One Month, Four AI Meeting Tools, One Uncomfortable Conclusion

For one month, every meeting I attended was recorded by four different AI notetakers at once. The transcription, I can report, is a solved problem: all four produced accurate text, decent speaker labels, and summaries that would pass a casual skim. The differences that mattered were elsewhere.

The first differentiator was honesty about uncertainty. One tool confidently attributed a decision to a person who had only mentioned it in passing. Another flagged the same moment as unclear. Attribution errors are the dangerous failure mode in this category, because a wrong name in a summary becomes a wrong accountability in a week.

The second was what happened to action items. Every tool extracted them; two extracted them eagerly, inventing tasks out of discussion that had explicitly rejected the task. The best tool asked before committing: it listed candidate actions separately from confirmed ones. That distinction, between what was said and what was decided, is the whole game.

The uncomfortable conclusion: I stopped reading any of them carefully by week three. The transcripts sat unread, the summaries got skimmed, and my memory of meetings stayed about the same. The tools solved a problem I only pretended to have, which was 'I need a record,' when my real problem was 'I need to have been paying attention.'

I kept one tool, the one that flags uncertainty and asks about actions. But the month taught me to use it as a safety net, not a memory. The people whose follow-up game improved from these tools were the ones who never read them; the ones who read everything got slightly worse at listening. Choose your failure mode deliberately.