[alexpotato]

But those things were considered on the same level of current LLMs in the sense of "well, a computer might do part of my job but not ALL of it".

No, algorithmic trading didn't replace everything a trader did but it most certainly replaced large parts of the workload and made it much faster and horizontally scalable.

[happymellon]

The problem here is that you are cherry picking examples of successful technology.

The inverse would be to list off Theranos, Google Stadia, and other failed tech and claim that people said that there was massive steps that subsequently didn't materialise. In fact a lot of times it was mostly fabricated by people with stuff to gain from ripping off VCs.

Look at how bad it is with Microsoft in Windows despite their "all in on AI".

Ultimately no one really knows how it will pan out, and if we will end up with Enron or an Apple. Or even if it's a combination of a successful tech that ultimately is mishandled by corporations and fails, or a limited tech that regardless captures the imagination through pop culture and takes over.

[exsomet]

The two key differences to me are infrastructure and specificity of purpose.

Autoland in plane requires a set of expensive, complex, and highly fine-tuned equipment to be installed on every runway in the world that enables it (which as a proportion is statistically not a majority of them).

And as to specificity, this system does exactly one thing - land a specific model of plane on a specific runway equipped with instrumentation configured a specific way.

The point being: it isn’t a magic wand. Any serious conversation of AI in these types of life or death situations has to recognize that without the corresponding investment in infrastructure and specificity of purpose, things like this blog post are essentially just science fiction. The fact that previous generations of technology considered autoland and algorithmic trading to be magic doesn’t really change anything about that.