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.
Data centres in space: they’re a brilliant idea, but a herculean challenge
Data centres beyond Earth’s atmosphere would have access to continuous solar
energy and could be naturally cooled by the vacuum of space. Away from
terrestrial issues like planning permission, such facilities could be rapidly
deployed and expanded as the demand for more data keeps increasing. It may
sound like something from a sci-fi novel, but this concept has been gaining
more attention as space technology has advanced and the need for sustainable
and scalable data centres has become apparent. ... Space weather, such as
solar flares could disrupt operations, while collisions with debris are a
major worry – rather offsetting the fact that space-based data centres don’t
have to fear earthquakes or floods. Advanced shielding could protect against
things like radiation and micrometeoroids, but it will probably only do so
much – particularly as Earth’s orbit becomes ever more crowded. To fix damaged
facilities, advances in robotics and automation will of course help, but
remote maintenance may not be able to address all issues. Sending repair crews
remains a very complex and costly affair, and though the falling cost of space
launches will again help here, it is still likely to be a huge burden for a
few decades to come. In addition, disposing of data centre waste takes on a
whole new level of complexity off-planet.
India’s Digital Data Protection Framework: Safety, Trust and Resilience
The draft rules cover various key areas, including the responsibilities of
Data Fiduciaries, the role of Consent Managers, and protocols for State Data
Processing, particularly in contexts like the distribution of subsidies and
public services. They also detail measures for Breach Notifications,
mechanisms for individuals to exercise their Data Rights, and special
provisions for processing data related to children and persons with
disabilities. The Data Protection Board, central to the enforcement of the
Act, is set to function as a fully digital office, streamlining its operations
and improving accessibility. Additionally, the rules outline procedures for
appealing decisions through the Appellate Tribunal, ensuring accountability at
every stage. One of the defining aspects of the draft rules is their alignment
with the SARAL framework, which emphasises simplicity, clarity, and contextual
definitions. To aid public understanding, illustrative examples and
explanatory notes have been included, making the document accessible to
stakeholders across industries, government bodies, and civil society. Both the
draft rules and the accompanying explanatory notes are available on the MeitY
website for public review and consultation. While legislative measures are
being formalised, the government has swiftly addressed recent data breaches.
The Rise of AI Agents and Data-Driven Decisions
“In 2025, AI agents will take generative AI to the next level by moving beyond
content creation to active participation in daily business operations,” he
says. “These agents, capable of partial or full autonomy, will handle tasks
like scheduling, lead qualification, and customer follow-ups, seamlessly
integrating into workflows. Rather than replacing generative AI, they will
enhance its utility by transforming insights into immediate, actionable
outcomes.” Kawasaki emphasizes the developer-centric benefits as well. “AI
agents will become faster and easier to build as low-code and no-code
platforms mature, reducing the complexity of creating intelligent, AI-powered
scenarios,” he says. ... “AI will play a transformative role in the
fortification of cyber security by addressing challenges like scalability,
prioritization and speed to detection. Unfortunately, cyber threats have
become commonplace on the network and attackers are becoming more
sophisticated in their methods – many times operating at a threshold that is
very difficult to detect. As a result, organizations that fail to integrate an
AI capability into their defense strategy risk being exposed to
business-altering vulnerabilities. AI’s ability to monitor vast networks for
imperceptible anomalies allows organizations to prioritize the most critical
threats in real-time.”
New HIPAA Cybersecurity Rules Pull No Punches
Since the beginning, HIPAA has always been the best, yet insufficient,
regulation dictating cybersecurity for the healthcare industry. "[There's] a
history of the focus being in the wrong place because of the way HIPAA was
laid out in the mid-1990s," says Errol Weiss, chief information security
officer (CISO) of the Healthcare Information Sharing and Analysis Center
(Health-ISAC). ... The newly proposed Security Rule aims to fix things up,
with a laundry list of new requirements that touch on patch management, access
controls, multifactor authentication (MFA), encryption, backup and recovery,
incident reporting, risk assessments, compliance audits, and more. As Lawrence
Pingree, vice president at Dispersive, acknowledges, "People have a love-hate
relationship with regulations. But there's a lot of good that comes from HIPAA
becoming a lot more prescriptive. Whenever you are more specific about the
security controls that they must apply, the better off you are." ... Joseph J.
Lazzarotti, principal at Jackson Lewis P.C., says provision 164.306 allowed
for the kind of flexibility businesses always ask for: "That we're not
expecting the same thing from every solo practitioner on Main Street in the
Midwest versus the large hospital on the East Coast. There are obviously going
to be different expectations for compliance."
Quote for the day:
“Do the best you can until you know
better. Then when you know better, do better.” -- Maya Angelou
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