Operator’s scalability and stickiness through network effects depends on reinforcing two tightly linked flywheels: one at the model actuation layer, and another at the user cohort personalization layer.
At the model layer, capturing reasoning traces across every workflow becomes critical. Even as architectures evolve beyond pure vision capabilities, reasoning data, showing how tasks are decomposed, trade-offs considered, and clarifications handled, will be essential for improving higher-order reasoning. These reasoning traces will allow Operator to incrementally optimize task planning, preference anticipation, and error recovery, compounding improvements across both consumer and enterprise domains. At the user layer, Operator must replicate a pattern similar to Google’s early web strategy, systematically identifying core user intents across diverse verticals through conversational interactions. Unlike Google’s link-based indexing, Operator must abstract intents dynamically from user conversations, where the underlying structure is more fluid and varied. Understanding aggregated user intents will help OpenAI prioritize which verticals warrant deeper integrations and dedicated product development. This will enable the creation of vertical-specific flywheels, where curated workflows, native partnerships, and custom agent behaviors make the agent execution for the Nth user more valuable. Finally, flywheel durability will differ across markets. In enterprise, structured workflows and explicit feedback will compound CUA’s advantages; In consumer, vertical-specific personalization and outcome-driven optimization will be key to maintaining relevance as user interaction patterns shift.
Network Effects