Is human supervision becoming the next bottleneck for AI?

Many technologies seem to pass through two different stages. First, their raw capability improves. Then, the human burden required to use that capability begins to fall. Computers did not become widely accessible through faster processors alone. Operating systems, graphical interfaces, and abstraction layers reduced the expertise and attention required from the user. The internet did not spread through network performance alone. Browsers, search engines, shared standards, and simpler interfaces made its underlying power usable without requiring everyone to understand the infrastructure. AI may now be approaching a similar transition. Models are becoming more capable, but serious AI use still pushes a significant amount of work back onto the human: - maintaining context - repeatedly explaining intent - detecting silent errors - verifying outputs - recovering from failed runs - deciding when to stop or redirect the system AI already reduces many forms of work. But greater capability can also create new supervision costs, especially when the system operates across longer tasks or more complex workflows. As model capability rises, the limiting factor may gradually shift from access to the model toward the human capacity required to supervise it. Historically, powerful technologies became broadly useful not only when their maximum performance increased, but when ordinary users no longer had to carry so much of the operational burden themselves. Are we still treating model capability as the main bottleneck when human supervision capacity may already be becoming equally important? And what would the AI equivalent of the GUI look like—not something that makes the model smarter, but something that makes its intelligence less costly for humans to control?