venturebeat
Scaling AI into production is forcing a rethink of enterprise infrastructure

Presented by NutanixAcross industries, organizations are focused on how to move from AI pilots, proofs of concept, and cloud-based experimentation to deploying it at scale — across real workloads, for real users, in real business environments. VentureBeat spoke with Tarkan Maner, president and chief commercial officer at Nutanix, and Thomas Cornely, EVP of product management, about what that transition demands, and what it will take to get it right.“AI in general is shifting everything we do, not only in technology, but across all vertical industries, from regulated industries like banking, health care, government, education to non-regulated industries like manufacturing and retail,” Maner said. “As a complete platform company, we welcome this change. It’s creating more opportuni [...]

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venturebeat
As enterprises confront AI agent sprawl, xpander wants them to own their own control and context layer

Enterprise AI has a new infrastructure problem: companies are accumulating agents faster than they are developing systems to govern them.Gartner estimates that the average global Fortune 500 company w [...]

Match Score: 117.53

venturebeat
Enterprise AI agents are only as reliable as the messiest documents behind them

Enterprise AI has largely been built around context engineering. Teams connect enterprise systems, generate chunks and embeddings, build retrieval pipelines, and assemble the context needed by individ [...]

Match Score: 93.89

venturebeat
Vercel rebuilt v0 to tackle the 90% problem: Connecting AI-generated code to existing production infrastructure, not prototypes

Before Claude Code wrote its first line of code, Vercel was already in the vibe coding space with its v0 service.The basic idea behind the original v0, which launched in 2024, was essentially to be ve [...]

Match Score: 77.50

venturebeat
Train-to-Test scaling explained: How to optimize your end-to-end AI compute budget for inference

The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use inference-tim [...]

Match Score: 75.36

venturebeat
Nimble claims its new, domain-specialized Web Search Agents cut token costs in half while boosting retrieval accuracy

Nimble, a New York City-based tech startup VentureBeat previously covered for its efforts to re-invent web search for enterprises by using multiple AI agents to improve accuracy and depth, is taking a [...]

Match Score: 75.18

venturebeat
Enterprises put non-Nvidia chips 14 points ahead of Nvidia's next-gen GPUs on their evaluation lists

When enterprise buyers build out their next AI accelerator evaluation list this cycle, they're more likely to put a non-Nvidia chip on it than Nvidia's own next-generation GPU. According to [...]

Match Score: 73.89

venturebeat
Perplexity takes its ‘Computer’ AI agent into the enterprise, taking aim at Microsoft and Salesforce

Perplexity, the AI-powered search company valued at $20 billion, announced on Wednesday at its inaugural Ask 2026 developer conference that its multi-model AI agent, Computer, is now available to ente [...]

Match Score: 73.87

venturebeat
The Agentic Reckoning: Enterprise AI organizations have a runtime problem, not a model problem — and most are building the wrong solution

In Q1 2026, VentureBeat's Pulse Research surfaced the “Governance Mirage”: the gap between the governance org charts enterprises had drawn and the control layers they had actually built. Fort [...]

Match Score: 72.41

venturebeat
Alibaba's AI video model rises to No. 2 in global rankings, as OpenAI's Sora and ByteDance's Seedance fall away

Alibaba Cloud on Sunday released HappyHorse 1.1, a major upgrade to its AI video generation model that the company says delivers production-ready video synthesis across core content creation scenarios [...]

Match Score: 70.14