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Amazon To Test AI-Created Material For Carbon Capture In Data Centers

Amazon is stepping up its environmental efforts by testing a groundbreaking carbon-removal material for its data centers. The company, which is tackling the growing emissions linked to the artificial intelligence systems powering these centers, has partnered with Orbital Materials, a startup that used AI to design the innovative substance.

Jonathan Godwin, CEO of Orbital Materials, explained that the new material acts like an atomic-level sponge, with cavities precisely sized to capture CO2 without interacting with other elements. This targeted approach could be a game-changer in carbon filtration.

One of the appealing aspects of the new material is its cost-effectiveness. Godwin estimates that the material could account for just 10% of the cost associated with renting a GPU chip for AI training, significantly less than the price of traditional carbon offsets.

Meanwhile, the demand for energy in data centers is rising, as AI’s rapid development requires more power and cooling solutions. This surge poses a challenge for Amazon, which is committed to achieving net-zero carbon emissions by 2040.

Amazon Web Services (AWS), the world’s largest cloud provider by revenue, plans to begin piloting the AI-designed carbon removal material in one of its data centers starting in 2025. This initiative is part of a three-year collaboration with Orbital, which will also gain access to AWS’s technology and open-source AI tools for further development.

Howard Gefen, General Manager of AWS Energy & Utilities, stated that the partnership would promote sustainable innovation, but financial details remain undisclosed. Orbital, with offices in Princeton, New Jersey, and London, began its journey about a year ago by setting up a lab to synthesize AI-designed materials. The startup aims to work with AWS to test additional AI-generated solutions, addressing water usage and cooling requirements in data centers. Godwin co-founded Orbital, which currently employs 20 people and is supported by investors such as Radical Ventures and Nvidia’s venture arm. Before this, Godwin contributed to materials science work at Alphabet’s DeepMind until 2022.

Amazon Launches OpenSearch Upgrade To Support AI Agent Workloads

Cloud infrastructure was largely designed around human activity, such as searching, browsing, streaming and interacting with websites. The rise of AI agents is creating a different type of demand, characterized by rapid bursts of automated activity involving database queries, document searches and API calls. As enterprises deploy more AI-powered systems, cloud providers are adapting infrastructure to support increasingly complex machine-to-machine workloads.

Adapting To The New Age Of Agentic Traffic

Recognizing the fundamental shift in traffic patterns, Amazon Web Services (AWS) has reimagined a foundational element of its cloud offering. On Thursday, AWS launched its next generation of OpenSearch Serverless. This advanced, fully managed search and vector database is engineered specifically for agentic workloads, scaling instantly when task bursts occur and minimizing costs by scaling down to zero during idle periods.

Meeting the Demands Of Machine-Generated Traffic

Industry leaders now understand that infrastructure optimized for human-driven internet is ill-suited for the exponential growth of machine-generated traffic. Cloudflare recently reported that bots accounted for 31% of HTTP traffic over the last six months, with AI crawlers and search assistants driving a significant portion of these requests. As Lai Yi Ohlsen, Senior Product Manager at Cloudflare, noted, “Non-human traffic will exceed human traffic sometime in the first half of 2027.”

AI Agents Move Into Production

Recent announcements across the technology sector indicate that AI agents are moving beyond experimentation and into wider commercial use. At Google I/O, Google introduced tools designed to help users delegate tasks such as research and travel planning to AI systems. Businesses are also deploying internal AI agents to automate workflows, increasing the volume of machine-to-machine interactions across enterprise networks.

Technical Changes To OpenSearch

Tia White said the updated platform separates compute resources from storage, allowing capacity to scale more efficiently as demand changes. According to AWS, the model is intended to help organizations manage unpredictable traffic spikes generated by AI systems while reducing infrastructure costs during idle periods.

Integrations and Industry Implications

At launch, OpenSearch Serverless will integrate natively with AI development platforms such as Vercel and Kiro, enabling developers to deploy robust search and vector backends without the overhead of infrastructure management. This innovation aligns with broader industry trends, as companies such as Databricks, Snowflake, Microsoft, and Cloudflare pivot their services to support AI-driven memory and retrieval for enterprise data. As AI adoption accelerates, the pressure for infrastructures that optimize for machine-generated workloads will only intensify.

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