Despite the recent signs of weakness in the US economy, the data center business is booming. A combination of converging factors including AI, cloud computing and cryptocurrency have data center construction projects underway across the country.
But with progress comes a challenge: what happens to the mountains of outdated servers? The rapid innovation in GPU hardware technologies by companies like NVIDIA means that the typical replacement cycle is accelerated (sometimes as short as 1 year) so companies can take advantage of the benefits of improved hardware.
At Rose City Robotics, we think the answer is mathematics. Machine learning model improvement through advanced mathematics techniques can improve two key factors: first automating the disassembly and recycling of servers and second increasing the energy efficiency of data centers through algorithmic improvements.
But with progress comes a challenge: what happens to the mountains of outdated servers? The rapid innovation in GPU hardware technologies by companies like NVIDIA means that the typical replacement cycle is accelerated (sometimes as short as 1 year) so companies can take advantage of the benefits of improved hardware.
At Rose City Robotics, we think the answer is mathematics. Machine learning model improvement through advanced mathematics techniques can improve two key factors: first automating the disassembly and recycling of servers and second increasing the energy efficiency of data centers through algorithmic improvements.
The Need for Server Recycling
Across the U.S., over 5,000 data centers house millions of servers, many of which are replaced every 2–3 years (PCX Corporation). Some organizations, particularly those leading AI and cloud computing, upgrade hardware annually. While this ensures peak performance, it also accelerates the accumulation of obsolete servers.
Maximizing Value: More Than Just Scrap
Organizations that approach recycling strategically can benefit in multiple ways:
- Component Resale: CPUs, memory, and storage devices hold significant resale value. Separating these high-value parts before recycling can maximize returns.
- Refurbishment Opportunities: With 23% of server upgrades incorporating refurbished components, extending hardware lifespans can be a cost-effective alternative (Datacenter Dynamics).
- Market Timing: The first quarter of the year presents prime selling opportunities, as enterprises roll out new budgets and upgrade hardware.
- Partnerships: Collaborating with specialists in data center decommissioning can optimize value recovery and ensure compliance with data security regulations like GDPR and CCPA.
Cost-Saving Strategies in Recycling
Beyond direct resale value, smart recycling can significantly reduce operational costs:
- Hazardous Waste Reduction: By separating non-hazardous components from hazardous ones, companies can cut disposal costs and improve regulatory compliance.
- Efficient Processing: Proper sorting streamlines recycling, reducing labor and transportation expenses.
- Resource Recovery: Extracting rare metals from servers minimizes costs by feeding materials back into the manufacturing process.
The Manual Nature of Disassembly and the Need for Innovation
Despite advances in automation, server decommissioning remains largely manual. Technicians earning around $70K annually are responsible for dismantling, sorting, and packing components. While barcode scanners and automated data erasure tools improve efficiency, the physical labor involved remains a bottleneck. Innovations in automated disassembly could revolutionize this process.
Machine Learning and Robotics: Automating Data Center Recycling
One of the most promising innovations in data center sustainability is the application of machine learning to robotics, enabling automation in the server recycling process. At Rose City Robotics, we are developing and advancing mathematic techniques to enable ML-powered robotics that do not require explicit programming but instead learn tasks through human demonstration using Transformer Neural Networks.
This approach, demonstrated in our prototype Rosie (read more about Rosie here), allows robotic arms to efficiently disassemble and sort server components, reducing reliance on manual labor and increasing efficiency. By training machine learning models on large datasets of server decommissioning tasks, we can improve precision, speed, and adaptability in recycling operations.
This approach, demonstrated in our prototype Rosie (read more about Rosie here), allows robotic arms to efficiently disassemble and sort server components, reducing reliance on manual labor and increasing efficiency. By training machine learning models on large datasets of server decommissioning tasks, we can improve precision, speed, and adaptability in recycling operations.
Sustainable Innovation is our Mission
At Rose City Robotics, our mission is to drive sustainable innovation in the technology sector by bridging the gap between cutting-edge advancements and responsible resource management. We believe that sustainability and technological progress go hand in hand, and our commitment extends beyond robotics to broader issues like data center recycling. By applying engineering principles to optimize the lifecycle of data center hardware, we aim to create solutions that are both economically viable and environmentally responsible.
Teaming up with Portland State University Mathematics Department
To further this effort, Rose City Robotics is in the process of completing our NSF SBIR grant application in collaboration with the mathematics department at Portland State University. Our goal is to develop algorithm efficiency-optimized foundational models that reduce computational overhead while maintaining performance. By leveraging advanced mathematical frameworks, we aim to create AI models that require fewer data center resources, ultimately contributing to a more sustainable AI infrastructure. At Rose City Robotics, we advocate for an approach that not only advances AI but does so responsibly, ensuring that future innovations are both powerful and sustainable.
As AI adoption accelerates, data centers are being pushed to their limits, requiring more power and infrastructure expansion. However, optimizing machine learning models can significantly reduce the computational workload, thereby decreasing the need for additional data center construction. By enhancing algorithm efficiency organizations can achieve the same AI-driven results while consuming less energy. This, in turn, improves sustainability by cutting both operational costs and the environmental footprint of data centers.
At Rose City Robotics, we advocate for an approach that not only advances AI but does so responsibly, ensuring that future innovations are both powerful and sustainable.
At Rose City Robotics, we advocate for an approach that not only advances AI but does so responsibly, ensuring that future innovations are both powerful and sustainable.
The Growth Opportunity in Data Center Recycling
Market trends underscore the rising demand for data center capacity. The global cloud data center market is expected to grow from $32.28 billion in 2025 to $75.40 billion by 2034, driven by:
- AI Expansion: AI-ready data centers will account for 70% of total capacity demand by 2030, with an annual growth rate of 33% (McKinsey & Company).
- Energy Efficiency Pressures: With data centers projected to increase global power consumption by 50% by 2027 and up to 165% by 2030, energy-efficient solutions—including recycling—are gaining traction (Goldman Sachs Research).
- Tariffs and Supply Chain Constraints: Rising component costs make recycled parts more valuable, presenting opportunities for second-life hardware.
- European Regulatory Trends: Big tech companies with European operations face stringent data privacy and recycling mandates, increasing demand for sustainable server disposal solutions.
The Future of Data Center Impact: A Smarter, More Sustainable Approach
Despite economic uncertainties, the data center industry continues to expand, driven by AI, cloud computing, and cryptocurrency. However, with rapid progress comes the pressing challenge of managing outdated hardware. The frequent replacement of GPUs and servers—sometimes as often as every year—demands a more strategic, sustainable approach to recycling and resource optimization.
At Rose City Robotics, we believe that the key to addressing this challenge lies in mathematics and machine learning. By leveraging cutting-edge AI models and robotics, we can transform how data centers recycle their outdated equipment. Our work with Portland State University’s mathematics department and our NSF SBIR grant application represent significant steps toward making algorithm efficiency a cornerstone of sustainable AI and data center management.
Through automation, machine learning can not only streamline server disassembly but also reduce the need for excessive infrastructure expansion by optimizing energy efficiency at the algorithmic level.
Through automation, machine learning can not only streamline server disassembly but also reduce the need for excessive infrastructure expansion by optimizing energy efficiency at the algorithmic level.
The future of data center recycling isn’t just about hardware, it’s about smarter systems. Those who embrace advanced robotics, efficient AI models, and sustainable resource management will not only reduce costs but also help shape a greener, more responsible digital landscape.
At Rose City Robotics, we are committed to making this vision a reality.
At Rose City Robotics, we are committed to making this vision a reality.
Duncan Miller
Co-founder / CEO
Duncan is a software enginner with 20 years of experience as a founder in ed-tech, AI and clean-tech. He has expertise in automation software, test-driven AI development, user interface design and clean energy technologies. Duncan earned an MBA in Entrepreneurship from Babson College and teaches with the PSU center for entrepreneurship and business accelerator. He lives with his wife and two children in Portland Oregon on an extinct cinder code volcano. He is passionate about artificial intelligence, climate solutions, public benefit companies and social entrepreneurship.