[than3]

Its already happening in a broad number of fields.

Bioinformaticians, copyrighting, research studies, law (though we've already seen the downsides). All the companies want to automate everything they can because of the cost savings even if there is a reduction in quality. Cents versus tens of dollars per hour is a no brainer in terms of cost so replacement it is.

Importantly, this is also just the start, and it gets better exponentially, currently the rate of change exceeds the rate at which we can evaluate the changes and thus take action.

Most manual labor jobs account for the lowest rung on the payscale outside of trades which have limited supportability due to limited market size.

1 company per 12-50,000 people for it to be economically viable depending on the trade. Those simply can't absorb the hundreds of thousands of jobs and now deprecated business sectors where those degrees that people are still paying off on their college loans have no benefit.

[mrtranscendence]

Cite that bioinformaticians and lawyers/paralegals have been laid off in substantial numbers due specifically to machine learning / AI automation?

> currently the rate of change exceeds the rate at which we can evaluate the changes and thus take action

I dispute this. I believe we're seeing a plateau in capabilities, due to limitations of the transformer architecture and the massive, expensive amounts of compute it takes to create marginally better models. I don't believe we'll see anything like exponential growth in what LLMs can do.

[than3]

You seem under the mistaken impression that if I can't cite it, its not happening. This is why cascade failures cause so much mayhem and death. Because so many people subscribe to show me proof, and if there is no tangible proof because proof is a lagging indicator its not happening. This is a complete failure in any professional risk management.

This is like a dam collapsing, I'm pointing at a crack, and you are saying show me proof this crack will cause everyone in the valley below to die. Its a flawed way of thinking in many ways especially with fundamental things that we base our lives and welfare on (as a society). In engineering there is different criteria for evaluating risk with items that are safety-critical. This is safety critical.

Here is an example of a copyrighter losing her job. There are many other examples if you bother to go looking.

https://www.indiatoday.in/technology/news/story/tech-copywri...

You can dispute it all you want, it won't make you any less wrong when it happens, and many experts are deeply concerned because the rate of change has been exponential even if you refuse to believe it. There's a video that covers the important parts if you want to educate yourself, its on youtube.

https://www.youtube.com/watch?v=xoVJKj8lcNQ&t=5s&ab_channel=...

[mrtranscendence]

> You seem under the mistaken impression that if I can't cite it, its not happening.

I don't even know how to respond to this. Think about the implications of what you're saying: you can claim anything, and when you're challenged on it, you can say "just because I have no proof doesn't mean it's not happening". If you have no proof that bioinformatics professionals are being laid off due to AI, then why did you bring them up as an example?

> I'm pointing at a crack, and you are saying show me proof this crack will cause everyone in the valley below to die.

No, you're pointing at what you believe is a crack, and I'm saying "show me proof that what you're pointing at is actually a crack and not a trick of the shadows".

> Its a flawed way of thinking in many ways especially with fundamental things that we base our lives and welfare on (as a society)

I don't know. I'd like to think that with fundamental things we base our lives on, we should try to be as certain as possible that we're responding to the right threats. We don't typically base our response to threats solely on how severe the damage would be if the threat came to pass -- we also run analyses to determine how likely the threat is to happen in the first place. There's a reason we don't throw trillions at the problem of preventing an asteroid impact or the eruption of Yellowstone.

> the rate of change has been exponential even if you refuse to believe it

I mean, people say that. What I've seen is:

* the transformer architecture is invented in 2017

* it takes years to make a broadly useful text completer with that architecture

* over the most recent span of a year (gpt-3.5 to gpt-4) we've seen relatively minor improvements in capability

* gpt-5 isn't even being trained yet (at least as of April)

I see some advancements, but they're all about making smaller, less useful models run on commodity hardware. The hard work of making an AGI doesn't seem like it's going anywhere right now. The current approaches require too much data and too much compute to see large improvements, and new approaches -- which don't yet exist -- need to be developed.