Our latest forecast indicates that it will take a decade or more for artificial intelligence factory operators and model builders to reach breakeven on their massive capital outlays. Our projections call for nearly $4 trillion in cumulative capital spending outlays by 2030, with just under $2 trillion in cumulative AI revenue generated in that timeframe. ...

The data center as we know it is being reimagined as an “AI factory” – a power- and data-optimized plant that turns energy and information into intelligence at industrial scale. The tech stack is flipping from general purpose central processing unit-centric systems to graphics processing unit-centric accelerated compute, optimized for parallel operations and purpose built ...

The White House’s recent executive orders on artificial intelligence and the launch of America’s AI Action Plan have made one thing clear: The United States intends to lead the world in AI. To be sure, the policy direction is ambitious: Accelerate innovation, strengthen infrastructure and ensure fairness and safety. But as important as these goals are, rules ...

Large language models seem like magic. These artificial intelligence models write poetry, draft legal arguments and debug code with a fluency that suggests true understanding. But there is no magic, only patterns. An LLM learns language the same way a cryptographer cracks a code: by analyzing a massive volume of text and inferring the rules ...

For the past decade, software has been the primary constraint in most technology development. At the edge, hardware advances in processor speed, multi-threading and memory were incremental, while the real breakthroughs came from software innovation. Today, that dynamic has flipped. Hardware has quickly become the critical factor as multiple trends converge, including renewed focus on ...

As organizations continue their digital transformation, the demand for timely, consumption-ready data has never been higher. Yet simply adopting data operations tools is not enough to improve data delivery. Accelerating velocity and improving efficiency in data operations requires more than tool implementation. It demands a cohesive strategy built on platform engineering, proven practices, the right ...

Salesforce Inc. is moving from the hype phase of generative AI into doing the harder engineering work to create the agentic enterprise. The Dreamforce 2025 conference this week showed us that the company that created the original software-as-a-service model now wants to lead what we call service as software. In our view, this represents a profound revolution, ...

At Oracle Corp.’s CloudWorld 2025 in Las Vegas this week, Riverbed Technology LLC unveiled its new Data Express Service, a software-as-a-service offering designed to address one of the biggest bottlenecks in enterprise AI adoption: AI data movement. This involves moving massive datasets quickly, securely and cost-effectively. Built on Oracle Cloud Infrastructure, the new service enables petabyte-scale ...

The shift to service as software will bring learning curve advantages, software-like marginal economics, and winner-take-most dynamics to all companies across every industry, not just tech vendors. We believe those firms that can more quickly jump on the artificial intelligence experience curve will see substantially increased benefits relative to their competitors. However, our research suggests ...

Agentic artificial intelligence is the new belle of the software ball. C-level executives want their companies to use AI agents to move faster, therefore driving vendors to deliver AI agent-driven software, and every software delivery team is looking for ways to add agentic capabilities and automation to their development platforms. By parallel coding with co-pilots, ...