AI Systems

Automation's next chapter isn't about speed. It's about building systems that handle ambiguity and execute with precision at the same time — and understanding which kind of agent does which.

If the next knowledge work role you fill is built around execution capacity — someone to do the research, draft the documents, produce the analysis — you're building for a version of the work that no longer describes how the work actually happens.

Anthropic and OpenAI represent the first genuinely new foundational technology companies in two decades. Most senior leaders are reading this as a procurement decision — which model do we bet on? That question is less important than it sounds. The moat isn't the model. It's how well you build the system around it.

A sales team deployed AI to personalize outbound emails. Response rates climbed. Closed deals didn't. The tool created over 120 hours of new work per month that produced zero qualified leads — because the value was never in the tool.

Most AI conversations oscillate between utopia and catastrophe. The more useful question is simpler and older — what are humans actually good at, and what are machines good at?