> When doing automations that are perfectly handled by deterministic systems why would I put the outcomes of those in the hands of a non-deterministic one?

The stuff I'm punting isn't stuff I can automate. It's stuff like, "build me a quick command line tool to model passes from this set of possible orbits" or "convert this bulleted list to a course articulation in the format preferred by the University of California" or "Tell me the 5 worst sentences in this draft and give me proposed fixes."

Human assistants that I would punt this stuff to also consume a lot of wattage and power. ;)

> We didn't have these tools 5 years ago. 5 years ago you dealt with said "drudgery". On the other hand you then say it can't do "most things I do".

I'm not sure why you think this is paradoxical.

I probably eliminate 20-30% of tasks at this point with AI. Honestly, it probably does these tasks better than I would (not better than I could, but you can't give maximum effort on everything). As a result, I get 30-40% more done, and a bigger proportion of it is higher value work.

And, AI sometimes helps me with stuff that I -can't- do, like making a good illustration of something. It doesn't surpass top humans at this stuff, but it surpasses me and probably even where I can get to with reasonable effort.

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Mentlo 5 days ago | parent [-]

It is absolutely impossible that human assistants being given those tasks would use even remotely within the same order of magnitude the power that LLM’s use.

I am not an anti-LLM’er here but having models that are this power hungry and this generalisable makes no sense economically in the long term. Why would the model that you use to build a command tool have to be able to produce poetry? You’re paying a premium for seldom used flexibility.

Either the power drain will have to come down, prices at the consumer margin significantly up or the whole thing comes crashing down like a house of cards.

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mlyle 5 days ago | parent [-]

> It is absolutely impossible that human assistants being given those tasks would use even remotely within the same order of magnitude the power that LLM’s use.

A human eats 2000 kilocalories of food per day.

Thus, sitting around for an hour to do a task takes 350kJ of food energy. Depending on what people eat, it's 350kJ to 7000kJ of fossil fuel energy in to get that much food energy. In the West, we eat a lot of meat, so expect the high end of this range.

The low end-- 350kJ-- is enough to answer 100-200 ChatGPT requests. It's generous, too, because humans also have an amortized share of sleep and non-working time, other energy inputs/uses to keep them alive, eat fancier food, use energy for recreation, drive to work, etc.

Shoot, just lighting their part of the room they sit in is probably 90kJ.

> I am not an anti-LLM’er here but having models that are this power hungry and this generalisable makes no sense economically in the long term. Why would the model that you use to build a command tool have to be able to produce poetry? You’re paying a premium for seldom used flexibility.

Modern Mixture-of-Experts (MoE) models don't activate the parameters/do the math related to poetry, but just light up a portion of the model that the router expects to be most useful.

Of course, we've found that broader training for LLMs increases their usefulness even on loosely related tasks.

> Either the power drain will have to come down, prices at the consumer margin significantly up

I think we all expect some mixture of these: LLM usefulness goes up, LLM cost goes up, LLM efficiency goes up.

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Mentlo 4 days ago | parent [-]

Reading your two comments in conjunction - I find your take reasonable, so I apologise for jumping the gun and going knee first in my previous comment. It was early where I was, but should be no excuse.

I feel like if you're going to go down the route of the energy consumption needed to sustain the entire human organism, you have to do that on the other side as well - as the actual activation cost of human neurons and articulating fingers to operate a keyboard won't be in that range - but you went for the low ball so I'm not going to argue that, as you didn't argue some of the other stuff that sustains humans.

But I will argue the wider implication of your comment that a like-for-like comparison is easy - it's not, so leaving it in the neuron activation space energy cost would probably be simpler to calculate, and there you'd arrive at a smaller ChatGPT ratio. More like 10-20, as opposed to 100-200. I will concede to you that economies of scale mean that there's an energy efficiency in sustaining a ChatGPT workforce compared to a human workforce, if we really want to go full dystopian, but that there's also outsized energy inefficiency in needing the industry and using the materials to construct a ChatGPT workforce large enough to sustain the economies of scale, compared to humans which we kind of have and are stuck with.

There is a wider point that ChatGPT is less autonomous than an assistant, as no matter the tenure with it, you'll not give it the level of autonomy that a human assistant would have as it would self correct to a level where you'd be comfortable with that. So you need a human at the wheel, which will spend some of that human brain power and finger articulation, so you have to add that to the scale of the ChatGPT workflow energy cost.

Having said all that - you make a good point with MoE - but the router activation is inefficient; and the experts are still outsized to the processing required to do the task at hand - but what I argue is that this will get better with further distillation, specialisation and better routing however only for economically viable task pathways. I think we agree on this, reading between the lines.

I would argue though (but this is an assumption, I haven't seen data on neuron activation at task level) that for writing a command-line tool, the neurons still have to activate in a sufficiently large manner to parse a natural language input, abstract it and construct formal language output that will pass the parsers. So you would be spending a higher range of energy than for an average Chat GPT task

In the end - you seem to agree with me that the current unit economics are unsustainable, and we'll need three processes to make them sustainable - cost going up, efficiency going up and usefulness going up. Unless usefulness goes up radically (which it won't due to scaling limitations of LLM's), full autonomy won't be possible, so the value of the additional labour will need to be very marginal to a human, which - given the scaling laws of GPU's - doesn't seem likely.

Meanwhile - we're telling the masses at large to get on with the programme, without considering that maybe for some classes of tasks it just won't be economically viable; which creates lock in and might be difficult disentangle in the future.

All because we must maintain the vibes that this technology is more powerful than it actually is. And that frustrates me, because there's plenty pathways where it's obvious it will be viable, and instead of doubling down on those, we insist on generalisability.

	▲	mlyle 4 days ago \| parent \| next [-]
		> There is a wider point that ChatGPT is less autonomous than an assistant, as no matter the tenure with it, you'll not give it the level of autonomy that a human assistant would have as it would self correct to a level where you'd be comfortable with that. IDK. I didn't give human entry level employees that much autonomy. ChatGPT runs off and does things for a minute or two consuming thousands and thousands of tokens, which is a lot like letting someone junior spin for several hours. Indeed, the cost is so low -- better to let it "see its vision through" than to interrupt it. A lot of the reason why I'd manage junior employees closely are to A) contain costs, and B) prevent discouragement. Neither of those apply here. (And, you know -- getting the thing back while I remember exactly what I asked and still have some context to rapidly interpret the result-- this is qualitatively different from getting back work from a junior employee hours later). > that maybe for some classes of tasks it just won't be economically viable; Running an LLM is expensive. But it's expensive in the sense "serving a human costs about the same as a long distance phone call in the 90's." And the vast majority of businesses did not worry about what they were expending on long distance too much. And the cost can be expected to decrease, even though the price will go up from "free." I don't expect it will go up too high; some players will have advantages from scale and special sauce to make things more efficient, but it's looking like the barriers to entry are not that substantial.
	▲	og_kalu 4 days ago \| parent \| prev [-]
		The unit economics is fine. Inference cost has reduced several orders of magnitude over the last couple years. It's pretty cheap. Open AI reportedly had a loss of $5B last year. That's really small for a service with hundreds of millions of users (most of which are free and not monetized in any way). That means Open AI could easily turn a profit with ads, however they may choose to implement it.