A data center in international waters is immensely more practical in most regards, if legal jurisdiction is your complaint.

Setting aside economic impact, I’m skeptical that this is actually a good idea, purely in terms of political capital.

Like…here in the US, gas taxes have a wildly-disproportionate political impact, in part because they’re constantly thrown in your face. Signs all over outdoors with them.

If every time a French user of Steam, say, wants to download a game that they already bought and they’re presented with a fee from Steam or something, I think that that’s probably going to make the tax highly-visible to French voters, and probably unpopular.

It looks like Call of Duty is over 1TB. Okay, so Valve is probably telling you to pay your 55 euro or whatever French Internet Tax every time you want to install the game. That’s probably going to generate upset pretty quickly.

If that goes through, it’s going to make local caching a lot more financially-appealing in France.

CPUs in the sense of serial compute speed haven’t really been advancing nearly as quickly for the past 20 years.

https://www.science.org/doi/full/10.1126/science.aam9744?referrer=https%3A%2F%2Fkagi.com%2F

Over the past 50 years, the miniaturization of semiconductor devices has been at the heart of improvements in computer performance, as was foreseen by physicist Richard Feynman in his 1959 address (1) to the American Physical Society, “There’s Plenty of Room at the Bottom.” Intel founder Gordon Moore (2) observed a steady rate of miniaturization and predicted (3) that the number of transistors per computer chip would double every 2 years—a cadence, called Moore’s law, that has held up considerably well until recently. Moreover, until about 2004, new transistors were not only smaller, they were also faster and more energy efficient (4), providing computers with ever more speed and storage capacity. Moore’s law has driven economic progress ubiquitously.

Unfortunately, Feynman’s “room at the bottom” is no longer plentiful. The International Technology Roadmap for Semiconductors [(5), p. 36] foresees an end to miniaturization, and Intel [(6), p. 14], a leader in microprocessor technology, has acknowledged an end to the Moore cadence. Indeed, Intel produced its 14-nm technology in 2014, but it stalled on producing its 10-nm technology, due in 2016, until 2019 (7). Although other manufacturers continued to miniaturize—for example, with the Samsung Exynos 9825 (8) and the Apple A13 Bionic (9)—they also failed to meet the Moore cadence. There isn’t much more room at the bottom.

Why is miniaturization stalling? It’s stalling because of fundamental physical limits—the physics of materials changes at atomic levels—and because of the economics of chip manufacturing. Although semiconductor technology may be able to produce transistors as small as 2 nm (20 Å), as a practical matter, miniaturization may end around 5 nm because of diminishing returns (10). And even if semiconductor technologists can push things a little further, the cost of doing so rises precipitously as we approach atomic scales (11, 12).

Few industries see exponential improvements for decades on end. Intel did very well when everyone needed a new CPU every few years because things had improved so incredibly much.

Today, we mostly have to settle for improvements in parallelism, and software that can leverage really large gains there has tended to take the GPU route—simple hardware, but designed around much more parallelism.

As we shall see, the primary advantage of hardware streamlining comes from providing additional chip area for more circuitry to operate in parallel. Thus, the greatest benefit accrues to applications that have ample parallelism. Indeed, the performance of hardware for applications without much parallelism has already stagnated. But there is plenty of parallelism in many emerging application domains, such as machine learning, graphics, video and image processing, sensory computing, and signal processing. Computer architects should be able to design streamlined architectures to provide increasing performance for these and other domains for many years after Moore’s law ends.

Hardware that is customized for an application domain can be much more streamlined and use many fewer transistors, enabling applications to run tens to hundreds of times faster (46). Perhaps the best example today is the graphics-processing unit (GPU) [(41), section 4.4], which contains many parallel “lanes” with streamlined processors specialized to computer graphics. GPUs deliver much more performance on graphics computations, even though their clocks are slower, because they can exploit much more parallelism. GPU logic integrated into laptop microprocessors grew from 15 to 25% of chip area in 2010 to more than 40% by 2017 (see Methods), which shows the importance of GPU accelerators. Moreover, according to the Top 500 website, which tracks high-performance computing technology, only about 10% of supercomputers that were added to the Top 500 list in the year 2012 contained accelerators (often GPUs), but by 2017, that share had grown to 38% (12). Hennessy and Patterson (45) foresee a move from general-purpose toward domain-specific architectures that run small compute-intensive kernels of larger systems for tasks such as object recognition or speech understanding. The key requirement is that the most expensive computations in the application domain have plenty of parallelism and locality.

Nvidia is a leader in GPUs, and people are willing to pay a lot for new GPUs, because those are still improving rapidly. Intel is a leader in CPUs, and CPUs aren’t.

I thought that the book in the series about that “darkness priestess” girl who spent time in those caves of hers was kind of depressing and meh. The Tombs of Atuán, maybe?

searches

“Atuan”.

I did like the other books in the Wizard of Earthsea series, though.

I don’t think that Trump is going to get an AUMF for it. His own party didn’t give him one with a trifecta in place. I saw quotes from multiple clearly-unhappy Republican legislators talking about how it’d be very difficult for Trump to get one, given that he didn’t consult with them prior to kicking this off.

I mean, I wouldn’t give Trump one either, certainly not with Hegseth as SecDef.

And if the Republicans aren’t going to give him one, then I’d say that the chances of the Democrats doing so are considerably less.

Thing is, though, I think that that’s more likely to extend it. Like, the American military rolling in in force would end the situation, but if that doesn’t happen…shrugs Iran doesn’t have much reason to stop. I don’t know who else would go in and use force.

Uber spent a decade telling cities that protecting taxi drivers was anti innovation. Now that robotaxis threaten its own business, suddenly limits on new competitors sound reasonable.

Uber has also been working on and I’m pretty sure has been operating robotaxis.

My impression is that human drivers were probably going to be a temporary situation for them as they built out their system, which would provide a strategically-advantageous position relative to competing robotaxi operators for them to do the transition to robotaxis.

searches

https://en.wikipedia.org/wiki/Uber

The company has launched or is in the process of launching robotaxi services in several cities worldwide in partnership with operators, software designers, and car manufacturers including Lucid Motors, Nuro, Baidu, Pony.ai, Avride, Nissan, Rivian, Zoox, Stellantis, Wayve, and Nvidia (to use the Nvidia Drive software).[3][4][5][6]

As some comments here have pointed out, it’s probably more like data that goes into the training corpus.

I’d also suggest that some demand won’t be from AI companies, but rather from other consumers of data storage, even if a boom in AI-driven spending is at the root of things. Like, maybe your ideal storage medium is SSD, but AI-related demand has driven prices up to 5x what they were earlier. Okay, so some users get priced out and fall back to rotational drives, because those are good enough for their use case. Prices on those go up by 2x. Well, the next fallback for some of those users who get priced out, who maybe used hard drives to store backups, say, is gonna be magnetic tape storage.

The October 14 Consumer Price Index showed that overall, prices for goods and services across the United States have risen 3.7 percent over the last 12 months, exacerbated by massive increases in energy costs. Gasoline shot up a punishing 34.2 percent and fuel oil (primarily used by consumers in residential heating) has skyrocketed 74.2 percent, driven in large part by oil and gas trade disruptions stemming from the United States and Israel’s war in Iran.

“That paints a dreary picture for Americans struggling with high costs, who likely won’t feel any relief at the pump in the near future — especially as more potential military action looms [in Iran],” Business Insider reported on Monday.

It’s a pretty cheerful picture if one’s primary concern is carbon dioxide emissions.

There are three ways that carbon dioxide emissions come down:

1. Voluntary limiting of use. I am pretty confident that this isn’t going to be a dominant factor, based on how people have acted en masse thus far.

2. Government rationing or similar restrictions on access to fossil fuel. I don’t think that this is terribly politically likely.

3. Price increases on fossil fuels. This is what’s been happening.

Now, I don’t know for how long those price increases will persist. But if you want less carbon dioxide emissions, you basically want one of #1, #2, or #3 to happen, and I think that #3 is probably going to be the most-realistically-plausible scenario.

“Reddit is the last platform to provide basically unfettered logged-out access, and that’s largely because of me. I’ve been holding that line,” Huffman said. “I think users will probably go through the same understanding that I have, which is that it’s a sign of the times. It’s an evolution of how these platforms work.”

Listens with interest from the Threadiverse, which has many instances that provide logged-out access.

“This is almost certainly the largest theft of any kind in history,” said Tom Robinson, chief scientist at crypto consultancy Elliptic.

If it’s North Korea, and if they get to make use of it, it’d be over 1/6th of their GDP, which is pretty bonkers.

There was that massive bank fraud case in Moldova a few years back.

https://en.wikipedia.org/wiki/2014_Moldovan_bank_fraud_scandal

In 2014, $1 billion disappeared from three Moldovan banks: Banca de Economii, Unibank and Banca Socială.[3] This bank fraud was a coordinated effort involving all three banks working together to extract as much loan finance as possible from the banks without any obvious business rationale. Ilan Șor, a Moldovan businessman, together with Vladimir Plahotniuc and Serghei Iaralov “masterminded” the scam. Șor was chairman of the board at Banca de Economii (Savings Bank, BEM) up to 28 November 2014.[4]

Funds worth $1 billion were transferred to the United Kingdom and Hong Kong shell companies used to conceal the real owners of assets,[3][5] then deposited into Latvian bank accounts under the names of various foreigners.

The total loss from the scheme was equivalent to 12% of Moldova’s GDP.[6]

A carousel borrowing scheme was applied, loans at one bank were paid off with loans from another. The massive expansion of bank lending was funded with heavy borrowing from Russian companies.

That’d be a larger relative impact than the bank fraud was on Moldova (albeit on the receiving side, not the stolen-from side).

“A lot of people want local inference,” he said. “And somehow, they assume, that the problem is solved now. Because we are going to have open-source models. We’re going to be able to run them on our own computers. And so we’re not going to need all the stuff in the data center.”

“But from a global allocation perspective, actually, it’s quite terrible to have everybody have a bunch of the world’s limited supply of [dynamic RAM] sitting in their own computer,” he added.

Yeah, I’ve made that point here many times myself. Capacity utilization is going to be much higher in the cloud, so it’s cheaper to do in the cloud than to have everyone with their own inference hardware.

But…that depends on the constraint being limited on hardware. That’s the case in 2026, but unless much more hardware-intensive models keep showing up and being much better than came before, so that someone like OpenAI can get value that increases tremendously out of more hardware for a model, that hardware constraint won’t persist.

Local compute also provides some benefits. You don’t rely on Internet connectivity, so you don’t have things go down if your network connection does. While we don’t have many latency-critical applications out there today, you can get better latency. Unless and until OpenAI is going to provide service with some pretty hard and convincing data privacy guarantees, you get data privacy. Whether it’s a company that might be exposing their core IP or what their people are working on, an individual who doesn’t want their sexy chatbot sessions showing up places they don’t want them one day, a government who has national security concerns, or anyone who is just worried about a cloud LLM provider leveraging lock-in down the line to extract concessions or more money for them, local LLMs provide a lot of benefits.

I am confident that the world as a whole won’t shift to local AI for at least four or five years, and probably more. It can’t—most memory right now is going to cloud AI, and reduced capacity utilization means that getting the same compute capacity would require more memory than is out there, we don’t have construction for many times existing memory capacity underway yet, and it would take four to five years for that to happen if we started today.

But I do think that it will tend to happen over time.

I think that in the long run, if cloud AI companies are going to persistently keep people on remote compute, it’ll need to be because their models stay private and their performance is so much better than open-weight models that people choose them for quality, not for hardware scarcity.

My guess—and I’m not privy to any inside information here—is that operations at places that are building neural-net-based models look something like this:

1. “Harvest” people are responsible for obtaining data and as much associated information as possible, stuff like where data was obtained from, timestamps, any identity information, etc (handle “X” on platform “Y”). If a comment or webpage or whatever gets revised, maybe they keep multiple copies. They’ll avoid things like following infinite loops on websites, deal with corrupt archives, all that. That probably goes into some giant database. Maybe some of these guys buy access, like Google did with Reddit. A lot are just going to write spiders, scraping bots, whatever. A search engine fundamentally has to have a spider slurp down data to create its index to work at all, so I’m sure that Google is going to have a bunch of expertise in this area, built up over many years. They’ll probably retain this data in pretty raw, unprocessed form.

2. “Unification” people are going to work on the above. They write code to convert the above into a standardized-across-all-platforms format. Maybe they convert stuff to unify timezones or time formats on comments. Maybe they strip off formatting stuff added by particular platforms. Markdown or HTML titles, or titles in Microsoft Word documents, for example, probably wind up represented in one way. Where possible, maybe usernames get attached, if data is from a forum.

3. “Scoring” people work on the unified dataset. They are going to be developing some system—and there might be a number of parallel projects—for generating a score of various sorts that might be a useful value for either a model or training information. With this as well, search engines have to do this; Google’s PageRank, for example, tries to assess the “value” of a page, determine what’s most-useful and reliable information, so that it can order results (and maybe blacklist some). A lot of what search engines do isn’t just getting you hits, but putting the most-valuable ones at the top. If you can develop heuristics to rank information based on reliability or political position or any number of other things, those are going to be useful numbers.

4. “Pruning” people use information, probably including the “scoring” information, to winnow down the size of the data, eliminate data where the cost of inclusion in the training set—it’s costly to train a neural net, and reducing the size of the training corpus reduces that cost—outweighs the benefits. They probably try to cut out garbage, irrelevant binary data, poisoned data, duplicate data, misinformation, whatever.

5. “Training” people are going to do the training and probably develop systems for rating the value of the above “scores” in producing desirable output. Maybe some “scoring” heuristic is really super-useful in producing accurate and useful output as defined by some benchmark that these guys set up. Like maybe, oh, one “score” is based on whether information was obtained from a .gov TLD, or another whether a text classifier says that the language is English and the Lix school year readability is above a certain level. Could be many of these. Some of what these guys find may go back to the “scoring” guys in trying to guess what heuristics should be further developed (“known time of domain age to time content was initially spidered is particularly valuable in identifying SEO-generated spam website content; see if you can produce more scores that identify mass-created spam websites”), some to the “pruning” guys to increase the low-value data that they prune out, and some of what they find may go into weighting importance of text for the training process. Some may even go all the way back to the “harvest” guys to determine how frequently to spider data from a given source—Google doesn’t respider information at a fixed rate, but hits “valuable” sources, like major news sites, very frequently. For non-pure-LLM neural nets—I don’t know if Google is doing this yet with Gemini, but I’d imagine that eventually, probably more-advanced AIs will need to move past just being an LLM—this might also be an input used in making a decision at runtime.

There are probably going to be a lot of points in this process where not-valuable data gets identified and removed or deprioritized. Even if no engineer at somewhere like Google has tried to write a “scoring” heuristic to identify text specifically to identify output from a tool that mass-edits comments on Reddit, my guess is that text consisting of nonsense words, which is what some of the Reddit mass-comment-editing-tools I’ve seen insert, probably intrinsically get identified as part of that process by multiple classification tools that generate heuristic scores. If all of a user’s comments look like the output of some simple text generation tool, or if all of them change in a given short period, that’s probably kind of a big red flag. That does mean that the information won’t be in the output model, but I think that people who are aiming to poison LLMs during training probably are expecting more impact from putting nonsense in their comment versus just blanking the comments than they’ll likely have. Google—and more broadly, people who are running search engines and even more, building neural nets trained on data on the Internet—are just going to have to already have a lot of fairly-sophisticated work in place to assess the value of and filter their inputs.

I can no longer create a new one without using an email tied to my identity

I haven’t bothered to look into it, but historically, there were a lot of sites that let one create throwaway email addresses to avoid handing over email addresses and tying an online handle to personal information.

Sites that harvested this email data fought back by trying to blacklist those “throwaway email address” sites, but it’s a game of whack-a-mole—like, those throwaway email address sites also set up and discard domains from a pool—so I imagine that it’s likely possible to manage to find one that works, if you’re determined to do so.

Note that throwaway sites do create security issues in that password recovery and similar often relies on a registered email address, so you’re potentially entrusting some random throwaway email site with access to your account, since it can receive email from the site. I wouldn’t use that for something important, but depending upon use, it could be useful.

Also, a number of sites—I don’t know about what Reddit will be doing, but I think that Google requires this for account registration these days—require a phone number, and at least in the US, while you can get VoIP SIP service, don’t need to tie that to a phone, I understand that there have been legal restrictions added on SMS service from this crowd in the past few years. I don’t know if Google permits for validating a phone number via voice or requires demonstrating SMS capability.

I’d imagine that that probably increases the magnitude of the effect I mentioned in another news article that I linked to here in another comment, where Google users are just using Google’s Gemini LLM rather than searching for information on Reddit. Google will already have incorporated that information into its training corpus for Gemini back when it paid Reddit for access to it, so it makes the LLM the only route for a user to get access to that knowledge.

(Not criticizing your move, just commenting on what I imagine the broader impact will be, particularly if the “logged-in only” move increases the rate of other people wiping their posts and comments as well.)

your data, which is the only thing of value your company has.

I mean, I agree that it certainly has value, but the userbase does too; it’s the userbase that generates that valuable data when it comments and posts.

While referral traffic is probably mostly people using Reddit as a store of knowledge, doing things like site:reddit.com searches on Google, not principally use-Reddit-as-a-forum users, one does imagine that trying to require users to use Reddit logged-in will probably cause more users to leave Reddit. This is probably an opportunity for the Threadiverse to pick up refugee users from Reddit. Might be good to simplify and smooth the path for new Threadiverse users as best as possible, since I imagine this’ll probably be the last “big moment” with potential friction for Reddit users in getting Reddit users over the hump to using a walled garden.

Reddit will start testing the login requirement in “select,” non-US markets in Q4. The company will use those tests to decide what a logged-in-only Reddit will look like “globally,” per Huffman.

It won’t be all at once, then; Reddit will probably want to assess what how severe the impact on userbase is, how many people they can convert to using their app or having a login, and what tactics will work best in converting those users. So one might expect influx of new Threadiverse users over time as Reddit gradually shifts regions to being login-only.

“During October, we have seen that search traffic has dropped by more than 1 million referrals on average per week. If this rate of decline continues, current trends would suggest that total DAUqs [or daily active unique users] could fall in Q4, by a low-double-digit percentage from our current base of 130 million DAUqs,” Huffman wrote.

Huffman would like Reddit to become less dependent on search referrals. “Logged-in and app DAUq are our most valuable users, and they are our primary focus,” he wrote.

https://www.reuters.com/business/reddit-pinterest-shares-tumble-weak-user-revenue-growth-2026-10-30/

Reddit plunges as Google AI search squeezes traffic

Oct 30 (Reuters) - Shares of Reddit (RDDT.N), tumbled 23% in premarket trading on Friday after it warned Google’s growing use of AI in ​search was hurting referral traffic and could shrink its user ‌base, offering fresh evidence of AI-driven disruption.

• Reddit’s warning highlights how Google’s ​shift toward AI-powered search, which can answer queries directly, is ⁠disrupting the flow of traffic to websites that have traditionally relied ​on the search engine for users
• Total daily active unique visitors could fall ​by a low double-digit percentage in the fourth quarter from about 130 million if referral traffic continues to fall, Reddit said

Do a search, and in the results page, click on the “Tools” dropdown, go to “Any time”, and then “Custom range…”.

To take it a step further, I work adjacent to OEM manufacturers for industrial control systems. Half of our customers are running windows 98.

Hah! I’d actually consider industrial automation to be one of the more-likely applications for LLMs. Not necessarily LLMs driving the system, but outputting software to drive PLCs and similar. My understanding from past reading is that industrial automation is one of the principal things that China is interested in using LLMs for.

searches

https://faxiangongchang.com/en/reports/china-industrial-ai-agent-2026

What AI Agents in Factories Are Actually Doing

Down on the shop floor, industrial AI agents are currently running most solidly in four types of scenarios.

Quality inspection. Vision large models are replacing traditional rule-based machine vision. Among the 12 lighthouse factories newly added by the World Economic Forum in September 2025, AI and generative AI use cases account for up to 50%, with demonstration factories reporting results including a 41% reduction in product defect rates.

Process parameter optimization. In the Baosteel hot-rolling line case disclosed by Huawei Cloud, the Huawei Pangu large model improved finishing-mill width-spread prediction accuracy by more than 5% and raised yield by 0.5%, which by its own account translates into more than 90 million yuan in additional annual revenue. It should be noted that such figures are all vendor-disclosed and still lack independent third-party verification, but directionally they are consistent with public data from lighthouse factories.

Engineering and maintenance assistance. Siemens Industrial Copilot improves the efficiency of generating programmable logic controller code by roughly 60%, and pilots of its maintenance edition show an average 25% saving in reactive maintenance time; the product won the Hermes Award at Hannover Messe 2025. The factory operations agent Microsoft launched at Hannover Messe 2025 lets employees ask in natural language, “Why did Line 3 stop yesterday?”

Scheduling and order management. This is the entry point closest to AI agents for small and mid-sized factories. An AI tool aimed at small and mid-sized factories has disclosed a set of figures: after adopting AI scheduling and order tracking, factories’ average order delay rate fell from 25% to within 5%; one factory in Dongguan cut its order-splitting preparation time by 60%; and an annual fee of about 10,000 yuan corresponded to a return of roughly 1:12. These are again vendor figures, but they touch on an important fact—the value of AI agents for small and mid-sized factories lies first not in sophisticated process optimization, but in freeing up the labor tied down by high-frequency, tedious work such as order tracking and scheduling.

They mention PLC code generation, but I guess some other areas too.