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This month I spent $50 on OpenRouter credits. I find it very useful for coding assistance (not coding per se). It's $50 that I never spent before on this kind o

This month I spent $50 on OpenRouter credits. I find it very useful for coding assistance (not coding per se). It's $50 that I never spent before on this kind of service. That's what we call growth - it quite literally came out of nowhere.

oblio 3d ago
Your $50 isn't enough to sustain trillions of dollars in CapEx investments. This investment cycle - at this scale - only makes sense if within the next 5 years AI replaces something crazy like 5-10% of jobs, if not more. Not changes, replaces completely. And I'd argue the timer started in 2023.
- brunoarueira 3d ago
  It's the marketing they were doing all the time that AI will replace developers and other jobs, then the companies started this bullshit firing thousands of people without really thinking through. It's more sane to advertise that AI will augment us and I guess if they followed this path a more sustainable growth is expected to happen!
- saberience 3d ago
  I've spent several 1000 dollars on AI products this month, roughly 3000 dollars. Trust me, there are many other people like me in the world and the enterprises are spending even more. There is more money flowing into the overall AI ecosystem (by far) than the money Nvidia puts in. The idea that Nvidia is artificially creating the whole demand is laughable and doesn't add up.
  - ReflectedImage 3d ago
    The AI numbers only work if it replaces white collar work. Power tools for knowledge workers, which is what we are getting, isn't enough to save it.
  - oblio 3d ago
    > I've spent several 1000 dollars on AI products this month, roughly 3000 dollars. > Trust me, there are many other people like me in the world and the enterprises are spending even more. Awesome, let's do some math here. I'll do both $1k/month and $3k/month, please follow along. To make the math even simpler to follow, I'll actually reduce your amounts. ~$800/month will get us about $10k/year and ~1600/month will mean about $20k/year, makes for easier divisions. Current total investment into AI is at least: https://isaiprofitable.com/ https://isaiprofitable.com/ -> $1.8tn. So $1 800 000 000 000. From what we know about current AI tech, about ever increasing hardware prices, about ever increasing electricity prices, about the ever increasing DC construction prices, AI companies need to invest a fair chunk of money each year to keep the whole thing going, let's be SUPER conservative and put that amount at $200bn per year. So: 1 800 000 000 000 + 200 000 000 000 = $2tn next year. Then at least another 200 000 000 000 per year = $0.2tn/year. So $2tn next year divided by $10k/year means that means that they will need 200 million yearly subscriptions to recover the money already invested. At $20 k/year would mean 100 million yearly subscriptions. Spread over 5 years that would mean 40 million yearly subscriptions and 20 million yearly subscriptions. Then for each year, just to cover the costs, at $10k/year 20 million yearly subscriptions would be needed, and at $20k/year 10 million yearly subscriptions are needed. So that's 60 million yearly subscriptions and 30 million yearly subscriptions. I used Claude to extract some numbers. The total global addressable workforce that makes more than $80k per year (where an employer would dare spend $10k/$20k per year on AI) is about 100 million people. The total private population that has $10k/20k per year in disposable income is about 500 million people (excluding China, since they will for sure not use Western AIs en masse). So that's about 600 million users (just stacking private users + how much companies would pay for their workers). So just to break even each year, 10% of those would need to pay those crazy high subscriptions, and 5% the extra crazy high subscriptions. For private users if they get 1% of that rate, it means that a lot of private individuals have fallen on their collective heads. For enterprises, nobody's going to increase their salary expenses from $80k to $90k-$100k for benefits that we can't even quantity, let alone guarantee an upside of 10-25%. That kind of budget will be allocated for people making $150k or above, which makes the total global addressable workforce something like 50 million, most likely less. I want to have what you're smoking. Yes, demand is there. Demand to prop up how much we're investing. NO WAY. At actual prices and actual LLM productivity gains, we should probably be investing 20% of what we're investing. A lot of people will be wiped because of Nvidia and friends. Even worse, a lot of regular people will suffer because we've distorted our societies so much due to this hype train.
- neonstatic 3d ago
  I merely pointed out, that there is real growth behind this - 50$ from my wallet went to this, that did not go towards it at any point in the past. Your argument that my 50$ is not enough is duly noted, but also intellectually lazy.
hypercube33 3d ago
This month I reduced my spend on cloud AI and started using local models as primary - it's maybe 2 or 4 times slower for some things but it still is getting the job done just fine. I talked to a friend of mine who's been doing the same running smaller Qwen models locally for coding.

oblio 3d ago

Your $50 isn't enough to sustain trillions of dollars in CapEx investments. This investment cycle - at this scale - only makes sense if within the next 5 years AI replaces something crazy like 5-10% of jobs, if not more. Not changes, replaces completely. And I'd argue the timer started in 2023.

brunoarueira 3d ago
It's the marketing they were doing all the time that AI will replace developers and other jobs, then the companies started this bullshit firing thousands of people without really thinking through. It's more sane to advertise that AI will augment us and I guess if they followed this path a more sustainable growth is expected to happen!
saberience 3d ago
I've spent several 1000 dollars on AI products this month, roughly 3000 dollars. Trust me, there are many other people like me in the world and the enterprises are spending even more. There is more money flowing into the overall AI ecosystem (by far) than the money Nvidia puts in. The idea that Nvidia is artificially creating the whole demand is laughable and doesn't add up.
- ReflectedImage 3d ago
  The AI numbers only work if it replaces white collar work. Power tools for knowledge workers, which is what we are getting, isn't enough to save it.
- oblio 3d ago
  > I've spent several 1000 dollars on AI products this month, roughly 3000 dollars. > Trust me, there are many other people like me in the world and the enterprises are spending even more. Awesome, let's do some math here. I'll do both $1k/month and $3k/month, please follow along. To make the math even simpler to follow, I'll actually reduce your amounts. ~$800/month will get us about $10k/year and ~1600/month will mean about $20k/year, makes for easier divisions. Current total investment into AI is at least: https://isaiprofitable.com/ https://isaiprofitable.com/ -> $1.8tn. So $1 800 000 000 000. From what we know about current AI tech, about ever increasing hardware prices, about ever increasing electricity prices, about the ever increasing DC construction prices, AI companies need to invest a fair chunk of money each year to keep the whole thing going, let's be SUPER conservative and put that amount at $200bn per year. So: 1 800 000 000 000 + 200 000 000 000 = $2tn next year. Then at least another 200 000 000 000 per year = $0.2tn/year. So $2tn next year divided by $10k/year means that means that they will need 200 million yearly subscriptions to recover the money already invested. At $20 k/year would mean 100 million yearly subscriptions. Spread over 5 years that would mean 40 million yearly subscriptions and 20 million yearly subscriptions. Then for each year, just to cover the costs, at $10k/year 20 million yearly subscriptions would be needed, and at $20k/year 10 million yearly subscriptions are needed. So that's 60 million yearly subscriptions and 30 million yearly subscriptions. I used Claude to extract some numbers. The total global addressable workforce that makes more than $80k per year (where an employer would dare spend $10k/$20k per year on AI) is about 100 million people. The total private population that has $10k/20k per year in disposable income is about 500 million people (excluding China, since they will for sure not use Western AIs en masse). So that's about 600 million users (just stacking private users + how much companies would pay for their workers). So just to break even each year, 10% of those would need to pay those crazy high subscriptions, and 5% the extra crazy high subscriptions. For private users if they get 1% of that rate, it means that a lot of private individuals have fallen on their collective heads. For enterprises, nobody's going to increase their salary expenses from $80k to $90k-$100k for benefits that we can't even quantity, let alone guarantee an upside of 10-25%. That kind of budget will be allocated for people making $150k or above, which makes the total global addressable workforce something like 50 million, most likely less. I want to have what you're smoking. Yes, demand is there. Demand to prop up how much we're investing. NO WAY. At actual prices and actual LLM productivity gains, we should probably be investing 20% of what we're investing. A lot of people will be wiped because of Nvidia and friends. Even worse, a lot of regular people will suffer because we've distorted our societies so much due to this hype train.
neonstatic 3d ago
I merely pointed out, that there is real growth behind this - 50$ from my wallet went to this, that did not go towards it at any point in the past. Your argument that my 50$ is not enough is duly noted, but also intellectually lazy.

hypercube33 3d ago

This month I reduced my spend on cloud AI and started using local models as primary - it's maybe 2 or 4 times slower for some things but it still is getting the job done just fine. I talked to a friend of mine who's been doing the same running smaller Qwen models locally for coding.