mistrust between teams, especially when the people seeing the upside are not the same people absorbing the costs.
From there, Majors pushes for a more grounded approach: share the full story, keep the conversation focused on engineering tradeoffs, and avoid turning AI adoption into a rhetorical match. Rather than defaulting to slogans about whether AI is good or bad, the post calls for practical questions about what would make AI-generated work safe enough to ship.
Majors also argues that strong engineering discipline and tight feedback loops matter more than ever. The post leans on the idea that credibility comes from owning consequences, which means the people shaping AI workflows need both technical standing and a willingness to confront tradeoffs honestly.
There is more in the full essay, including a closer look at how leaders can keep teams moving without letting trust, reliability, or morale collapse. Read the original piece for the complete argument.
Source: Charity Majors