Today's digest examines the intersection of geopolitical strategy, hardware innovation, and the persistent challenges of algorithmic bias and safety.

1. OpenAI is scared of open-weight models. Should the US be?

Debates over restricting foreign open-weight models highlight the tension between protecting national interests and maintaining competitive AI business models.

_{Source: TechCrunch AI}

2. Google is working on a new AI chip designed to make Gemini more efficient

Google is developing custom silicon to optimize the computational efficiency of its Gemini model architecture.

_{Source: TechCrunch AI}

3. AI is more likely than humans to form biases when hiring

Recent findings indicate that automated hiring systems may exhibit significant biases, potentially surpassing human recruiters in discriminatory screening practices.

_{Source: MIT Tech Review}

4. Safety and alignment in an era of long-horizon models

OpenAI's research into long-horizon models emphasizes the necessity of iterative deployment to identify and mitigate emerging safety risks.

_{Source: OpenAI}

5. Who's afraid of Chinese models?

Geopolitical concerns regarding Chinese AI development are fueling internal divisions among policymakers and industry leaders over regulatory strategy.

_{Source: Hacker News}

6. The Download: AI hiring biases, and weather data sabotage

Automated recruitment tools risk perpetuating systemic inequality by encoding human and data-driven biases into the hiring process.

_{Source: MIT Tech Review}

7. NVIDIA data center hardware is being cooled with water 'hotter than a hot tub'

NVIDIA is utilizing high-temperature liquid cooling systems to manage the thermal output of modern data center hardware.

_{Source: Engadget}

8. China’s AI models have Trump’s AI world at war with itself

Conflicting perspectives within the current administration regarding Chinese AI capabilities are creating instability for domestic technology firms.

_{Source: MIT Tech Review}

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