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 accelerates that demand at an unprecedented rate.
From data to nature: A new mission takes root
After the sale of Cleversafe, I found myself at a crossroads. I had spent over a decade building a company that was at the cutting edge of technology, but I felt a growing desire to reconnect with something more tangible and enduring. That desire led me to co-found The Forge Charitable Adventures, a nature sanctuary with a mission to restore and protect some of the most vulnerable and rare ecosystems in the world, in 2016. What began as a passion project quickly evolved into a much larger mission: to create a self-sustaining system that could have a lasting impact on both the environment and the community.
At The Forge, we’ve begun restoring 300 acres of rare ecosystems, bringing back native species, and turning once dying habitats into thriving ecosystems. It’s a project that requires decades of work, careful planning, and a commitment to sustainability. But the initial results have been nothing short of remarkable. Where invasive species once choked out life, we now have prairies buzzing with bees and birdsong. It’s a testament to the power of long-term thinking and the importance of creating systems that can sustain themselves over time.
The experience of building The Forge taught me something crucial: Just like in the tech world, true impact takes time, patience, and a systems-based approach. Whether you’re restoring an ecosystem or scaling a tech company, the goal should be to create something that can endure for decades, if not centuries.
The parallel threats of the natural and digital worlds
The parallels between preserving nature and managing the growth of AI are striking. Both require us to think in systems, not just one-off solutions. In the natural world, you can’t just plant a few trees and call it conservation. True ecosystem restoration requires a deep understanding of how different species, habitats, and environmental factors interact over time. In the same way, building sustainable digital infrastructure requires more than just deploying AI models or adding more servers. It’s about creating a resilient, scalable system that can evolve as demands change.
The proliferation of AI and big data has created a digital “gas guzzler” effect, where the benefits of innovation come with a hefty energy price tag. According to the International Energy Agency, global data center energy consumption could more than double by 2026, reaching levels that exceed large nations. The irony is that while we’re using AI to solve some of the world’s biggest challenges—from climate modeling to health-care breakthroughs—we’re also contributing to an environmental crisis of a different kind.
AI’s energy consumption is no longer a theoretical concern; it’s a rapidly growing problem that is already impacting businesses and communities. As the application of AI spreads across industries, many companies are finding that their energy costs are skyrocketing, often outpacing the benefits of the technology itself. The challenge is that most businesses are still focused on the immediate gains to be made with AI, without fully considering what the long-term costs—especially the energy costs—of maintaining these systems will be in the years to come. It’s a global problem, and a rapidly accelerating one at that.
The AI energy crisis: a looming threat
At Ocient, we’re focused on tackling this problem head-on by strengthening the foundational elements AI is built upon: data storage, processing, and analytics. By designing Ocient from the ground up to integrate data storage and compute functions more efficiently, we enable organizations to streamline the most energy-intensive aspects of data movement, management, and analysis and reduce their overall energy consumption by up to 90% less than traditional systems. In an era where AI applications are driving unprecedented demand, the efficiency of the underlying data infrastructure has never been more critical.
The reality is that as the application of AI continues to spread, it’s not just the cutting-edge algorithms that matter—it’s also the efficiency of the data technologies that power them. We’re entering an era where energy availability and cost will be the biggest constraints on growth, even more so than computing capabilities. The shift we’re witnessing is a recognition that, much like preserving ecosystems, building sustainable digital infrastructure requires more than just quick fixes. It requires a long-term commitment to efficiency and a system-wide approach.
If current trends continue, planet Earth will run out of additional power for data center growth in the coming years. That’s why we need to bend down the curve of currently accelerating data center energy demand by substantially increasing the energy efficiency of new computing systems. For the first time in the history of computing, future plans for data center development and new computing system deployments are being met with “you can’t do that,” because the additional energy needed isn’t available. Even the hyperscalers and cloud providers can’t get the energy they need and have begun telling their largest customers there’s not enough energy available to power their future AI plans.
AI’s energy demands are particularly concerning when you consider that we’re still in the early stages of its adoption. As more businesses integrate AI into their operations, the energy required to support these systems will only grow, likely exponentially. And unlike other areas of technology, where efficiency gains have kept pace with increased usage, AI is different. The more powerful the models become, the more energy they require. There’s a real risk that, without significant advances in energy efficiency, AI could become one of the largest drivers of energy consumption—and by extension, carbon emissions—in the coming decades.
Building a sustainable foundation for AI
For businesses and enterprises, the foundation of any AI strategy should begin with optimizing the underlying data infrastructure. That makes sense, given AI is built upon layers of data pipelines, storage, and analytics systems, each with their own energy footprints. The more efficient those layers are, the more scalable and sustainable AI deployments can be. That’s where companies like Ocient come in. Our focus isn’t on creating the AI models—it’s on ensuring that the data systems feeding those models are as efficient, sustainable, and cost-effective as possible.
In the rush to deploy AI solutions, it’s easy to overlook the foundational elements of data infrastructure. But without the right systems in place, AI’s potential becomes limited by the inefficiencies that creep into the process—whether through excessive energy consumption, data bottlenecks, or escalating costs. By focusing on sustainable data management, we’re not just addressing today’s needs; we’re setting the stage for an AI-powered future.
Practical steps for business leaders
For business leaders navigating the complex AI landscape, I recommend anchoring on three fundamental principles. First, think long term. AI and data strategies shouldn’t be built for immediate gains but for sustainability over decades. What you design today should be adaptable and scalable as demands change. This means building systems that can evolve, not just exist as one-off solutions.
Second, consider the full lifecycle cost of your technology investments, including energy consumption. As AI becomes more embedded in everyday operations, the energy required to power those systems will become a major cost driver. Start planning for it now. This includes evaluating the energy efficiency of the infrastructure you’re building and selecting vendors who prioritize sustainability.
Finally, embrace a systems mindset. Just as restoring an ecosystem requires more than planting a few trees, building AI capabilities requires more than just deploying models. It’s about creating a resilient, sustainable infrastructure that can evolve over time. Investing in energy-efficient data architectures, optimizing storage and compute resources, and thinking beyond short-term gains will benefit your systems long term.
The road ahead: a call to action
As someone who has spent years working in both the digital and natural worlds, I’ve seen how deeply interconnected they are. The choices we make in one will inevitably impact the other. We have a rare opportunity—and responsibility—to shape a future where both natural and computing ecosystems can thrive. The question is, will we rise to the challenge?
Read more:
- Why an AI energy crisis may not unfold how you think: IBM sustainability chief
- AI’s massive appetite for energy will make or break climate-change goals
- AI could gobble up a quarter of all electricity in the U.S. by 2030 if it doesn’t break its energy addiction, says Arm Holdings exec
- OpenAI’s Sam Altman is funding a green-energy moonshot as AI’s power demands grow to ‘insatiable’ levels
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.
