AI-First Work Requires Organizational Redesign
The durable advantage will come from redesigning workflows, roles, and incentives around AI—not simply adding tools to existing processes.
- future of work
- organizational design
- AI strategy
Writing archive
Writing on AI, product strategy, software systems, and the future of work.
The durable advantage will come from redesigning workflows, roles, and incentives around AI—not simply adding tools to existing processes.
A small customer-conversation evaluation suggests focused decision models may offer a useful cost and latency advantage for narrow classification tasks.
Clear targets usually guide a language model better than a context window filled with the behaviours it should avoid.
New creative AI tools reduce the cost of turning a passing question into an experiment, making exploration faster and more playful.
Two Markdown layers can preserve durable project knowledge across coding-agent sessions without turning memory into noise.
Long-running, hard-to-reproduce datasets can become a durable advantage when new AI capabilities need a faithful picture of the real world.
Keep uncertain workflows manual until customer value and repeated behaviour show the team what is worth automating.
A period-specific model could help storytellers recover the assumptions, details, and language that modern models smooth away.
A small leadership metric for making appreciation deliberate instead of accidental.
Why retrieval works better as a navigation system than as a larger pile of disconnected text chunks.
Reliable AI systems separate probabilistic reasoning from deterministic state, rules, data routing, formatting, and execution.
AI prototyping tools are moving product managers from static artifacts toward functional prototypes that can be tested before engineering scales them.
MCP may solve AI's interoperability problem, but technical openness, ecosystem effects, and neutral governance will determine whether it becomes lasting infrastructure.
A batching analogy for finding efficiency in products that make large numbers of language-model calls.
AI infrastructure is moving from aggressive capital allocation to the harder work of operating complex systems profitably.