On airSeason 01EP 0218 min
What an LLM actually changes
Where generative features earn their cost in a B2B roadmap, and where they quietly don't.
HOMODYNStandby
What an LLM actually changes
The cut lives on YouTube. Open the channel, or paste a video id onto this episode to play it here.
Play on YouTubeA companion to the written note. We walk four candidate queues — search, summary, triage, compliance draft — and only two survive contact with error cost at volume.
The tempting targets are the visible ones. They demo beautifully and they sit outside the operation.
Downloads
1 fileField notes
Don't start where it demos
Search boxes and onboarding assistants sit outside the operation. They look like AI and they change nobody's week. The work worth touching is a human reading unstructured input and emitting a short, checkable output.
Fence before fluency
A deterministic policy layer has to be allowed to reject the model. If the only brake is 'the model sounded unsure', you will ship confident mistakes into the next system.
Plot the ugly number
Accuracy is a vanity metric here. Plot annual error cost against salary saved, by decision class. Most candidate queues fall on the wrong side. That chart is the episode.