You are using a web browser version that is no longer supported. Please make sure you are using the most updated version of your browser, or try using our supported browser Google Chrome to get the full Applied Materials experience.
Sundeep Bajikar is Vice President of Corporate Strategy and Market Intelligence at Applied Materials where he is responsible for shaping Applied’s strategy related to AI, in addition to tracking and analyzing Applied’s core business performance. He joined Applied in 2017 after spending ten years as a Senior Equity Research Analyst covering global technology stocks including Apple and Samsung Electronics, for Morgan Stanley and Jefferies. Previously he worked for a decade as Researcher, ASIC Design Engineer, System Architect and Strategic Planning Manager at Intel Corporation. He holds an MBA in finance from The Wharton School and M.S. degrees in electrical engineering and mechanical engineering from the University of Minnesota. He holds 13 U.S. and international patents with more than 30 additional patents pending. Sundeep is also author of a book titled, “Equity Research for the Technology Investor—Value Investing in Technology Stocks.”
In our Battle of Exponentials framework, data grows exponentially as Moore’s Law slows, creating increased demand for silicon and wafer fab equipment. Are there enough valuable applications to fuel data growth as capital intensity rises? We believe so. In this blog post I focus on the promise of tokenization to digitize and democratize virtually all kinds of transactions.
We’ve received great feedback on our “Battle of Exponentials” framework, which examines the market impacts we’ve observed since around 2015 as data generation continued to expand at an exponential rate while classic Moore’s Law scaling slowed. In this blog post we focus on the step-up in NAND equipment capital intensity that occurred during the transition from 2D to 3D NAND architectures.
After declining significantly from a peak in 2000, wafer fab equipment (WFE) intensity has been on an uptrend in recent years. As a result, semiconductor equipment revenue growth has been outpacing semiconductor growth.
My previous blog explained the computing architecture requirements for AI workloads. Now, I take a deep dive into the types of materials engineering breakthroughs needed to enable these new architectures.