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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.
It is surprising how much can you achieved with good prompt and harnesses nowadays with how little tokens. The problem is that the majority of people using AI are too stupid to be lazy in the proper ways. I think that a tornado is coming. Probably later than anticipated, but the white collars brains are afraid (insert starship troopers movie meme here) - especially the ones who deep down always knew that their intellectual labor is neither extremely intellectual nor much useful. I am already seeing proposals for excise tax on tokens. And I think that the big hyperscalers grossly underestimate how much optimizations are left in the pipeline.
The compute cost on tools is low, agents are becoming quite adept at tool calling - so agents creating their own tools and tool calls is totally expected ... in a way this is what programmers have always done.
There is lots of performance left to be squeezed out of each token. And relatively small hyper focused models also doesn't seem to be getting the attention it deserves.
I'm always amazed at how often this refrain comes up, with different explanations every time. For some reason, he idea of bullshit jobs is one has immense staying power.
Whenever it does come up, I often wonder how one would separate the useless, lazy, stupid jobs from the essential ones. When I was younger I held a similar view, but over time I realized that the single strongest predictor for whether I thought a job was bullshit or not was how little I knew about its actual day to day work.
As a simple example, take project managers. A bad one is terrible, and is probably one of those things that a lot of people woud say is neither "intellectual" nor "useful". I had that opinion once upon a time. Eventually, I worked on a project with a good project manager and realized that they actually do an insane amount of work and provide a significant force multiplier for the rest of the people involved. It felt fantastic to just... work on the problem.
That's one of my biggest concerns about the current LLM frenzy. It's largely being driven by a small, cloistered group of people who really buy into the "bullshit jobs" premise, and spend more time saying "well couldn't you Just X" instead of figuring out why things are the way they are. Systems evolve into specific shapes for a reason. Tribal knowledge is real.
I feel like we're going to be forcefully reminded of those facts if we keep it up.
I mean this is kinda the point. A lot of these roles if you get the right person into the right situation they can definitely actually manifest a lot of value, but there's also a lot of jerking off and people dissapearing into huge bureaucratic machines. I also believe that 'bullshit jobs' and 'the current state of the economy evolved for a reason' aren't really mutually exclusive. My expectation is that in 20 years time there'll be a broad reshuffle of the deck but whatever percentage is largely superfluous today will also still be there in slightly different job titles.
Tend to agree.
Also, part of the issue is that a job can very much look like bullshit right up until some extremely important necessity arises.
Some amount of 'busywork' is there so that someone can stay occupied while they're being paid to be present in case that [event] occurs, which can be at almost any time, and the work has to be easy and unimportant enough that they can set it aside to attend to the event without something else catching on fire.
Rough example, the security guard at the bank might sit around watching videos on his phone for most of a year, but he is expected to jump to it if a guy with a ski mask appears.
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