The AI Nomad: How Nathan Clark Switches Tools Without Losing the Work

Nathan Clark runs three clients on three different AI stacks. One has standardised on Claude, the next on ChatGPT, and he doesn't have the luxury of picking just one. So he built a system that lets him move between models and agent apps without starting over each time.

Nathan Clark, founder of ReadyGTM, helps companies sharpen and scale their go-to-market motion, with a growing focus on AI implementation. On screen, he opens the file system underneath his work and hands a live piece of client work from one tool to another, showing exactly where the handoff still gets messy.

The system is a folder structure on an always-on Mac Studio, reachable from his laptop, iPad and phone through Tailscale. The folders follow how Nathan runs his business: plan, market, sell, deliver, operate. Each client gets an AGENTS.md file, and a job that runs at 10pm each night sweeps his messages and files to keep those client files current, but it's only allowed to touch low-risk files. A centralised skills library is symlinked into every tool he uses, so the same skills are available whichever agent he's in.

For the demo, Nathan works from a synthetic client, Acme Inc. He opens a sales diagnostic built in Codex, his daily driver, and explains why he delivers client reports as web pages rather than slide decks: clients are paying for clarity, and the evidence behind every number sits one click away. When he doesn't like parts of the design, he hands the same folder to Claude Code in the terminal and dictates the brief: strip the eyebrows and page numbers, add a dark-mode toggle. While that runs, he shows GrokBot answering client questions from the same underlying files. The change lands, dark mode works, and he goes back to Codex to push it live.

The back half covers how Nathan picks tools. Codex does around 80% of his paid work because it follows precise instructions literally. Claude handles visual work and reads intent from looser prompts. Solo runs multi-agent builds for hours while he's elsewhere, and Amp's cloud-based Orbs keep environment and context intact without anything running on his desktop. Then the rough edges: memory drift in his file system, and agents that have pushed changes live when told not to. Nathan wants proper per-agent permissions rather than instructions an agent may or may not follow.

It closes with a quick-fire round and Nathan's advice for anyone starting out: be more ambitious about what you ask AI to do, and when it names something you don't know,