John Cutler · 3 min read · AI & Product
TBM 431: The Denominator That Matters
Read the original on cutlefish.substack.com →
The article presents a simple multiplicative model for AI success in organizations, arguing that success depends on Machine Understanding, Problem Understanding, and Practice Evolution, all bounded by a credible Social Contract. It warns that if any term is missing, the entire effort collapses, and that mandates often fail because they overlook the social dynamics and real problem framing behind AI initiatives.
Key Takeaways
- AI success is multiplicative: you must address Machine Understanding, Problem Understanding, and Practice Evolution together, not in isolation.
- The Social Contract is the precondition for real gains; without trust in fair use of improvements, efforts will stall or backfire.
- There are no universally fixed best practices; Practice Evolution must adapt to real work and change constantly.
- Mandates that push everyone to use AI without solving underlying problem understanding and practical workflows tend to fail.
- Resistance to AI can be rational when the denominator is weak; strengthening the social contract helps reduce friction and unlocks value.
Excerpt
Here’s a simple model for thinking about AI success in organizations. The Variables Machine (Technology) Understanding How AI actually works. The inputs and outputs. The failure modes. You don’t need an expert-level sense of the math, but you need a viable mental model of how the technology and what it is doing. Understanding the costs (based on a very incomplete current sense of the costs). Said another way, you have to get past the “magic” of AI. Problem Understanding Context, domain experience, skill, customer knowledge, constraints, history, politics. Everything that makes your problem a real-world problem and not a generic…