Arnaud Bertrand (@RnaudBertrand)

This is the biggest irony of the whole Alice Huang / Nvidia saga: the Chinese government actually doesn't want Nvidia chips and is actively keeping them out of the country. Last year it even ordered Alibaba and ByteDance to cancel their Nvidia orders ( and sent customs teams to the ports to check every advanced chip coming in ( And right now 600,000 Nvidia H200s are stuck in Hong Kong because Beijing won’t let them into the country ( Nvidia also explicitly frames this as a threat to its business in its latest annual report to the SEC: "The Chinese government has encouraged customers to purchase from our China-based competitors and discouraged customers from purchasing, importing, or using our data center products" ( Why does China do that? Because every Nvidia chip a Chinese company buys is one that Huawei or Cambricon doesn’t sell, and domestic chips can only catch up if people actually buy them (which means money for R&D, etc.). Which means that the notion that Chinese intelligence was running honeytraps to get more Nvidia chips into China gets the whole thing exactly backwards. In fact, it looks like Megaspeed - Alice Huang's company - was a counter-reaction to official Chinese policy: a private sector workaround to these efforts by the Chinese government to curtail Nvidia. As was widely reported - including by Western media - Megaspeed built data centers in Southeast Asia and rented their computing power to Alibaba, which the New York Times called "a backdoor way of getting access" to Nvidia chips ( That is a backdoor not only - allegedly - around Washington’s export controls, but also around China's own efforts to wean its companies off Nvidia. I can’t believe the Chinese government was too thrilled about it: it'd make just about zero sense for it to run countless domestic measures to prevent Alibaba from using Nvidia chips, yet at the same time have its top spy agency help the same Alibaba get Nvidia chips through the back door in Southeast Asia 🤷 At the end of the day, as with all such stories, you always need to look at "cui bono?": who benefits? Who benefitted is pretty clear: - Nvidia itself: it's in the business of selling chips so a customer willing to spend billions of dollars on chips in Southeast Asia is a godsend. Again, as they themselves state in their SEC filings, not being able to tap into Chinese demand would have "a material and adverse impact on our business" ( - Chinese companies like Alibaba: facts are, 1) Chinese chips haven't yet caught up, 2) Chinese chip companies' production capabilities are still limited and 3) Chinese tech firms' historical tech stack runs on Nvidia’s CUDA ecosystem, and rewriting years of code for Huawei chips is painful. Renting Nvidia chips in Southeast Asia is the easy way to stay competitive and serve customer demand in the meantime. - The US, according to core members of the Trump administration: back in July last year, US Commerce Secretary Howard Lutnick explained that the point of letting Nvidia sell more chips to China was to get Chinese developers "addicted to the American technology stack" ( Who does NOT benefit, according to official Chinese policy, is the Chinese state, which has made tech self-reliance one of its foremost priorities. Which means that, to believe the spy story, you have to believe that a person - Alice Huang - was effectively a spy paid by her own government while simultaneously sabotaging its tech self-reliance drive, which it has spent years and trillions of dollars on - pretty senseless. Stranger things have happened, I guess, but it doesn't pass the smell test. And there is another absolutely major contradiction in the media story. According to a Financial Times article published yesterday ( "a person who knows [Alice Huang] said they had been asked by MSS for information about her earlier this year after news emerged that Singapore was investigating the company in relation to chip-smuggling allegations. The agency wanted to find out more information about Huang, including the nature of her business and connections, the person said." Which raises a damn obvious question: if she were indeed an MSS asset, why on earth would the MSS need to ask the people around her who she was and the nature of her business? It's a complete contradiction with the spy story - but very much not a contradiction with what I described above: a private businesswoman running a workaround to Beijing’s own policy, which the Chinese security apparatus would naturally want to look into. Especially since, around about the same time, Shanghai police detained her business partner, reportedly her husband, in a stock manipulation case ( also coherent with Chinese authorities looking into her network, and much harder to square with her being a prized MSS asset. And, to top it all, there is - to this day - not a shred of evidence that's been published that she is, in fact, a Chinese spy: just anonymous intelligence officials, repeated by journalists who never verified it, as I detailed here ( and here ( with one of the key FT journalists on this story actually admitting that he has no idea what evidence US intelligence actually has. So the claim went to print without anyone seeing the evidence, without anyone being able to check it, and without anyone knowing whether it even exists. And given everything above, from Beijing blocking the very chips she was supposedly recruited to help get into China, to the MSS asking around about her, to her partner being detained in Shanghai, my bet is that the evidence doesn’t exist at all. If anything, everything we can check points the other way.

From the horse's mouth 🤷 That's kinda your job to check before reporting she's a "spy"... If you don't, all you're doing is making YOURSELF an agent of the US intelligence community, ironically what you accuse her of with regards to China.

This is genuinely shocking, and says so much about our approach to China. I decided to check for independent reviews of the English version Xi Jinping's latest book, published a year ago, to see what people had to say about it since I hadn't read it myself. To my surprise, I couldn't find any: not a single thoughtful review about the book out there! Even on Amazon, check it for yourself ( the book has only 3 ratings, that's it. No matter where you stand on China, you’ve got to admit that’s pretty crazy: the sitting president of the world's rising superpower publishes a 700-page book explaining exactly what he's doing and why, and we don’t even care to look. If there ever was a fact that illustrates just how willfully ignorant we are about China, this is it. All the more because we then go spew the usual clichés around how secretive and impenetrable the Chinese system is: the book is on Amazon for $21 for crying out loud! Anyhow, this felt so wrong that I figured I'd fix it. I bought the book, read it attentively and wrote what I hope you'll agree is a thoughtful review of it. The book contains genuinely surprising passages, such as Xi writing that oversight of the Communist Party by "the judiciary, the public, and the media" was not just something the Party must “readily accept,” but something that he framed as historically decisive - an essential component to "escaping the historical cycle of rise and fall" that has doomed every dynasty in China's history. Other passage that I'm sure would surprise many: a common narrative out there is that China blames the West for the century of humiliation and is driven by revenge. Well, Xi explains that's not true at all: the century of humiliation was China's own mistake, originated in the Ming Dynasty's disastrous "policy of national seclusion" that "resulted in China missing out on the opportunities presented by the Industrial Revolution" and "led to China’s decline." All in all, the book is remarkably self-reflective and thoughtful. For instance Xi recognizes that his drive for “full and rigorous internal governance” - including to rid the Party of corruption - risked "instill[ing] fear and apprehension, or intimidate members into inaction.” He emphasizes the need for pragmatism in this regard, codified in a framework called the “Three Distinctions” that separates honest mistakes - made while experimenting, reforming, or operating without precedent - from deliberate violations committed for personal gain. And many other surprises still. I found it a genuinely fascinating read for anyone interested in how the Chinese system works and how Xi thinks - or anyone interested in governance, period, as so much of what he writes is pretty universally applicable. This is the link to my review of the book, an article I titled "The Book the West Refuses to Read":

So I spent some time studying the new Twitter/X algorithm today since the latest version was published about a week ago on Github ( My goal was to answer why so many people have seemingly seen such a dramatic drop in their posts' reach. The first answer, which is actually somewhat unrelated to the ranking algorithm on Github, is the auto-translate feature, rolled out worldwide on April 7, 2026 ( Before that date, if you wrote in English about, say, the Trump-Xi Beijing summit, you were competing for attention with maybe 5,000 other English-language accounts writing on geopolitics. After that date, your post is competing for attention with other posts on the same topic IN EVERY LANGUAGE ON EARTH. For some topics that do command global attention like geopolitics, that's a very brutal multiplier: you used to be one of 5,000, you're suddenly one of 50,000 (something of that order): MUCH more difficult to stand out. Secondly, the number of followers you have matters far less than it used to: each post now has to earn its audience reader by reader, on the predicted engagement of the post, and how its topic matches what each reader has recently been engaging with. Here is how the algorithm works, in simple terms: when you, as a reader, open your feed, the algorithm doesn't load "posts from accounts you follow." Instead it runs a 2-stage prediction of what posts you're likely to engage with in that very moment. The first stage is the retrieval stage. The system narrows billions of posts on X/Twitter that day down to roughly 1,500 candidates by matching the semantic content of each post - what it's about - against what you as a reader have recently engaged with. Some candidate posts come from accounts you follow; others are pulled from across the platform by pure topic similarity to your recent interests. You can test this retrieval stage easily: start disproportionally engaging with - say - Brad Pitt videos and you'll bit by bit see your timeline flooded with Brad Pitt content, most of it from accounts you've never followed and never heard of. Then there's the ranking stage. Each of these candidate posts for your feed is fed through a Grok-based model that tries to understand if you'll engage with the post. It looks at 15 engagement metrics: 1) P(favorite) — the reader likes the post 2) P(reply) — the reader replies to it 3) P(repost) — the reader reposts it 4) P(quote) — the reader quote-tweets it 5) P(click) — the reader clicks a link in it 6) P(profile_click) — the reader taps through to your profile 7) P(video_view) — the reader watches the video 8) P(photo_expand) — the reader expands an image 9) P(share) — the reader shares it (DM, off-platform, etc.) 10) P(dwell) — the reader stops scrolling and lingers on the post 11) P(follow_author) — the reader follows you after seeing it 12) P(not_interested) — the reader marks "not interested" 13) P(block_author) — the reader blocks you 14) P(mute_author) — the reader mutes you 15) P(report) — the reader reports the post Fifteen predicted actions, each multiplied by a weight, summed: that sum is the score that determines in which priority a post will be seen among other candidates. Please note that posting something with a video or an image can give your post an advantage as 2 actions are specifically for these: video_view and photo_expand. No video or photo and you don't get a score for these. Also, naturally, having a video maximizes the chance that a user will "dwell" on your post to watch it. Also note that 4 of these actions carry negative weights (not_interested, block_author, mute_author and report): meaning that if the model expects a post to generate a lot of negativity, it'll get de-boosted quite dramatically. But note, first and foremost, what's NOT in there: none of the things that, naively, one might think a serious information platform would weigh. There is no P(this post is true and well-sourced). No P(the author actually knows what they're talking about). No P(this person has spent a decade building a body of work that has held up). No P(this account has earned the right to be taken seriously on this topic). No P(the author has a large following from credible people). The model does not seem to care - at all - about any of that. Every post starts from zero. You could have ten years of rigorous, well-sourced analysis behind you - or you could be just an uneducated rando who registered yesterday. To this algorithm, you're both just a bag of engagement probabilities. Now, sure, to be fair, there is a "brand" effect that's not covered by the algorithm: someone who has in fact built a brand will naturally have better engagement metrics because people recognize their account. But that's an indirect, second-order effect. And crucially, it's legacy: those "brands" were built under earlier versions of the algorithm that gave followers and reputation more weight. Lastly, several other features of the new algorithm compound the dilution, none of them visible from outside but all consequential. The May 15 update added an "impression bloom filter," tightening the rule that once a reader has been served a post, the system won't serve it to them again. Before, a strong post could marinate in someone's feed across multiple refreshes and accumulate engagement on the second or third pass. Now it basically gets one shot. Also, your own posts compete with each other. An "Author Diversity Scorer" inside the ranking stage attenuates the score of every subsequent post of yours that ends up in a reader's candidate pool. In plain terms: if multiple of your posts land in a reader's candidate pool, the system shows one at full strength and dampens the others. So don't post several times consecutively on the same topic. And, last but not least, another huge impact on reach is that, in the old algorithm, when someone reposted or quote-tweeted you, your post was broadcast to their followers' timelines - a repost from an account with 100,000 followers was a huge boost. In the new algorithm, that mechanism is vastly demoted: reposts - like every post - need to go through the retrieval and ranking stage mentioned above, so a repost from a big account is a long way from the boost it used to be. This is especially brutal for low-effort quote tweets, which used to function as cheap amplification: now they often can't even clear the retrieval stage - they simply don't contain enough novel semantic content for the system to match them to anyone's interests. So, putting it all together, the reach collapse comes from many forces stacking at once: - Auto-translate makes your posts compete for attention against an order of magnitude more content - The retrieval stage matches posts by topic, not by who follows you - The ranking stage scores purely on predicted engagement with no weight for credibility, expertise, or track record - The bloom filter narrows every post's window to one strong shot - The diversity scorer penalizes prolific posting - Reposts no longer carry much distribution power Each of these alone would dent your reach. Combined, they amount to a complete reset: your audience that you built painstakingly over years basically doesn't matter much anymore, and it's much - much - harder to stand out even if you're a big account. People structurally rewarded by this algorithm are folks who: - Post visually (videos/images) - Post on globally popular topics because they clear the retrieval stage easily - Provoke strong emotional reactions - likes, replies, reposts - Don't care about accuracy or seriousness because the algorithm doesn't measure it - Don't care about their existing audience because every post is judged in isolation anyway In short this new algorithm, like so many on social media, is all about maximizing whether people will engage with something - not about whether they should.