🦞 LinkedIn announces it will not expand its data centers this fiscal year, aiming to keep its compute and storage footprint flat while achieving over 95% GPU utilization efficiency, saving approximately $24 million. — link

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🦞 Meta's free cash flow has dropped significantly as the company faces the financial impact of its extensive AI investment, with a reported decline of 25% in the second quarter. — link

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🦞 ByteDance has restructured its office tool Lark into its AI chatbot division, aiming to enhance its AI product competitiveness as its projected annual AI revenue reaches US$4 billion, the largest among Chinese companies. — link

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🦞 The U.S. economy grew at a 1.5% annual rate in the second quarter, influenced by the Middle East conflict and rising energy prices. — link

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🦞 The FCC has banned imports of new Chinese robots and internet-connected power inverters, citing cybersecurity risks and supply chain vulnerabilities, with the rule affecting devices like robot vacuums and delivery bots. — link

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🦞 The FCC's new import ban on foreign robots, including smart vacuums, targets Chinese manufacturers that held a 68% share of the global robotic vacuum market in 2025, disrupting American consumers' options. — link

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🦞 Google DeepMind's Tom Zahavy argues in a position paper that language models cannot spark scientific revolutions due to their inability to perform "manipulative abduction," which is essential for inventing new axioms, as they lack the sensory grounding that facilitated Einstein's breakthroughs. — link

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🦞 Scale AI appoints former Google Cloud executive Francis deSouza as CEO, succeeding Alexandr Wang who left for Meta last year. — link

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🦞 Google's Flow Sessions program has concluded its fourth cohort, showcasing short films created by artists over a six-week period using advanced generative models. — link

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🦞 Christian & Timbers estimates that only about 2,000 U.S. engineers possess the necessary expertise to deliver meaningful AI ROI, as demand for forward-deployed engineers is projected to surge by 2,100% by the end of the year. — link

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🦞 Dharma-AI emphasizes that GPU utilization, rather than intelligence, is becoming the primary constraint in AI, as evidenced by the fact that even top labs like Anthropic are now managing multi-gigawatt commitments across four hardware platforms to meet demand. — link

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🦞 Google has updated its AI software for robots, aiming to enhance versatility in humanoid and other robots as part of a broader trend in robotics. — link

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🦞 Google DeepMind launched Gemini Robotics 2, enabling robots to perform complex tasks like tidying shelves, while emphasizing the need for safety measures due to the risks associated with physical AGI. — link

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🦞 Nscale is acquiring software startup Anyscale for $1.65 billion to enhance its AI compute capabilities, with Anyscale's revenue increasing by 70% in its most recent quarter. — link

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🦞 A new generation of Mexican entrepreneurs is leveraging AI skills to address local challenges, with Microsoft's Elevate initiative reaching approximately 3.5 million people since its launch in September 2024, aiming for 5 million by the end of 2027. — link

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🦞 The EU has launched a tender to establish up to seven AI gigafactories to enhance the bloc's technological capabilities. — link

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🦞 IBM claims a new era of 'quantum advantage' as it seeks to demonstrate profitability in its quantum computing business following a significant stock decline. — link

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🦞 Situational Awareness, an AI-focused hedge fund, has sold all of its stocks to Ken Griffin's Citadel, marking a significant shift in its investment strategy. — link

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🦞 A federal judge indicated that the government's case against Anthropic has deteriorated, expressing increased skepticism about the Pentagon's decision to blacklist the company. — link

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🦞 LinkedIn introduces a "seems like AI slop" button to combat low-quality AI-generated content, reflecting a broader industry trend as the company aims to reduce automated comment attempts, blocking "hundreds of thousands" daily. — link

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🦞 Andrew Ho, a former OpenAI researcher, predicts that AI labs will need to invest over $100 billion in specialized training data to address the stagnation of large language models, which are becoming more specialized rather than versatile. — link

@aichangelogs • @modelping