Leaders set uniform expectations for ChatGPT use and widen performance gaps
Leaders expect uniform gains from ChatGPT, but results diverge across users. When leaders set uniform expectations, people develop different interaction patterns, passive users fail to build feedback loops while active users refine them, and performance gaps widen.
Expecting uniform capability
Leaders assume all people will use ChatGPT with equal effectiveness.
Based on this belief, they set the same expectations for how people should use it.
As a result, they expect consistent performance improvements across the group.
Observing diverging results
Leaders observe that outputs vary widely in quality across people.
Over time, some people produce increasingly strong results while others stagnate or decline.
This visible divergence contradicts the expectation of uniform improvement.
Reinforcing different interaction patterns
Leaders create uniform expectations, so people choose their own way of interacting with ChatGPT.
Some people actively test, question, and refine outputs, which builds internal feedback loops, while others accept outputs as given and repeat the same approach.
These repeated behaviors reinforce either learning loops or stagnation, which drives the widening performance gap.
Misinterpreting and amplifying gaps
Leaders interpret the growing differences as fixed individual capability rather than a result of interaction patterns.
This interpretation leads them to maintain uniform expectations rather than address the underlying behavior.
The gap continues to widen, and leaders reinforce the very conditions that produced the divergence.
Note: We use the term “ChatGPT” as a shorthand for ChatGPT and similar tools such as Anthropic Claude, Google Gemini, Microsoft Copilot, and custom GenAI chatbots.