[ssl-3]

Good point.

So update the device from 1993's new-fangled digital answering machine to 2009's Google Voice, and have it do the transcription from voicemail to text.

Someone will still have to call Bill back about his Honda (which is actually the Kia he bought for his daughter -- Bill is not a very technical guy these days[1] and he confuses such concepts regularly) in order to get any trading of money for services done.

It doesn't take an LLM to get there, and Bill would probably prefer to avoid being frustrated by the bot's insistent nature.

[1]: https://news.ycombinator.com/item?id=47356166

[camillomiller]

Look, you‘re kicking an open door. I think LLMs applied like this are just a layer of complexity that os mostly replacing lower level programming solutions that could do the same thing

[Mrngl1991]

The transcription + callback loop is honestly underrated. Most of the value here is just capturing intent accurately ("Honda" vs "Kia" aside) so the mechanic can prioritize callbacks. A dumb voicemail-to-text pipeline handles that fine. The LLM layer adds complexity without solving the actual bottleneck, which is someone qualified picking up the phone.

[ssl-3]

You nailed it.

But I'm not sure that a bot can be trusted to make good decisions about priority, either. So even if it makes good decisions based on context (which it can increasingly-often do, but does not always do), it lacks the context that is necessary to form the basis of good decisions.

Suppose a message comes into the box with this form: "This is Wendy, can you call me? My car is making that noise again."

The bot might deprioritize that call because it lacks actionable contextual information. "My job as a bot is to get more jobs into the shop. This call does not have enough data to do that, so I'll shove to the bottom of list of callbacks behind more-actionable jobs."

But the mechanic? The mechanic knows Wendy's Ford very well, and he also knows Wendy. She's a been a good customer for over a decade. The mechanic also knows the noise, and that Wendy has 3 little kids and that she's vacationing 900 miles away on a road trip with those kids in that Ford. The context is all there inside of the mechanic's brain to combine and mean that this might be the highest-priority call he gets all week.

Wendy may not have actively relayed any urgency in her message, but the urgency is real and she needs called back right away. She needs answers about what to do (keep driving and look into it when she gets back? pull over immediately and get a tow to a decent local shop? maybe she even needs help finding such a shop?) pretty much immediately. Not because it means more business today, but because it means more business for years.

The mechanic can spot this from a list of transcripts in an instant and give her a ring back Right Now. The bot is NFG at this.

The addition of the bot only adds noise to the process, and that noise only works to Wendy's detriment. When the bot adds detrimental noise to Wendy's situation, it also adds detriment to the shop's longevity.

The presence of the bot -- even as a prioritizing sorting mechanism -- asymptotically shifts the state from an excellent shop that knows their customers very well to a bot-driven customer-averse hellscape.

(And no, the answer isn't to make the bot into an all-knowing oracle that actively gets fed all context. The documentation burden would be more expensive, time-wise (and thus money-wise) than hiring a competent human receptionist who answers the phone, handles the front door traffic, and absorbs context from their surroundings. A person who chatted with Wendy last Thursday right before she left for her trip is always going to be superior to a bot.)