In today’s world, data is often heralded as the new oil, fueling everything from global financial markets to the algorithms behind our favorite apps. But just like oil, our growing dependence on data has a serious environmental cost that is too often overlooked. As the CEO of a data analytics software solutions company, I’ve had a front-row seat to witness how the explosion of AI and big data is driving an insatiable demand for computing power. The environmental toll is mounting, and it’s happening faster than most people realize.
My journey from selling a billion-dollar tech startup to founding a nature sanctuary has given me a unique perspective on the parallel threats our natural and digital environments face. It’s time we rethink how we manage the energy demands of our digital world—before it’s too late.
Lessons from scaling a billion-dollar tech company
In 2004, I founded Cleversafe, a data storage startup that sought to revolutionize how the world managed massive datasets. At the time, we were a small, scrappy team with a bold vision: to create a new way to store and manage unstructured data at scale. Our technology dispersed data across multiple servers, offering enhanced security, price-performance and reliability. But beyond the technology itself, what truly set us apart was our team’s relentless drive to solve complex problems and build lasting relationships with our customers.
The success of Cleversafe was rooted in more than just our products; it was built on trust and expertise. We were a small company in an industry dominated by giants, yet our customers—some of the largest enterprises in the world—chose to work with us because they believed in our people. By 2015, our growth and impact had scaled to the point where IBM acquired us for $1.4 billion. It was a moment of validation for our work, but it also marked a turning point in how I began to think about the broader impact of technology.
In the tech world, we often talk about scaling efficiency, but that efficiency comes with hidden costs. Every byte of data stored, every AI model trained, requires energy—and a lot of it. As we pushed the boundaries of what was possible with data storage, I also witnessed how the demand for computing power was growing exponentially, creating an insatiable appetite for energy. The challenge was no longer just about storing more data, but about doing so sustainably—a lesson that’s even more relevant today as AI…
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