All the problems you mentioned can be reframed as positive feedback for the economy we’re evolving into. Let me dream the arguments for each of your bullet points:
1. Power fail-over (battery + generator backup) in every house?
- I recently listened to a Planet Money episode about how DC/AI infrastructure needs are driving up electricity prices in Ohio. Ordinary households end up paying higher bills while big entities plan/build for reliable power.
- Maybe household-level infrastructure could be improved as part of making this kind of model viable. This applies to networking infrastructure too
3. Could get expensive flying a technician to every household to upgrade hardware in the racks
- People with enough education can be trained, and with the incentive of being paid, households themselves could become the technicians.
4. Probably don’t want everyone at home having physical access to storage devices
- Same idea: if households are being paid and it’s “their role” to manage, the access concern gets reframed as operational responsibility.
5. Massive theft risk
- Theft risk already exists today (even in good neighborhoods). The incremental risk might be negligible.
6. Homeowner’s insurance would probably…
- If we squint hard enough, there are arguments here too (e.g., payments not missed, additional compensation).
It has less to do with "oligarchs" and more to do with protectionism over domestic industry: retain jobs in America, preserve worker income taxes revenue, capture taxation of corporate profits, tilt the scales in favour of an American business becoming a global exporter of their products, keep development of high-tech assets under American regulatory control.
I'd expect all the free-market capitalists and libertarians to make a lot of noise against government interference, even if the purpose is to retain domestic jobs.
This is a well-written, well-researched piece, but some of the narratives are off the mark. For example:
> Phase 1 (Courtship, 2010-2014): TSMC needed Apple for legitimacy
TSMC was already the world's largest pure-play foundry long before Apple walked through the door, controlling more than 50% of market revenue. Although vertically-integrated microprocessor businesses had better-performing processes (Intel, AMD/IBM with PD-SOI), TSMC was head of the pack in the foundry world (competitors: UMC, Chartered, SMIC, etc).
> What if Apple chose Intel in 2014?
How would they have done that? Intel didn't even offer a foundry service at that time, and it would have taken years for Intel to adapt to a foundry model (one which publishes a "process design kit" for use by industry-standard EDA software to design and simulate circuits).
I agree, but governments intentionally shifted from corporate taxes (taxes on net, corporate income) to payroll taxes (taxes proportional to employee wages) because businesses were either finding creatives ways of deferring/diverting income, or they just weren't taking profit (and, thus, nothing to tax).
Corporate taxes are inefficient as well as they discourage reinvestment by the company, since the State taxes a rake each year. Taxing on dividends paid (and buybacks) is better, even if it leads to higher prices to compensate for the lower capital profitability for shareholders (who compete with bonds).
The bill in favour of the Elizabeth Line was only put to parliament in 2005, receiving royal assent in 2008. Construction work began in 2009, faced some delays during COVID, but was completed in 2022 (total construction time: 13 years)
Construction on New York's Tunnel #3 began in 1970. It was 28 years before any part of it was operational. A second section came online 15 years later (2013). The final stage isn't expected to be completed until 2032, a full 62 years after construction began. I'm unaware of any comparable tunnel project which has progressed at this slow of a pace.
You still need an interface which does at least two things: handles incoming read/write requests using some kind of network protocol, and operates as a memory controller for the RAM.
Texas Memory Systems was in the business of making large 'RAM Drives'. They had a product line known as "RamSan" which made many gigabytes/terabytes of DDR available via a block storage interface over infiniband and fibre channel. The control layer was implemented via FPGA.
I recall a press release from 2004 which publicized the US govt purchase of a 2.5TB RamSan. They later expanded into SSDs and were acquired by IBM in 2012.
> To be clear - the shock wasn’t that OpenAI made a big deal, no, it was that they made two massive deals this big, at the same time, with Samsung and SK Hynix simultaneously
That's not "dirty." That's hiding your intentions from suppliers so they don't crank prices before you walk through their front door.
If you want to buy a cake, never let the baker know it's for a wedding.
What they mean is that they bought 40% of all RAM production, they managed to do that by simultaneously making two big deals at the same time. It's buying up 40% of all RAM production with the intention to have most of it idle in warehouses that is "dirty". And in order to be able to do that, they needed to be secretive and time two big deals at the same time.
The incentive suggested in the article is to block other competitors from scaling training, which is immensely RAM hungry. Amongst other things. Even Nvidia could feel the pressure, since their GPUs need RAM. It could be a good bargaining chip for them, who knows.
I'm not saying it's true, but it is suspicious at the very least. The RAM is unusable as it stands, it's just raw wafer, they'd need a semiconductor fab + PCB assembly to turn them into usable RAM modules. Why does OpenAI want to become a RAM manufacturer, but of only the process post-wafer.
> The RAM is unusable as it stands, it's just raw wafer, they'd need a semiconductor fab
The wafers are processed. That means Samsung/Hynix have taken the raw ("blank") wafers, then run them through their DRAM lithography process, etching hundreds of DRAM dies ("chips") onto the wafer.
You could attach test probes to individual chips on the wafer and you'd have a working DRAM chip. In fact, that's how testing is performed: you connect to each die one at a time with a "probe card" which supplies power & ground, plus an electrical interface for functional testing.
If OpenAI takes possession of the processed wafers and wants finished RAM modules, they need to do a few things: test each die (expensive), saw the wafer into individual chips (cheap), package them (moderately expensive), test them again (medium expense), and then assemble the final module (inexpensive). Modern semiconductor test facilities cost billions of dollars and take years to build, so they'd need to immediately outsource that work (typically done in Southeast Asia)
OpenAI likely doesn't want to do any of this. They probably just want to make sure they're in control of their own destiny with regard to DRAM, then decided the best place to accomplish that was by cutting deals directly with the DRAM semiconductor producers. This will allow them to take the wafers to the existing supply chain, then contract them to turn the wafers into finished modules.
This wasn't buying a cake from a baker, this was a bakery buying 40% of the flour in the world so nobody else can sell wedding cakes, but now there's gonna be no bread for a year
That's not the dirty part. This is the dirty part:
> OpenAI isn’t even bothering to buy finished memory modules! No, their deals are unprecedentedly only for raw wafers — uncut, unfinished, and not even allocated to a specific DRAM standard yet. It’s not even clear if they have decided yet on how or when they will finish them into RAM sticks or HBM! Right now it seems like these wafers will just be stockpiled in warehouses
> OpenAI isn’t even bothering to buy finished memory modules
And? Why should they be obligated to pay for all the middleman steps from fab down to module? That includes: wafer-level test, module-level test (DC, AC, parametric), packaging, post-packaging test, and module fabrication. There's nothing illegal or sketchy about saying, "give me the wafers, I'll take care of everything else myself."
> not even allocated to a specific DRAM standard yet
DRAM manufacturers design and fabricate chips to sell into a standardized, commodity market. There's no secret evolutionary step which occurs after the wafers are etched which turns chips into something which adheres to DDR4,5,6,7,8,9
> It’s not even clear if they have decided yet on how or when they will finish them into RAM sticks or HBM
The implication here is that the primary goal is to corner the market, not to use the supply. If you aren't going to use them anyways then of course it is silly to pay for them to be finished.
Do you think that's fine, or do you think that implication is wrong and OpenAI does actually plan to deploy 40% of the world's DRAM supply?
> The implication here is that the primary goal is to corner the market
You have no evidence of that. Even at face value, the idea of "cornering the market" on a depreciating asset with no long-term value isn't a war strategy, it's flushing money down the toilet. Moreover, there's a credible argument OpenAI wanted to secure capacity in an essential part of their upstream supply chain to ensure stable prices for themselves. That's not "cornering the market," either, it's securing stability for their own growth.
Apple used to buy-up almost all leading-edge semiconductor process capacity from TSMC. It wasn't to resell capacity to everyone else, it was to secure capacity for themselves (particularly for new product launches). Nvidia has been doing the same since the CUDA bubble took off (they have, in effect, two entire fabs worth of leading-edge production just for their GPUs/accelerators). Have they been "cornering" the deep sub-micron foundry market?
> the idea of "cornering the market" on a depreciating asset with no long-term value isn't a war strategy, it's flushing money down the toilet
OpenAI's entire business strategy thus far can be summarized as "flushing money down the toilet", so that isn't actually as unlikely as you're making it sound.
Yes, they've made insane scaling bets before and they have paid off.
If what we've heard about no acceptable pre-training runs from them in the last two years trying to increase the memory for training by two orders of magnitude is just a rehash of what got them from gpt2 to gpt3.
One of the pain points of scaling Zookeeper is that all writes must go to the leader (reads can be fulfilled by followers). I understand this is "leader of a shard" and not a "global leader," but it still means a skewed write load on a shard has to run through a single leader instance
> given that horizontal scaling of metadata requires no rebalancing
This means a skewed load cannot be addressed via horizontal scaling (provisioning additional shards). To their credit, they acknowledge this later in the (very well-written) article:
> This design decision has downsides: TernFS assumes that the load will be
The world's greatest spy agency at work.
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