Cloud
AI adoption helps sustain confidence in the mainframe
The latest BMC survey shows that 94% of mainframe professionals and decision-makers expect long-term investment in mainframe technology, with AI tool adoption as a top priority (45%), and over a third plan to invest in in-house or third-party agents. The industry is shifting from AI experimentation to trusted operations, with mainframes seen as part of AI infrastructure, a younger talent structure, and security and privacy concerns reinforcing their position.
Google rolls out flexible billing, cost controls for AI agents
In a blog post on Wednesday, Google said it will provide enterprises with new ways to manage AI costs by expanding flexible billing options, AI agent cost controls, and including developer platforms in the Gemini Enterprise subscription.
Legacy IT forces enterprises to delay AI projects
Cloud data management provider Cloudera surveyed 1,500 global enterprise architects, data architects, and cloud infrastructure leads, finding that legacy architecture is failing to meet AI demands. Nearly three-quarters of enterprises say their infrastructure needs revamping, and most have delayed or canceled AI initiatives due to governance and compliance challenges. Hybrid-first strategies are gaining traction, with 1 in 4 enterprises prioritizing them over the next two years.
AI infrastructure spending shifts in latest sign of deployment maturity
According to the latest Gartner report, global spending on AI-optimized infrastructure as a service (IaaS) is projected to grow 96% to $42 billion in 2026, with inference spending ($23.3 billion) exceeding model training spending ($19 billion) for the first time. This shift indicates that enterprise AI deployment is moving from the experimental phase to production at scale, with inference workloads becoming mainstream.
Meta launches coding agent Muse Code in latest enterprise AI push
Meta released its terminal programming agent Muse Code (beta) on Wednesday, aiming to improve software engineering task efficiency, and simultaneously upgraded its foundational model Muse Spark. This move is Meta's latest effort in the enterprise AI space, as the company seeks to monetize AI through increased infrastructure investment. Executives raised the full-year capital expenditure forecast to $130 billion during the earnings call and are considering renting out data center computing power.