zar1048576

I think we are in largely uncharted territory here, especially given the implications. Is Anthropic's approach optimal? Probably not. But given the stakes involved, gating access seems like a reasonable place to start.

I'm curious about how gated access actually holds over time, especially given that historically with dual-use capabilities containment tends to erode, whether through leaks, independent rediscovery, or gradual normalization of access.

I think that concern is valid in general terms, but it’s not clear to me that it applies here.

The goal here seems to be removing low-value output; e.g., sycophancy, prompt restatement, formatting noise, etc., which is different than suppressing useful reasoning. In that case shorter outputs do not necessarily mean worse answers.

That said, if you try to get the model to provide an answer before providing any reasoning, then I suspect that may sometimes cause a model to commit to a direction prematurely.

Have had similar issues with costs sometimes being all over the map. I suspect that the major providers will figure this out as it’s an important consideration in the enterprise setting

My sense is that the asymmetry is non-trivial issue here. In particular, a threat actor needs one working path, defenders need to close all of them. In practice, patching velocity is bounded by release cycles, QA issues / regression risk, and a potentially large number of codebases that need to be looked at.

I definitely agree w/ you that big organizations are generally better able to navigate the enterprise sales process, but mainy trying to say that customers might choose to work with a bigger company's products for reasons that typically go way beyond that (e.g., better integrations, support resources, etc.).

My sense is that if a threat actor were able to build a quantum computer to the scale of being able to compromise public-key primitives based on the difficulty of integer factorization and discrete logarithms under the key sizes used in practice today, one of the highest-valued targets will be Bitcoin.

I wonder if there will be a different phenomena — namely everyone just developing their own personal version of what they want rather than relying on what someone else built. Nowadays, if the core functionality is straightforward enough, I find that I just end up building it myself so I can tailor it to my exact needs. It takes less time than trying to understand and adapt someone else’s code base, especially if it’s (mostly) AI generated and contains a great deal of code slop.

Nice weekend project! Even though there are copious resources out there (textbooks, videos, etc.), those may not appeal to everyone. People have different preferred modalities for consuming information and there is always value in (correctly) reframing concepts in a way that can be better understood by people who don’t resonate with traditional textbooks and YouTube videos. I’m glad you found a formulation that works for you, and judging by the number of upvotes, it resonated with others as well. At the very least, I’m sure that working on this improved your understanding as well!

Definitely miss those!

I don’t know if that’s necessarily true. I do think that a big part of enterprise sales involves building a comprehensive solution that works well within the customer’s ecosystem. Start-ups usually tend to build point products, which have value, but are still missing functionality (even if that functionality is not scintillating) that customers really desire to easily deploy and maintain solutions. Also, customers do care about things like stability of their vendors and the level of available support.

I agree with this. What you focus on depends on the circumstances. I believe PaulG likes to say that premature optimization is the root of all evil. Early on, you’re trying to ship and get a functioning product out the door — if spending a bit of money on extra RAM at that time helps you, it’s worth it. Over time, as you are trying to optimize, it makes sense to think more about memory management, etc.

I suspect the big jump came from the release of Claude Opus 4.5/4.6 and GPT-5.x-Codex between Nov ‘25 and Feb ‘26, which were trained with heavy reinforcement learning on long coding projects, rewarding only real success (like running code, using terminals, self-fixing bugs, and passing tests) while adding better memory for huge codebases and extra coding-specific training.