David Mitchell
I study the relationship between interest rates and semiconductor markets, with particular attention to government bond yields, monetary policy expectations, valuation, and the cost of capital. My research examines how changes in rates can influence semiconductor companies and technology-sector investment. I write for this website because semiconductor valuations are often sensitive to changes in interest rates. Higher or lower discount rates can influence how investors value future earnings, while monetary policy can also affect corporate investment and consumer demand. My work follows Treasury yields, central-bank policy expectations, real interest rates, semiconductor valuations, earnings expectations, and capital expenditure. I compare rate movements with semiconductor indices and major technology subsectors. I also examine how interest-rate changes can affect different semiconductor segments differently, particularly capital-intensive manufacturing, high-growth technology companies, and memory markets. Through my articles, I aim to explain the transmission mechanism between interest rates, financial conditions, semiconductor fundamentals, and market valuation.
Macro-semiconductor analyst focused on interest rates, Treasury yields, monetary policy, discount rates, semiconductor valuations, and capital expenditure.
David Mitchell is a macroeconomic researcher specializing in the relationship between interest rates and semiconductor markets. His work focuses on Treasury yields, monetary policy expectations, financial conditions, and semiconductor valuation.
David examines how changes in interest rates can influence the valuation of semiconductor companies and the broader technology sector. He also follows the relationship between discount rates, earnings expectations, and capital-intensive semiconductor investment.
His research compares interest-rate movements with semiconductor indices, capital expenditure, demand conditions, and different industry subsectors. He pays particular attention to the different ways financial conditions can affect manufacturers, equipment companies, and high-growth chip businesses.
Through his articles, David explains the transmission between monetary policy, interest rates, semiconductor fundamentals, and market valuation.
His Articles
Active Correction Strategies for the Systematically Underweighted Memory Weight in AI Compute Indices
Most AI compute indicators and ETFs are built around the obvious heroes: GPUs, accelerators, and custom AI chips. Memory—the bandwidth and capacity that feed those chips—often shows up as a supporting cast with smaller index weights. That systematic underweighting of memory in AI compute indicators is understandable from a market-cap and narrative perspective, but it is increasingly at odds with how AI infrastructure actually works. If memory is a bottleneck, underweighting it is not just a technical quirk; it is a structural allocation error.
2026 Global Semi CapEx Map: The Divergence Between Advanced and Mature Nodes
The semiconductor industry entered 2026 with a clearer, more bifurcated capital expenditure landscape than at any point in the past decade. As hyperscalers and AI-driven computing demands continue to escalate, foundries and integrated device manufacturers (IDMs) prioritize advanced-node capacity (3 nm, 2 nm and next-generation gate-all-around architectures).
The Nonlinear Relationship Between AUM and Tracking Deviation for Memory ETFs
AI has turned memory—from DRAM and HBM to NAND and enterprise storage—into the central bottleneck of computing. As that happens, memory-focused ETFs have gone from niche to headline, with assets under management (AUM) exploding in a short span of time. Along the way, another, quieter variable starts to matter: tracking deviation. How closely do these funds actually follow the memory indices they promise to track? And how does that tracking behave as AUM grows from tiny to massive?
Tracking the Spot vs. Contract Price Spread Dynamics in HBM Market
High-Bandwidth Memory (HBM) has transitioned from a boutique, high-margin specialty to a strategic commodity within high-performance computing and AI infrastructure. That transition created two distinct pricing channels: spot-market transactions (ad-hoc purchases, inventory sales, and opportunistic trades) and contract-based procurement (multi-year supply agreements, prepayments, and capacity reservations).
The Profit Erosion Effect of Soaring Tape-Out Costs for China’s AI Chip Designers
In the last few years, China’s AI chip industry has moved from early experimentation to large‑scale commercial deployment, with dozens of design houses racing to tape out increasingly complex accelerators for data centers, edge devices, and vertical applications. At the same time, the cost of moving a design from layout to silicon—tape out—has risen sharply, driven by advanced process nodes, more complex packaging, and growing verification demands.