In TechCrunch, executives from PolyAI (customer service) and Otter (meeting note-taking) explain why voice AI hasn't had its 'ChatGPT' tipping point yet. The first hurdle is speed: as long as the model takes too long to reason and answer, the conversation doesn't feel natural. The second is transcription itself — speech recognition 'often misses important keywords,' and the moment the system acts on a mis-transcribed text, 'that is wrong, and you lose trust in the platform.' Hence the emphasis on transparency: flagging that 'the meeting is being recorded,' or that you're 'talking to an AI' on an enterprise call. The cases that already work stay narrow: transcription, notes, scoped customer service.
The lesson for an app is simple: voice isn't a switch you flip because it's fashionable. It shines where hands are busy, where dictation beats typing, or for summarising what was said — but it disappoints fast the moment a transcription error triggers a wrong action. At Devside, we treat voice as a feature that has to earn its place: we reserve it for moments where it genuinely helps, we always show the recognised text before acting, we keep a clear way out (correct, fall back to touch, reach a human), and we say plainly when it's an AI listening. Trust isn't won by talking faster, but by getting it wrong less often — and owning it when it happens.