David GerardMar 31

the precise timeline of how OpenAI fucked over the RAM market

> October 2025: Sam Altman flies to Seoul and signs simultaneous deals with Samsung and SK Hynix for 900,000 DRAM wafers per month. That's 40% of global supply. Neither company knew the other was signing a near-identical commitment at the same time.

https://xcancel.com/aakashgupta/status/2038813799856374135

edit: this guy is a seriously bot-pilled pumper, but this seems to be a good summary of known facts. doubt the AI memory use trick he mentions is load bearing tho.

Aakash Gupta (@aakashgupta)

The timeline on this is genuinely insane. October 2025: Sam Altman flies to Seoul and signs simultaneous deals with Samsung and SK Hynix for 900,000 DRAM wafers per month. That's 40% of global supply. Neither company knew the other was signing a near-identical commitment at the same time. Those deals were letters of intent. Non-binding. No RAM actually changed hands. But the market treated them as gospel. Contract DRAM prices jumped 171%. A 64GB DDR5 kit went from $190 to $700 in three months. December 2025: Micron kills Crucial, its 29-year-old consumer memory brand, to reallocate every wafer to AI and enterprise customers. The company explicitly said it was exiting consumer memory to "improve supply and support for our larger, strategic customers in faster-growing segments." Translation: the AI demand signal was so loud that selling RAM to PC builders stopped making financial sense. March 2026: Google publishes TurboQuant, a compression algorithm that reduces AI memory requirements by 6x with zero accuracy loss. Cloudflare's CEO called it "Google's DeepSeek." The entire thesis that AI would consume infinite memory forever just got a six-month expiration date on it. Same month: OpenAI and Oracle cancel the Abilene Stargate expansion. The $500 billion data center vision that justified the RAM deals couldn't survive its own financing terms. Bloomberg attributed the collapse partly to OpenAI's "often-changing demand forecasting." MU is now down ~33% from its post-earnings high. Revenue up 196% year over year, EPS up 682%, and the stock is in freefall because the company restructured its entire business around a demand signal that came from non-binding letters and is now being compressed out of existence by a research paper. Micron bet the consumer division on Sam Altman's signature. The signature was worth exactly what the paper said: nothing binding.

Nitter
Show threadAriadne Conill 🐰Mar 31
@davidgerard thanks sam!
Show threadBillSaysThisMar 31

@ariadne @davidgerard “Google publishes TurboQuant, a compression algorithm that reduces AI memory requirements by 6x with zero accuracy loss.”

This algorithm is somehow only applicable to AI??

Show threadAriadne Conill 🐰Mar 31
@BillSaysThis @davidgerard yes, it is possible to create domain-specific compression algorithms that are better than general ones.
Show threadVorsosMar 31
@ariadne @BillSaysThis @davidgerard Really? I’ve been using pngcrush for audio files.
Show threadC++ Wage SlaveMar 31

@Vorsos

I can't tell if you're serious, but Ariadne is right. Simple example: Flac will losslessly compress audio better than zip or gzip will. That's why it was invented. 😄

@ariadne @BillSaysThis @davidgerard

Show threadkatlin

@CppGuy @Vorsos @ariadne @BillSaysThis @davidgerard

Interestingly enough, Chinchilla 70B was trained mostly on text and beat domain-specific compressors PNG and FLAC in one experiment.

https://arxiv.org/abs/2309.10668

Not saying you are wrong. I assume that newer, domain-specific algorithms would still outperform the general Chinchilla algorithm, and there can be practical downsides if they involve large memory requirements, even if they result in more efficient compression.

Language Modeling Is Compression

It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors. In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning. For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively. Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model.

arXiv.org
Mar 31 at 5:25pmWeb