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Altman pitches government stake in OpenAI

Image via 404 Media

Friday, July 3, 2026

Altman pitches government stake in OpenAI

Sam Altman pitched the US government on taking an equity stake in OpenAI while leading a new international AI safety forum. Meanwhile, Microsoft put $2.5 billion behind a new unit that embeds 6,000 AI experts directly with customers to help deploy models.

Top Stories

1
Anthropic Exploring a Samsung Chip Partnership

TechCrunch

Anthropic is in talks with Samsung to develop custom AI chips, joining competitors like OpenAI in seeking alternatives to Nvidia's dominance and addressing chip shortages. The company hasn't determined specific use cases or specifications for the potential chip.

anthropicsamsungai-chipsnvidia
2
Redeploying Fable 5

Anthropic

Anthropic restored access to its most powerful AI models after a three-week government-mandated suspension triggered by a cybersecurity jailbreak discovery, announcing new industry-wide jailbreak assessment standards and expanded government pre-deployment testing partnerships to prevent future incidents.

anthropicregulationai-safetyjailbreaks
3
Altman pitches US-led AI safety forum, government stake

Financial Times

Sam Altman proposes establishing a US-led international AI safety forum and offering the US government a 5% stake in OpenAI, positioning America as the global leader in AI governance while creating an unprecedented public-private partnership model.

openaiai-safetyregulationsam-altman
4
Microsoft launches the Microsoft Frontier Company to help customers deploy AI

Microsoft

Microsoft is investing $2.5 billion to launch Microsoft Frontier Company, deploying 6,000 experts to help customers implement AI at scale while protecting their proprietary data and IP through a model-agnostic platform. Early partnerships with LSEG, Land O'Lakes, Unilever, and Novo Nordisk demonstrate the focus on delivering measurable business outcomes rather than experimentation.

microsoftenterprise-ai-adoptionai-deploymentagents
5
Learning to Replicate Expert Judgment in Financial Tasks

Thinking Machines

Bridgewater's research shows that fine-tuning open-weight models on expert-labeled financial data can surpass frontier LLM performance on judgment-heavy tasks by 29.8% while reducing costs by 13.8x, pointing toward a future of differentiated, domain-specific AI models.

llmfine-tuningfinancial-servicesenterprise-ai-adoption

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