126 stories
Token economics are becoming a defining measure of artificial intelligence infrastructure efficiency. As AI workloads move beyond training, the economics of producing useful intelligence are starting to shape infrastructure decisions.
Artificial intelligence is usually discussed as software, but its limits are increasingly physical. Every model depends on processors housed in data centres, continuous electricity, cooling systems, fibre connections and engineers who can keep the equipment running.
Microsoft is turning Fabric, its integrated data platform, into the place enterprise agents go to learn how a company works, whether The post Microsoft Fabric is where AI agents learn how the business works appeared first on The New Stack.
Founded by Apache Flink veterans, the startup is taking on workflow heavyweight Temporal.
Taiwan-based neocloud GMI Cloud Inc. wants to capitalize on what it says is an “unprecedented” demand for artificial intelligence computing power after raising a massive $663 million in funding from two sources.
If you rely too heavily on one AI provider, switching can mean rebuilding workloads and moving data while you face The post Eclipse wants companies to be free to switch AI providers. Today, doing so can mean a costly rebuild. appeared first on The New Stack.
Perplexity has released Photon, an in-house retrieval and ranking engine written in Rust. It replaces an open-source engine Perplexity had forked for its AI-native search stack. Photon now handles retrieval and ranking for all production traffic.
Video world models can render convincing clips that still break physics. Butter spreads like paint. Balls pass through walls. A team from NVIDIA, MIT and the University of Oxford argues the fix can come from language itself, not from extra visual, latent or numerical signals.
Liquid AI has released d1, a decision model built for structured choices instead of text generation. You give it context and a set of typed questions. It returns calibrated probabilities across a fixed set of outcomes in a single call, with zero generated tokens.
Nvidia Corp. is extending the data processing unit from infrastructure offload to a broader security role across the artificial intelligence factory. The opportunity is to make agentic AI safer to operate at scale.
As director of HPE Labs, Andrew Wheeler (pictured) is uniquely positioned to observe rapid advancements in artificial intelligence and new developments for the emerging field of quantum computing.
Getting AI into production has become the real test for enterprises. As the proof-of-concept phase ends, harder questions about cost, data and control are taking its place.
OpenAI just introduced dots at their DevDay today. Dots are persistent AI agents powered by GPT-6 Astra. Each dot gets its own cloud computer and browser. It works across 4,000+ apps through ChatGPT plugins and keeps going after you log off. Is it deployable today? Yes, as a managed product.
Nebius and NVIDIA are running the 2026 Physical AI Awards for startups with products in the field. Five category winners each get $150,000 in compute credits, joint promotion, executive mentorship, and seats at an executive dinner. Applications close October 25.
Fireworks Research launched Ember-1 on September 23 as a research preview. Built on Moonshot’s open-weight model, Kimi K3, it claims The post Ember-1 vs. Kimi K3: Nearly identical results at 3.4 times the speed appeared first on The New Stack.
Google Cloud AI Research has open-sourced RRSI, a framework that lets LLM agents rewrite their own prompts, tools and memory while model weights stay frozen. It adds a leakage critic, a noise floor, a cost rule and pruning so gains carry over to new tasks.
H Company has released Holo4, a family of generalist computer-use models for AI agents. One set of weights clicks and types on screens. It also writes code and calls MCP or API tools. Holo4 ships in 2 sizes: Holo4 27B (dense) and Holo4 35B-A3B (Mixture of Experts, 3B active).
The enterprise artificial intelligence conversation is moving past models and compute. As companies push projects into production, the harder problem is increasingly what sits underneath them: getting distributed, messy and often sensitive enterprise data ready for systems that need reliable…
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