Codex vs Fable: Which AI Agent Picked the Better Problem?

This video compares how two AI agents, Fable and Codex, selected different automation problems from the same business context, revealing insights into AI's ability to identify high-leverage tasks versus safe, finishable ones.

What it covers

Same brief, two different outcomes
Both Fable and Codex were given the same open-ended task to identify automatable problems in a real business, but they chose different issues to address.
Why agent scale changes the workflow
As AI agents grow more capable, they shift from executing defined tasks to helping define which problems are worth solving.
Fable's preflight build
Fable conducted a deeper analysis before building, identifying a higher-leverage automation opportunity.
Codex's handoff proof
Codex focused on a safer, more immediately finishable task, prioritizing execution over strategic discovery.
Strategic discovery versus execution
The video contrasts AI agents that prioritize finding impactful problems versus those that focus on completing predefined tasks.
The reusable automation skill
The process of letting AI help identify automatable problems can be a repeatable, strategic skill for improving workflows.
The final verdict
Fable identified a higher-leverage problem, demonstrating the value of AI in strategic discovery, though human judgment remains essential.