enterprise ai5 articles
Half a Billion Dollars in One Month: What Happens When Nobody Watches the AI Tab
An unnamed company reportedly spent $500 million on Anthropic's Claude in a single month after failing to set usage limits on its AI licenses, highlighting how quickly enterprise AI costs can spiral out of control. Broader industry examples, such as employees using AI to check the weather or misusing large models for simple tasks, point to widespread inefficiency in how companies deploy AI tools. Experts argue that businesses need greater internal AI expertise, better model selection, and smarter usage controls to manage costs and ensure quality outcomes.
Cloud Bare Metal Is Now Undercutting On-Prem on Price and Availability, Says Nutanix CEO
Nutanix CEO Rajiv Ramaswami says hyperscalers' bulk purchasing power means bare metal cloud servers are now often cheaper and more readily available than on-premises hardware, pushing some traditionally on-prem customers toward the cloud. However, he noted a countertrend of customers favouring on-prem infrastructure for AI workloads to keep costs predictable, as ROI on AI remains uncertain. Nutanix reported Q3 2026 revenue of $703 million, a 10% year-on-year increase, and added 730 new customers, many migrating from VMware.
Trajectory Wants to Give AI Products a Memory. Can It Deliver?
A group of former researchers from Google DeepMind, Apple, OpenAI, and Meta have launched a startup called Trajectory, which aims to build a platform enabling AI products to continuously learn and improve from real-world user interactions. The company has raised $15 million in seed funding and already works with AI-native clients like Clay and Harvey, using post-training techniques to update models as frequently as weekly based on instances where the AI falls short. While critics note this falls short of true continual learning in the traditional sense, Trajectory's founders argue it represents an early step toward AI that could eventually update in real time — every hour or even every interaction.
Who Owns Your AI? The Sovereignty Question Enterprises Can No Longer Ignore
Enterprises that rushed to adopt third-party AI tools are now reconsidering the trade-off between capability and control, as concerns grow about losing proprietary data and competitive advantage to external providers. This has sparked a broad movement toward **AI and data sovereignty** — building independent control over models and data infrastructure rather than relying on centralised cloud providers. A survey of over 2,050 senior executives by EDB found that 70% believe a sovereign data and AI platform is essential to their success.
GPT-5.5 and Codex Are Inside NVIDIA Already. Here's What That Actually Means.
OpenAI's Codex coding application, now powered by GPT-5.5, runs on NVIDIA's GB200 NVL72 rack-scale systems, delivering dramatically faster and cheaper AI inference. Over 10,000 NVIDIA employees across various departments are already using it, reporting significant productivity gains such as debugging cycles shrinking from days to hours and weeks-long projects completing overnight. The rollout reflects a decade-long partnership between NVIDIA and OpenAI, with OpenAI committing to deploy over 10 gigawatts of NVIDIA systems for future AI infrastructure.