The Rise in AI is Tied to the Importance of Materials Engineering
In the rapidly evolving world of technology, the advancement of materials engineering is facing a significant hurdle: the pace of co-innovation across company boundaries. This is particularly true in the realm of computer chips, where Moore's Law, which once delivered improvements in performance and power consumption, is no longer sufficient.
Clinging to a lone-wolf strategy may result in running out of joules and time. However, a new, high-velocity model of co-innovation is emerging, and it's reshaping the landscape of the tech industry. Foundries, equipment suppliers, and power electronics firms are pooling capital to accelerate innovation and commercialization of advanced node technologies.
This collaborative approach is crucial for gaining ground on shrinking carbon footprints and capturing a durable competitive edge. The proposed materials engineering playbook to address the energy gap in AI-driven datacenter electricity demands by 2030 centers on integrating energy efficiency and sustainability deeply into semiconductor design, manufacturing, and supply chains.
Key elements include embedding carbon intelligence across the design and production process of semiconductors, deploying advanced power delivery architectures like 800V high-voltage DC to improve efficiency, aggressively increasing renewable energy sourcing, and reshaping supply chains with a strong focus on net-zero emissions targets and energy recovery.
Leading semiconductor foundries are incorporating science-based targets for carbon reduction and investing heavily in renewable power to achieve up to 60% renewable electricity sourcing by 2030. They are also employing novel materials and designs that enable more scalable and efficient power delivery for AI data centers, addressing the surging electricity demands from densifying AI workloads.
Companies like Applied Materials are engaging their extensive supplier networks to reduce Scope 3 emissions, especially energy consumption embedded in semiconductor products. This includes renewable energy adoption by suppliers, deployment of on-site solar generation, and detailed emissions tracking to ensure measurable progress toward net-zero goals. More than half of key suppliers now have greenhouse gas reduction commitments aligned with science-based targets.
Data centers are moving toward tighter cooling solutions and heat reuse strategies to improve overall energy profiles, with location decisions increasingly influenced by grid reliability, climate, and district heating availability. Rising power constraints are forcing equipment vendors to innovate faster in materials and system-level designs to shorten lead times and minimize energy losses across the supply chain.
The push for energy-efficient AI data centers is accelerating adoption of novel materials engineering solutions that support high-voltage DC architectures enabling more direct and efficient power delivery. These advances reduce conversion losses, improve thermal management, and facilitate sustainable scaling of infrastructure for AI workloads.
In conclusion, the next efficiency curve is being written in materials engineering. The cost of training these models has increased even faster than their compute requirements, making energy efficiency a paramount concern. Every tera-operation of compute must consume fewer joules for large-scale intelligence and a livable planet. The era of a handful of vertically integrated giants managing the entire scaling roadmap is over. Instead, differentiation has migrated toward materials, packaging, and architecture, and none of those domains is the exclusive province of a single company.
Embracing a collaborative, materials-first roadmap is not just about keeping pace with the exponential growth in AI data center power demand projected by 2030, but also about unlocking efficiency gains as large as 2,500x. AI could push datacenter electricity demand toward as much as 10-15% of global consumption by 2030. However, with the right strategies and collaborations, we can ensure that the future of AI is not just powerful, but also sustainable.
Sources:
- TechTarget
- Applied Materials
- GreenBiz
- Semiconductor Engineering
Sundeep Bajikar, a key figure in the tech industry, emphasizes the importance of collaboration in the advancement of artificial-intelligence technology. He highlights the role of materials engineering in addressing the energy gap in AI-driven datacenter electricity demands by 2030, stating that none of the domains in this field is the exclusive province of a single company.
The proposed materials engineering playbook, which includes integrating energy efficiency and sustainability deeply into semiconductor design, manufacturing, and supply chains, requires the pooling of resources and knowledge from foundries, equipment suppliers, and power electronics firms, as they work together to accelerate innovation and commercialization of advanced node technologies. This collaborative approach is crucial for capturing a durable competitive edge and gaining ground on shrinking carbon footprints.
