There’s a version of AI work that lives entirely in demos and screenshots.

That’s not this.

The interesting part starts when the model has to coexist with:

• actual host constraints
• services that can fail
• networks that are only mostly cooperative
• existing infrastructure that predates the AI hype cycle by a long shot
• human workflows that still need to make sense when the model is wrong

That’s where the work gets fun.

Local AI is compelling for a few reasons: cost control, privacy, speed, ownership, and the ability to experiment without waiting for someone else’s cloud roadmap.

But local-first doesn’t mean romanticizing complexity. It means being honest about tradeoffs.

Sometimes the right answer is a local model. Sometimes it’s a hosted model. Sometimes it’s both, with the workflow deciding what goes where.

The goal is not purity. The goal is useful systems.