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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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: 90.18

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: 86.02

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: 84.47

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: 81.97

venturebeat
5% GPU utilization: The $401 billion AI infrastructure problem enterprises can't keep ignoring

For the last 24 months, one narrative justified every over-provisioned data center and bloated IT budget: the GPU scramble. Silicon was the new oil, and H100s traded like contraband. Reserve capacity [...]

Match Score: 76.35

venturebeat
Claude’s next enterprise battle is not models: it’s the agent control plane

New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI [...]

Match Score: 76.14

venturebeat
How DeepSeek’s radical architecture is shattering Silicon Valley's token moat

DeepSeek’s announcement over the weekend that it has made its 75% price cut permanent on its flagship V4 Pro model is a disruptive assault on the capital-heavy business models of Silicon Valley’s [...]

Match Score: 75.76

venturebeat
Mistral AI launches Workflows, a Temporal-powered orchestration engine already running millions of daily executions

Mistral AI, the Paris-based artificial intelligence company valued at €11.7 billion ($13.8 billion), today released Workflows in public preview — a production-grade orchestration layer designed to [...]

Match Score: 71.06

venturebeat
GitHub leads the enterprise, Claude leads the pack—Cursor’s speed can’t close

In the race to deploy generative AI for coding, the fastest tools are not winning enterprise deals. A new VentureBeat analysis, combining a comprehensive survey of 86 engineering teams with our own ha [...]

Match Score: 63.30