At the World Economic Forum in Davos, technology leaders agreed that 2026 will mark the transition of artificial intelligence from pilot projects to large-scale deployment. The speed and safety of this shift will depend on three factors: access to energy, cybersecurity, and companies’ ability to integrate AI with critical decision-making systems.
Medical records just became the hottest commodity in Silicon Valley. In a stunning 72-hour span this January, OpenAI and Anthropic launched competing platforms that transform fragmented health data into AI-powered personal health advisors, signaling a digital colonization of America’s healthcare system. While regulators spent a decade mandating interoperability through the 21st Century Cures Act, tech giants are exploiting the resulting data floodgates, deploying sophisticated language models to ingest, analyze, and monetize patient information at unprecedented scale.
Have we traded medical privacy for the convenience of conversational health intelligence?
An analysis of the memory market indicates that in 2026 data centers — especially those powering artificial intelligence and large-scale computing platforms — could absorb as much as 70% of global production of DRAM and related memory chips. This raises the risk of serious shortages of this critical component across other technology sectors and consumer markets.
The United States has announced a 25% tariff on advanced artificial intelligence processors, including Nvidia’s H200 AI chips and AMD’s competing MI325X accelerators, that are shipped to China and other countries via U.S. territory or otherwise fall under the new trade measures. The decision has already been signed by President Donald Trump.
AI in pharma and life sciences is an easy “sell,” yet delivering real value remains a formidable challenge. Progress is most often hampered by closed data, weak data sharing, and inflated expectations. In this interview with Marcin Wawryszczuk (PhD, MBA) — Head of AI at Andersen and an AI researcher — we discuss how data platforms are becoming the bottleneck, whether it is possible to distinguish a viable project from a mere demo, and why, in production, resilience to drift and explainability of results outweigh impressive metrics.
The Anthropic Economic Index indicates that the pace and nature of artificial intelligence adoption still differ markedly between countries, potentially widening existing economic and social inequalities worldwide. The data show that, despite the rapid development and spread of AI tools, their use in the workplace is strongly correlated with income levels and employment structures in individual countries, reinforcing the advantage of wealthier economies over less affluent ones.
The biggest barrier to deploying artificial intelligence tools in the UK’s NHS stems from problems integrating them with electronic patient record (EPR/EHR) systems, according to a new report by the Royal College of Physicians (RCP). The survey of 541 RCP members shows that a lack of interoperability between EPR systems is the key factor preventing clinicians from using AI effectively in everyday practice.
The genre of the LLM interview emerged the moment the first model was released to the public. Since then, “artificial intelligence” has been asked to prophesy the future, debate the philosophical nature of being, or simply engage in heart-to-hearts. This creates the illusion of conversation with a sentient being — a phenomenon that simultaneously frightens, astonishes, and inspires awe. Yet, we have never encountered an interview where the AI is addressed honestly, with its mask of humanity stripped away.
Microsoft is testing a new feature in Windows 11 that could bring its AI assistant, Copilot, directly into the File Explorer application, according to leaks from the latest preview builds of the operating system.
Nvidia and U.S. pharmaceutical giant Eli Lilly have announced the creation of a joint research laboratory in San Francisco, with a planned investment of $1 billion over the next five years.