I have been wondering if it's more geared at reducing resource usage, given that at the moment there's a known constraint on AI datacenter expansion capability. Perhaps they are struggling to meet demand?

100% this, I’ve posted the same sentiment here on HN. I hate the chilling effect of the bans and the lack of clarity on what is and is not allowed.

it's a bug only if they get a harsh public response, otherwise it becomes a feature

I've used it with a sub a lot. Concurrency of 40 writing descriptions of thousands of images, running for hours on sonnet.

I have a lot of complaints. I've cancelled my $200 subscription and when it runs out in a few days I'll have to find something else.

But claude -p is fine.

... Or it was 2 week ago. Who knows if they've silently throttled it by now?

It kind of reminds me of the joke where a plumber charges $500 for a 5 minute visit. When the client complains the plumber says it's $50 for labor and $450 for knowing how to fix the problem.

It's pretty clear that OpenAI has consistently used bots on social networks to peddle their products. This could just be the next iteration, mass spreading lies about Anthropic to get people to flock back to their own products.

That would explain why a lot of users in the comments of those posts are claiming that they don't see any changes to limits.

I'm on the enterprise team plan so a decent amount of usage.

In March I could use Opus all day and it was getting great results.

Since the last week of March and into April, I've had sessions where I maxed out session usage under 2 hours and it got stuck in overthinking loops, multiple turns of realising the same thing, dozens of paragraphs of "But wait, actually I need to do x" with slight variations of the same realisation.

This is not the 'thinking effort' setting in claude code, I noticed this happening across multiple sessions with the same thinking effort settings, there was clearly some underlying change that was not published that made the model get stuck in thinking loops more for longer and more often without any escape hatch to stop and prompt the user for additional steering if it gets stuck.

It is not in the interests for Anthropic to screw its customer base. Running a frontier lab comes with tradeoffs between training, inference and other areas.

You will be backstabbed

You will be squeezed for all they can.

And you will be betrayed.

> Phase N: People can't afford computing anymore. Everything is thin clients and rented. It's become like the private railroad industry. End of the PC era. Like kids growing up on smartphones, there's nothing to tinker with anymore. And certainly no gradient for entrepreneurship for once-skilled labor capital.

Thankfully none of them actually makes money and just runs on investment so there is a good chance bubble will drop and the price of PC equipment will... continue to rise as US gives up Taiwan to China

I've been using GLM for over 6 months and pretty happy.

I think it has something to do with mode collapse (although Claude certainly has its own "tells"), but I'm not sure.

It sounds trivial but even for Agentic, I found the writing style to be really important. When you give Claude a persona, it sounds like the thing. When you give GPT a persona, it sounds like GPT half-assedly pretending to be the thing.

---

Some other interesting points about Anthropic's models. I don't know if any of these relate to my LLM style question, but seems worth mentioning:

Claude models also use way less tokens for the same task (on ArtificialAnalysis, they are a clear outlier on this metric).

And there's a much stronger common sense, subjectively. (Not sure if we have a good way to actually measure that, though.) It takes context and common sense into account, to a much greater degree.

(Which ties in with their constitution. Understanding why things are wrong at a deeper level, rather than just surface level pattern matching.)

Opus is great but it should be bigger. You notice the difference between Sonnet and Opus, but with heavy use you notice Opus's limitations, too.

It all boils down to a brilliant but extremely expensive technology. Both to build and to run.

We've been sold a product with heavy subsidy. The idea (from Sam) scale out and see what happens.

Those who care to read between the lines can see what's happening. A perfect storm of demand that attract VCs who can't understand they are the real customers. Once they understand that it will be too late.

Regarding open weight models: eventually we will, as humanity, benefit from the astronomical capital poured into developing a technology ahead of its time. In a few years this and even more will run on edge.

Written by open source developers, likely former openai and anthropic employees who got so much cash in the bank they don't need to worry about renting their knowledge.

If you're objective it to democratize AI, sure. But for those fed up with it and the devastating effects it's having on students, for example, can opt to actively avoid paying for products with AI (I say this as someone who uses it every day, guilty). At some point large companies will see that they're bleeding money for something that most people don't seem to want, and cancel those $100k/mo deals. I've already experienced one AI-developer-turned company crash and burn.

Personally, I don't think this LLM-based AI generation will have any significant positive impacts. Time, energy (CO2) and money would have been far better spent elsewhere.

This one seems too far fetched. Training models is widespread. There will always be open weight models in some form, and if we assume there will be some advancements in architecture, I bet you could also run them on much leaner devices. Even today you can run models on Raspberry Pis. I don't see a reason this will stop being a thing, there will be plenty of ways to tinker.

However, keep in mind the masses don't care about tinkering and never have. People want a ChatGPT experience, not a pytorch experience. In essence this is true for all tech products, not just AI.