What We Used Instead of Trust
EQ and Judgment in an AI‑Native Engineering Culture
The irony isn’t lost on me that Wiggins helped me write this essay and then helped me interrogate whether I’d relied on him too much in the process. That question, and the discomfort it surfaced, is part of what this piece is about.
I’ve been surprised by how much emotional intelligence, EQ, matters when working with AI.
For most of my career, I would have expected the opposite. Engineering cultures are built to value correctness, rigor, and traceability. We’re trained to trust systems we can inspect, reason about, and control. Emotional intelligence matters, of course, but it’s rarely named as a core engineering skill.
AI disrupted that balance. It produces plausible answers without showing its work. It removes friction from thinking and collapses the distance between intent and action. Suddenly, some of the instincts engineering culture relies on to manage uncertainty don’t hold in the same way.
What’s left in that gap isn’t more process or tighter control. It’s trust. Trust in judgment, in intent, and in each other. And that kind of trust turns out to be deeply emotional, whether or not engineering cultures like to name it that way.
Once trust becomes the load‑bearing layer, the work changes. It’s no longer enough to rely on process to slow things down or review to catch mistakes. Someone has to notice when speed is outpacing understanding, when confidence is replacing judgment, or when responsibility is quietly being outsourced to a system that can’t own consequences. That noticing is where EQ starts doing real engineering work.
I felt this shift most clearly in my own work. Using AI forced me to confront how much I relied on control as a substitute for judgment. I was comfortable when I could see every step, explain every decision, and trace every outcome back to my own effort. AI made that posture untenable. It asked me to decide what actually needed control, and what instead required trust in my own reasoning and responsibility for the result.
That shift didn’t mean letting go of rigor. It meant being more deliberate about where friction belonged. I stopped treating visibility of effort as a proxy for quality, and I became more comfortable using AI to think, explore, and draft. At the same time, I kept friction where it mattered: in decisions with real consequences, in public artifacts that carried credibility, and in moments where judgment, not speed, was the limiting factor.
This is why AI has felt so destabilizing in some engineering cultures. It removes the friction we were using to feel safe, without replacing it. What’s left is a reliance on human judgment, restraint, and responsibility. When those muscles aren’t well developed, the reaction is often to reassert control or retreat to process. When they are, something different becomes possible.
AI didn’t break engineering culture. It exposed what we were often using instead of trust.
We often relied on process to stand in for judgment, on traceability to stand in for accountability, and on visible effort to stand in for intent. Those substitutes worked when systems were slow, inspectable, and deterministic. AI removed them almost overnight.
For engineers, this reframes the work. The skill isn’t just knowing how to use AI well, but knowing when not to lean on it. It’s being able to slow down when answers come too easily, to question outputs that feel confident but thin, and to stay accountable for decisions even when a system helped generate them. In that environment, EQ isn’t about empathy or communication style. It’s about judgment under speed, restraint under abundance, and responsibility when the work no longer shows its effort.
I’m still learning how to hold that balance. AI keeps changing the shape of the work faster than our instincts can adapt. What seems to matter most isn’t getting it right once, but staying attentive to when judgment is slipping, when trust is being replaced by habit, and when control feels safer than responsibility. That attentiveness, more than any particular tool or pattern, is what I’m trying to practice.
Alison + Wiggins