China and Uber, the US's Cookie Monsters

TLDR;

Welcome back! This week, we have a long list of interesting news to talk about. From China’s latest SOTA model to the US’s next great small model, we also tackle NVIDIA and Microsoft super events, Uber clamping down on AI costs, Bernie Sanders’ latest AI rant, and many more.

Enjoy!

MODELS

Minimax M3, New Chinese SOTA?

Minimax has released a new model that, on paper, is competitive with the best the US has to offer. Across several benchmarks, it holds its own against Opus 4.7 or GPT-5.5, alongside Opus 4.8, the bleeding edge of the industry.

Despite being considerably smaller than its rivals (at least ten times smaller), it competes and offers a massive one-million-context window, which suggests this model punches well above its weight class.

Alibaba also released a model, Qwen 3.7-Plus, but I believe M3 is the most interesting model here.

I wrote about it in way more detail here, but the crucial thing to highlight is that, much like all other Chinese Large Language Models (LLMs), they perform some sort of compression over context.

This stems from the fact that, while you might not remember what you had for dinner three days ago, you can recall decades-old childhood memories with ease, because the human brain is opinionated about what’s “worth remembering”.

However, this is extremely complicated to do with AIs, to the point that we largely avoid doing so if we have the compute means to avoid it.

This results in models that do not compress context; if you send them an entire 50-page report, they’ll store in context every single word in it, which means that an LLM’s context grows proportional to its length (and in a quadratic fashion, actually; tripling sequence length nine-folds the computational requirements).

Chinese Labs, much more compute-constrained, are forced to innovate in this regard, and they do so by forcing this context compression.

But how?

Say you’re reading a 100,000-word book and you want to predict what will happen in the next few hundred words. To do so, you’ll probably bear in mind what has recently occurred, while also taking into account a summarized view of what happened in earlier chapters. You don’t remember every single thing that happened, only those things your brain considers important.

But if we give an AI a 100,000-word sequence to predict the 100,001st word, the model will store all previous 100,000 words. Not a single one is ignored. Even if a word is ‘uhm’ or ‘ehhh’, they are also attended to.

This is as if, for you to decide what to eat today, you considered not only what you ate recently, but also what you ate a year ago from today, while also taking into consideration yesterday’s debate with your husband about which cushion color works best on your sofa.

This seems dumb, but it’s exactly what is going on; all options are considered.

To handle this, Minimax proposes a “hardware-aware” compression. The context gets considerably compressed, in the same way other Chinese models…

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