Posted by bmulholland 16 hours ago
Or at least Nvidia GPUs will become slightly cheaper for regular consumers again
There is probably a ~50% chance that the next Dem candidate for presidency runs on a national datacenter moratorium or something equally as crippling.
The current datacenter owners love a compute-bound world anyhow. A moratorium on new datacenters would increase their valuation, encourage efficiency and make computers cheap again. If Chinese labs can ship frontier models under 1T parameters, why not American labs too?
It's fine if you're one of the people selling shovels to gold miners for a while, but sucks to be building houses in the boom town?
Maybe the money will still flow into this industry after all
Once models are more or less interchangeable, the price of LLMs will drop to essentially the price of energy required to run them, and the big labs will be able to run them cheaper than anyone else.
With competition we will actually have the fair split, whatever that is, and thus much lower prices.
At the moment, to have a big AI firm, or really AI firm at all, you need to be blessed by NVIDIA, in the form of receiving circular financing for your compute. They know that their prices aren't fair, or competitive.
Commoditization of inference is the end of that. The end of the mega-premium on inference hardware, and it's good not only for people who like running their LLMs, but it's the first step towards commoditization of training.
If what you said were true, you would hardly see people complaining about the quality of Opus 5 or good writing from Sol. But people do.
They are all converging.
I’m not an expert in metallurgy by any means, but this seems really off. There are many recipes for steel and varied processes that also impact the final product.
> However, as previously mentioned, Jalapeño’s results are obtained without speculative decoding and Vera Rubin’s results use speculative decoding. Speculative decoding leads to a ~3-5x reduction in cost per token. When speculative decoding is implemented on Jalapeño, this will enable Jalapeño to serve tokens even more cost effectively.
How much speculative decoding improves throughput is workload-dependent. Yes, it can improve performance by 5x, but it can also slow down performance by 2x. It also requires a vastly more complex decode loop than the standard token-by-token decode. The tech journos didn't ask themselves if speculative decoding improves perf so much why wasn't it on by default? Perhaps, because speculative decoding is not optimal for OpenAI's ASIC? Perhaps that is also why they were only able to benchmark the not-very-representative single-user-decode case?
I went down a rabbit hole after watching Dylan Patel on Dwarkesh today: https://www.youtube.com/watch?v=aV26V1UvkJw
I was initially just surprised by how bullish Dylan is on OpenAI/Anthropic and how bearish he is on China, despite Chinese labs getting closer to US SOTA while offering inference at dramatically lower prices.
So, I started digging while waiting for various day-job inference calls to return, ha.
Dylan says he spent years obsessively posting on hardware forums, moderating hardware subreddits, and running anonymous hardware blogs/videos before SemiAnalysis. But he also says most of that history is now gone, including from the Internet Archive, because he asked for it to be removed.[1]
In a 2024 interview he described his post-college job as “data science” around hurricane/earthquake/wildfire simulations for a financial company.[1] In a 2026 Sequoia interview he described himself as having been a “quant at a small quant risk firm” who generated $10M+ of “risk-free revenue.”[2] The Information reports that he declined to identify the employer and doesn’t list it on LinkedIn.[3]
Even harmless/silly stuff seems to drift. In February he said he kept bees for ~1.5 years. Today it was “few months, few months.”[4][5] I know, sort of silly and doesn't matter.
The Information reports that Patel owns stakes in ~20 startups in the same ecosystem SemiAnalysis covers, organized a $50M Fluidstack SPV, and is now targeting a $400M venture fund.[3][6]
And, in a 2022 HN discussion about SemiAnalysis disclosures, after saying his reports had moved smaller stocks by 20% in a day, Patel wrote: “If I thought I could move the stock, I'd make the position in the morning alongside my clients, and publish shortly after.”[7]
I don’t know that any of this is false or that anything improper happened (I’m definitely not claiming that). More that 1-2 of these things would just be odd. Taken together, though, they made me question how much trust I was putting in the broader story.
The dynamic of reminds me of crypto, WeWork, Theranos, Citron, etc. Once enough important people validate someone, things that would normally invite basic diligence somehow stop getting questioned.
[1] https://www.dwarkesh.com/p/dylan-jon
[2] https://sequoiacap.com/podcast/dylan-patel-of-semianalysis-w...
[3] https://www.theinformation.com/articles/dylan-patel-semianal...
[4] https://www.latent.space/p/dylanpatel-cooking
[5] https://www.dwarkesh.com/p/dylan-patel-3
[6] https://www.theinformation.com/briefings/exclusive-semianaly...