AI models are getting cheaper to run, but the real cost of artificial intelligence begins after deployment. From model drift and retraining to human oversight and the hidden cost of poor decisions, this article examines what it really takes to keep AI useful, and whether continuous learning could change the economics.

AI systems can become less reliable after deployment. These nine companies are developing different ways to detect drift, diagnose performance decay and help production AI respond.

Semiconductor shares have fallen sharply as investors reassess the financing behind the AI infrastructure boom and question whether rapidly rising expenditure will produce sustainable returns.

AMD's planned investment of up to $5 billion in Anthropic marks more than a funding deal. It reflects a growing shift towards strategic AI infrastructure partnerships, as chipmakers and frontier AI developers compete to secure the computing power needed for the next generation of artificial intelligence.

Midjourney has built a working full-body ultrasound prototype, but its 60-second scan remains a target. Nuvastra examines the hardware, AI, regulatory position and missing clinical evidence.

AI systems are beginning to learn from experience after deployment, but current benchmarks cannot always distinguish genuine improvement from memory retrieval, overfitting or hidden regression.

Nine startups are competing to make AI less static, using persistent memory, production feedback, reinforcement learning and new architectures designed to improve after deployment.

AI founder Tim Gülke explains why the next generation of artificial intelligence must move beyond static models. In this interview, the Wakeline founder explores continuous learning, adaptive intelligence and the ideas pushing the boundaries of global AI technology.