Add Row
Add Element
cropper
update

[Company Name]

Agility Engineers
update
Add Element
  • Home
  • Categories
    • SAFe
    • Agile
    • DevOps
    • Product Management
    • LeSS
    • Scaling Frameworks
    • Scrum Masters
    • Product Owners
    • Developers
    • Testing
    • Agile Roles
    • Agile Testing
    • SRE
    • OKRs
    • Agile Coaching
    • OCM
    • Transformations
    • Agile Training
    • Cultural Foundations
    • Case Studies
    • Metrics That Matter
    • Agile-DevOps Synergy
    • Leadership Spotlights
    • Team Playbooks
    • Agile - vs - Traditional
Welcome To Our Blog!
Click Subscribe To Get Access To The Industries Latest Tips, Trends And Special Offers.
  • All Posts
  • Agile Training
  • SAFe
  • Agile
  • DevOps
  • Product Management
  • Agile Roles
  • Agile Testing
  • SRE
  • OKRs
  • Agile Coaching
  • OCM
  • Transformations
  • Testing
  • Developers
  • Product Owners
  • Scrum Masters
  • Scaling Frameworks
  • LeSS
  • Cultural Foundations
  • Case Studies
  • Metrics That Matter
  • Agile-DevOps Synergy
  • Leadership Spotlights
  • Team Playbooks
  • Agile - vs - Traditional
November 23.2025
2 Minutes Read

Is the Rapid Growth of AI Sustainable? Energy and Water Concerns

Surreal depiction of AI's environmental impact and sustainability.

When AI Meets Sustainability: The Untold Energy and Water Costs

The rapid advancement of artificial intelligence (AI) technologies, particularly large language models (LLMs) like ChatGPT, is reshaping industries but also raising significant sustainability concerns. As these models continue to grow in complexity, their energy consumption and water requirements are surging, pushing the limits of our natural resources. In a world where water and energy are becoming increasingly precious, the environmental impact of AI cannot be ignored.

Understanding the Energy Demands of AI

The energy consumption related to AI systems is staggering. Reports indicate that AI, data centers, and cryptocurrency operations consumed about 460 terawatt-hours of electricity in 2022, which is nearly 2% of the world’s electricity demand. This figure is expected to grow significantly, raising alarms regarding the sustainability of AI’s rapid expansion. With estimates projecting AI could require as much as 134 terawatt-hours of energy annually by 2027, countries like the UK are bracing for a potential energy crisis as AI-driven electricity demand could skyrocket sixfold in the next decade.

Water Usage: An Overlooked Challenge

While energy demands capture widespread attention, water usage in data centers poses an equally pressing concern. Cooling systems in data centers, a vital component for maintaining operational efficiency, require considerable quantities of water. A notable statistic reveals that Google's data centers draw approximately 78% of their water from potable sources, raising critical questions about competition for this vital resource. Regions facing water shortages may find the expansion of AI data centers problematic, prompting governments to consider regulatory measures to curtail further growth.

Innovative Solutions for Sustainable AI Infrastructure

To address these growing environmental challenges, organizations are exploring innovative data management strategies aimed at reducing resource consumption. One effective method involves implementing tiered data architectures, where frequently accessed data is stored in high-speed locations while less critical data is archived in lower-speed, energy-efficient zones. This approach not only curtails energy and water usage but also enhances data accessibility without sacrificing performance.

Future Predictions: Does AI Have a Sustainable Path Forward?

The pressing question remains: can AI innovation coexist with sustainable practices? To develop a future in which AI and environmental responsibility go hand in hand, a multi-faceted approach is imperative. This includes investing in green computing technologies, enhancing transparency in water and energy consumption, and implementing regulatory frameworks that ensure corporate accountability. By leveraging new cooling technologies that optimize water use and focusing on energy-efficient hardware, companies can make strides toward a balanced relationship between AI expansion and resource sustainability.

Agile-DevOps Synergy

9 Views

0 Comments

Write A Comment

*
*
Related Posts All Posts
12.13.2025

Azul’s Acquisition of Payara: A Game-Changer in Java App Development

Update Azul's Strategic Move: Acquiring Payara to Strengthen Its Java Platform This week, Azul, a key player in the Java ecosystem, announced its acquisition of Payara, expanding its capabilities beyond Java runtimes into application server and microservices territory. This partnership isn't new; it builds on nearly eight years of collegial collaboration that began in 2018 when Payara integrated the Azul Platform Core into its Payara Server Enterprise. Enhancing Open-Source Java Solutions With both companies deeply rooted in open-source communities—contributions to projects like OpenJDK and the Eclipse Jakarta EE Platform are credible evidence—it is clear that this acquisition represents a significant stride towards providing enterprises with alternatives to legacy application server platforms like IBM WebSphere and Apache Tomcat. According to Azul CEO Scott Sellers, this initiative aims to empower businesses to modernize their Java applications without needing to rewrite them entirely. The Potential of Payara Micro Payara’s innovations, particularly Payara Micro—a lightweight application server designed for building and running cloud-native Java microservices—will dovetail seamlessly with Azul’s existing offerings. This functionality enhances flexibility and scalability for organizations looking to leverage the monetary benefits of hybrid and cloud-native deployments while retaining their existing Java investments. A Market Opportunity Worth $26 Billion Interestingly, the merger creates an exciting avenue for Azul as it taps into an estimated $26 billion total addressable market for application servers, projected to grow at a compound annual growth rate (CAGR) of 11% to 14%. Such statistics fuel the anticipation that Azul could redefine its market presence significantly. Support for Enterprise Transition Processes The evolution toward cloud-native architecture in enterprise solutions is key. With Payara now part of the Azul family, the combined resources will facilitate more organizations' transitions from traditional systems. Industry expert Mitch Ashley highlights that many enterprises are seeking new paths without the need to discard existing applications—making this acquisition not just strategic, but timely. Collaboration with Technology Partners Additionally, Azul’s recent partnerships with Moderne, which provides tools to accelerate application modernization, further enrich the capabilities available to customers. As companies assess their reliance on proprietary platforms, this holistic approach positions Azul and Payara as leaders in the shift towards open source solutions in enterprise Java. Future Prospects for Java Development As Java remains one of the most widely utilized programming languages in enterprise IT, reliance on open-source solutions seems destined to grow. Azul’s commitment to reducing the operational costs associated with legacy Java applications while enhancing their performance signals an exciting journey ahead for developers and businesses alike. In closing, the Azul-Payara merger exemplifies a strategic advance for enterprise Java solutions, aligning with modern trends in agility and open-source software. By creating a more cohesive Java platform, individuals and organizations can expect to benefit from enhanced performance, security, and innovation across the Java ecosystem.

12.13.2025

Navigating the Chip Smuggling Debate: How Nvidia’s Allegations Impact China’s AI Future

Update Chip Smuggling Claims Ignite Debate Over Export Controls In a dramatic escalation of tensions surrounding chip exports, allegations have emerged linking China’s DeepSeek to illicit activities involving Nvidia’s advanced Blackwell chips. These chips are not just any hardware; they are pivotal for developing power-hungry AI models amid escalating geopolitical stakes. Understanding the Background of Chip Restrictions The backdrop to this controversy dates back to October 2022 when the U.S. government prioritized export controls to curb China's technological advancements. These restrictions specifically targeted powerful chips like the Nvidia A100, integral for training sophisticated AI algorithms. While this move aimed to bolster U.S. dominance in AI technology, it inadvertently compelled China to innovate within its confines, leading to the development of less powerful yet effective chip alternatives. The Landscape of Chip Smuggling Despite strict controls, illicit pipelines have flourished. Recent investigations by U.S. prosecutors highlight extensive smuggling operations, revealing that banned Nvidia H100 and H200 GPUs have likely made their way into the Chinese market. This ongoing black-market crisis demands new measures, prompting Nvidia to introduce sophisticated tracing technologies aimed at curtailing unauthorized chip access. This introduces a new layer of digital enforcement in a continuously evolving war over global chip supremacy. DeepSeek's Alleged Operations: Implications on AI Development The accusations against DeepSeek suggest the company is harnessing smuggled chips to train cutting-edge AI systems using an extensive network of undisclosed data centers. If validated, such operations would signal a significant threat to the U.S. position in the AI arena, as China’s ability to deliver competitive models could accelerate exponentially. Market Response: Nvidia's Position in a Competitive Landscape In the face of these allegations, Nvidia has dismissed them as 'far-fetched' but acknowledged the need to investigate credible claims. This response underscores the precarious balance between safeguarding national interests and maintaining lucrative partnerships with the Chinese market. Notably, while U.S. manufacturers like Nvidia and AMD benefit significantly from China’s appetite for their older chips, this dynamic also fosters a climate where Chinese companies strive for self-sufficiency. Future Predictions and Trends in AI Development As hardware scarcity looms, China may gear up to cultivate its own AI ecosystem, potentially collaborating with allied nations in Southeast Asia and the Middle East. Such developments could reshape the global tech landscape, leading to the birth of competitive local brands capable of challenging U.S. dominance. The stakes are high, with the U.S. and China vying not just for market share, but for leadership in the critical AI space. Conclusion: Navigating the Path Forward The debate surrounding chip smuggling allegations serves as a reminder of the complex interplay between technology, politics, and ethics. As nations race to equip themselves with the most advanced tools for AI, the outcomes will significantly influence both local and global technology landscapes.

12.12.2025

Training Transformation: The Growing Demand for Hands-On Learning in DevOps

Update How Hands-On Training Closes the Skills GapIn a rapidly evolving digital landscape, organizations are increasingly recognizing the value of hands-on training as a solution to critical skills shortages. With nearly 90% of businesses acknowledging significant skills gaps within their teams, the urgency for effective training methods has intensified. INE’s recent initiatives highlight this imperative as companies allocate end-of-year budgets towards experiential learning—training that goes beyond theoretical knowledge, preparing employees for real-world challenges.The Impact of AI on TrainingAs artificial intelligence continues to transform the workplace, the need for flexible and adaptive training solutions has never been more pressing. In fact, the LinkedIn Workplace Learning Report emphasizes that 74% of employees prefer experiential, hands-on learning formats that enable them to apply concepts practically. AI is now playing a pivotal role in personalizing these training experiences, ensuring that they cater to individual skill levels and learning preferences. By embracing AI-driven training solutions, L&D teams can improve competency faster and more effectively.The Shift from E-Learning to Experiential ModelsWhile traditional e-learning methods still have a place in corporate training, enterprises are increasingly shifting towards hands-on training models. Certification programs that once dominated L&D strategies are now viewed as inadequate in equipping employees with the necessary skills to excel in today’s tech-driven world. The key to addressing these evolving demands lies in the implementation of skills pathways that are richly tied to practical, performance-based learning outcomes.Benefits of Immersive Learning TechniquesOrganizations looking to improve workforce readiness are prioritizing immersive training experiences. Research shows that practice-based training facilitates knowledge retention of up to 75%, compared to just 5% to 20% with lecture-based learning. This stark difference highlights the effectiveness of experiential training methods in rapidly scaling up employee capabilities and directly contributing to organizational resilience. Initiatives from INE, such as simulation labs and adaptive learning paths, exemplify how companies can strategically invest Q4 budgets for immediate returns on workforce performance.Global Trends in Corporate LearningGlobally, corporate learning platforms are adapting to meet the demands of diverse workforces transitioning into hybrid work environments. These platforms promote a culture of continual learning, fostering engagement through integrated resources that encourage self-paced learning. As the popularity of remote work continues to rise, organizations that leverage flexible, digital-first training solutions will find themselves at a competitive advantage.Conclusion: The Future of Training is Hands-OnThe convergence of AI, hands-on training, and immersive learning experiences presents an opportunity for organizations to revolutionize their approach to employee development. As the market demands a workforce ready to tackle complex challenges, enterprises can no longer rely solely on traditional training methods. Employees and corporations alike stand to benefit vastly from embracing a holistic, strategy-driven approach to learning that integrates practical skills with evolving industry needs.

Terms of Service

Privacy Policy

Core Modal Title

Sorry, no results found

You Might Find These Articles Interesting

T
Please Check Your Email
We Will Be Following Up Shortly
*
*
*