Janauary 05, 2025

Phantom data centers: What they are (or aren’t) and why they’re hampering the true promise of AI

Fake data centers represent an urgent bottleneck in scaling data infrastructure to keep up with compute demand. This emerging phenomenon is preventing capital from flowing where it actually needs to. Any enterprise that can help solve this problem — perhaps leveraging AI to solve a problem created by AI — will have a significant edge. ... As utilities struggle to sort fact from fiction, the grid itself becomes a bottleneck. McKinsey recently estimated that global data center demand could reach up to 152 gigawatts by 2030, adding 250 terawatt-hours of new electricity demand. In the U.S., data centers alone could account for 8% of total power demand by 2030, a staggering figure considering how little demand has grown in the last two decades. Yet, the grid is not ready for this influx. Interconnection and transmission issues are rampant, with estimates suggesting the U.S. could run out of power capacity by 2027 to 2029 if alternative solutions aren’t found. Developers are increasingly turning to on-site generation like gas turbines or microgrids to avoid the interconnection bottleneck, but these stopgaps only serve to highlight the grid’s limitations.

Understanding And Preparing For The 7 Levels Of AI Agents

Task-specialized agents excel in somewhat narrow domains, often outperforming humans in specific tasks by collaborating with domain experts to complete well-defined activities. These agents are the backbone of many modern AI applications, from fraud detection algorithms to medical imaging systems. Their origins trace back to the expert systems of the 1970s and 1980s, like MYCIN, a rule-based system for diagnosing infections. ... Context-aware agents distinguish themselves by their ability to handle ambiguity, dynamic scenarios, and synthesize a variety of complex inputs. These agents analyze historical data, real-time streams, and unstructured information to adapt and respond intelligently, even in unpredictable scenarios. ... The idea of self-reflective agents ventures into speculative territory. These systems would be capable of introspection and self-improvement. The concept has roots in philosophical discussions about consciousness, first introduced by Alan Turing in his early work on machine intelligence and later explored by thinkers like David Chalmers. Self-reflective agents would analyze their own decision-making processes and refine their algorithms autonomously, much like a human reflects on past actions to improve future behavior.

The 7 Key Software Testing Principles: Why They Matter and How They Work in Practice

Identifying defects early in the software development lifecycle is critical because the cost and effort to fix issues grow exponentially as development progresses. Early testing not only minimizes these risks but also streamlines the development process by addressing potential problems when they are most manageable and least expensive. This proactive approach saves time, reduces costs, and ensures a smoother path to delivering high-quality software. ... The pesticide paradox suggests that repeatedly running the same set of tests will not uncover new or previously unknown defects. To continue identifying issues effectively, test methodologies must evolve by incorporating new tests, updating existing test cases, or modifying test steps. This ongoing refinement ensures that testing remains relevant and capable of discovering previously hidden problems. ... Test strategies must be tailored to the specific context of the software being tested. The requirements for different types of software—such as a mobile app, a high-transaction e-commerce website, or a business-critical enterprise application—vary significantly. As a result, testing methodologies should be customized to address the unique needs of each type of application, ensuring that testing is both effective and relevant to the software's intended use and environment.

This Year, RISC-V Laptops Really Arrive

DeepComputing is now working in partnership with Framework, a laptop maker founded in 2019 with the mission to “fix consumer electronics,” as it’s put on the company’s website. Framework sells modular, user-repairable laptops that owners can keep indefinitely, upgrading parts (including those that can’t usually be replaced, like the mainboard and display) over time. “The Framework laptop mainboard is a place for board developers to come in and create their own,” says Patel. The company hopes its laptops can accelerate the adoption of open-source hardware by offering a platform where board makers can “deliver system-level solutions,” Patel adds, without the need to design their own laptop in-house. ... The DeepComputing DC-Roma II laptop marked a major milestone for open source computing, and not just because it shipped with Ubuntu installed. It was the first RISC-V laptop to receive widespread media coverage, especially on YouTube, where video reviews of the DC-Roma II  collectively received more than a million views. ... Balaji Baktha, Ventana’s founder and CEO, is adamant that RISC-V chips will go toe-to-toe with x86 and Arm across a variety of products. “There’s nothing that is ISA specific that determines if you can make something high performance, or not,” he says. “It’s the implementation of the microarchitecture that matters.”

The cloud architecture renaissance of 2025

First, get your house in order. The next three to six months should be spent deep-diving into current cloud spending and utilization patterns. I’m talking about actual numbers, not the sanitized versions you show executives. Map out your AI and machine learning (ML) workload projections because, trust me, they will explode beyond your current estimates. While you’re at it, identify which workloads in your public cloud deployments are bleeding money—you’ll be shocked at what you find. Next, develop a workload placement strategy that makes sense. Consider data gravity, performance requirements, and regulatory constraints. This isn’t about following the latest trend; it’s about making decisions that align with business realities. Create explicit ROI models for your hybrid and private cloud investments. Now, let’s talk about the technical architecture. The organizational piece is critical, and most enterprises get it wrong. Establish a Cloud Economics Office that combines infrastructure specialists, data scientists, financial analysts, and security experts. This is not just another IT team; it is a business function that must drive real value. Investment priorities need to shift, too. Focus on automated orchestration tools, cloud management platforms, and data fabric solutions.

How datacenters use water and why kicking the habit is nearly impossible

While dry coolers and chillers may not consume water onsite, they aren't without compromise. These technologies consume substantially more power from the local grid and potentially result in higher indirect water consumption. According to the US Energy Information Administration, the US sources roughly 89 percent of its power from natural gas, nuclear, and coal plants. Many of these plants employ steam turbines to generate power, which consumes a lot of water in the process. Ironically, while evaporative coolers are why datacenters consume so much water onsite, the same technology is commonly employed to reduce the amount of water lost to steam. Even still the amount of water consumed through energy generation far exceeds that of modern datacenters. ... Understanding that datacenters are, with few exceptions, always going to use some amount of water, there are still plenty of ways operators are looking to reduce direct and indirect consumption. One of the most obvious is matching water flow rates to facility load and utilizing free cooling wherever possible. Using a combination of sensors and software automation to monitor pumps and filters at facilities utilizing evaporative cooling, Sharp says Digital Realty has observed a 15 percent reduction in overall water usage.

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