Shelf three · Language models & generative AI
Generative AI at Work
Kitchen table
Older computer programs could only sort what already existed. This pile is spam, that photo is a cat. The new ones can make new things: a letter that never existed, a picture nobody drew, a summary of a meeting in ten lines. It is like the difference between a librarian who finds books and a friend who writes you one. Wonderful, as long as somebody checks the work, because making things up is precisely what it is built to do.
Whiteboard
The generation call is the easy tenth of the system. The engineering is the review path (who approves, at what confidence), the fallbacks when output fails validation, the audit trail of what was generated from which inputs by which model version, and the honest list of tasks that should never be generated at all. Structured outputs with schema validation beat free text everywhere the result feeds another system. Design for the failure modes first. The demo already works. The demo always works.
Boardroom
The dependable wins are in drafting, summarising and triage – high-volume language work where a human still signs off and the AI removes the blank page, reliably clawing back thirty to fifty percent of the time. Full automation of customer-facing output is a different risk class entirely: every generated sentence carries your letterhead, and 'the AI wrote it' is not a defence any regulator accepts. Sequence accordingly – assistance first, autonomy only where the error cost is genuinely low and measured.
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