AI is being built in public, but it is not arriving evenly. Access, habits, infrastructure, institutions, and timing all matter. This thread tracks what it means to live through that transition while the ground is still moving.

The central question is not whether AI is good or bad in the abstract. It is where a person, team, or institution stands relative to the change, what that position makes possible, and what waiting costs when the tools compound faster than the systems around them.

Who this is for

• Knowledge workers deciding how seriously to engage with AI now.
• Operators, founders, and investors thinking about timing, access, and position.
• Readers who find both AI boosterism and AI doom unconvincing.

What I'm watching

• Pricing and access changes at the major AI labs.
• Evidence that early AI adopters are compounding faster than late adopters.
• Cases where waiting was rational, and cases where waiting became expensive.
• Institutional adoption patterns across firms, schools, governments, and professions.
• Shifts in how people talk about AI itself: tool, coworker, infrastructure, threat, status marker, default layer.