Agreed. I do think the metaphor still holds though.

A financial jackknifing of the AI industry seems to be one very plausible outcome as these promises/expectations of the AI companies starts meeting reality.

Not a great metric, research in academia doesn't necessarily translate to value. In the US they've poached so many academics because of how much value they directly translate to.

Volume is easy: they have far more people, it is quality that counts.

DeepSeek 3.2 is frankly fairly close to that. GLM 4.7 as well. They're basically around Sonnet 4 level.

I think the least politically divisive issue within the US is concern about China’s growth as it directly threatens the US’s ability to set the world’s agenda. It may be politically divisive if you are aligned with Chinese interests but I don’t see anything politically divisive for a US audience. I expect Chinese CEOs speak in similar terms to a Chinese audience in terms of making sure they’re decoupled from the now unstable US political machine.

From the perspective of competing against China in terms of AI the argument against open models makes sense to me. It’s a terrible problem to have really. Ideally we should all be able to work together in the sandbox towards a better tomorrow but thats not reality.

I prefer to have more open models. On the other hand China closes up their open models once they start to show a competitive edge.

I mean, there's no way it's about this right?

Being critical of favorable actions towards a rival country shouldn't be divisive, and if it is, well, I don't think the problem is in the criticism.

Also the link doesn't mention open source? From a google search, he doesn't seem to care much for it.

I'm in the same boat. Sonnet was overkill for me, and GLM is cheap and smart enough to spit out boilerplate and FFMPEG commands whenever it's asked.

$20/month is a bit of an insane ask when the most valuable thing Anthropic makes is the free Claude Code CLI.

I've used a bunch of the SOTA models (via my work's Windsurf subscription) for HTML/CSS/JS stuff over the past few months. Mind you, I am not a web developer, these are just internal and personal projects.

My experience is that all of the models seem to do a decent job of writing a whole application from scratch, up to a certain point of complexity. But as soon as you ask them for non-trivial modifications and bugfixes, they _usually_ go deep into rationalized rabbit holes into nowhere.

I burned through a lot of credits to try them all and Gemini tended to work the best for the things I was doing. But as always, YMMV.

Amazingly, just yesterday, I had Opus 4.5 crap itself extensively on a fairly simple problem -- it was trying to override a column with an aggregation function while also using it in a group-by without referring to the original column by its full qualified name prefixed with the table -- and in typical Claude fashion it assembled an entire abstraction layer to try and hide the problem under, before finally giving up, deleting the column, and smugly informing me I didn't need it anyway.

That evening, for kicks, I brought the problem to GLM 4.7 Flash (Flash!) and it one-shot the right solution.

It's not apples to apples, because when it comes down to it LLMs are statistical token extruders, and it's a lot easier to extrude the likely tokens from an isolated query than from a whole workspace that's already been messed up somewhat by said LLM. That, and data is not the plural of anecdote. But still, I'm easily amused, and this amused me. (I haven't otherwise pushed GLM 4.7 much and I don't have a strong opinion about about it.)

But seriously, given the consistent pattern of knitting ever larger carpets to sweep errors under that Claude seems to exhibit over and over instead of identifying and addressing root causes, I'm curious what the codebases of people who use it a lot look like.