Delegation is the AI Metric that Matters

AI delegation is the most telling sign of how capable a model has become. When experts start handing specific decisions over to an agent, it shows the model has earned enough trust to replace human judgement in that domain. The article frames this as a metric that matters more than benchmark scores, because real-world delegation directly reflects user confidence and product value.

The piece draws a parallel with the early days of e-commerce, when shoppers only bought low-risk items and needed third-party guarantees like PayPal and Amazon's A-to-Z guarantee to feel safe. Over time, trust grew through social proof and incremental improvements, eventually making online shopping normal. That history illustrates how adoption, frequency, share, and assortment can be tracked to understand a technology's diffusion.

Applying the same lens to AI, the author defines a five-dimensional framework: adoption (how many use AI), frequency (how often), share (percentage of tasks), assortment (task types), and posture (the degree of delegation-from avoidance to supervision to full delegation). This "AI Posture" spectrum captures not just whether AI is used, but how much control users retain, providing a nuanced view of trust.

For product owners, the framework offers a concrete way to monitor user stories. A programmer might avoid AI for core architectural decisions, supervise test-case generation, and fully delegate refactoring scripts. By charting these patterns, teams can spot which capabilities are ready for broader rollout and which still need reliability improvements.

Leaders can use expert delegation as an early indicator of AI maturity, spotting emerging capabilities before they become mainstream. Watching where experts move from supervision to delegation highlights both technical breakthroughs and upcoming regulatory or cultural challenges, helping teams prioritize investments and manage risk.