{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/69ab3b7c7036d739021982df/6a5995c362e50b14bd6e7804?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"I asked Fable and Codex what to automate. They disagreed.","description":"<p>For deeper playbooks and analysis: <a href=\"https://natesnewsletter.substack.com/p/let-ai-pick-what-to-automate\" rel=\"noopener noreferrer\" target=\"_blank\">https://natesnewsletter.substack.com/p/let-ai-pick-what-to-automate</a></p><p><br></p><p>What's really happening when you stop telling an AI what to automate and ask it to discover the problem itself?</p><p>The common story is that AI agents need a tightly specified task — but the reality is that the strongest systems can inspect real work, identify recurring friction, and propose different high-leverage automations.</p><p><br></p><p>In this video, I share the inside scoop on giving Fable and Codex the same open brief and getting two very different answers.</p><ul><li>Why picking the problem is becoming part of the agent's job</li><li>How Fable found a strategic editorial preflight opportunity</li><li>What Codex built to validate completed content handoffs</li><li>Where human judgment still matters</li><li>How to turn the method into a reusable automation-discovery skill</li><li><br></li></ul><p>For operators, builders, and leaders, the shift is from asking which tool to use to asking which recurring problem is worth solving completely.</p><p>Subscribe for daily AI strategy and news.</p><p><br></p><p>Hosted on Acast. See acast.com/privacy for more information.</p>","author_name":"Nate B. Jones"}