Articles from WebProNews

For the better part of two years, the artificial intelligence trade has been the most powerful force in American capital markets. Hundreds of billions of dollars have poured into data centers, semiconductor fabrication, and AI software startups. Nvidia alone has seen its market capitalization swell past $3 trillion. Yet behind the euphoria, two of Wall Street’s most influential banks have arrived at a striking conclusion: AI’s actual contribution to U.S. GDP growth may be, for all practical purposes, zero.

For decades, economists have debated the effects of automation on labor markets, trade balances, and productivity growth. But a new and far more urgent question is emerging among policymakers, technologists, and Wall Street strategists: What happens when artificial intelligence agents — autonomous software programs capable of making decisions, executing transactions, and interacting with other agents without human oversight — begin operating at scale across the global economy?

When the Cloud Native Computing Foundation announced the lineup for KubeCon + CloudNativeCon Europe 2026 in London, one co-located event stood out for its ambition and scope: Open Source SecurityCon. Scheduled as a full-day program running alongside the main conference, the event signals that open-source software security has moved well beyond the domain of engineering teams and into the strategic planning conversations of enterprise leadership worldwide.

OpenAI’s latest and most ambitious model, GPT-5, has encountered an unexpected problem — not in its raw intelligence, but in how the company and its customers can actually measure whether it works. As the San Francisco–based AI giant pushes deeper into autonomous AI agents capable of performing multi-step tasks on behalf of users, the traditional benchmarks that once served as reliable yardsticks for model quality are proving woefully inadequate.