Comment by me551ah

Comment by me551ah 3 days ago

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Scaling AI will require an exponential increase in compute and processing power, and even the current LLM models take up a lot of resources. We are already at the limit of how small we can scale chips and Moore’s law is already dead.

So newer chips will not be exponentially better but will be more of incremental improvements, so unless the price of electricity comes down exponentially we might never see AGI at a price point that’s cheaper than hiring a human.

Most companies are already running AI models at a loss, scaling the models to be bigger(like GPT 4.5) only makes them more expensive to run.

The reason why internet, smartphones and computers have seen exponential growth from the 90s is due to underlying increase in computing power. I personally used a 50Mhz 486 in the 90s and now use a 8c/16t 5Ghz CPU. I highly doubt if we will see the same form of increase in the next 40 years

aaronblohowiak 3 days ago

We are either limited by compute, available training data, or algorithms. You seem to believe we are limited by compute. I've seen other people argue that we are limited by training data. It is my totally inexpert belief that we are substantially limited by algorithms at this point.

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  • mattnewton 3 days ago

    I think algorithms is a unique limit because it changes how much data or compute you need. For instance, we probably have the algorithms we need to brute force solving more problems today, but they require infeasible compute or data. We can almost certainly train a new 10T parameter mixture of experts that continues to make progress in benchmarks, but it will cost so much to train and be completely undeployable with today’s chips, data, and algorithms.

    So I think the truth is likely we are both compute limited and we need better algorithms.

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    • aaronblohowiak 3 days ago

      There are a few "hints" that suggest to me algorithms will bear a lot more fruit than compute (in terms of flops):

      1) there already exist very efficient algorithms for rigorous problems that LLMs perform terribly at! 2) learning is too slow and is largely offline 3) "llms aren't world models"

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    • PUSH_AX 2 days ago

      General intelligence exists in this world, the inability to transfer it to a machine does seem like an algorithm problem. When it’s here we don’t even know if it will be an llm, no one knows the computer requirements.

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  • sarthaksingh99 3 days ago

    We are limited by both compute and available training data.

    If all we wanted was to train bigger and bigger models we have more than enough compute to last us for years.

    Where we lack compute is in scaling the AI to consumers. Current models take too much power and specialized hardware to be be profitable. If AI was able to improve your productivity by 20-30% percent but it costed you even 10% of your monthly salary, none would use it. I have used up $10 worth of credits using claude code in an hour multiple times. Assuming I use it continuously for 8 hours every day in a month, 10 * 8 * 24 = $1920. So its not that far off the current costs or running the models. If the size of the models scales faster than the speed of the inference hardware, the problem is only going to get worse.

    I too believe that we will eventually discover an algorithm that gives us AGI. The problem is that we cannot will a breakthrough. We can make one more likely by investing more and more into AI but breakthroughs and research in general by their nature are unpredictable.

    I think investing in new individual ideas is very important and gives us lot of good returns. Investing in a field in general hoping to see a breakthrough is a fool's errand in my opinion.

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  • joegibbs 3 days ago

    If the LLM is multimodal would more video and images improve the quality of the textual output? There’s a ton of that and it’s always easy to get more.

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azinman2 3 days ago

> I highly doubt if we will see the same form of increase in the next 40 years

People would have predicted this at 1GHZ. I wouldn’t discount anything about the future.

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threecheese 3 days ago

We are a few months into our $bigco AI push and we are already getting token constrained. I believe we really will need massive datacenter rollouts in order to get to the ubiquity everyone says will happen.

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czk 3 days ago

it only requires exponentially MORE money for linear returns!

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