Organizations are rethinking hybrid cloud around data gravity, governance and AI economics, and as AI scales, workload ...

The line between art and digital tools has become extremely thin. Painters work with neural networks, sculptors prototype in ...

Future conflict will stress sustainment systems in contested and distributed environments. Aviation units will require rapid ...

Old-school security playbooks can't handle AI glitches, so companies need a fresh approach to catch model errors and legal ...

Enterprise AI is at an inflection point. What began with centralized, cloud-scale large language models (LLMs) is moving ...

As artificial intelligence continues its rapid expansion, the industry's greatest challenge is no longer building more capable models-it is building the infrastructure required to run them efficiently ...

AI demand is colliding with two hard constraints: grid capacity and local politics around hyperscale. Our industry’s most ...

Meta has provided a rare glimpse into the company's storage infrastructure, claiming the system underpinning its AI ...

The next phase of AI infrastructure will not be defined by a single destination called “the cloud” or “the edge.” ...

DeepReinforce today released Ornith-1.0, a family of open-source coding models built around a mechanism most RL-trained agents avoid: the model itself writes the training harness that guides its own ...

Running AI is totally draining Earth's power grids, so your company's next data center might actually be launched into space.

Training a foundation LLM from scratch costs millions and requires internet-scale data — which is why most enterprises don't bother. Sapient thinks it has a cheaper path. To overcome this brute-force ...