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Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority intend to switch or add providers within the year, many within a quarter. Buying decisions turn on integration and total cost of ownership rather than headline token price — which is fortunate, because most enterprises cannot yet see their unit economics clearly: GPUs sit at half utilization or less, and fewer than half rigorously track what their compute actually costs. The result is a compute gap — heavy, fast-moving investment running ahead of the visibility needed to control it. This wave of VentureBeat Pulse Research examines enterprise AI infrastructure and compute: where organizations are in their deployment journey, what they run AI on today, how satisfied they are, what would make them switch, where they plan to evaluate their investments, and — most revealingly — how well they can measure and control the economics of the compute underneath it all. The central finding is a compute gap — the distance between how aggressively enterprises are investing in AI infrastructure and how little of its economics they can see. Only about one in five (21%) run AI in production at scale, yet spending intentions are outrunning that maturity: the single largest planned area enterprises plan to evaluate over the next year is AI-specialized clouds (45%), a layer almost none of these enterprises use today. Meanwhile the compute already in place runs cold — 83% report GPU utilization of 50% or less — and fewer than half (44%) can rigorously track what their AI compute costs. Enterprises are buying more infrastructure faster than they can account for what they already own. Enterprises are not settled on their infrastructure vendors, either: A clear majority (64%) plan to sw

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Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context source, and provider-native retrieval has quietly overtaken the dedicated vector databases that define the category — yet a majority of enterprises have already watched their agents produce confident, wrong answers traced to missing or inconsistent context. A governed semantic layer is emerging as the fix, but most are still building it; the field is converging on hybrid retrieval; and even as provider-native tools lead in practice, a plurality say they intend to keep best-of-breed. The result is a context gap — agents that sound authoritative running on a foundation their owners do not yet fully trust. This wave of VentureBeat Pulse Research examines the enterprise RAG and context layer: what feeds AI agents their business context, which retrieval systems enterprises run, how they buy and measure them, where the architecture is heading, and — most revealingly — how often that context is already failing them. The central finding is a context gap — the distance between how confidently enterprise agents answer and how reliable the context beneath them actually is. A majority of enterprises (57%) report that in the past six months their AI agents produced confident but wrong answers they traced to missing or inconsistent business context, and more than half of those said it happened more than once. This is not a fringe failure: retrieval is the primary context source for 38% of enterprises, more than any other approach, so when retrieval is thin or inconsistent, the errors it produces are wearing the agent’s authority. The infrastructure to fix it is being built — 58% already run or are building a governed semantic layer — but for most it is not yet in production. Underneath, the market is consolidating in a direction that surprises. Provider-native retrieval — OpenAI’s file se

Beehiiv is launching an AI Copilot to help publishers with user growth and analytics.

Today, the European Union ordered Google to give its AI rivals greater access to Android, the open-source operating system that powers billions of devices worldwide. The demand is hardly surprising. It may look like a defeat on paper for Google, which has spent years resisting exactly this kind of access, but it is a regulatory […]

Disclaimer: NO AI bullshit here. We are in April 2024, I am working in Mexico City and a friend of mine contacts me. She is an architect and she works in Merida. Her and her colleague would like to have a solution to analyze architectural cadastral documents. She told me that she called friend for Mexico and USA and they all say it is impossible. Interested by the challenge I said: "Hold my beer" and I made a POC in 75h of intensive work. However, it was not enough. They had other requirements and getting to this was much more complicated. Complicated but still not impossible. As a Data Scientist, I started to develop a full solution (mostly Computer Vision) to read and analyze automatically specific cadastral documents. I decided to continue working on the project. Hours and hours. There was no money involved. Only challenge. Thanks to that I was able to do everything with the time I wanted. Putting all the passion in my side-project. With time the project grew and grew. But I was prepared for this. I made every part of the code separable, isolated. 6 repositories: aitt-core: core backend + Streamlit app -> Docker image deployment on ECR aitt-core-deploy: service deployment on the server via Ansible aitt-symbol-clf: ML + API -> Docker image deployment on ECR aitt-symbol-clf-deploy: service deployment on the server via Ansible 5 .aitt-infra: Terraform on AWS aitt-platform: Vue.js frontend (private repo) I finally came to a solution for what they needed in more than 625 hours of work. My work contract ended in 2025 in Mexico so I came back to France (I am French, yes I love pain - joke for bilingual). This project made me realized several things: I am capable of doing great things. We all are. The only thing that matters is discipline. This is not some sort of personal development bullshit. This is an advice for life. Einstein said: "I have no special talents. I am only passionately curious." Time is a powerful thing . With time and dedication you can achieve great t

Hey HN, we’re Xia and Varun, the founders of Traceforce ( https://www.traceforce.ai/ ). Traceforce provides visibility and control over AI apps such as ChatGPT, Claude etc directly on all devices (laptops, sandboxes, virtual machines) by discovering not just which apps are being used but also how they are connected to other data sources via MCPs. We also have an open-source dynamic MCP pentesting tool https://github.com/traceforce/mcp-xray to detect vulnerable MCPs. The purpose of Traceforce is

Roblox is about to let people make games with AI right inside its mobile app, which could make a platform that's already filled with content of questionable quality feel even more overloaded. The company has embraced AI with open arms, including a preview of an ambitious take on AI world models similar to Google's Project […]

Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap — the distance between how much autonomy enterprises are handing their agents and how far they trust the tests that are supposed to catch the failures. This wave of VentureBeat Pulse Research examines how technical leaders measure agent performance: which reliability and evaluation platforms they use, how they select and trust them, what breaks in production, and how far they are willing to let agents run without a human in the loop. The central finding is an evaluation gap — the distance between the autonomy enterprises are granting their agents and the trust they place in the evaluations meant to govern it. Half of organizations (50%) have, in the past year, deployed an agent or LLM feature that passed their internal evaluations and then caused a customer-facing failure, and a quarter have seen it happen more than once. Trust in the tests themselves is thin: only 5% say they fully trust automated evaluation today, and the single most-cited limitation is that evaluations align poorly with real-world outcomes (29%). Enterprises are discovering that a passing eval is not the same as a working agent. What makes the gap consequential is the direction of travel. Two-thirds of organizations (66%) already permit fully automated, zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to allow it within twelve months (33%). At the same tim

X will use Grok AI to better detect stolen content, redirect payouts to original creators, and crack down on engagement bait.

You can now make playlists, design flyers and compile shopping lists using AI in Search.

Travel agency Fora announced a $60 million Series D round led by Forerunner and Tactile Ventures, valuing the company at $1 billion.