Nvidia’s pullback on DGX Cloud shows a simple truth: even giants struggle when cloud distribution clashes with hyperscalers. DGX Cloud was costly for builders and threatening to the very clouds that buy most of Nvidia’s chips. Adoption slowed, partnerships strained, and

🚨 Microsoft’s $30B bet on UK AI infrastructure is proof of one thing: compute is the new gold. But while hyperscalers pour billions into data centers and supercomputers, the demand curve for AI is growing even faster. Centralized expansion alone can’t keep up. That’s where

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Google’s “Learn Your Way” isn’t just a new ed-tech tool, it’s a signal that the industrial education model is breaking. For centuries, personalization at scale was impossible, but now AI can rebuild learning around the way each student thinks and what they actually care about.

Google’s pivot proves it: the public cloud isn’t a one-size-fits-all solution. Enterprises demand flexibility, the ability to balance cost, speed, and control across diverse workloads.

🚨 Stargate expands with 5 new AI data center sites, pushing capacity toward 10GW and $500B in buildout. The signal is clear: compute is no longer just tech infrastructure, it’s national infrastructure. But hyperscale sites like these take years and billions before they’re

Remember we announced a partnership with Fluence ⏀ a while back? Team meeting today: a pre-release demo of DePIN VM deployments with dKloud KloudDeploy! Containers + VMs across multiple chains, with traditional payments, all in one dashboard. Closed Alpha is coming

Oracle’s 20B talks with Meta show just how valuable compute power has become. AI at scale is not limited by talent or ideas, it is limited by access to GPUs. That is where dKloud comes in. By aggregating decentralized GPU supply into one liquid marketplace, dKloud makes compute

AI agents are changing how work happens. They remove the sunk cost trap by making experimentation nearly free. Code, design, and research can now be rebuilt from scratch in minutes, not months. But every experiment, every iteration, still needs serious compute power. That is

Intel’s new enterprise GPU shows how AI infrastructure is evolving from training to large scale inference. As GPUs become more virtualized and workload specific, demand for flexible high performance compute continues to rise.

AI’s power problem is becoming too big to ignore. Goldman Sachs forecasts that by 2030, data center power demand from AI will rise 25% and account for nearly 20% of total global electricity consumption. McKinsey projects that U.S. data centers could triple their usage from 25 to

The Future Isn’t General Purpose. It’s Purpose Built. The cloud landscape is undergoing a seismic shift. As AI adoption accelerates, traditional one-size-fits-all cloud platforms are hitting their limits, especially when it comes to cost efficiency, performance, and compliance.

The shift toward purpose-built clouds marks a turning point in how enterprises think about infrastructure. As AI workloads grow more demanding and compliance rules tighten, the old one-size-fits-all cloud model no longer fits. According to InfoWorld, companies are now turning to

Denmark’s launch of the Gefion supercomputer with NVIDIA shows how nations are investing in their own AI infrastructure to strengthen digital independence. As computing power becomes a strategic asset, the ability to access and scale GPU resources efficiently is now central to

China’s ambition to lead global AI by 2030 highlights how national competitiveness is increasingly defined by access to computing power. Advanced chips and GPU infrastructure have become the backbone of innovation across research, defense, and technology. dKloud provides the

Turning AI research papers into executable code represents a major leap in how knowledge is applied. Systems like these demand significant computing resources to analyze models, refactor code, and run verification at scale. dKloud provides the high performance compute

China’s progress in developing analog AI chips capable of outperforming traditional GPUs shows how fast computing technology is evolving. As architectures diversify, one constant remains—the growing need for massive, reliable compute to power advanced models and experiments.

Andreessen Horowitz’s new $10B raise highlights how central infrastructure has become in the AI economy. With $1.5B specifically aimed at AI infrastructure, investors are signaling that compute capacity and scalability are now as strategic as the models themselves. It reflects