The global tariff crisis initiated by the US administration is expected to have strong, long-lasting effects on the data center sector, driving up prices and slowing growth.
While AI infrastructure build-out may focus on performance today, over time data center operators will need to address efficiency and sustainability concerns.
AI vendors claim that “reasoning” can improve the accuracy and quality of the responses generated by LLMs, but this comes at a high cost. What does this mean for digital infrastructure?
Data center builders who need power must navigate changing rules, unpredictable demands — and be prepared to trade.
Quantum computing progress is slow; press releases often fail to convey the work required to make practical quantum computers a reality. Data center operators do not need to worry about quantum computing right now.
Customers are responsible for architecting resiliency into their cloud apps. However, the cloud's consumption model means resiliency comes at a price. Enterprises must evaluate availability against cost before building on the cloud.
Major and damaging publicly reported outages are increasingly likely to be due to a deliberate attack — whether cyber or physical, according to Uptime Intelligence public outage data for 2024.
Critics argue that data center water use is excessive and poorly managed. Operators should select a cooling system to fit the local climate and available water supply, explaining water use within the context of local conditions.
Chinese large language model DeepSeek has shown that state of the art generative AI capability may be possible at a fraction of the cost previously thought.
High-end AI systems receive the bulk of the industry’s attention, but organizations looking for the best training infrastructure implementation have choices. Getting it right, however, may take a concerted effort.
Data shows that cuts to workforce initiatives and mentorships had little effect on the proportion of new hires in 2024. Owners and operators can consider reassessing and restructuring these programs.
The high capital and operating costs of infrastructure for AI mean an outage can have a significant financial impact due to lost training hours
The US’ SEC has withdrawn requirements for climate risk reporting, and the EU is revising its rules. Despite this, strong drivers remain for operators to measure their environmental impact
Scalability and cost efficiency are the top reasons enterprises migrate to the cloud, but scalability issues due to application design flaws can lead to spiralling costs — and some workload repatriation to on-premises facilities
Compared with most traditional data centers, those hosting large AI training workloads require increased attention to dynamic thermal management, including capabilities to handle sudden and substantial load variations effectively.
Seb Shehadi
Daniel Bizo
Tomas Rahkonen
Max Smolaks
Andy Lawrence
Owen Rogers
Rose Weinschenk
Jay Dietrich
John O'Brien
Peter Judge
Douglas Donnellan