Google🤝Apple, TSMC Makes History, & More

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THEWHITEBOX

TLDR;

Welcome back! In this issue, we cover AI’s leap into genuine scientific discovery, with GPT-5.2 Pro solving historic math problems and Stanford’s SleepFM predicting diseases from brain waves.

We also track the massive flow of capital into hardware and infrastructure, highlighted by TSMC’s 35% profit surge and Anthropic’s $350 billion target valuation. Finally, we analyze Google’s aggressive "Gemini everywhere" strategy, including its deep integration into Gmail and a landmark deal to power Apple’s Siri, among many other relevant news items you can’t miss.

This is a long but very interesting one. Enjoy!

HEALTHCARE

Using Sleep to Identify Illnesses

Researchers from Stanford University have published a study in Nature Medicine describing SleepFM, a multimodal foundation model trained on polysomnography (PSG) data.

The model was developed using 585,000 hours of sleep recordings from approximately 65,000 participants across multiple cohorts. PSG captures brain waves (EEG), heart activity (ECG), breathing, muscle tone, eye movements, and other signals during a full night's sleep.

On standard sleep tasks (e.g., sleep staging and apnea detection), it performs competitively with or better than specialized models, and when linked to participants' electronic health records (with follow-up up to 25 years and predictions made only for future diagnoses), SleepFM estimated risk for over 130 diseases.

These predictions outperform models based only on demographics (age, sex, etc.) for many outcomes. The authors note that PSG populations differ from the general public, and the model identifies associations rather than causation. It remains a research tool, with potential future adaptation to simpler devices, such as wearables.

TheWhiteBox’s takeaway:

This work is very interesting because it reminds us, once again, that not everything has to be about Large Language Model (LLM) chatbots and that simple applications of AI can already offer substantial benefits to society.

Interestingly, the underlying mechanism is similar to that of LLMs, as SleepFM also relies on the attention mechanism to process and find patterns in data. Just like ChatGPT uses attention to capture meaning in words and sentences, SleepFM uses it to capture hidden patterns in sensory data.

Also, it reminds us that one of the best opportunities in AI is pattern-matching across vast amounts of data and modalities, which is particularly valuable for healthcare use cases. While we don’t know if we will build a machine as smart as a human, this pattern-matching ability is already life-saving and worth investing in.

THEWHITEBOX

Google Launches UCP

Google has announced the release of the Universal Commerce Protocol (UCP), an open standard meant to give “agentic commerce” a common language, so AI agents and commerce systems can work together across the full shopping flow (discovery, buying, and post-purchase support) without every retailer needing a custom integration for each agent.

It has been developed by Google in partnership with prominent retail companies such as Walmart, Shopify, Etsy, Target, and a long tail of others, ensuring immediate integration.

Moreover, they have released Business Agent, a shopper-facing conversational layer that Google is adding to Search: shoppers can chat with a…

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