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Comment by littlestymaar | Hacker News Reader

No they aren't. Some jobs are being scaled down because of the increased productivity of other people with AI, but none of the jobs you listed are within reach of autonomous AI work with today's technology (as illustrated by the AirCanada hilarious case).

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I would split the difference and say a bunch of companies are /trying/ to replace workers with LLMs but are finding out, usually with hilarious results, that they are not reliable enough to be left on their own.

However, there are some boosts that can be made to augment the performance of other workers if they are used carefully and with attention to detail.

Yes. “People make mistakes too” isn’t a very useful idea because the failure modes of people and language models are very different.

I completely agree, that's exactly my point.

Doesn't the Air Canada case demonstrate the exact opposite, that real businesses actually are using AI today to replace jobs that previously would have required a human?

Furthermore, don't you think it's possible for a real human customer service agent to make such a blunder as what happened in that case?

Possibly, a human customer rep. could make a mistake, but said human could correct the mistake quickly. The only responses I've had from "A.I" upon notifying it of its own mistake, is endless apologies. No corrections.

Anyone experienced ability to self-correct from an "A.I" ?

> Doesn't the Air Canada case demonstrate the exact opposite, that real businesses actually are using AI today to replace jobs that previously would have required a human?

It shows that some are trying, and failing at that.

> Furthermore, don't you think it's possible for a real human customer service agent to make such a blunder as what happened in that case?

One human? Sure, some people are plain dumb. The thing is you don't give your entire customer service under the responsibility of a single dumb human. You have thousands of them and only a few of them could do the same mistake. When using LLMs, you're not gonna use thousands of different LLMs so such mistakes can have an impact that's multiple order of magnitude higher.

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