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Bloomberg[1] has a breakdown based on ASML filings. For Q2, Japan, South Korea, and Taiwan made up 77% of ASML's orders, with China representing another 14%. Given China is on the list, I imagine this includes non-EUV tech as well given the sanctions.

The fact that Europe has incredibly limited frontier chip making capability is the issue, though. Even if they can produce other chips, its position is not at all resilient.

[1] https://www.bloomberg.com/news/articles/2026-09-22/asml-exec...

Thanks, that makes sense. I wasn't sure why The Economist would make this distinction, though I can see why certain companies (e.g., 'poorer' non-hyperscalers) might opt for cheap/loud options.

Given your experience with data centers, what do you think of this quote from the article?

> In any case, even the noise worry is overstated: many viral videos of supposed AI data centre noise are really Bitcoin mines, not state-of-the-art hyperscaler facilities.

Are there really major differences between a hyperscaler, neocloud, and crypto mining data center in terms of noise pollution or other negative effects?

I'm a founder who went through YC (S12), raised VC funding, etc. Now working on other businesses. I also know a few other founders who raised tons of VC funding on their first go, some who have succeeded and some who shut down.

Anecdotally, I feel like the second-time founders see VC as a very specific tool and many prefer not to raise... They'd rather bootstrap and scale more slowly, retaining control, freedom, and flexibility. Granted, a lot of founders also have bad experiences with VCs.

I think your framing VC as a tool is the right one. Not sure if first-time founders are still like this, but ~15 years ago, it seemed like raising VC funding was validation in and of itself and you were perceived as successful due to the raise. The second-time folks know this is neither validation of your product nor is it any sort of guarantee of success... If you need to raise lots of money to capture a speculative market or build something that won't see revenue for a while, VC is great. If you want to build a decent business that grows 20% to 30% per year and compounds over decades, then maybe it's not for you.

Finally, VC is a broad category. Raising money from former founders or from strategic VCs who can legitimately mentor you is very different than getting a check from a fund that makes 100s of investments per year and simply manages you as an arms length portfolio entry.

I recently read a few articles about how the harness is a bigger factor to successful LLM usage and wish they discussed this here.

I use GLM-5.3, Qwen3.8, Claude (all of 'em), GPT Sol/Luna/Terra across direct API calls + local models where I can (128GB Macbook Pro)... The harness and whether the model or underlying system prompts know how to make the best use of iterative LLM calls makes such a big difference...

For example: one-off articles on a news topic (e.g., "Update me on the US-Canada relations") yields very similar results across all models... But "run a web search, write a draft perspective from three points of view, and structure data around it" will make everything but Claude + GPT struggle.

Meta is also one of Microsoft's largest AI customers.

https://www.bloomberg.com/news/articles/2026-08-20/meta-has-...

> Citadel has sold more than 80 per cent of the portfolio it took on from buying the majority of Situational Awareness’s stock bets last month after the AI-focused hedge fund was almost toppled by the recent tech sell-off.

...

> “Over our nearly thirty-six-year history, we have prided ourselves on being front-footed and proactive during periods of market dislocation,” Griffin wrote. The firm’s flagship fund was up 6 per cent in July, while many of its rivals lost money or were relatively flat.

I see. Well, feel free to disagree with my insights and ideas; it makes us all better for it.

But refrain from unfounded allegations of AI slop or incompetence. Gatekeeping is even worse.

EDIT: you keep editing your comments. I'm going to stop engaging with you since one can't even have a linear discussion. Good luck with your research and ideas. If you are an expert on Japan, I encourage you to share your views in the general comments here.

Author here. Everything is written by me, by hand. What makes you think it's AI slop?

I see you edited your comment. Yes, we use LLMs to monitor news --- otherwise we wouldn't be able to read Japanese news sites or policy documents. However, the articles and analysis are written "manually". I take this very seriously --- the only "AI" is spelling + grammar checking.

This goes into the details of the mechanics of what an intervention entails---who does what, why, etc.

I'd be more concerned about private credit socializing losses and being bailed out, than bad AI loans leading to private credit problems which then lead to the bailout.

Private credit has a million other problems right now, like bad loans to auto parts dealers[1], iron traders[2], and so many more.

The issue is lack of due process in private credit. THAT is the huge @#$%ing problem.

[1] https://www.bloomberg.com/news/articles/2026-07-28/first-bra...

[2] https://www.bloomberg.com/news/articles/2026-07-31/vitol-car...

I generally agree with you but given your comments, you might enjoy some additional details... Or please challenge me if you think I am wrong.

I've been paying for order-level data feeds on stocks and one thing you'll find is that a lot of the 'sensitive' trades will be anonymized or broken down in different ways to obfuscate who is trading. Citadel would still be able to see there's a surprising level of interest in a certain stock but might not be able to deduce it's one actor. A broker working for SA should know they need to do this, as it helps the broker do better via commissions, etc. too.

My understanding is that Citadel negotiated directly with SA to buy the book, so the final trades were likely taking place outside of the formal market feeds.

Author here. There is absolutely no AI used in any of the writing I do. We use AI to track news and better understand global and economic developments.

You'll have to clarify -- do you mean the fund, or our hypothesis on reflexivity and the value of AI assets?

LTCM didn't crash the global economy thanks to a bailout orchestrated by the US government. If anything even close to this is currently happening with Situational Awareness, then we're either about to crash hard or realize that AI is too big to fail.

Situational Awareness will be a good case study for whether or not circular deals unravel, or if the AI industry is more resilient than critics fear. Here's why:

1. Situational Awareness was recently leading Fluidstack's $830 million Series A.

2. Fluidstack is helping lead Anthropic's $50 billion data center buildout.

3. Fluidstack is one of TeraWulf's major customers.

4. Some of Terawulf's bonds are tied to Fluidstack's leasing for the Anthropic operation.

5. Google is backstopping the bonds above, should Fluidstack not be able to pay TeraWulf.

I heard via a number of sources (e.g., CNBC) that some of Situational Awareness' private deals might not move forward as a result of what's going on.

It will be interesting to see if any data centers, leases, etc. become problematic as a result of this.

... so far, what's really odd is how positive markets are today despite a massive hedge fund focused on AI falling apart.

Two contradictory thoughts based on the article:

1. One thing you could do is look at free cashflow. The actual cash a company generates and keeps on its balance sheet would be a clear indicator of retaining profit or resources after circular deals.

2. The above doesn't preclude a company from committing to backstops and offtake agreements, so you still need to track those.

... so you'd want to look at how much cash a company is retaining and whether it is committing to act as a sort of lender of last resort.

Up until this week, I'd argue that Nvidia is was a pretty solid player in this space. If it's generating $10s of billions in cash, then who cares if it put $10 billion into a risky startup? It can afford it.

Now, however, it is also exploring committing $250 billion as a backstop for OpenAI's data center projects, which moves it to the riskier side of things.

I know I'm not providing you with data; I don't have that... But I think the above is a signpost to watch out for.

Not sure if you're referring to dollars as an alternative to other forms of cash (e.g., treasuries, money market funds, etc.) or as an alternative to other currencies (e.g., Euro, Yen, etc.). If it's the latter, then I think we've seen recessions and booms where the opposite happens, depending on the balance of payments situation.

SpaceX is a really interesting one, tbh, especially in the context of circular deals... they're making $2b/month leasing their data centers to Anthropic and Google, which is a huge markup.

... but then again... if they are leasing the compute then it implies their own AI work isn't paying off?

In short: I agree with you.

Good point. Do you think the NASDAQ selloff will recover once Korea stabilizes?

This has been bugging me quite a bit. I think the more "off-balance-sheet" the deal is, the shadier it gets... But it's really hard to tell early on.

I actually think Nvidia's deals have been above-board, generally speaking, because they are transaprent.

Less true for Meta and Terawulf.

Observing how the US stock market is behaving this week (up-down-up-down...), I feel like many investors don't appreciate the distinction between good and bad circularity.

Have you considered consulting? Could be a good way to explore other industries and geographies while making use of the skills you have today. I'm sure many people would want to hire you or work with you.

You might appreciate this book, too: https://www.amazon.ca/Second-Mountain-David-Brooks/dp/081299...

I’m a supporter of the technology and believe it is transformative.

I've been reflecting on Generative AI in the context of broader sociological and cultural theories. This article is reminding me of these things and I'm curious what other think.

#1: George Soros' concept of reflexivity, where human biases begin informing, distorting, and supporting asset prices not because of their underlying fundamentals, but because of the human biases that have contributed to their prior appreciation. As per the essay being cited, if you are a CEO committing to AI as a strategy, you will also commit resources to double down on the technology. Your own identity becomes tied to it, whether you realize it or not, and you'll keep pushing for it and maybe even ignore facts that challenge the success of your investment.

#2: Marshall McLuhan (of "The Medium is the Message" fame) argues that we need to understand communication and entertainment technologies in terms of the structure they impose on us. While social media is seen as a societal ill by many, its original idea of connecting people is fundamentally, well, social... GenAI is very much a non-social (i.e., you experience it on your own) convenience technology. It gives you answers, it writes code, and it implies an authoritative perspective that is always available to you, as an imperfect human. What will this mean for our own identities as human beings?

I am very much a supporter of foundation models, LLMs, AI, etc. but can't help and think about some of the ideas above. Curious what others think.

Meguro Parasitological Museum is fantastic and en...grossing. :) It's a fun area to visit in general. Highly recommend it.

Also...

Yamato History Museum: https://www.yamato-hd.co.jp/museum/en/

... and if you are near Hiroshima, the Toto Museum is a history of toilets + washroom innovation and surprisingly fascinating. https://jp.toto.com/en/knowledge/visit/museum/

They are trying to diversify into consumer hardware and also are further along in owning data centers. The consumer hardware product, assuming it launches and does decently well, puts them in a very unique position relative to pretty much every other frontier lab. It's more speculative, but I'd argue it can change things quite a bit for them if it works out.

> To that end, making our compute available for rent means that we can only take it back if we can make more money on it ourselves; the only way we can do that is by leaning into what we are good at, not what I have spent too long wanting us to be. To put it another way, our best product decisions have been intuition validated by data and revealed preference; that’s how we’re going to approach AI.

What a great way to frame the strategy + opportunity cost. "We will make a bare minimum via reselling, and it's now up to us to prove we can do better."

Does anyone know what a model like this would cost to build with today's compute?

Your concept sounds interesting. I highly recommend you add a link to your startup on the job board or in your HN profile. Searching for Pascal with NYC, CPG, etc. key words doesn't yield your home page and I can't tell if teampascal.com has anything to do with you given it doesn't discuss CPG.