Blog

Knowledge work can be very open ended. You have tasks, to be sure. But they aren’t as cut and dry as you might think. You can always iterate, or ship the next version. What does done look like? If you aren’t careful done will elude you.

What is novelty?

Consider the following three queries. Something behind the scenes is triggering Gemini. Should the end user have a say about whether they want vintage Google (web search) or a GenAI output?

How much have your workflows and routines changed in the last year? The last 5 years? The last 10 years? What part of those changes would you attribute to technology? What part would you attribute to maturity, learning, and personal development? Are the two areas intertwined?

Are the resources required for AI fundamentally different? Or do they exist on a continuum?

Maintenance is hard.

Spooky action at a distance. What about when it’s spooky activity in your pocket?

If there is a main phase and a reflection phase, how do they integrate between each other? How do you switch between, or weight each when using them at the same time?

Related to the prior post on reflection, reflection requires space. Space is needed to explore and investigate, test, probe, and question. And adapt.

What is the value of reflection, and how can it be incorporated into AI?