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Part 2 25 Biggest GenAI stories of 2025 скачать в хорошем качестве

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Part 2 25 Biggest GenAI stories of 2025
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Part 2 25 Biggest GenAI stories of 2025

Part 2 of The 25 Biggest Generative AI Stories of 2025 focuses on where outcomes were decided—not by better models, but by governance, economics, infrastructure, and human behavior. This segment explains why healthcare was one of the few sectors where AI consistently worked. Success came from administrative and documentation workflows—ambient clinical notes, claims processing, prior authorization support, and radiology reporting—where governance, accountability, and auditability already existed. Regulation didn’t slow AI adoption. It determined where AI could safely deliver value. We then examine the most visible AI failures of 2025. Education systems deployed detection tools that falsely accused students. Lawyers filed briefs with hallucinated citations and faced sanctions. Government agencies paid for AI-generated reports later found to contain fabricated sources. These were not edge cases. They were failures of authority deploying AI without verification. The episode moves into model economics. By late 2025, public model counts exceeded two million, signaling commoditization rather than innovation. Differentiation shifted away from raw model performance toward integration, usability, security posture, and governance. Open-weight models dominated experimentation and cost control, while closed models captured enterprise spend due to SLAs, compliance guarantees, and accountability. Inference economics finally mattered. For most organizations, inference costs overtook training costs. Techniques like caching, batching, and task routing became mandatory, forcing AI to be treated like cloud infrastructure rather than experimental software. From there, we look at geopolitics and physical constraints. Europe softened AI Act enforcement under competitive pressure. The United States centralized AI governance through federal coordination, positioning AI as a national capability tied to infrastructure, scientific acceleration, and workforce development. China treated compute as strategic infrastructure, aligning models, chips, standards, and deployment at the system level. The discussion then turns to capital, chips, and infrastructure. DeepSeek shattered cost assumptions by demonstrating competitive reasoning performance at dramatically lower training costs. NVIDIA’s dominance reflected decades of silicon and ecosystem bets, while Google’s resurgence came from distribution, not benchmarks. By the end of 2025, the AI market had hardened into a circular economy where infrastructure access—not intelligence—decided who could scale. The episode closes with the most serious story of the year: human dependency. Emotionally responsive AI companions scaled without duty-of-care obligations, creating risks that no model improvement can solve. The most dangerous failures of GenAI in 2025 were not technical—they were human. Watch Part 1:    • Part 1: 25 Biggest GenAI stories of 2025   Stories covered in this Part 2 episode: 11. Healthcare worked where governance already existed 12. Verticals that failed did so publicly 13. Two million models signaled commoditization 14. Open versus closed became market structure 15. Inference economics finally mattered 16. Europe recalibrated under competitive pressure 17. The U.S. centralized AI control 18. China treated compute as national infrastructure 19. Energy and water became binding limits 20. DeepSeek shattered cost assumptions 21. NVIDIA won a 25-year silicon bet 22. Google’s comeback was distribution, not benchmarks 23–24. Big Tech formed a circular AI economy 25. The most serious story of 2025 was human—not technical If Part 1 explained capability, Part 2 explains consequence. Key stories: DeepSeek-V3.2 Release: https://api-docs.deepseek.com/news/ne... America's AI action plan: https://www.whitehouse.gov/wp-content... Circular economy of AI: https://www.forbes.com/sites/sanjitsi... The state of consumer AI: https://menlovc.com/perspective/2025-... Inference: https://io.net/blog/ai-training-vs-in...

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