This week
Suggest a nameObligation Marketing for AI Builders
The 7-Rung LadderThe 8-Type TaxonomyWrapper or WinnerThe Sample ColumnThe Signal
Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
The delegation asymmetry finding, willingness to send an agent versus willingness to receive one, is a design constraint any two-sided marketplace deploying agentic assistants needs to plan for. Product teams building agent-mediated matching or negotiation features should expect adoption to stall on the receiving side long before the sending side does.
This is a solid explainer of why LLM outputs vary run to run, framed around a real finding that a fifth of one startup's support tickets stemmed from users not understanding sampling behavior. Useful if you're writing product docs or building UX around non-deterministic outputs, but it's educational content, not new information for anyone technical. File under onboarding material for junior team members.
Reverse-engineering pieces like this matter because OpenAI rarely documents its agent architecture in detail, and competitors building agent products need a working model of what 'good enough' proactive scheduling and memory integration looks like at scale. If you're building an agent product, this is a useful blueprint of the surface area you need to cover to compete with ChatGPT Work.