Jagoda Joanna Zdańska’s Post

The data center market is heading toward USD 1.8 trillion by 2035. AI, cloud and data‑intensive workloads are driving unprecedented demand for scalable, high‑performance infrastructure. Hyperscale and edge aren’t future bets anymore — they’re essential for growth today. The question now isn’t if to invest, but how fast you can scale. Read more: https://bit.ly/4nkq6x0

Data is driving transformation in every sector, from finance to space exploration. But to turn data into real business outcomes, you need hybrid compute that is AI-optimized, secure by design, and built for agility from edge to cloud. smc.int.hpe.com/s/e2e31

If 83% of your data lives on-prem, why send your AI workloads to the cloud? With reasoning workloads skyrocketing by 320x, routing everything through the cloud creates massive latency and cost bottlenecks for enterprises. The most sustainable approach is bringing AI directly to where your data already lives — and Dell Technologies is building the exact infrastructure to make that seamless. Check out this great breakdown from Jeff Clarke on how we are helping organizations bring AI agents safely on-prem: 👇 #DellTechWorld #AgenticAI #EnterpriseInfrastructure #DataGravity #OnPremAI #DellTechnologies #IWork4Dell

The CFO wants lower cost. The CTO wants more power. Most cloud platforms force you to compromise between the two. 😅 Because traditional cloud infrastructure was built for shared workloads, not AI at scale. So as training jobs grow, data moves farther, latency increases, and costs start compounding through bandwidth, congestion, and egress fees. The CTO sees inconsistent training times and throughput bottlenecks. The CFO sees unpredictable cloud bills. CloudLogics changes the architecture itself. The Fastest Cloud for AI Workloads brings high-performance compute closer to where data already lives using dedicated infrastructure and private dark fiber engineered for deterministic performance. ☁️ #AIInfrastructure #CloudComputing #AIWorkloads

A new survey report uncovering how enterprises are simplifying their infrastructure, scaling cloud workloads, managing energy demands, and closing the AI skills gap. Key findings from the report: 🗞️ 97% say cloud platforms are critical to scaling AI  93% are optimizing infrastructure for energy efficiency  83% have already seen measurable ROI from their AI infrastructure investments  38% of top performers simplified their stack to accelerate time-to-value

CIOs Shift AI Workloads to Colocation Facilities by 2026 Discover why industry leaders are choosing to transition AI operations from the public cloud to colocation services, ensuring enhanced security and performance. #AI #Colocation #TechTrends2026 DataBank

A strong signal for where Sovereign AI is heading: Red Hat (Ariel Adam + Axel Saß) + Duality are showing what secure, collaborative AI infrastructure can actually look like in practice. Key idea: You shouldn’t have to choose between using the cloud and protecting your data. With confidential computing + Duality’s platform: - Data stays protected, even during computation  - Partners can collaborate without exposing underlying assets - Enterprises retain full control over workloads and access - This also opens up a meaningful shift for on-premise environments - the ability to leverage cloud infrastructure without giving up control over sensitive data. Expect to see more architectures like this as Sovereign AI moves from concept to deployment. Read the full blog here: https://lnkd.in/e3ZzG2Fh Let’s connect if you're exploring how to extend on-prem workloads into the cloud securely.

We are moving from "renting" AI (risky) to "owning" it (private). But how do you scale Sovereign AI without compromising security? This latest piece from Red Hat and Duality Technologies highlights a massive shift in the industry: the rise of Confidential Computing. It’s the "missing link" that allows organizations to collaborate on sensitive datasets without ever exposing the raw data or AI models to the underlying infrastructure. Why is this important? We can finally achieve "trust-by-design." By using hardware-attested Trusted Execution Environments, you're not just hoping your cloud provider is secure—you are guaranteeing it through architecture. Is "Sovereignty" a priority for your 2026 AI roadmap, or are you still in the experimentation phase? #SovereignAI #ConfidentialComputing #DigitalSovereignty #RedHat #EnterpriseAI

Iowa’s role in cloud and AI is growing faster than many realize. From hyperscale data centers to high‑performance connectivity, the state is emerging as a national compute hub. We unpack what’s driving it, and why it matters, here: https://buff.ly/XHpgJ8v #DigitalInfrastructure #IowaInnovation #CloudComputing

In Australia, the cloud conversation is shifting, from infinite scale assumptions to operational reality. AI workloads are exposing what traditional cloud models were never designed for: Sustained GPU demand, latency sensitivity, and the need for tighter control over where compute meets data. Azure Local is emerging as a practical correction, bringing cloud capabilities closer to the enterprise, where performance, compliance, and control can be engineered more precisely. But here’s what most miss: Deploying the infrastructure is the easy part. The real challenge begins in Day-2, optimising GPU utilisation, managing stack dependencies, and sustaining performance under real-world load. Because in this new model, success isn’t defined by how fast you deploy, but by how well you operate. Read the blog by Miitul Rajjput to know more: https://lnkd.in/dcfJxk9s Anunta Australia: Ajit Aloz, Miitul Rajjput, Subramaniam Krishnan, Jerin Raju #AzureLocal #AustraliaTech #AIInfrastructure #HybridCloud #CloudStrategy #DigitalTransformation #Anunta