When I wrote this week about Anthropic’s announcement that all Claude models, worldwide, would soon begin “watermarking” everything they generate, including text, to comply with this EU regulation, we were left to speculate how this was going to work, because Anthropic offered not even a vague description of how it would work — despite the fact that the title of the announcement was, absurdly and insultingly, “How Claude Marks AI-Generated Content”.

My initial speculation was that maybe they’d hide invisible non-printing Unicode characters in the text. Just spitballing. Turns out that’s not what they’re going to do. What they’re going to do is apply a form of steganography, where the choice of words (or other token output) at inference time will leave fingerprints that can later, maybe, be detected probabilistically.

I initially guessed “invisible characters” not because I didn’t think of the semantic word-choice technique, but because I was a fool who took Anthropic at its word in their description of what they would do. Their original support document claims:

When a supported Claude model generates text, it weaves an imperceptible watermark directly into the text itself. You won’t see it, and it doesn’t change the meaning, quality, or readability of Claude’s response.

They say “imperceptible” and “doesn’t change the meaning, quality, or readability”. Their words. Not almost imperceptible. Not slightly changes the meaning, quality, or readability. That made sense to me, because that’s absolutely what I want — nay, demand — from any tools I use personally. It’s unacceptable for a tool to sacrifice an iota of clarity, coherence, meaning, quality, etc. for the purpose of embedding hidden clues within the text to suggest its provenance. That’s what I would and will demand. And Anthropic’s (original) support document unambiguously claims that’s what their system will enable. So if that were true, I couldn’t see what was left other than hiding invisible characters within the text.

My error was believing Anthropic that their system wouldn’t adulterate and corrupt the semantics of the text their models generate. That is in fact exactly what they plan to do. I should have my head examined for believing a single word of a document titled “How Claude Marks AI-Generated Content” that doesn’t explain, at all, how Claude marks (or will mark) AI-generated content.

How It’s Actually Going to Work

Yesterday, on an entirely different website than the original “How Claude marks AI-generated content” article (the one that didn’t explain anything at all about how it works), Anthropic published “How Claude’s Text Watermark Works”, which does actually explain in layman-accessible terms how it’s going to work. I will return to Anthropic’s new highly euphemistic and slightly misleading description below.

There’s a bunch of research on this topic, some of which I have also linked to below. But the very best description of the general idea behind the technique is an interactive essay by James Padolsey, “How AI Text Watermarking Works”. It’s a wonderfully cogent read, and the interactive elements splendidly illustrate the main concepts. A+ work. If you have any interest in this at all, I dare say you must read — and play with — Padolsey’s piece.

But here’s my stab at a layman’s high-level summary. If you toss a coin N times and note the results, you can determine with a degree of certainty whether the coin is fair or biased. LLMs are, in their popular incarnations, non-deterministic. Ask the same question of the same model and you often get at least slightly different answers. Maybe the same meaning, but different phrasing. At each decision point for generating the next token, the model makes a choice. With these semantic watermarking techniques, they make different choices for some tokens based on word lists that could be called “green” and “red”. At each decision point, they’re a little more likely to pick a word from the green list than the red list. That doesn’t mean they never choose words from the red list. Just that they’re less likely to than they would if the adulterated marking technique weren’t in place. (Same way that a crooked 51-49 coin will still land “wrong” side up 49 times out of 100 on average.)

Words or word phrases are sorted into the green and red lists deterministically on the fly, at each “next token” generation point. So sometimes a specific word will be on the green list, and other times it will be on the red list. Someone with the secret key can determine which list a word will be on at each token generation point (which is how the watermarking is detected); those without the secret key cannot. This means there will never be a list of words that Claude prefers or eschews.

With coin flipping, the higher N is — the more times you flip — the more confident you can be that the coin is fair or biased. So too with this semantic watermarking. The more words in the text, the more accurate the analysis will be that the text was generated by a specific AI model or not. With too few coin flips, you can’t achieve any confidence at all regarding a coin’s fairness. With too few words (or tokens), there’s no way to achieve any confidence whether a string of text was AI-generated or not.

Given a string of text to examine for signs of a specific watermarking system, if there are more words tagged as green and fewer tagged as red than would otherwise be expected, the text can be flagged — with some degree of confidence — as having been generated, or merely modified, by the AI system that applies the specific secret-key watermarking system. The amount of confidence in the determination will obviously vary, significantly, based on the size of the text string and randomized weights given to words on the green and red lists. But only Anthropic will be able to determine if text was seemingly generated by Claude, and Anthropic will only be able to detect the watermarks that are applied by Claude. Claude can’t detect the hidden watermark signals generated by, say, Gemini, and Gemini can’t detect the hidden watermark signals created by Claude, because each implementation is predicated on secret keys held only by the LLM provider.

Objections to the Technical Premise

One of my fundamental problems with this is that no two synonyms carry the exact same meaning. “He leaped at the chance” and “He jumped at the opportunity” are very similar sentences expressing the same general sentiment, but they are not the same. The exact words we choose when writing matter. I want any LLM I use to choose the very best, most precise words at every single decision point. An obvious constraint that I accept is time and computation. Within the constraint of executing inference quickly, and at a certain cost per token, I want the best words. This constraint matches human writing. I could surely write a better column by taking longer to write it. I write with a sense of how much care I should put into every word and punctuation choice I make. I take more time with certain paragraphs, sentences, or even individual word choices when my gut feeling says I should.

In other words, these are necessary trade-offs. These factors are all in my interest: speed, cost, quality. Ideally I would like perfect writing, at instantaneous generation speed, at zero cost. None of those things are possible. Computation is not free of charge (and cloud-based LLM inference with leading models is actually expensive). Inference is not instantaneous. And great writing, whether natural or artificial, can only approach perfection.

The idea that anything other than my needs should factor into the generation of text for me is patently offensive.

This isn’t just about text one might generate with the intention of passing it off as their own natural work. This isn’t even about LLM proofreading of work written by hand. Anthropic is saying that all new Claude models are going to adulterate every single bit of text longer than 200 tokens (~150 words) they generate, including everything it presents to its users to read. So even in a private conversation between a user and Claude, which will never be read by anyone other than the user, Claude will begin making word choices in the name of marking its output in statistically predictable ways rather than maximizing clarity and precision.

Even today’s so-called frontier models are already decidedly lacking in lucidity. Claude, ChatGPT, Grok, et al. are “better writers” than most humans and produce better prose than the median human. But: no shit. Most people are terrible writers. The “average person” is pretty stupid and half of all people are stupider than that. And there are many smart, interesting people who are miserable writers. So as impressive as LLMs are, the bar is low. The best writing I see come out of these models is worse than anything I would choose to read for pleasure. And now Anthropic is saying they’re going to make it worse, on purpose, for purposes that do not benefit me in any way? Even if only slightly worse?

Get fucked.

Objections to the EU Regulation

Speaking of objections, the relevant EU regulation motivating all of this, “Code of Practice on Transparency of AI-Generated Content”, is red-tape nanny-state pipe-dream nonsense. Here’s Ben Thompson’s summary from a paywalled Stratechery update this week:

• The regulation applies to text longer than 200 tokens.
• The provider must mandate in their terms-of-service that users not remove the watermarking.
• The solution should be robust in terms of evading “typical processing solutions” like screen shots, scanning and OCR, copy-and-pasting, translations, etc.

Taken literally, compliant LLM terms of service must forbid users from rephrasing the output from models that comply with this regulation, because the word choices are the marks. But it’s not the European Union that is trying to impose their absurd, impractical, witch-hunt-fueling regulation on the entire world. That falls on Anthropic.

Complying with this, particularly with regard to text, is only going to create problems for honest users. Dishonest users attempting to pass off AI-generated text as their own writing (students, employees, whoever) will simply circumvent detection through non-compliant AI paraphrasing tools.

James Padolsey — whose interactive visual explanation of how these schemes work I linked to above — explains this in a post titled “Anthropic’s Weak Watermarks Appease a Weak Law” (which, if it rings a bell, I linked to in a standalone post earlier today):

The same thought that led to this law could have applied to calculators at the time of their inception, had their outputs revealed themselves through artefacts. Thankfully, a sum borne of the brain is treated no differently from one produced by a calculator. Likewise with spellcheckers. To make assistance suspect only once the tool becomes capable enough to compose a whole sentence is not a principled boundary. It is a moral premium placed on difficulty itself.

Anthropic has nevertheless chosen a blanket, model-level implementation that appears broader than the law’s minimum requirement. That may be convenient compliance engineering, but it discards distinctions the law expressly attempted to preserve. The result is a signal broad enough to implicate harmless and assistive use, yet fragile enough to be removed by a motivated person through substantial recomposition. It risks concentrating suspicion on ordinary and assistive users while remaining weakest against deliberate deception.

Padolsey is the creator of Declaude, a delightfully simple web app that allows you to “Paste in AI-flavored text and get the same content back as plain prose”. Declaude’s original purpose is cleaning the saccharine Claude personality stink from text (whether it was created by Claude or any other LLM), but, if Anthropic persists in its stated plan to begin adulterating all text Claude generates, Declaude will also serve as a copy-paste single-extra-step way to eliminates those marks. Declaude is interesting and useful already, but it exemplifies how ill-considered and futile this EU regulation is when it comes to prose.

Google SynthID

Google has a watermarking system in place that they call SynthID, which they apply to AI-generated images, video, audio, and text. I’m concerned in this article only with text. With multimedia, embedded watermarks can be metadata within files, and truly not affect the experiential quality of the work when viewed or listened to. With text, we are talking about the actual words that are chosen. From the “AI-generated text” section of Google DeepMind’s own description of SynthID:

We’ve expanded SynthID to watermarking and identifying text generated by the Gemini app and web experience. Large language models generate text one word (token) at a time. Each word is assigned a probability score, based on how likely it is to be generated next. So for a sentence like “My favorite tropical fruits are mango and…”, the word “bananas” would have a higher probability score than the word “airplanes”. SynthID adjusts these probability scores to generate a watermark. It’s not noticeable to the human eye, and doesn’t affect the quality of the output.

In a group chat, a friend of mine quoted the above, and I responded that if a chatbot wrote “My favorite tropical fruits are mango and airplanes”, I’m pretty sure I’d fucking notice. Another friend then responded with this:

Days later, that still cracks me up.

But Google’s absurd description puts the lie to their own claim that it isn’t noticeable, and it serves to show just how little regard the people behind these generated-text fingerprinting schemes have for the actual craft of writing. Of course bananas has a higher probability score than airplanes, because airplanes aren’t fruit. But what about pineapple? Should the sentence complete to “mango and bananas” or “mango and pineapple”? That’s a good question, and the only acceptable answer for why an LLM should choose bananas instead of pineapple (or coconut, or guava, or papaya...) is that it has determined that it’s the best fit for the intended meaning, tone, and sentiment of the text. Not because bananas is on the watermarking “green” list and pineapple is on the “red” list, even though pineapple might be the better fit. Google’s own supposedly jocular description of how SynthID works in fact captures how the scheme perverts the text it generates.

They’re saying you won’t notice because if it only chooses bananas over pineapple for these fingerprinting purposes, well, they’re both tropical fruits and who cares. But it’s utter nonsense that the difference is “not noticeable to the human eye”. The semantic difference between banana and pineapple is just as noticeable to the human eye as the taste of the two are to the human tongue.

If it did produce “My favorite tropical fruits are mango and airplanes”, it’d be incredibly stupid, but it wouldn’t be offensive because we’d all recognize that something completely off-key happened. What’s offensive is that with a system like SynthId in place, where the fingerprinting decisions are motivated by a secret key, we have no idea whether it completed to “mango and bananas” because bananas was determined to be the best next token, or because bananas is in the “green” bucket of words. It calls every single word choice into question.

Here’s a paper published in Nature where Google’s team behind SynthID published their work, after putting it into production with Gemini (née Bard):

We analysed approximately 20 million watermarked and unwatermarked responses and computed the thumbs-up and thumbs-down rates (both as a fraction of the total number of thumbs-up and thumbs-down feedback received). We found that the thumbs-up rate for the two models differed by 0.01% (with the watermarked model being higher); and the thumbs-down rate differed by 0.02% (with the watermarked model being lower). We found both of these differences to be statistically insignificant, and well within the 95% confidence intervals.

From this experiment, we conclude that over a wide variety of real chatbot interactions, the difference in response quality and utility, as judged by humans, is negligible. Subsequently, non-distortionary SynthID-Text has been productionized and is currently watermarking responses in Gemini and Gemini Advanced. To the best of our knowledge, this evaluation represents the first systematic watermarking investigation of its kind within a large-scale production system.

To this I say:

• Gemini/Bard’s thumbs-up/thumbs-down buttons are not a good experiment for evaluating the effect on quality. If a chatbot tells me “My favorite tropical fruits are mango and bananas” instead of “mango and pineapple”, I’m not going to give the response a thumbs down because of the fruit it chose. I’d give it a thumbs down if it said “airplanes”, yes, but that’s a strawman. (The paper in Nature even uses “My favourite tropical fruit is ...” as an illustration, but in the paper, the only four next tokens considered are, in order of probability distribution, mango, lychee, papaya, and durian. No airplanes. And, conveniently, in the paper’s example, the “winner” of the watermarking “tournament” just happens to be mango, the one that would have been selected as the best if the watermarking weren’t in place.)

• A “difference in response quality and utility, as judged by humans” that is “negligible” does not mean imperceptible. What they really mean is that it’s only slightly worse and that everyone is either too stupid to notice or too indifferent to care.

• It’s widely considered that Gemini is behind ChatGPT and Claude in quality. Perhaps the fact that they’ve put SynthID-text into production is one of many reasons why. I personally agree that Gemini’s prose is inferior. Maybe the use of SynthID has nothing to do with the fact that I, along with the general public consensus, consider Gemini to be a second-rate chatbot — but in that case, maybe it’s the fact that Gemini is a second-rate chatbot that makes the difference “negligible” when Google started mixing in SynthID-motivated tokens in its results. It’s a lot more likely that your restaurant customers won’t notice that you replaced your regular coffee with Folgers Crystals if your regular coffee is second-rate to start with.

Anthropic

Now, finally, back to Anthropic’s new “How Claude’s Text Watermark Works”, published yesterday. I have some comments.

To summarize:

• We use a method of watermarking that does not have any practical impact on the quality or content of Claude’s outputs;

• The difference between watermarked and un-watermarked text will not be distinguishable to readers;

Translation: Specific words do not matter and we don’t think anyone reads anything closely.

• Nothing is added to the text and there are no hidden characters;

This would have been worth clarifying at the outset.

• Watermarking won’t be specific to Claude. As of August 2, the EU requires AI providers serving its market to mark AI-generated content. Other major model developers have signed the same Code of Practice and will be implementing their own watermarks.

No other AI provider has stated that they will apply such marking, adulterating all generated text, outside the EU.

Take the sentence “The weather today was cold and…”. The next word is very unlikely to be “sugary.” But it is quite likely to be “overcast” or “grey.” Under most circumstances, it doesn’t matter much to the reader which of these latter two words the model ultimately chooses — the meaning of the sentence is largely the same either way. In cases like this, the choice is settled by a random number.

Arguing that grey vs. overcast “doesn’t matter much to the reader” is the crux of my argument that this entire endeavor is a perverse adulteration of what it means to write — or to read. That it’s subtle in some ways makes it more perverse, because it’s sneaky.

In internal testing, we’ve seen no impact of watermarking on the content, level of creativity, or readability of Claude’s text. In the SynthID-Text paper, which introduced the technique we use, Google DeepMind tested this impact by serving a model that used watermarking to a portion of their Gemini traffic and comparing thumbs-up and thumbs-down ratings. They found no statistically significant differences from the unwatermarked model. And in a controlled study, human raters comparing watermarked and unwatermarked answers side-by-side saw no difference in quality.

See above for my argument that this thumbs-up/thumbs-down data is absolutely worthless in evaluating whether the SynthID-style word-bias watermarking makes text worse. By definition it must make text worse, unless the underlying LLM model’s scoring is wrong, because the nature of the watermarking algorithm requires it to sometimes increase the probability of selecting a worse word choice and decrease the probability of selecting the model’s best choice. It’s only a question of how much worse. What Google’s thumb-counting data shows is only that it isn’t so much worse as to make Gemini users click the thumbs-down button.

Watermarking doesn’t change the meaning or experience for the person reading it, but if you wanted to check after the fact whether the text was likely generated by Claude, the watermark allows you to do so.

No, it does not. Because the entire scheme is tied to secret keys held only by the AI provider, it only allows Anthropic, not “you”, to check anything.

When Claude proofreads text written by a person, what it gives back has generally only been lightly edited; because nearly all the words are the person’s, there’s very little (if anything) for the watermark to attach to. Depending on the length of the text and how heavily Claude has edited it, those changes might not be enough to make Claude’s involvement detectable. The more Claude writes, the more decisions it has to make, and the more space there is for a watermark.

Translation: No one can ever again use Claude for proofreading their own prose unless they’re willing to risk that the whole thing might be flagged as having been generated by Claude.

For example, once the model has written “2 + 2 =”, there is a very clear best choice for the next token (if the model is completing the sum, there isn’t an answer that’s equally as good as “4”; if it’s talking about George Orwell’s Nineteen Eighty-Four, there isn’t an answer that’s equally as good as “5”). The “nudge” of the watermark wouldn’t be applied here. For the same reason, code — which in very many cases has to be exact — has generally less watermarking than some other forms of text.

Having said that, in areas where there is an arbitrary choice between particular words or terms within the code, the watermark can be used, such as comments within code. But by definition, it will have a negligible effect on the actual code produced.

Translation: We value precision in programming code; we do not in prose.

And it is exceedingly rich to cite George Orwell’s Nineteen Eighty-Four, approvingly, in the context of justifying a text adulteration scheme premised on the notion that specific words do not matter. I mean what the actual fuck? Orwell!

Lastly, as to why they’re doing this:

We’re implementing watermarking to comply with the EU AI Act. Anthropic, along with several other major AI model providers and around 190 total signatories, signed the EU Code of Practice on Transparency of AI-Generated Content in July 2026. This requires AI system providers to use methods of “marking” AI-generated text. We’re applying watermarking globally at launch because we don’t yet have a durable way to scope it by region.

This, from a company that the Financial Times just reported is weeks away from an IPO with an intended valuation of $2 trillion, which would make it one of the 10 highest-valued companies in the world — as of today, placing it at #7, between TSMC ($2.2T) and Broadcom ($1.9T).

This leaves us to believe that one of the following must be true:

• It’s perfectly reasonable that a technology company valued on par with Amazon and TSMC is technically incapable of complying with an EU regional law only within the EU itself.¹ Not a cause for concern at all.

• Anthropic is in over their heads, wields shockingly little control over their own tech stack, and their imminent IPO is likely to be remembered only as a new high-water mark in the manic global AI bubble.

Also, what happens if another major global market makes it unlawful for AI to secretly watermark generated text?

OpenAI

From an OpenAI support document titled “Provenance Signals (Content Credentials, SynthID) in OpenAI-Generated Content”:

Consistent with our commitments under the European Commission’s Code of Practice on Transparency of AI-generated content, our goal is to expand provenance signals to all modalities including text, so customers and developers have clear ways to meet their own transparency obligations as standards and tooling continue to mature.

There’s a lot of wiggle room in this brief statement, and it could just as well mean that OpenAI models will only adulterate text with fingerprint markers when users or developers ask for it. Or that it will only be mandatory for users in the EU. If I were at OpenAI I’d go hard on this and publicly say that ChatGPT will never watermark text it generates unless you ask it to, and that if you want tools that secretly work behind your back without telling you how they work to flag your words in ways you can’t see, go ahead and use Claude.

Further Reading

Three papers on ArXiv:

• “A Watermark for Large Language Models”, last revised May 2024.
• “Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models”, last revised May 2025.
• “Robust Text Watermarking for Large Language Models via Dual Semantic Embeddings”, last revised June 2026.

I will admit that while I’m profoundly offended by the idea of personally using tools that attempt to leave such watermarks in text they produce or touch, the mathematics behind it are fascinating.

Michael Lopp, at Rands in Repose, “RIP Claude”:

As a human who has had to wrangle with EU regulations in the past, I am abundantly clear what’s involved in the laborious bureaucratic process. I can guess what threats Anthropic is facing. However, this is a tone-deaf, clumsy, and alarming opening salvo in their watermark strategy. [...]

My writing is my work, and Anthropic’s current strategy is aggressively writer-hostile.

Jeff Gamet, “Anthropic’s Claude Watermark Is Akin to an AI Poison Pill”:

To be clear, the watermarking is embedded in pretty much any text Claude touches. Along with text Claude generates, it also applies to text it processes, such as proofreading and summarizing. I expect we’ll see too many inaccurate accusations of using Claude to write documents where the content was human-written, but AI-proofread.

The watermarking sticks with documents through copy-and-paste, too. Imagine copying text from a blog post or email only to have what you wrote tagged as potentially AI-generated. In fact, that could very well happen with this post. I personally write all of my content without AI tools, but I copied the quote at the top of this piece directly from Anthropic’s website. Does that mean what I wrote here will show as AI-generated? If they used their own models to generate or edit what I quoted, then the answer is very likely “yes.”

One of the papers published at ArXiv I cited above claims that such watermarking even persists when an article of text originally generated in English is translated into German.

Secrets are the poison here. When only Anthropic holds the secret keys that both produce the watermarking and perform the probabilistic detection of those marks, we’re all left to wonder. To wonder if what we’re reading is secretly watermarked, what we’re quoting is secretly watermarked, and whether what we ourselves are writing will be unjustly accused of being AI-generated based on secrets we don’t know and can’t see. Poisonous is exactly the right word.

Or should I say toxic? Or airplanes?

Cory Weinberg, Jemima McEvoy, Jessica E. Lessin, and Stephanie Palazzolo, writing for the paywalled-without-gift-links The Information:

As Amodei has hopscotched the globe to preach about the potential and risks of AI — from New Delhi to Davos to Sun Valley — Clark has almost always been near his side. Several people who know the couple describe Clark as Amodei’s emotional ballast, someone he has sought counsel from during the turbulence of Anthropic’s growth and clashes with Washington over the future of AI.

Clark may well be the most consequential person within Anthropic’s orbit who doesn’t have a formal role at the startup. You might even call her the first lady of Anthropic. [...]

And before Female Algorithm Technologies, Clark tried her hand at building a pornographic film company, Eddice, that planned to make movies with high-production values and female protagonists. She also hoped it would have online shopping capabilities. In 2011, she tried to raise money for Eddice from Jeffrey Epstein, who’d pleaded guilty to charges of soliciting a minor for prostitution three years earlier, according to emails made public as part of the Epstein files released by the Department of Justice earlier this year. She emailed Epstein the script for the first of a series of films they hoped to make (title: “American Girl in Paris”).

“We thought you and the ladies might enjoy,” Clark wrote. She then appended a winking warning: “A little nsfw,” an acronym for “not safe for work.” Epstein didn’t invest.

While enormous attention has fallen on AI and the people behind the companies developing it, Clark has largely flown under the radar.

James Padolsey, on the Claude-text-watermarking-to-comply-with-an-EU-regulation imbroglio:

The same thought that led to this law could have applied to calculators at the time of their inception, had their outputs revealed themselves through artefacts. Thankfully, a sum borne of the brain is treated no differently from one produced by a calculator. Likewise with spellcheckers. To make assistance suspect only once the tool becomes capable enough to compose a whole sentence is not a principled boundary. It is a moral premium placed on difficulty itself.

Anthropic has nevertheless chosen a blanket, model-level implementation that appears broader than the law’s minimum requirement. That may be convenient compliance engineering, but it discards distinctions the law expressly attempted to preserve. The result is a signal broad enough to implicate harmless and assistive use, yet fragile enough to be removed by a motivated person through substantial recomposition. It risks concentrating suspicion on ordinary and assistive users while remaining weakest against deliberate deception.

Padolsey is the creator of Declaude, a delightfully simple web app that allows you to “Paste in AI-flavored text and get the same content back as plain prose”. Declaude’s original purpose is cleaning the cutesy Claude personality stink from text (whether it was created by Claude or any other LLM), but, if Anthropic persists in its stated plan to begin adulterating all text Claude generates, Declaude will also serve as a copy-paste single-extra-step way to eliminates those marks. Declaude is interesting and useful already, but goes to show how ill-considered this EU regulation is.

Padolsey also wrote and programmed a splendid interactive essay that explains and illustrated how the “watermarking” scheme Anthropic is adopting (and Google Gemini has already adopted) works.

The Wall Street Journal (gift link):

To help alleviate the supply crunch, Apple is looking to Chinese manufacturers.

“The Trump administration is not in favor of that,” Lutnick said in an interview after touring an Apple manufacturing facility in Houston. There have to be “other solutions to the memory issue, but it’s not great American companies using Chinese memory.”

Asked if he has relayed that message to Apple, Lutnick said “plainly.”

But:

U.S. government rules require American companies to secure a license before sharing product information with CXMT and YMTC. The Chinese chip makers would need such information if they were to make customized chips for Apple. But Apple is free to buy off-the-shelf parts from the companies, and to negotiate prices with them.

Apple’s chief operating officer, Sabih Khan, declined to confirm that Apple was testing Chinese memory chips in an interview Thursday. But he said that given the magnitude of the supply shortage, “we have to look at all options,” including working with existing suppliers to increase U.S. production.

XCancel:

XCancel is an instance of Nitter.

Nitter is a free and open source alternative Twitter front-end focused on privacy and performance. The source is available on GitHub at https://github.com/zedeus/nitter [...]

Using an instance of Nitter (hosted on a VPS for example), you can browse Twitter without JavaScript while retaining your privacy. In addition to respecting your privacy, Nitter is on average around 15 times lighter than Twitter, and in most cases serves pages faster (eg. timelines load 2-4× faster).

I personally don’t care for Elon Musk’s X-rebranded Twitter. I also realize that some of you outright despise it, and, worse, that under Musk, they make it difficult to view content if you’re not signed into an account or if you have JavaScript disabled. So, sometimes, when I link to posts or threads on Twitter/X, I include a link to the same content at XCancel.

XCancel isn’t perfect, alas. Just this week I linked to a tweet from Google Design that contained an animation of an Android app that they were inexplicably proud to show off. The XCancel cache of that tweet only showed it as animated to some people. Others just saw a static image. I don’t know why. That’s XCancel’s problem, not mine.

Also, it’s tiresome and repetitive to include an extra link to XCancel every time I link to a tweet on Twitter/X. If you would prefer to view x.com links on xcancel.com, you should automate the redirection. If you use Safari, XCancel Redirect is a free extension that claims to do just what you think it does. (I haven’t tried it.) Or you can use a general-purpose redirect extension. RedirectWeb is a good one that I can recommend. Also, Jeff Johnson’s excellent StopTheMadness — an extension I rely on for multiple purposes and frequently recommend. With StopTheMadness, you can redirect all x.com URLs to xcancel.com with the following rules (you need the leading and trailing slashes in the pattern to tell STM that it’s a regex pattern, not a plain text string):

Pattern: /https?://(www[.])?x[.]com/
Replacement: https://xcancel.com
Enabled on platforms: All

I understand not wanting to visit Twitter/X. But if you feel that way, I do sympathize, but that’s your decision, and you should use tools to redirect Twitter/X links automatically. And if you’re wondering why I don’t just refuse to link to anything on Twitter/X, that just isn’t practical. I wish it weren’t so, but many people, including senior Apple executives, and many organizations post interesting content exclusively to Twitter/X. If something posted to Twitter/X is also available elsewhere, I link to the elsewhere version. But if it’s exclusively available on Twitter/X and interesting, I link to the original.

My linking to original content on Twitter/X does not perpetuate Twitter/X’s relative popularity. Original content appearing exclusively on Twitter/X does.

My thanks to Drata for sponsoring last week at DF. Their message is short and sweet: Leverage autonomous AI agents to automate compliance, manage internal and third-party risk, and continuously prove your security posture.

Following up on my post yesterday about modern iPhones and scratch resistance, and my personal habits of (a) almost never using an iPhone case, and (b) never setting the iPhone face down except on soft (cloth) surfaces.

I should have anticipated this, because I’ve had this conversation with real-world normies repeatedly in recent years, but I got a bunch of questions from people who say they’re wary of ever putting their iPhone down on its back because they’re worried about scratching the camera lenses. That’s not a silly thing to worry about. It looks like those lenses are glass; glass scratches; and it sure seems like a scratched camera lens might forever ruin all the photos and videos you take with the camera.

There are a couple misconceptions here though. First, the glassy flat circles exposed on the back of your phone aren’t the camera lenses. Those are covers over the lenses, which are smaller, spherical (convex, not flat), and recessed. You can look through the covers and see the actual spherical lenses inside. The exposed lens covers are made of sapphire, not glass, and are thus incredibly scratch-resistant. They’re by far the most scratch-resistant parts of the phone. I’ve never once found even a tiny scratch on any of my iPhone camera lens covers, and I’ve never taken any particular care to avoid scratching them. I mean, I don’t drag the lenses face-down on surfaces. I’m not trying to scratch them. But I set my phones down on hard surfaces lenses-down all the time and they never seem to pick up even fine scratches.

Zack “JerryRigEverything” Nelson is the guy on YouTube who makes videos where he scratches the hell out of products to see how durable they are. (And he bends them, burns them, and abuses them in other gruesomely creative ways.) In his iPhone 17 Pro video, starting around the 4:10 mark, he tries scratching the sapphire lens covers with a sharp razor blade. No effect. Sapphire is far more likely to shatter than scratch, because it’s so hard. (You may recall that a decade ago Apple pursued using sapphire for the displays of iPhones but it didn’t work out.) Don’t try scratching your lenses with a diamond hardness pick and you’ll be fine.

Also, believe it or not, a fine scratch on the lens cover will almost certainly not affect image quality at all. Try sticking a strand of hair (which is probably much thicker than a typical scratch) to the surface of your iPhone 1× main camera lens. Take a picture. Clean the hair off the lens. Retake the same picture. You almost certainly won’t see any difference at all. Here’s a great video from DPReview back in 2020 showing just how much dust or scratching you need to put on the outside of a lens to degrade image quality. It’s counterintuitive but the surface of the lens is not where light is focused — the sensor, inside the camera, is.

This page from Google Design on their “Material 3” UI language came to my attention after my snarky post about the ungainly new to-do app they bizarrely bragged about on Twitter/X this week. I don’t think this “Material 3” page is new — I think it’s a few years old — but I’d never seen it before.

First, it’s crazy that (in desktop browsers with a mouse cursor) they change the I-beam cursor for text selection to ... a circle. I guess it looks kind of fun but the whole point of the I-beam cursor is to enable precise character selection. They changed that to something that makes precise selection as difficult as possible. It’d be better to just use an arrow pointer than to replace the I-beam with a goddamn circle. This, from the UI design team.

Second, this paragraph made me honestly wonder if I was reading a spoof, a years-old unfunny April Fool’s “prank”. But as far as I can tell this is totally straight, not a prank:

These factors can be quantified in users’ responses to new M3 Expressive designs. We found a 32% increase in subculture perception, which indicates that expressive design makes a brand feel more relevant and “in-the-know.” We also saw a 34% boost in modernity, making a brand feel fresh and forward-thinking. On top of that, there was a 30% jump in rebelliousness, suggesting that expressive design positions a brand as bold, innovative, and willing to break from convention.

Nothing says rebellious and “in-the-know” like assigning precise percentages to “rebelliousness”, “modernity”, and “subculture perception”.

Update: Believe it or not there is video of the Google Design team putting this research together. Worth a watch.

Philip Michaels, writing last September for Tom’s Guide:

iPhone 17 torture test videos done by JerryRigEverything indicate that Ceramic Shield 2 certainly resists scratching, with scratch testing leaving only light scratches at level 7 on the Mohs scale of hardness. Scratches typically show up on glass at levels 5 or 6 on that scale.

“Ceramic Shield 2 is indeed the best we’ve ever seen,” JerryRigEverything remarks in the iPhone 17 Pro testing video.

That backs up Apple’s own Ceramic Shield 2 video, in which a mineral tip can be seen rubbing against a Ceramic Shield 2-coated display. There’s residue left on the screen, but it’s material from that tip, as it wipes away fairly easily.

I mentioned yesterday that I try never to place my iPhones face down, to avoid scratches, and that my nearly year-old iPhone 17 Pro seemingly has not one visible scratch on the front glass. Not even a single micro abrasion I can see, even when tilting it with the display off to hunt for them. Surely Apple’s Ceramic Shield 2, which is intended to provide best-ever scratch resistance, is a factor too — and likely the biggest factor. Apple at its best.

A few years ago I’d have looked at this post and maybe leaned toward the idea that a precocious 8th grader somewhere hacked into the @GoogleDesign Twitter account and tried to pass off their little to-do app as having come from Google’s design team. But this is apparently real. I almost hope it’s AI slop and that there aren’t any human designers there who think anything in this app has appropriate proportions or is aesthetically pleasing.

(Here’s an XCancel link for the x.com averse. You can just change any x.com/* URL to xcancel.com/*. But, alas, the XCancel mirror of the original tweet loses the animation showing the UI in motion — for some, but not all, users.)

Joanna Stern, writing at The New Things (gift link):

I used to love the blinking notification light on my BlackBerry, and later my Droid 2. It was a simple way to know I had a message without actually looking at my messages. Then BlackBerry let you customize the color, and it was a rainbow dream.

Google’s HiLight takes it a step further by letting you assign different colors to VIP contacts. So when your phone is face down, you can tell who’s trying to reach you without picking it up. It looks cool. The big bummer? At launch, it only works for phone calls.

Google told me it’s “continuing to invest in this technology” and that messaging notifications are coming. Still, it’s odd they aren’t there at launch, especially since Google found plenty of colors for Gemini. The ring changes hue depending on whether the AI is listening, thinking or responding.

I have no interest in this because a few years ago I stopped ever putting my iPhone on any surface face down. I don’t use a case, so setting the phone face down is a good way to pick up scratches. I stopped doing that, ever, and now I’ve got a nearly year-old iPhone 17 Pro that seemingly doesn’t have even a micro abrasion on the display glass.

And it seems downright goofy that it’s launching with support only for phone calls. Google can’t even launch a blinking light right on these Pixel phones.

David Imel, The Verge:

But in our current moment, the photos people are drawn to are not flawless — and that’s created a real problem for the people making smartphone cameras. “The gap between what two random people want from their camera is growing dramatically,” says Isaac Reynolds, who leads the Pixel camera team at Google. Some people want a perfectly optimized photo, Reynolds says. “But there’s a growing number of people who want something that they feel is more authentic or traditional, by their definition.”

That’s what Reynolds and the Pixel team are setting out to solve with Camera Looks, a total rethink of how the camera captures and styles an image on the Pixel 11 series. The phones include a new suite of processing tools aimed at giving people much more control over the camera’s output. At first glance, they look a lot like filters, with names like Black Tie, Classic, and most strikingly, Digi, in reference to the growing trend of buying cheap digital cameras from the early aughts. But unlike a filter, which generally applies editing tweaks after the processing happens, Camera Looks is changing how image data is processed at the sensor level.

This sounds a lot like Apple’s “Photographic Styles” in the iPhone 13 and later, but with the addition of some control over grain and sharpness. I love grain, and the thing I like least about Apple’s built-in photo processing is the way it attempts to eliminate all noise. I like digital cameras that embrace noise and try to make it pleasing, rather than over-processing to try to pretend these tiny sensors aren’t inherently noisy. So I say kudos to Google for loosening up on “everyone gets the over-sharpened, over-smoothed look”.

But, that said, what’s made me happy over the last year is shooting almost all my photos with third-party apps — in particular, Halide, Not Boring Camera, and Analogue — that give me low-level control over processing through LUTs (or in Halide’s parlance, “looks”). These apps give me the looks I want just through point-and-shoot photography. No need to process images after shooting. But because they (optionally) shoot RAW, I can reprocess in post without losing any original sensor data. I can shoot in black-and-white and subsequently change my mind and reprocess with a color look later. These apps enable honest film-look photography without looking at all gimmicky, and without baking any sort of filtering into the image data in my camera roll. It’s the best of both worlds. It’s also way, way fussier than the built-in Camera app should be.

Anyway, all this Pixel announcement news today has me thinking that these days, I never hear much about Pixel cameras or image processing. In the early years of the Pixel lineup, there was a common view that Pixel phones were the best camera phones in the world. I never believed that, but I did think they were an interesting alternative, taking different but equally valid philosophical choices than Apple. Apple has always insisted on instant processing. You snap the shutter and the iPhone saves your image instantly. Pixel cameras often undertook seconds-long processing. That enabled features the iPhone couldn’t offer.

I think it’s pretty easy to pinpoint when things changed for Pixel — when Marc Levoy left Google for Adobe in 2020 (where he now leads the team making the very intriguing Indigo camera app, that is exclusively available for iPhone and which offers a slew of innovative computational photography features).

When Levoy was leading Google’s camera photography team, there was a credible argument that Pixel phones were the best still camera phones in the world. (They were never even close on video.) Ever since, I never hear about them except in the middle of August each year, when Google announces new Pixel hardware. See you in 12 months.

Ivan Mehta, TechCrunch:

With this year’s Pixel 11 series launch, Google is thinking more agentic AI to complete your tasks. With Gemini, U.S.-based users will be able to order groceries, book rides, or get coffee, for instance. Plus, Gemini can call businesses on users’ behalf for table reservations or appointments. Google said that users can take over or stop tasks at any point in time and also review transcripts for AI-operated calls.

Over the next few weeks, a new slate of connected apps will be able to work with Gemini to get things done, including Granola, Otter.ai, Wix, Fever, Get Your Guide, Localiza, OpenTable (U.K.), Ticketmaster, iHeartRadio, Pandora, Angi, Thumbtack, and Zocdoc.

I’ve never heard of most of those apps, or, if I have heard of them, I thought they went defunct years ago. The one app amongst those I do use, OpenTable, apparently only supports this Gemini integration in the U.K.?

Another knockout year for the Pixel phones and Android.

Sam Rutherford, writing for Engadget:

Granted, the P11 Pro Fold still includes IP68 dust and water resistance, which is better than what you get on the Z Fold 8 line (IP48). But after making an absolute tank of a foldable phone with last year’s model, I said Google really needed to cut weight and thickness this generation, and it just hasn’t. Instead, it seems like Google has leaned even more into the Pro Fold’s sturdiness with a new glass fibre material for its rear panel that the company says is nearly impossible to crack. This is great in theory, but obviously I wasn’t in a position to smash one of Google’s demo units in order to test that claim.

I have never seen anyone, anywhere, with a folding Pixel phone. Pretty sure I won’t this year either.

I played with a Pixel 10 Fold or whatever its name was last year at the Google Store on Newbury street in Boston, which store felt like a small library or museum. The Pixel Fold display was so plasticky that I had a moment where I wondered if the display units were fake props, like at Ikea. But no, it was the real thing. And it cost $1,800 to start.

Victoria Song, The Verge:

The $399 Google Pixel Watch 5 isn’t about the hardware. Sure, there’s a new satin pyrite case finish, a few new strap colors, and a Steph Curry Special Edition. Under the hood, there’s a slightly faster Qualcomm processor and an itty-bitty battery bump. There’s a $50 price hike from last year, too, because the Pixel Watch 5 isn’t immune to RAMageddon — none of us are. Otherwise, no one would blame you for looking at this watch and thinking absolutely nothing’s changed. That’s because the big updates this year are all software-based.

I have never seen anyone in real life wearing a Pixel Watch. (I’ve only seen a handful of people using Pixel phones, and every single one of them was at a tech event of some sort.)

You can also use AI to generate watchfaces. I created a watchface featuring “fat cats and an elegant font.” It didn’t quite do that. The fat cats weren’t characters, so much as pleasantly plump, cat-shaped clock numbers. It’s a gimmicky way to shoehorn the Nano Banana model onto the watch… but if that’s your thing, it works.

Well I’m sure now I’ll see plenty of people wearing Pixel Watches.

Mia Sato, The Verge:

Earlier this summer, Amazon customers began noticing that emails related to their online orders looked sparse: Order confirmation emails didn’t name specific items anymore, and instead listed only item categories.

“Your Beauty item is confirmed!” an email about my retainer cleaning tablets read. Shoppers have posted other iterations of the redacted emails as well: “Ordered: 1 Hardware item,” “Your Drugstore, Shoes, and other items are here!” and “1 Nutrition & Wellness, 1 Wireless Accessories,” for example. The emails have clip art-style illustrations of general product categories, and a shopper has to exit their email and go to Amazon to see what item it’s actually referring to.

Complaints about the vague emails appear to have started in July; orders placed as recently as June contain thumbnails and names of the exact item purchased. [...]

“As customers shop with us more frequently, including on their phones, and use the ‘Your Orders’ page in the Amazon app to get real-time, consolidated order details and delivery status, we’ve simplified several order-related emails to direct customers to our app and website for the latest information on their orders,” spokesperson Maxine Tagay said in an email. “This also reduces customer information shared outside the Amazon app and website to further improve customer privacy.”

That statement from Amazon is almost honest, if you squint at it and ignore the bit about it having anything to do with “simplification”. If you order a Torx screwdriver, what’s simple is getting a confirmation email with the exact name, brand, and photo of the item you ordered, not “a hardware item”. The honest part is “reduces customer information shared outside the Amazon app”.

The fact is, a lot of people use apps like Gmail for email. In fact, a lot of people use Gmail in particular. There’s a good chance you do. When you use Gmail, Google learns everything in your email. Amazon sees this as a competitive problem for them, not a privacy problem for users. Everyone who uses Gmail basically knows that Google “knows” what’s in their email, and they’re voluntarily agreeing to that. Amazon just doesn’t like that any of its competitors — perhaps especially Google — can glean so much information about what people are buying from Amazon. So Amazon, in an act of competitive spite that corrupts its relationship with its own customers, is limiting all useful purchase information to channels that it controls — its website and its own apps.

If customers relied solely on the “Your Orders” page at Amazon, they wouldn’t need to send any emails at all. The obvious fact is, people go to Amazon when they want to buy something. They don’t come back until they want to buy something else. They enjoy getting confirmation and shipping notifications in email because they check their email regularly.

Jeff Johnson:

These purported reviews, all but one of which is 5 stars, are all in English. Yet none of them is in the US App Store. I also looked at other English-speaking countries such as Australia and Canada but found no reviews. Also suspicious is that the name of every reviewer was a first name followed by an initial for the last name. If you’ve ever looked through App Store user reviews, it’s unusual to find this naming format. In most cases, the name on App Store user reviews is a uniquely chosen username, with no space character, often suffixed by a number.

The final and most conclusive evidence that these reviews are fake, totally invented out of thin air by the developer (or more likely, by an LLM), is that the dates on the reviews, such as “Mar 12,” predate the release of TabControl Extension in the App Store!

God only knows what this obvious AI slop scam is doing with the data from users’ Safari tabs. It’s currently the 12th or 13th most popular Safari extension in the App Store.

Watch this. No words needed, everyone can feel it. Genes are a hell of a thing.

Basic Apple Guy:

Apple just dropped the hardest icon glow-up for the new Chess icon in macOS 27 Beta 5.

This is not just a tweak or small improvement. This is the difference between total shit and a really nice icon.

The Economist (‘twas a gift link, but alas, I guess gift views have been used up — here’s an archive link in case the gift link is vexing you):

You can discover AI’s hallmarks by comparing the writing of man and machine. To do this you need a baseline that is distinctive and familiar. The Economist turned to prose that we’re sure is human and that readers will recognise: our own. We designed a study to ask top LLMs — OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini and xAI’s Grok — to write versions of our articles without consulting the web. (As a prompt, we gave them the AI-generated summaries that we have experimentally added to some of our articles.)

This gave us a corpus of human and AI creations and we compared them across 55,940 sentences and 1.2m words. To make sure we were detecting AI quirks rather than our own, we also checked the AI texts against journalism from CNN, the New York Times and the Washington Post. Excerpts from hit novels published between 1950 and 2022 offered another test.

Our findings are surprising. AI prose is distinguishable by word and punctuation choice as well as sentence and paragraph structure. But its hallmarks are not what you might expect, partly because its writing style has changed with software updates. That does not mean that LLMs are great writers: their prose lacks lucidity and elegance and is often formulaic. So those aspiring to be impressive (human) storytellers should avoid the following peculiarities in their own prose.

On point for today.

Some specific findings:

Much of this language could be described as what George Orwell called “pretentious diction”. He railed against writers who “dress up simple statements” with complicated words and jargon to sound clever. Such pontificating penmen, Orwell observed, also believe that “Latin or Greek words are grander than Saxon ones”. (Bots agree: more Latinate suffixes crop up in their writing than in human texts.)

Then look at punctuation. Many believe LLMs stuff their prose with em-dashes, but that is not true after the most recent updates. Today only Claude uses more em-dashes than human writers, with ChatGPT using markedly fewer than any other writer in our study. Humans rejoice — and start using dashes again.

A better way to spot AI-generated writing would be to look for texts without much punctuation at all. LLMs are very Joycean about it: they use fewer commas and semicolons than humans (and hardly any parentheses). They use less punctuation in part because they write longer sentences — “and” is their most overused word — and in part because they do not quote experts.