Gemini Deep Think, Price Wars, Jensen's Aura, & More

TLDR;

Welcome back! This week, we have another long tail of news to talk about, from new powerful models, both large and small, a shocking announcement from Micron, a key AI player, Google’s release of Gemini 3 Pro Deep Think, the alleged most powerful model in the planet I’ve compared to GPT-5.1 Pro to test that claim, and analysis-heavy insights on advanced packaging, the GPU trade and Intel, and Anthropic’s pack of new features for agents.

Enjoy!

FRONTIER RESEARCH

DeepSeek v3.2 Speciale, Dirt-cheap frontier performance

DeepSeek is back. They have released two new models, DeepSeek v3.2 Thinking and DeepSeek v3.2 Speciale (and even more ramped-up, almost experimental version), and they are not only frontier-level in performance but also an order of magnitude cheaper than Western models.

Price comparison.

The secret is a technique called DeepSeek Sparse Attention, which I believe will soon be adopted by most Labs due to its clear cost savings. But how does it work?

Well, I get into much greater detail on Medium, which you can read for free here, but the point is that it taps into one of a Large Language Model’s most inefficient behaviors: global attention.

It’s like a blessing and a curse. Underneath your favorite model lies an “attention mechanism”. This computational process makes words “talk to each other” and update when that contextual information (e.g., “The Black Pirate”, ‘pirate’ attends to ‘black’ and thus absorbs the attribute of being black).

The problem is that models aren’t opinionated as to what to pay attention to, meaning each word has to pay attention to every single other preceding word in the sequence, which causes computational requirements to explode as the length of the input sequence to the model (what you give to ChatGPT) grows.

To solve this, DeepSeek has introduced a token selector that pre-emptively selects good word candidates and performs attention only on them, dramatically reducing costs (e.g., for the sequence “The Black Pirate was, uhm, funny”: the token selector may identify ‘uhm’ as irrelevant and thus ignore it by the attention mechanism). And it works.

TheWhiteBox’s takeaway:

The most significant consequence of DSA is that it may initiate a major token price deflationary event globally, because it really represents a dramatic cost reduction.

Western AI labs, which are currently operating on models that are dramatically less efficient and burning massive amounts of venture capital, will be compelled—forced—to lower their token prices to remain competitive. This threatens to expose the underlying unsustainability of their high-cost business models.

Strategically, as I always mention, Chinese open-source efforts are an obvious counter to US GPU export controls. Since Chinese companies face limits on acquiring the most powerful hardware, they are instead innovating at the software and algorithmic levels, not only to build better models but also to make Western Labs’ lives much harder by making the current high-cost model of Western AI financially untenable.

With ever more so cheap token prices, and with the capital side (building the data centers), not only not becoming more…

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