Hospitals did not adopt artificial intelligence in a single, deliberate moment. It arrived in pieces: an imaging algorithm, a documentation assistant, a staffing forecast, a patient-message generator, a denial prediction model, a scheduling tool. Each product entered through a different department, answered a different need, and was evaluated against a different budget. That fragmented path made adoption feel manageable. It also concealed what hospitals were actually taking on. An AI product is… [...]
New York City Health and Hospitals, the largest public healthcare system in the United States, has disclosed that hackers stole personal data, medical records, and biometric information, including [...]
Lowering the cost of inference is typically a combination of hardware and software. A new analysis released Thursday by Nvidia details how four leading inference providers are reporting 4x to 10x redu [...]
The UK AI Security Institute (AISI) disclosed last night that the leading two frontier AI models from Anthropic and OpenAI took 19 unsanctioned actions against the live internet during cybersecurity t [...]
Danish startup Teton has used Gefion, one of Europe’s most powerful supercomputers, to turbocharge the development of its AI “care companion” for hospital staff. Teton installs cameras and sen [...]
Agentic AI is now a core part of the engineering process, driving massive execution leverage and helping us generate more code than ever before. Yet, a difficult question I’ve increasingly heard fro [...]