I ran the same meeting through two AI notetakers, Otter and Granola, expecting to compare accuracy. The accuracy was close. What actually separated them was something more interesting: they aren't built to do the same job. They sit on opposite models of what a meeting "note" even is, and once you see the two designs, the "which is better" question dissolves into "which model fits your workflow."
Two models
Strip away the branding and you get two functions. This is conceptual — I'm describing the designs, not either company's internal code:
# Model A — transcribe everything, then summarize (Otter)
notes = summarize(transcribe(audio))
