We're not seeing the progress in those "frontier models" that we have previously seen. There's certainly still gas left in tank tank, but we're way into the diminishing returns by now.
Cloud inference still beats hardware investments by orders of magnitude of course, but that's only if your data doesn't really matter to you.
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.
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")?
https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=llm...
https://proceedings.iclr.cc/paper_files/paper/2026/hash/3b4e...
It’s also poor reasoning to you only pick one thing you think supports your view, and ignore vastly more things not supporting it, all from the same useful criteria.
Now if only you’d carefully read the report you chose, and spend equal time looking at ample presented evidence, you’d develop a more accurate understanding.
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.
Coorperate people likely just want “the best” and are happy to pay a lot for it to make their workforce more efficient.
It’s hard for me to guess where the data center buildout will go, eventually there will be an oversupply, but we haven’t hit that yet, and at the moment the companies owning these high capacity data centers appear to be making lots of very real money in leases to hyper scalers, hard to imagine this premium continuing, but we need to see where demand for cloud based LLMs tops out.
Who is so emotionally invested into random comment sections being purely positive about their pet.. uuuuuuuh.. tech?
Very weird.
But I also don't believe things just because I want them to be true.