Responsible AI Foundation

Image on Unsplash There is a problem with training AI on AI-generated data. The problem is that it works too well for too long, and then it stops working altogether. A researcher named Ilia Shumailov at Google DeepMind published a paper showing what happens when you feed an AI model its own output repeatedly. In the beginning, the output looks fine, but after a few iterations, it starts to degrade. After five or six generations, trained purely on its own data, the model conve

Image on Unsplash When tech companies announce AI breakthroughs, they show you the models, the capability gains, the benchmark scores and the roadmap to AGI. What they do not show you is the $7 trillion that goes behind it.  That is the estimated cost to build out the data center infrastructure needed to support AI through 2030 ( McKinsey, 2025 ). Alphabet alone is planning to spend $91-93 billion in 2025 on infrastructure, primarily for AI. Microsoft spent $34.9 billion in c

Image on Unsplash AI models are performing better on standardized tests than humans ever have, yet researchers keep finding they fail at tasks a person could solve in seconds. Google’s Gemini 3 just scored 37.4 on Humanity’s Last Exam, a benchmark designed to measure human-level reasoning ( Business Today, 2025 ).  OpenAI’s o1 and Anthropic’s Sonnet 4.5 follow similar patterns, each releasing impressive benchmark scores that dominate headlines. The pattern feels inevitable. M