Hyperscale cloud providers are doing what any aggressive buyer with deep pockets would do: purchasing enormous volumes of DRAM and high-bandwidth memory to feed AI factories, new cloud regions, and expanding platform services. By securing supply ahead of competitors, they lock in favorable terms and ensure their growth is not constrained by component scarcity. From their perspective, this is smart business. From the enterprise market’s perspective, it is something else entirely.
When the largest infrastructure providers absorb a disproportionate share of a finite supply of memory, prices rise for everyone downstream. Enterprises attempting to refresh on-premises servers, expand private clouds, or maintain hybrid architectures suddenly face a distorted market. Hardware lead times grow. Budget assumptions fail. Planned refreshes become much more expensive than expected. In some cases, the cloud begins to look attractive not because it is strategically superior, but because the economics
The race to build the world’s most powerful AI factories demands networking that keeps pace with the ambitions of AI itself. NVIDIA Spectrum-X Ethernet scale-out infrastructure stands at the forefront of that race as the most advanced AI networking technology available today, deployed by industry leaders who can’t afford to compromise on performance, resilience or […]
Residents say AI factories with unknown environmental impacts are being rushed into development as proponents argue Australia must ride the data boom or be left behind
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When West Footscray resident Sean Brown takes his 19-month-old boy to the park, their walk passes an imposing new building cheerily spruiked as “Australia’s largest hyperscale AI factory”, a datacentre called M3.
He hates it: the construction noise from its constant expansion, the looming towers and the insistent background hum, the exhaust from the growing array of diesel generators that power the ranks of servers inside.
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Companies are taking control of their own data to tailor AI for their needs. The challenge lies in balancing ownership with the safe, trusted flow of high‑quality data needed to power reliable insights. This conversation from MIT Technology Review’s EmTech AI conference examines how AI factories unlock new levels of scale, sustainability, and governance—positioning data…
According to Nikkei Asia, even as suppliers ramp up DRAM production, manufacturers are only expected to meet 60 percent of demand by the end of 2027. SK Group chairman has even said that shortages could last until 2030.
The world's largest memory makers - Samsung, SK Hynix, and Micron - are all working to add new fabrication capacity, but almost none of it will be online until at least 2027, if not 2028. SK opened a fab in Cheongju in February, but that is the only increase in production among the three for 2026.
Nikkei says that production would need to increase by 12 percent a year in 2026 and 2027 to meet demand. But according to Counte …
Read the full story at The Verge.
In a recent article chronicling the history of Microsoft Azure and its intensifying woes, we see a narrative that has been building throughout the industry for years. As cloud computing evolved from a buzzword to the backbone of digital infrastructure, major providers like Microsoft, Amazon, and Google have had to make compromises. Their promises of near-perfect uptime shifted from an expectation to “good enough,” influenced by economic pressures that have seen the cloud giants prioritize cost cuts and staff reductions over previously non-negotiable service reliability.
Frankly, many who follow the cloud space closely, including myself, have been warning about this situation for some time. Cloud outages are no longer rare, freak events. They are ingrained in the model as accepted collateral for the rapid growth and relentless cost-cutting that define this era of cloud computing. The story of Azure, as discussed in the referenced Register piece, is simply the latest and most prominent e