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Trillions of dollars are being spent on building datacenters for inference. Amazon software engineers are inventing bullshit work for AI to inflate their internal usage scores.
I’m no expert, but isn’t there a fatal flaw here? Most of the work LLM inference is used for is essentially busywork that wouldn’t exist in an automated economy. It’s writing emails, it’s code reviews, it’s asking dumb questions, it’s transcribing or summarizing research or zoom meetings. Even in software engineering, a lot of LLM tokens are used in the kind of inference that a hypercompetent solo-coding model with limited or no human oversight just wouldn’t need.
Think of an office with 10 human employees working in, say, payroll, constantly sending each other emails, messages, having meetings, calling and speaking to each other and other people, summarizing documents, liaising with other departments, asking AI question about how to use various accounting tools, or about the company’s employee benefits package. Now say this department is automated. An AI model acts as an agent to use an already-existing software package to do all the payroll work. No emails, calls or meetings - or at least far fewer. The total inference work required goes down. And the existing software package doesn’t use AI (even if it may have been coded with it), because you don’t need AI to compute payroll data once you have sufficiently complex and customized software for your business.
In the same way, if we imagine our automated future, super high intensity / high token usage inference is actually not really universally required in a lot of occupations. It will be for some multimodal work (plumbing, surgery, domestic cleaning in complex physical environments), but for many tasks, one-and-done software coded either by AI or that already exists can just be deployed at low intensity by an agent. The AI that replaces your job might at first do a lot of coding, but as time goes on, the amount of novel inference required will diminish. Eventually, software coded in a one-and-done way by the AI may actually handle almost all the workload, and token usage for generation may be very limited to just some high level agent occasionally relaying instructions or performing oversight.
In this scenario, why would we expect inference workloads to shoot up so dramatically? Much enterprise AI usage is currently “fake” in the sense that it would not be performed in a fully automated environment. It’s a between-times thing.
AI is not in the state to do a completely automated economy yet, many tasks still have to be done (or at least directed by) humans. Thus freeing humans up from busywork is still an important gain in our current situation even if eventually this will be become redundant as well.
Unfortunately, busywork is also subject to Jevon's Paradox.
Building a fully automated economy is going to require conscious effort to build systems that reduce/eliminate human participation. Otherwise the meatbags will just keep making more work for each other.
I don't think jevons paradox should be seen as unfortunate so long as the new usage is productive in some form. Efficiency is a key aspect of growing the pot and getting us all bigger portions.
Like this sounds good to me. New jobs getting created to meet previously underserved demands means more total demands being fulfilled and presumably better overall lives.
Jevons Paradox isn’t something you want to deal with, with crises like climate change looming on the horizon. When solving that you have to go to public policy, not to tech (1, 2). The problem with greater efficiency is that the effective production and precision of inputs isn’t necessarily the most optimal one when it increases fragility. That was the whole point Taleb was making when he wrote Antifragile a number of years ago. I’m all for efficiency and all that, but it doesn’t mean it’s without some massive drawbacks.
Externalities in consumption can actually be a problem, but that can be addressed in other ways such as carbon taxes.
I think there needs to be a line drawn between efficiency from cutting unnecessary things, and efficiency from removing all redundancies and backups.
If someone is making a sandwich and between every step they clap their hands for no reason, stopping them from doing that is objectively an improvement. But having another jar of peanut butter in the pantry you bought because you're running low and might need more for this sandwich is just long run efficiency, even if short term it might not be necessary.
I’ll refer you here to the episode that had “thermodynamics” in the title, if you’re interested to hear about the issues with a carbon tax.
And this is where the balance is. You saw it in the policy sphere as well after COVID struck, where people saw just how fragile shipping and supply lines were. I don’t know how many people were paying attention but within Biden’s cabinet, people were talking about the necessity of a large scale program of re-industrialization in the US; because of it.
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