Ok so Samsung, SK Hynix and Micron do not have the capacity to meet demand. Also, what little capacity they do have they are allocating to HBM over DRAM. Based on my limited knowledge HBM can not be easily repurposed for consumer electronics. Translation: main street is cooked for the next 3-4 years.<p>It doesn't stop there though. OpenAI is currently mired in a capital crunch. Their last round just about sucked all the dry powder out of the private markets. Folks are now starting to ask difficult questions about their burn rate and revenue. It is increasingly looking like they might not commit to the purchase order they made which kick-started this whole panic over RAM.<p>Soo ... how sure are we that the memory makers themselves are not going to be the ones holding the bag?
The Radeon VII came out in 2019 as a $700 consumer GPU with an 1TB/s HBM2 memory subsystem which is more than any consumer GPU you can get today, including the high-end ones afaik. At that point in time, there was a whole lineup of AMD GPUs with HBM going down into the midrange.<p>If they could make this stuff and sell it to regular people a decade ago for very palatable prices, why do they come up with the idea that this is the technology of the gods, unaffordable by mere mortals?
I am betting the pendulum swings faster to the other side to excess capacity as all the construction lies of Altman fall through with financiers waking up the the fact they can't build the infrasctructure as fast nor make any profits on that infrastructure that will get built.
To add a more local hurdle as well, the Dutch power grid is at capacity and its managing company is now telling companies that planned to build a datacenter that they can't be connected to the grid until 2030, even though said companies already paid for and got guarantees about that connection.<p>That is, memory capacity is reserved for datacenters yet to be built, but this will do weird things if said datacenter construction is postponed or cancelled altogether.
That guarantee is not as much of a guarantee as stated in the media. You get a guarantee it will be planned at a certain time (as in looked at), not that it will be build. The cost of doing business is taking risks and mitigating them. There is a reason the nuclear plant in Borsele was build: an aluminium smelter. Maybe you should arrange for something similar as a datacenter (no politician will fall on a sword for that but you can try). The (original) power draw is about the same 80-100MW.
But wouldn’t you rather hbm prices come down first ? Memory makers will be fine. There is practically infinite demand.
Unless you get china style rationing of compute per person world wide.<p>The real issue is everyone wanting to upgrade to hbm, ddr5, and nvme5 at the same time.
I would expect that OpenAI gets as much money as they ask for for the next 10 years.<p>There’s virtually infinite capital: if needed, more can be reallocated from the federal government (funded with debt), from public companies (funded with people’s retirement funds), from people’s pockets via wealth redistribution upwards, from offshore investment.<p>They will be allowed to strangle any part of the supply chain they want.
China already has a well developed DRAM industry, as DRAM is somewhat easier than logic, and can tolerate a much higher defect rate. The industry will figure this out.<p>Another point is I often see the money argument - like country X has more money, so they can afford to do more and better R&D, make more stuff.<p>This stuff comes out of factories, that need to be built, the machinery procured, engineers trained and hired.
love that theres virtually infinite capital there. meanwhile in the rest of the world there is virtually no food.
Maybe if they had no competitors...
I think you're massively overestimating how much money is really accessible here. The parent comment's right that all of the easily available VC & private equity investment is basically used up. OpenAI was struggling to sell $600M of private equity, the big multi-billion dollar investment packages had lots of conditions and non-cash in it.<p>> more can be reallocated from the federal government (funded with debt)<p>While this is the most reliable funding, it's still not very accessible. OpenAI is a money pit, and their demands are growing quickly. The US government has started a bunch of very expensive spending. If OpenAI were to require yearly bundles of it's recent "$120B" deal, that's 6% of the US' discretionary budget. 12.5% of the non-military discretionary budget. (And the military is going to ask for a lot more money this year) Even the idea of just issuing more debt is dubious because they're going to want to do that to pay for the wars that are rapidly spiralling out of control.<p>None of this is saying that the US government can't or wouldn't pay for it, but it's non trivial and it's unclear how much Altman can threaten the US government "give me a trillion dollars or the economy explodes" without consequences.<p>Further deficit-spending isn't without it's risks for the US government either. Interests rates are already creeping up, and a careless explosion of deficit may well trigger a debt crisis.<p>> from public companies (funded with people’s retirement funds)<p>This would be at great cost. OpenAI would need to open up about it's financial performance to go public itself. With it's CFO being put on what is effectively Administrative Leave for pushing against going public, we can assume the financials are so catastrophic an IPO might bomb and take the company down with it. Nobody's going to be investing privately in a company that has no public takers.<p>Getting money through other companies is also running into limits. Big Tech has deep pockets but they've already started slowing down, switching to debt to finance AI investment, and similarly are increasingly pressured by their own shareholders to show results.<p>> from people’s pockets via wealth redistribution upwards<p>The practical mechanism of this is "AI companies raise their prices". That might also just crash the bubble if demand evaporates. For all the hype, the productivity benefit hasn't really shown up in economy-wide aggregates. The moment AI becomes "expensive", all the casual users will drop it. And the non-casual users are likely to follow. The idea of "AI tokens" as a job perk is cute, but exceedingly few are going to accept lower salary in order to use AI at their job.<p>There's simply not much money to take out of people's pockets these days, with how high cost of living has gotten.<p>> from offshore investment.<p>This is a pretty good source of money. The wealthy Arabian oil states have very deep slush funds, extensively investing in AI to get ties to US businesses and in the hope of diversifying their resource economies.<p>...<p>...<p>"Was". Was a good source of money.
> Soo ... how sure are we that the memory makers themselves are not going to be the ones holding the bag?<p>We aren't. The remaining memory manufacturers fear getting caught in a "pork cycle" yet again - that is why there's only the three large ones left anyway.
Memory makers did get themselves into this situation by selling all wafers for empty promises and alienating everyone but OpenAI tbh. I do hope they end up holding the bag once again, cause after covid and the cartel thing they don't seem to ever learn their lesson on how to have the tiniest amount of integrity.
The people who fucked over consumers are left holding the back that they sold us out over?<p>Oh no!
I'm a bit surprised the article makes no mention of Google's TurboQuant[0] introduced 26 days prior.<p>Given that TurboQuant results in a 6x reduction in memory usage for KV caches and up to 8x boost in speed, this optimization is already showing up in llama.cpp, enabling significantly bigger contexts without having to run a smaller model to fit it all in memory.<p>Some people thought it might significantly improve the RAM situation, though I remain a bit skeptical - the demand is probably still larger than the reduction turboquant brings.<p>[0] <a href="https://news.ycombinator.com/item?id=47513475">https://news.ycombinator.com/item?id=47513475</a>
BTW, a number of corrections. The TurboQuant paper was submitted to Arxiv back in April 2025: <a href="https://arxiv.org/abs/2504.19874" rel="nofollow">https://arxiv.org/abs/2504.19874</a><p>Current "TurboQuant" implementations are about 3.8X-4.9X on compression (w/ the higher end taking some significant hits of GSM8K performance) and with about 80-100% baseline speed (no improvement, regression): <a href="https://github.com/vllm-project/vllm/pull/38479" rel="nofollow">https://github.com/vllm-project/vllm/pull/38479</a><p>For those not paying attention, it's probably worth sending this and ongoing discussion for vLLM <a href="https://github.com/vllm-project/vllm/issues/38171" rel="nofollow">https://github.com/vllm-project/vllm/issues/38171</a> and llama.cpp through your summarizer of choice - TurboQuant is fine, but not a magic bullet. Personally, I've been experimenting with DMS and I think it has a lot more promise and can be stacked with various quantization schemes.<p>The biggest savings in kvcache though is in improved model architecture. Gemma 4's SWA/global hybrid saves up to 10X kvcache, MLA/DSA (the latter that helps solve global attention compute) does as well, and using linear, SSM layers saves even more.<p>None of these reduce memory demand (Jevon's paradox, etc), though. Looking at my coding tools, I'm using about 10-15B cached tokens/mo currently (was 5-8B a couple months ago) and while I think I'm probably above average on the curve, I don't consider myself doing anything especially crazy and this year, between mainstream developers, and more and more agents, I don't think there's really any limit to the number of tokens that people will want to consume.
The work going into local models seems to be targeting lower RAM/VRAM which will definately help.<p>For example Gemma 4 32B, which you can run on an off-the-shelf laptop, is around the same or even higher intelligence level as the SOTA models from 2 years ago (e.g. gpt-4o). Probably by the time memory prices come down we will have something as smart as Opus 4.7 that can be run locally.<p>Bigger models of course have more embedded knowledge, but just knowing that they should make a tool call to do a web search can bypass a lot of that.
The net effect won’t be a memory use reduction to achieve the same thing. We’ll do more with the same amount of memory. Companies will increase the context windows of their offerings and people will use it.<p>That is the sad reality of the future of memory.
I am not convinced that more context will be useful, practical use of current models at 1mil context window shows they get less effective as the window grows. Given model progress is slowing as well, perhaps we end up reaching a balance of context size and competency sooner than expected.
You still need to hold the model in memory. If you have for example 16 GB ram, the gains aren't that much
You can still use as much memory, but fit more things into it, so I don’t think the current market hogs will let go easily.
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I fear the author and most commenters are not aware of the law of demand and supply. If there is demand for consumer RAM, there will be supply for consumer RAM. It just takes time and risk-assessment to scale up operations.<p>We have RAM shortage now, we will have very cheap RAM tomorrow. It’s not like production is bottlenecked by raw materials. Chip companies just need to assess if the demand by AI companies will last so it’s better to scale up, or perhaps they should wait it out instead of oversupplying and cutting into their profits.
I'm a bit surprised the article makes no mention of China's new memory companies.<p>[0] <a href="https://techwireasia.com/2026/04/chinese-memory-chips-ymtc-cxmt-nand-dram-expansion/" rel="nofollow">https://techwireasia.com/2026/04/chinese-memory-chips-ymtc-c...</a>
The era of optimisation is finally here. I'm excited.
It seems that RAM manufacturers are still reluctant to increase production. They know something that investors don't about long term RAM demands?
The same thing everyone who's paying attention to the real world (and not the financial fantasy world) does: that OpenAI's purchase commitments are wildly unrealistic and unsustainable.
What’s the lose scenario for them? They’re basically a cartel, and you need ram irregardless. If they make less it’s still a cost:demand, just not the most optimal for them. They’ve done that math, and figure this is the best risk and reward for them. Your goodwill or opinion doesn’t matter to them, because you need them more than they need you.
If only we have not allowed oligopolies to exist. Meanwhile, EU is not in the race at all and US has very few fabs.
I'm personally hoping that one of the AI or data center companies is suddenly unable to pay for their bills and deflate the entire industry. Probably the only hope of things getting better before the 2030s.
Thank god they shut down 3D XPoint.
I just checked my gaming PC I built a few years ago with 64GB of DDR5 RAM, its actually gone up in value, that is unheard of generally.<p>Think I will scrap my PC and sell its parts.<p>I wonder if there are any niche companies building decent rigs with DDR3 and 5/6th generation Intel CPUs out there, it is cheap and might be a business opportunity?
I fear that the real reason we do have a shortage, I mean, the real reason for the demand, is AI companies scooping what they can so that their competitors, whether existing or incumbent, can’t get to it.
can't read the article due to a paywall.
here is the source: <a href="https://www.reuters.com/world/asia-pacific/south-koreas-sk-group-chairman-expects-chip-wafer-shortage-last-until-2030-eyes-2026-03-16/" rel="nofollow">https://www.reuters.com/world/asia-pacific/south-koreas-sk-g...</a>
<a href="https://archive.is/QGU89" rel="nofollow">https://archive.is/QGU89</a>