Taking a closer look at AI's supposed energy apocalypse

Now the problem is that if someone puts a $10/month price on the AI functionality, they will stop paying for it because it doesn't really have a $10/month ROI.

Literally every AI thing I've seen people use has got an interest taper off way before the free trial even ends.

The constraining factor will prevent an energy apocalypse: It doesn't materially improve most people's lives at all.

> A similar trend will likely guide the use of generative AI as a whole, with the energy invested in AI servers tracking the economic utility society as a whole sees from the technology.

This comparison is inappropriate. Cryptocurrency was and is mainly an object of speculation, not of intrinsic economic value. I'm contrast, AI has clearly a lot of economic value, and that value increases with time, since it gets better with time.

Moreover, the rate of improvement over the last few years have been staggering. Things that seemed far out of reach science fiction technology ten years ago are now unremarkable reality. So there are solid empirical grounds to expect that progress in the coming 10 years will be transformative as well. Nothing like that was or is the case for cryptocurrency.

Does AI even use more energy than crypto ever did? Doubtful.

Besides, we're projected to ingest all of humanity's written knowledge by 2026, and after that there will be a cliff in power consumption, we will mostly switch to inference on purpose built hardware, which is vastly more efficient.

while there is value and money-grifting involved in both crypto and genAI, there is a major differentiating factor between the wastage argument - artificially-adjusted proof of work, which is/was used for major cryptocurrencies.

people spend time and energy (figuratively and literally) on the internet on many ways one could define as wastefully (take scrolling down the "for you" feed for example). but for majority of cases, the "efficiencies" or their lack of are not done intentionally and are often improved on (e.g. new codecs like AV1 needing less bandwidth).

but to make crypto tokens work, the original decision of using PoW has led to a massive surge of energy consumption to achieve the same results. while there might be some work on countering its negatives (e.g. with eth network), it is still a major driving force, despite an arms race to make more efficient hardware.

while a lot of current AI has major inefficiencies (will be too long to list down here!), the root cause is often the fast-moving innovations and everyone building on someone's imperfect work.

coming back to the original argument, you should expect more dedicated hardware to do the underlying matrix multiplications to make AI work. for most people who would only care about inference, it is already becoming a reality at consumer level. but expect people to push the new stuff to its limit to stay competitive, whether you find all of this ridiculous or not.

I get it, GenAI became popular as a consumer-facing product and tech industry PR blitz in that year. But people in the tech world should know better. AI as massive industry-wide GPGPU workload took off in the 2010s, including widespread usage of smaller models like CNNs throughout both FAANG's stack and in scattered startups, as well as supermodels used as recommendation engines by everyone and their grandma. Arguably the entire business models of every irresponsibly large tech company ran on this shit. All the telemetry. Ad targeting. Social Media feeds. Hell, GPT-3 came out before 2020. It was a scandal in this world when OpenAI exclusively licensed it to Microsoft, who was already probably using it in search at that point. None of this was actually that long ago, this is way too soon to have cultural amnesia. I get that I'm in somewhat of a bubble as an AI researcher but surely tech publications should at least know these basic facts, right? Is this yet another reason to be annoyed at the 2010s lingo for calling all these pervasive neural networks "the algorithms"?

From the perspective of energy expenditure from AI workloads, the statement that it's a major driving force of the rising energy demands of datacenters is a perfectly reasonable conclusion given a graph where the TWH more than triples between 2012 and 2024. The article sometimes specifies "generative AI" (which did exist in 2012, but was in a way less interesting state for most people and businesses until 2022), but often just says "AI", which is a big umbrella term people have at least consistently been using for most neural networks for that entire span of time (and longer, and for lots of other things, and it's hopelessly overloaded to the point of being nonsensical sometimes, but regardless this is an incredibly uncontroversial usage). So someone at a data center with a graph that basically tracks the rise of GPGPU neural networks and shows a big jump in energy expenditure over that period attributing this to "AI" is very reasonable!