It’s not true that people are only using it because it’s free.
It’s actually quite interesting to see these contradictory positions play out:
1. LLMs are useless and everyone is making a stupid bet on it. The users of llms are fooled into using it and the companies are fooled into betting on it
2. Llms are getting so cheap that the investments into data centers won’t pay off because apparently they will get good enough to run on your phone
3. Llms are bad and they are bad for environment, bad for the brain, bad because they displace workers and bad because they make rich people richer
4. AI is only kept up because there’s a conspiracy to keep it propped up by Nvidia, oracle, OpenAI (something something circular economy)
5. AI is so powerful that it should not be built or humanity would go extinct
It is true that none of the LLM providers are profitable though, so there is some number above free that they need to charge and I am not convinced that number is compelling
None of LLM providers being profitable is exactly the situation I would expect. Them being profitable is so absurd on the contrary! Why wouldn't they put the money back into R&D and marketing?
I'm not well versed with the accountant terminology, whatever the word is to describe the operating cost, I am not convinced consumers will ever pay enough to cover those costs
It's a competitive environment, no way the data centers manage to capture that 10x efficiency improvement. There would be an expectation of 10x reduced prices, because someone else is offering that.
The problem I see as someone who has implemented a bunch of AI solutions in a range of markets, the quality isn't good enough yet to even think about efficiency - even if the current AI is 100x more efficient it still wouldn't be worth paying for because it doesn't deliver reliable and trustable results...
A) Huge straw man, since it isn't the same people making those points. None of those need the other to be true to cause issues, they are independent concerns.
B) You're missing a few things like:
1. The hardware overhang of edge compute (especially phones) may make the centralized compute investments irrelevant as more efficient LLMs (or whatever replaces them) are released.
2. Hardware depreciates quickly. Are these massive data centers really going to earn their money back before a more efficient architecture makes them obsolete? Look at all the NPUs on phones which are useless with most current LLMs due to insufficient RAM. Maybe analogue compute takes off, or giant FPGAs, which can do on a single board what is done with a rack at the moment. We are nowhere near a stable model architecture, or stable optimal compute architecture. Follow the trajectory of bitcoin and etherium mining here to see what we can expect.
3. How does one company earn back their R&D when the moment it is released, competition puts out comparable models within 6 months, possibly by using the very service that was provided to generate training data.
> Imagine how many millions would drive a Ferrari if they gave them away for free
0.
Ferrari is a luxury sports brand. What's the point of it if it flooded the streets?
Good looking, powerful, reliable car?
> Good looking, powerful, reliable car?
How to say you don't own a Ferrari without saying you don't own a Ferrari.
It’s not true that people are only using it because it’s free.
It’s actually quite interesting to see these contradictory positions play out:
1. LLMs are useless and everyone is making a stupid bet on it. The users of llms are fooled into using it and the companies are fooled into betting on it
2. Llms are getting so cheap that the investments into data centers won’t pay off because apparently they will get good enough to run on your phone
3. Llms are bad and they are bad for environment, bad for the brain, bad because they displace workers and bad because they make rich people richer
4. AI is only kept up because there’s a conspiracy to keep it propped up by Nvidia, oracle, OpenAI (something something circular economy)
5. AI is so powerful that it should not be built or humanity would go extinct
It is true that none of the LLM providers are profitable though, so there is some number above free that they need to charge and I am not convinced that number is compelling
None of LLM providers being profitable is exactly the situation I would expect. Them being profitable is so absurd on the contrary! Why wouldn't they put the money back into R&D and marketing?
I'm not well versed with the accountant terminology, whatever the word is to describe the operating cost, I am not convinced consumers will ever pay enough to cover those costs
Do you think if LLM's become 10 times more efficient it might covert he costs? What efficiency increase would you think is enough?
It's a competitive environment, no way the data centers manage to capture that 10x efficiency improvement. There would be an expectation of 10x reduced prices, because someone else is offering that.
The problem I see as someone who has implemented a bunch of AI solutions in a range of markets, the quality isn't good enough yet to even think about efficiency - even if the current AI is 100x more efficient it still wouldn't be worth paying for because it doesn't deliver reliable and trustable results...
A) Huge straw man, since it isn't the same people making those points. None of those need the other to be true to cause issues, they are independent concerns.
B) You're missing a few things like:
1. The hardware overhang of edge compute (especially phones) may make the centralized compute investments irrelevant as more efficient LLMs (or whatever replaces them) are released.
2. Hardware depreciates quickly. Are these massive data centers really going to earn their money back before a more efficient architecture makes them obsolete? Look at all the NPUs on phones which are useless with most current LLMs due to insufficient RAM. Maybe analogue compute takes off, or giant FPGAs, which can do on a single board what is done with a rack at the moment. We are nowhere near a stable model architecture, or stable optimal compute architecture. Follow the trajectory of bitcoin and etherium mining here to see what we can expect.
3. How does one company earn back their R&D when the moment it is released, competition puts out comparable models within 6 months, possibly by using the very service that was provided to generate training data.