InformationWeek

Nvidia puts $105 billion behind new data center; Anthropic walks back an enterprise data retention policy; Pennsylvania sets new data center requirements.

How can CIOs lead their businesses through rising, unpredictable AI costs and build budgets that deliver value?

Macroeconomic trends, increased use of AI and geopolitical uncertainty are among the drivers of layoffs at tech companies this year. Follow InformationWeek's tracker for how the tech job environment is faring in 2026.

Per-seat pricing worked when humans were your only users. Now AI agents outnumber employees 25-to-1. The traditional software budget model is breaking, writes one CEO.

As AI models evolve on unpredictable timelines, CIOs must rethink how they plan investments, design applications and manage testing over time.

AI is about more than individual efficiencies, writes an IBM CIO. Organizations need to consider how automating cross-team processes can unlock strategic growth and reduce operational drag.

The QA process for application deployment isn't keeping up with the speed of AI coding. Here are five steps to speeding up QA in the AI era.

At a recent AICamp meetup, practitioners from FICO, Diagrid and StoneX discussed managing AI agents in financial services.

New research finds CIOs and CTOs increasingly responsible for AI as enterprises report measurable results and move beyond experimentation.

AI means different things in different contexts. An IT pro suggests companies define terms like LLMs, agentic systems and automation before investing in tools.

Nvidia makes $500 billion AI infrastructure push; Anthropic adds a watermark to Claude-generated content; Oracle announces quantum partnership with Quantinuum.

AI agents transform financial operations while creating security blind spots. The sector needs visibility and control over trusted activity, writes one CISO.

The road to vendor diversification is paved with promise — unless it falls victim to the multi-cloud AI trap.

AI-assisted coding is making software faster to produce but harder to confidently evaluate, attracting capital to code review and testing companies while forcing enterprises to rethink the broader development lifecycle.

AI code generation is hitting fundamental limits on security, writes one CTO. Organizations must treat LLM outputs as untrusted and require independent verification before deployment.