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Comment by lsy | Hacker News Reader

This law is true in some cases, but I think is too often used as the tech industry's version of "first they ignore you, then they laugh at you, then they fight you, then you win". The argument is that, sure, a technology's promise may have not panned out, but under Amara's law, we can expect a commensurately massive impact later.

As Carl Sagan pointed out, "…the fact that some geniuses were laughed at does not imply that all who are laughed at are geniuses." The same applies to technologies. The fact that a technology has shown slower-than-expected progress is not a foolproof indication that it will eventually have massive impact. It may just not be as useful as it initially seemed, or may even be supplanted by a completely different technology.

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Ever since I was a little kid I was always hearing those inspirational stories about how someone would get into an accident and then "The doctor told me I would never walk again" or "I wouldn't make it to my next birthday" and then the person made a miraculous recovery and "proved them wrong" or something along those lines.

It always seemed like an attention bias to me. Because for sure there are many more stories about the doctor being right and the person did never actually walk again. And we don't really pay attention to the stories about the doctor saying they would walk again. Those aren't interesting.

It can also be a bias with the triumphant person overestimating the negativity of the past. Like did the doctor really say you will never walk again, or that it will be tremendously difficult and unlikely?

People like an underdog story and will hold it with more attention than other stories in which the expectations met reality.

The general observation is of course true, but we're talking about a particular instance, and I think it could be a pretty good fit here.

The expectation when you first try modern image or text generators is sth like 'this will replace all artists, programmers, writers and probably most white-collar workers'. Upon closer inspection, it turns out that the image generators fail on details like hands, and the text generators are too unreliable for production-grade code or writing articles. So the result in the short term is that you still need the same people with the same skills babysitting the models' outputs. They've just become a bit more productive. But in the longer run, even plain efficiency gains can have a large impact, and it seems like we might not need any more fundamental breakthroughs to make those tools actually production-ready (at least largely) on their own. The hands in the images, the bugs in the code and the hallucinations in the texts can probably be fixed with clever, but ultimately straightforward, iterations over the existing models.

100%… not using it to advocate for anything in particular. Just find the phenomenon interesting… you can apply this retroactively to several innovations.

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