Satya Nadella’s Post

Quite debatable. AI relies on data centers that consume large amounts of water to cool servers, worsening the global water crisis. This phenomenon brings to light a modern version of the Jevons Paradox: as AI efficiency improves, its expanded use drives an increase in demand for resources like water and energy, counteracting any savings made in efficiency. If we don't implement truly sustainable regulations and technologies, we risk the environmental impact of AI—particularly in terms of water and resource consumption—growing disproportionately. This increase could contribute to serious ecological imbalances, especially in areas already affected by water scarcity, such as certain regions in the United States, Argentina, Chile, and Europe, where data centers consume more water than entire communities. In this context, it is urgent to reconsider how we optimize technology, ensuring that true progress for humanity is not achieved at the expense of the planet’s vital resources. #sustainability #datacenter #artificialintelligence

Exciting times in AI just keeps on going. With Deepseek's latest unveiling of their model and whitepaper, it's been proven that AI is no longer a big tech game.

The Jevons Paradox, first observed in the 19th century, explains how efficiency can lead to more consumption instead of less. Making steam engines more efficient didn’t reduce coal usage—it increased it by lowering costs and unlocking new applications. We’re seeing the same dynamic with AI today. Tools like GitHub Copilot boost developer productivity, but instead of reducing demand, they’re enabling bigger projects, new business models, and entirely new fields. AI is no longer a niche—it’s quickly becoming the backbone of how we work, create, and solve problems. The more accessible and powerful it becomes, the more we’ll find ways to integrate it into every aspect of our lives, driving innovation and reshaping industries in ways we’re only beginning to imagine!

In 1865, the English economist William Stanley Jevons observed that technological improvements that increased the efficiency of coal use led to the increased consumption of coal in a wide range of industries. We have started witnessing the same in AI - as the AI models become more efficient and cheaper, the use of AI is starting to become more ubiquitous...Satya Nadella, Chairman and CEO of Microsoft summarized it well in his recent comment. https://lnkd.in/dhPMNWi2

As Satya Nadella points out, the Jevon’s paradox holds true in AI and other fields such as cybersecurity. I wrote a blog last year on how this paradox will impact managed security services similar to what we will see with AI. Read here: https://lnkd.in/gfStyHeC #msp #mssp

There’s a lot of coping going on. The repricing is justified. Even if Jevons paradox applies, #AI compute will move from high priced high margin data center #GPUs to low margin GPUs in PCs and Phones! That means profits will drop dramatically for companies like #NVDA and #MSFT which depend on cloud compute. It may not show immediately since many orders have been placed in advance, but the trend will be clearly visible in time. 💡📉

Jevons Paradox is a powerful lens through which to view AI’s impact on cybersecurity and IT efficiency. As Satya Nadella pointed out, the more efficient we become, the greater the demand for what we enable. At Aiden Technologies, Inc., we see this firsthand in patch management and endpoint configuration. By automating OS builds and software deployment at scale, we’ve dramatically reduced the time and effort required to maintain secure and compliant environments. But rather than decreasing demand, this efficiency is accelerating it—organizations are now able to build, secure, and refresh endpoints at a pace that was previously unthinkable. The question isn’t just how we make security and IT operations more efficient, but how we scale intelligently to meet the new demands that AI-driven automation unleashes. Exciting times ahead! #AI #Cybersecurity #Automation #PatchManagement

AI is getting so efficient with tokens per watt per dollar that soon we’ll have models analyzing why they’re using so much energy… while using even more energy to do it. Jevons Paradox strikes again! 🧐 I think Satya Nadella’s recent focus on new metrics like “tokens per watt per dollar” is a great step, but balancing these efficiency measures with a focus on better data is essential to avoid the pitfalls of the Jevons Paradox in AI. It’s not just about doing more with less - it’s about ensuring those efficiencies lead to meaningful, data-driven outcomes. Thoughts?

One of the major risk of AI would be robbing intellectual development of human being because AI can do almost all the defined work… it would be interesting to see how we as human evolve in this new paradigm