RCRTech

NTT Global Data Centers argues that the next challenge for AI infrastructure is no longer demand but execution. In its new Can Data Centers Keep Pace with AI? report, developed with ThoughtLab, the company says global AI-driven data center demand is expected to grow between 23% and 30% annually through 2030, but warns that power availability, equipment shortages, land constraints, and labor availability could prevent supply from keeping pace.

A timely event reminder that the Quantum Safe Networks Forum is next week (July 14) – and you should be there. Because Q-Day, when quantum computers can crack today’s standard encryption, is around the corner, some time after 2028. The telco industry is at the centre of the scramble to manage the response. This is not theoretical; encrypted traffic being harvested today could be decrypted tomorrow, turning long-term data security into a live infrastructure problem.

Featuring 8 case studies from Tier 1 operators around the world, this white paper demonstrates how the next-gen Enea AAA Server transforms operational complexity into scalable, automated, and revenue-generating services. For decades, the AAA (Authentication, Authorization, and Accounting) server was treated as a static gatekeeper—tasked simply with granting or denying network access. That view no longer holds.

0 In sum – what to know: 15GW AI infrastructure target – SK Telecom plans to develop up to 15GW of AI data center capacity, with the first 5GW scheduled to come online in phases beginning in 2029. Regional AI expansion – The company will expand its Ulsan AI data center into a multi-gigawatt cluster while adding new capacity in southwestern Korea to support national AI infrastructure.

0 In sum – what we know: A two-year delay – Samsung has resumed 1.4nm development with mass production now targeted for 2029, versus the 2027 date it originally promised. 2nm comes first – Samsung redirected resources toward stabilizing 2nm and 4nm, a pivot that helped land a large Tesla foundry order. Equipment moving forward – Samsung has shared 1.4nm process plans with tool vendors like Applied Materials and Lam Research and installed ASML’s High-NA EUV system for the node.

Google says the AI era is changing how hyperscale infrastructure is designed and operated, making efficiency as critical as adding new computing capacity. In its 2026 Environmental Report, the company argues that AI growth will depend not only on building more data centers, but also on reducing the energy and infrastructure overhead required to run increasingly power-hungry workloads. The report underscores the scale of AI’s impact.

More from Sean, who follows up his DTW Ignite narrative from yesterday with another today. The same idea is at the heart of both pieces, as well as his recent Cisco and Qualcomm reports: how to safely delegate to AI inside systems that were not designed for such probabilistic behaviour. As yesterday: networks are engineered for repeatability, auditability, and control, while AI produces variable outputs shaped by context and likelihood. That mismatch defines where autonomy can even exist.

0 Nvidia arguably powered the AI boom, but well and truly into it, hyperscalers and other big players aren’t exactly thrilled with their reliance on the company. The major hyperscalers and frontier AI labs continue to lean on Nvidia for frontier model training and a large share of high-end inference, even as they spend billions trying to change that.

0 The vendor-side story coming out of DTW Ignite last week in Copenhagen was relatively clear. Agentic AI needs architecture, governed data, orchestration, APIs, observability, policy, security, domain-specific models and closed-loop control. It needs, in other words, a deterministic envelope around probabilistic systems. For more on that nascent convergence, and how Danish philosopher Soren Kierkeegard fits in, read this research note.

Firstly, good stuff from Sean Kinney, as always – from Cisco Live a couple of weeks back (with apologies for its tardiness) and DTW last week (right on time). The two pieces are worth taking as a pair: they discuss the tension as AI reshapes operational control in networks. The DTW writeup, an intellectual take, says probabilistic AI and deterministic telecoms are strange bedfellows – that inference and judgement are unreliable stewards of repeatability and predictability.