I agree that datacenters are not going to go away, but I have doubts that the buildup that has happened is really going to pay off for most operators.
But I suspect returns may have already diminished into negative territory for at least some other use cases. One of my least favorite job responsibilities in this brave new era is figuring out how to avoid performance and behavior regressions when an older model were using for some application reaches end of life. It’s getting uncommon for me to look at our benchmark results and say, “Oh, good, it does better on one of the newer models!”
Part of the reason harnesses work well is you can run a lot of agents in parallel. That doesn't slow down demand.
LLMs are amazing tech, but they're terrible without oversight. More agents faster just makes reality collapse on them quicker.
But yeah, you're right, temporarily, this will still push demand. But the topic was about "diminishing returns" as in "tech getting better". Not as in "customer spending".
New models keep being able to use more and more agents on longer time frames. Your hypothesis doesn't look like what we're measuring.
Is it just that the providers are generating tons of synthetic datasets on coding tasks so that the models get more exposure to the right thing to do? Every time someone points out an LLM stupidity they add some training data to patch over the weakness (trivial to generate "there are two 'l's in llama")?
Before that, we had 0. After that, we had more than 1.
A leap as far as that is hard to recreate.
But that wasn't my point. That's just trolling.
The actual point is that LLMs aren't gaining new capabilities anymore. They just get more reliable at the ones they already have; turning what was a coin flip to some higher probability.
That's (intuitively speaking, not strictly mathematically speaking) kinda the mathematical definition of diminishing returns.
Who is so emotionally invested into random comment sections being purely positive about their pet.. uuuuuuuh.. tech?
Very weird.