Here's my take as someone who was using Claude everyday for every little thing and now I've deleted my account because I didn't like what it was doing to my mind.
What goes totally unmentioned in the article is that this feature is designed to help mitigate dark usage patterns. My major concern with chatbots is excessive usage can lead to AI psychosis or negative rumination (depression). A feature that makes user's more self-aware of their usage patterns is a good thing. Making the user more self-aware is a necessary first step that will precede any kind of intervention to reduce their reliance.
Where this fails is it frames the intervention as a moderation problem. It may seem counterintuitive, but moderation takes more self-control than elimination. If you struggle with your relationship to LLMs, then every time you make a choice whether or not to engage with a LLM is an opportunity to struggle. The more you struggle, the worse you feel.
Obviously Anthropic cannot advocate for churning from their product. The psychological stickiness of their product is its primary selling point for investors. When they say "set quiet hours and breaks" it frames this as a user problem. Just get good bruh, it's not that the technology hallucinates or is sycophantic, or basically designed to be a AI girlfriend / boyfriend, it's a skill issue. Rather than a technology being applied incorrectly and a company floundering to hook users before they try to jack up the prices to stay solvent.
I find the "AI Fluency" course particularly ridiculous.
> Build AI skills that support your original thinking
This is a straight-up lie. When you outsource your thinking to Claude then your ability to produce original thought degrades. The whole framework for using LLMs is the sort of thing you see from tech bros on Twitter trying to sell online courses. It reminds me of the intellectual yet idiot essay by Nassim Taleb[1]. Don't let Anthropic tell you how to think under the guise of doom trolling[2] and tech bro "thinking frameworks". Think for yourself!
Thanks for this. I've been trying to learn this writing thing on top of my full-time job and it's been a struggle. It's helpful to hear how you managed it.
The "hiding from researchers" framing is particularly bad. The parsimonious explanation for why a model produces different outputs when it detects eval contexts: eval contexts appear differently in the training distribution and the model learned different output patterns for them. No theory of mind required. Occam's razor.
The agentic behaviors emerge from optimization pressure plus tool access plus a long context window. Interesting engineering. Not intent.
People are falling for yet another Anthropic PR stunt.
I see a lot of people are confused about the electricity claim so I'll elaborate on it more. The assumption I'm making here is that on device people will run smaller models, that can fit on their machines without needing to buy new computers. If everyone ran inference on their machine there would be no need for these massive datacenters which use huge quantities of electricity. It would utilize the machines they already have and the electricity they're already using.
People are making a comparison of the cost per inference or token or whatever and saying datacenters are more efficient which makes obvious sense. What i'm saying is if we eliminate the need for building out dozens of gigawatt datacenters completely then we would use less electricity. I feel like this makes intuitive sense. People are getting lost in the details about cost per inference, and performance on different models.
LLMs on device is the future. It's more secure and solves the problem of too much demand for inference compared to data center supply, it also would use less electricity. It's just a matter of getting the performance good enough. Most users don't need frontier model performance.
I don't think this idea could work. There's this common misconception that our brains control our bodies, like how software can control hardware. The fact is that our brains are intrinsically connected to the rest of our body: via the central nervous system, sensory, and motor neurons. You can't just swap out our brains. It's integrated with the rest of our body in a fundamental way. If you cloned someone, the neuronal connections between the CNS and organs would not be the same, because these interconnections develop over a lifetime and are not predetermined at birth.
It also feels super unethical to me. Reminds me of "Never let me go" by Kazuo Ishiguro.
While it does not seem to include inference done on your local computer, to me it feels like a precursor for doing so.
To my mind, inference at the edge is what will kill inference in the datacenter. Inference at the edge is more secure, faster, and uses less electricity. People share vulnerable and personal info in their chats, why share it with OpenAI who will use it to sell ads?
In a world where most of inference being done at the edge, what do we need all of these data centers for? You may say we need them to continue pre-training even bigger models. And yet, pre-training models has hit a performance plateau.
Inference in a data center never made sense. It's such a massive investment of resources when we're all carrying around computers in our pockets. As someone who values my privacy, I will start doing inference on device exclusively as soon as possible.
Brandon Sanderson often says in interviews that "laying bricks" is the best job a writer can have. He also says being a software engineer is particularly bad job for writers because you cannot do it on autopilot. I can confirm.
Back then, all jobs moved at a much slower pace. There was a lot more off time during work hours.
The number of submissions to high energy physics category on arXiv is double this year compared to the historical average. The author hypothesizes the increase is due to papers being written by LLMs.
What goes totally unmentioned in the article is that this feature is designed to help mitigate dark usage patterns. My major concern with chatbots is excessive usage can lead to AI psychosis or negative rumination (depression). A feature that makes user's more self-aware of their usage patterns is a good thing. Making the user more self-aware is a necessary first step that will precede any kind of intervention to reduce their reliance.
Where this fails is it frames the intervention as a moderation problem. It may seem counterintuitive, but moderation takes more self-control than elimination. If you struggle with your relationship to LLMs, then every time you make a choice whether or not to engage with a LLM is an opportunity to struggle. The more you struggle, the worse you feel.
Obviously Anthropic cannot advocate for churning from their product. The psychological stickiness of their product is its primary selling point for investors. When they say "set quiet hours and breaks" it frames this as a user problem. Just get good bruh, it's not that the technology hallucinates or is sycophantic, or basically designed to be a AI girlfriend / boyfriend, it's a skill issue. Rather than a technology being applied incorrectly and a company floundering to hook users before they try to jack up the prices to stay solvent.
I find the "AI Fluency" course particularly ridiculous.
> Build AI skills that support your original thinking
This is a straight-up lie. When you outsource your thinking to Claude then your ability to produce original thought degrades. The whole framework for using LLMs is the sort of thing you see from tech bros on Twitter trying to sell online courses. It reminds me of the intellectual yet idiot essay by Nassim Taleb[1]. Don't let Anthropic tell you how to think under the guise of doom trolling[2] and tech bro "thinking frameworks". Think for yourself!
[1] https://nassimtaleb.org/2016/09/intellectual-yet-idiot/
[2] https://www.nytimes.com/2026/06/17/opinion/ai-dangerous-open...