For all the progress in AI, one problem continues to slow everything down. Cost. Training models gets the attention, but running them at scale is where the real expense shows
A startup raising massive funding with a tiny team usually signals hype. In this case, it signals something more interesting. A growing belief that the current path of AI may
AI agents are no longer experimental—they’re active participants inside modern companies. They handle workflows, respond to customers, and even make decisions. But once multiple agents start working together, things break
A new category of AI tools is emerging—ones that don’t just assist users, but actively take action on their behalf. These systems are designed to manage everyday digital tasks, reducing
The next phase of artificial intelligence is moving beyond software and into the physical world. Companies are increasingly combining AI with physics-based simulation to design, test, and deploy real-world systems
Enterprises moving AI systems from experimentation to production have long faced a tradeoff. Flexible, model-agnostic frameworks offered adaptability but often underutilized advanced model capabilities. On the other hand, model-specific SDKs
Meta built its reputation in AI partly on openness. Now, with its latest model, the company is taking a very different direction—one that prioritizes control over community access. From open
As AI assistants become more capable of taking real-world actions, companies are deliberately holding them back. Instead of pushing for full autonomy, the focus is shifting toward controlled, step-by-step execution.
Small, powerful AI models are forcing enterprises to rethink how they approach governance and security. What once worked in a cloud-first world is quickly becoming outdated as AI shifts closer