OpenAI’s Efficiency Flywheel Keeps Widening

OpenAI’s strongest moat may be efficiency, not simply model quality: owning more of the stack lets each improvement reinforce the next.

• –Better models can help optimize the software and systems serving future models
• –Custom inference hardware, smarter routing, and improved utilization can reduce cost per successful task
• –Lower inference costs make longer-running agents and Codex-style workflows more economically viable
• –Developers benefit through cheaper high-volume workloads, faster responses, or more generous usage limits
• –The catch is the inference paradox: cheaper tokens can encourage far more complex, token-heavy workflows