> Have outputs from engineers traditionally been measured in cost and speed?

Yes. How long it'll take and how much it'll cost are going to be among pretty much any customer's first questions.

They're not the only considerations, and could potentially be outweighed by other concerns even when quality is the same, but I think they are the main drives of AI adoption in industry. If error rate is the same, a $1/hr (amortized) camera and machine vision model capable of checking 300ft of material for defects per minute will likely be preferred to a $10/hr human QA capable of checking 30ft per minute, for instance.

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ofjcihen 6 hours ago | parent [-]

Oh machine learning has been useful for measuring deterministic and non deterministic outputs for a long time.

But that’s not the argument here, is it?

So the question still stands.

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Ukv 6 hours ago | parent [-]

My understanding of your argument is (paraphrasing):

> > People try to excuse AI issues/failure modes by saying humans have them too, but even if they're equally bad then what would be the whole point of replacing a human worker with AI?

To which my response is that speed and cost are also important factors, which can often give AI the edge in considerations when quality/error rate is equal.

If you meant something other than that, you may have to specify.

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ofjcihen 5 hours ago | parent [-]

Sure, let me be blunt.

Speed and cost are nothing without quality and quality is partially a product of accountability (not even considering the technical or logistical issues this is enough on its own.

An AI cannot be held accountable. It does not desire to feed its family.

Your counter argument was outside the context of this articles claims, specifically that programmers and other knowledge workers can be replaced by LLMs.

Equating simple yes/no outcomes generated by vision based machine learning is quite different than “build me a product people will be happy with” being asked of a non-deterministic machine.

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Ukv 4 hours ago | parent [-]

> Speed and cost are nothing without quality

Quality was what the hypothetical was assuming had reached parity, no? ("humans are as bad as AI", "If AI really is at human level quality/error rate", etc.)

> accountability

Why could the company not still take accountability? That's already the case for non-ML automated systems, some with high failure rates. As a customer I rarely if ever care about blame being pinned on a specific employee.

> Your counter argument was outside the context of this articles claims, specifically that programmers and other knowledge workers can be replaced by LLMs.

AndrewKemendo's comment and your reply ("any humans", "replace humans") seemed to generalize, but speed and cost being important factors is still true for knowledge work. For some given level of quality, a web developer offering a lower quote with shorter turnaround time will be preferred to one offering a higher quote with longer turnaround time.

	▲	AndrewKemendo 3 hours ago \| parent [-]
		Correct For the set of [all tasks humans can do] there is a subset of [things automation can do at equvalent or better error rates]. And that’s the baseline the baseline is not some platonic ideal that is never reached the baseline for a business operator is cost per delivery /error. The idea that existing human systems are optimized or otherwise “not broken”, is the key fallacy that people keep making.