Jonathan Brooks
I focus on enterprise memory and storage infrastructure, especially the growing requirements created by AI workloads, cloud computing, and large-scale data processing. My research covers DDR5 server memory, high-capacity SSDs, NAND technologies, storage architectures, memory bandwidth, and the relationship between computing infrastructure and semiconductor demand. I write for this website because the requirements of modern data centers are changing rapidly. AI systems need large amounts of memory and storage, but the requirements are different across training, inference, cloud services, enterprise applications, and traditional workloads. Understanding these differences is important when evaluating future semiconductor demand. My research follows server DRAM adoption, DDR5 capacity requirements, enterprise SSD deployment, NAND technologies, storage performance, and data-center architecture. I also examine how the increasing scale of cloud and AI infrastructure affects the balance between compute, memory, networking, and storage. Another part of my work focuses on the economics of enterprise storage. Capacity, performance, endurance, power consumption, and total cost of ownership can all influence purchasing decisions. These factors can in turn affect demand for different memory and storage technologies. I also follow investment themes related to enterprise memory and storage. My goal is to help readers understand the semiconductor components behind large-scale computing infrastructure and explain how changes in AI and cloud investment may affect server memory, NAND, SSDs, and related semiconductor markets.
Enterprise memory analyst covering DDR5 server DRAM, SSDs, NAND, AI storage, cloud infrastructure, memory bandwidth, and data-center architecture.
Jonathan Brooks is an enterprise memory and storage analyst who examines the semiconductor technologies supporting cloud computing, AI infrastructure, and large-scale data centers. His coverage includes server DRAM, DDR5, NAND, enterprise SSDs, and storage architecture.
Jonathan studies how modern data centers balance compute, memory, networking, and storage requirements. His research considers the different infrastructure needs of AI training, AI inference, cloud services, enterprise applications, and traditional computing workloads.
Server memory is a major part of his coverage. Jonathan follows DDR5 adoption, increasing memory capacity requirements, server configurations, and the relationship between processor performance and available memory bandwidth.
His research also covers enterprise SSDs and NAND storage. Capacity, performance, endurance, power consumption, and total cost of ownership are important factors in enterprise storage decisions, and Jonathan examines how these considerations influence semiconductor demand.
Through his articles, Jonathan provides readers with a system-level perspective on enterprise memory and storage and explains how AI and cloud infrastructure investment is changing demand across the broader semiconductor ecosystem.
His Articles
A Full Market Tracking Error Test of Semi ETFs – Which Replicates Best?
When investors compare semiconductor ETFs, they usually start with returns, fees, or holdings. Those are all important, but there is another dimension that often matters just as much: how well the ETF actually replicates its benchmark. In a sector like semiconductors, where concentration is high and the largest names can move the whole market, tracking error becomes a serious test of product quality. A fund may look good on paper, but if it drifts too far from its index, it is not doing the job investors expect.