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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.
People have gigantically been freed up from busywork in the office versus what things were like 30-40 years ago. Aside from an expansion of internet pornography, what exactly has been accomplished? New busywork was found, actually manufacturing things was offshored and Western economies have largely trended towards overfinancialized circlejerks where nothing actually happens.
There's tons of stuff that has improved. Things that you want bigger are bigger like cars, TVs and homes. Things we want smaller are smaller like medical devices, computers and cameras. They're all typically much higher quality too. Those medical devices are saving more lives, those cameras take better pictures, those cars are less likely to kill you in a crash.
Stuff is generally cheaper now (per hours of work needed) and more accessible like how 2024 was the first year ever that >50% of Americans took at least one flight. And they did it without having to hire someone to handle bookings for them. Email/texting/etc allows for instant (and automatically stored!) correspondence with anyone I want, meaning we don't have to wait weeks to communicate back and forth. You can listen to almost any song ever recorded, watch basically any show ever made. I can keep track of my financials without having to keep meticulous and detailed records and receipts of where I went and what I spent.
Modern manufacturing is bigger!. Jobs are down because automation and robots are more efficient than people, but we make more now locally than we used to.
Global manufacturing has actually gone down though (for the reasons you alluded to). That’s not actually the gain people think it is however, because there’s a trade off between resilience and efficiency; especially as technological demands increase in industries like automotive.
I know satellite farming in agribusiness is one example where efficiency is really proving itself to cut down on waste and the poor industry practices of old, but not all industries are benefitting from efficiency. The social system hasn’t yet managed to adapt to rapid technological progress. Especially the government.
Case in point. A little more than a year ago I had to call the IRS to retrieve a document related to my father’s old tax return. It involved me having to send in information about myself and a few other things and they’re still requiring people to fax in paperwork to some random office, wait 2-3 days, with no direct callback number to the agent you’re talking to, to then get a single document physically mailed to me. It’s not like I could just, oh I don’t know, email them a passworded attachment of what they asked for and check it right there over the phone.
The youngest kids in my extended family don’t even know what a fax machine is. Or a VHS tape. Or a Cassette tape. Or how to write in cursive. Or know how to write a check. The IRS is still using fax machines…
Manufacturing is down in two ways. Number of jobs, and share of GDP. But output is much higher. It's down as a share of GDP because other parts of the economy in services and software grew even faster. In part thanks to the automation of factories and farms, which cleared up human labor to go into other fields. People no longer have to work out mowing the fields and picking crops or putting cars together in the assembly line, so they are now free to go do other work and that work has exploded in productivity.
Great example of how governments, without the competitive pressure to improve and outdated (sometimes even conflicting) regulations that lawmakers are too uncaring to address are unable to update themselves in the same way that private corporations generally can.
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