IBM doubles transistor density through vertical stacking at 0.7 nanometers and expects up to 70 percent energy savings — production readiness in approximately five years.
Autonomous AI agents require observability platforms that make decision-making fully traceable, display costs transparently, and enforce defined action boundaries.
AI agents rarely cite non-existent sources, but link to incorrect papers in 15.9% of cases and stop using tools at exactly the point where they would be most critical for difficult questions.
Output compression reduces inference costs by 1.4–3x, while input compression increases them by an average of 1.15x because models respond to imprecise prompts with longer answers.
iLLaDA demonstrates that fully bidirectional diffusion training from scratch can be a competitive path to strong language models, even without autoregressive training.
AI agents can be trained as data scientists to automatically generate high-quality synthetic training data, which continuously improves through meta-optimization.
Agentic Overlays are thin wrapper layers that convert REST-APIs into A2A-capable agents without code duplication, eliminating the need for parallel infrastructures.
Governance for agentic AI requires access control at every level – from tool discovery through query execution to response synthesis – not just at a single central checkpoint like in RAG.
