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Commented on this the other day, I think AI is fundamentally different from ride share apps and Uber for X services. It's more likely to follow a Moore's law tr

Commented on this the other day, I think AI is fundamentally different from ride share apps and Uber for X services. It's more likely to follow a Moore's law trajectory. Getting cheaper and better over time? Or at least cheaper.

Larrikin 1y ago
It will only get cheaper if the models can run locally. Anything that is a subscription service will always get more expensive over time.
- tjr 1y ago
  Local and preferable Free. It seems odd that the majority of the software development world is gleefully becoming dependent upon proprietary tools running on someone else's machine. https://www.gnu.org/philosophy/who-does-that-server-really-serve.html https://www.gnu.org/philosophy/who-does-that-server-really-s...
- celeritascelery 1y ago
  While running locally will no doubt get cheaper over time (and hence become much more viable), cloud compute cost will also drop significantly as better hardware and more specialized models are created. We have seen this process already where the cost per million tokens has been falling rapidly.
- simianwords 1y ago
  This is untrue simply based on the so many past instances of Gemini, OpenAI making their products cheaper. The ratelimits for GPT 5 are pretty high. The API costs have decreased by 50% over and above o3's reduction which was also massive. This is not even considering the fact that the performance has also increased.
VerminOctopus1 1y ago
It feels like a lot of the core LLM progress has plateaued or is approaching the end of the asymptote. We’re seeing a ton of great tooling being built around LLMs to increase their utility, but I wonder how much more juice we can really squeeze out of these things.
AtlasBarfed 1y ago
And Moore's law will last forever! Right? Where as to get AI to any sort of approximation of what it's hyped up to be, may involve exponentially higher hardware costs. So for the longest period of time, AI was sitting in about 90% accuracy. With the use of Nvidia hardware it's going to say 99 to 99.9%. I don't think it's actually 99.9% To replace humans, I think you effectively need 99.999% and even more depending on the domain like self-driving is probably eight nines. What's the hardware cost to get each one of those nines linear polynomial? Exponential?

Larrikin 1y ago

It will only get cheaper if the models can run locally. Anything that is a subscription service will always get more expensive over time.

tjr 1y ago
Local and preferable Free. It seems odd that the majority of the software development world is gleefully becoming dependent upon proprietary tools running on someone else's machine. https://www.gnu.org/philosophy/who-does-that-server-really-serve.html https://www.gnu.org/philosophy/who-does-that-server-really-s...
celeritascelery 1y ago
While running locally will no doubt get cheaper over time (and hence become much more viable), cloud compute cost will also drop significantly as better hardware and more specialized models are created. We have seen this process already where the cost per million tokens has been falling rapidly.
simianwords 1y ago
This is untrue simply based on the so many past instances of Gemini, OpenAI making their products cheaper. The ratelimits for GPT 5 are pretty high. The API costs have decreased by 50% over and above o3's reduction which was also massive. This is not even considering the fact that the performance has also increased.

VerminOctopus1 1y ago

It feels like a lot of the core LLM progress has plateaued or is approaching the end of the asymptote. We’re seeing a ton of great tooling being built around LLMs to increase their utility, but I wonder how much more juice we can really squeeze out of these things.

AtlasBarfed 1y ago

And Moore's law will last forever! Right? Where as to get AI to any sort of approximation of what it's hyped up to be, may involve exponentially higher hardware costs. So for the longest period of time, AI was sitting in about 90% accuracy. With the use of Nvidia hardware it's going to say 99 to 99.9%. I don't think it's actually 99.9% To replace humans, I think you effectively need 99.999% and even more depending on the domain like self-driving is probably eight nines. What's the hardware cost to get each one of those nines linear polynomial? Exponential?