Compute Chips
Over the past several AI chip generations, one trend has become impossible to ignore: thermal design power (TDP) is climbing at a pace that would have seemed extreme even a few years ago. When flagship accelerators and high‑end AI chips see TDP grow on the order of 50% per generation, the consequences extend far beyond chip packaging.
Compute Chips
As AI chips surge to the forefront of national technology strategies, attention has increasingly turned from fabs and packaging to a crucial but less visible layer: electronic design automation (EDA). For many years, domestic EDA tools lagged behind international leaders, limiting how far local firms could push advanced AI chip design without external dependencies.
Compute Chips
AI chip companies sit at the center of one of the most capital‑intensive and innovation‑driven industries in the world. Their success depends on sustained, heavy investment in research and development, yet they must also deliver financial performance that satisfies investors, funds next‑generation fabs and packaging, and supports competitive pricing. The question of how much to spend on R&D—expressed as an R&D expense ratio relative to revenue—cannot be answered in isolation.
Compute Chips
As AI chips move from niche accelerators to pervasive compute foundations in data centers, edge devices, and embedded systems, the way CPU and AI cores are licensed has become a strategic question rather than a mere technical detail. ARM’s established commercial IP licensing model and the rising ecosystem around RISC-V represent two contrasting philosophies: one offers mature, standardized cores under proprietary licenses;
Compute Chips
As AI chip companies mature from high‑growth stories into capital‑intensive platforms, investors increasingly focus on free cash flow (FCF) as the core signal of long‑term value creation. Revenue growth, unit shipments, and benchmark leadership still matter, but they no longer tell the full economic story. What ultimately determines whether a leading AI chip company justifies its valuation is when, and how, its investments in R&D, tape‑outs, capacity, and ecosystem translate into sustained, positive, and growing FCF. This transition moment is what we can call the FCF reflection point.
Compute Chips
The AI hardware landscape in 2026 is defined not just by traditional semiconductor vendors, but increasingly by hyperscalers building their own, deeply integrated silicon. Among the most important examples are Google’s TPU v7 and Amazon’s Trainium 3, two generations of in‑house accelerators aimed squarely at large‑scale AI training and inference in their respective clouds.
Compute Chips
For years, multi‑chip modules (MCMs) and chiplet‑based designs have promised a way around the limits of monolithic system‑on‑chips: better yield, more flexible integration, and modular reuse of IP. Yet one constraint kept their potential in check—the performance bottlenecks at the interfaces between dies. As long as die‑to‑die links behaved more like “mini‑PCIE slots” than true on‑chip fabric, MCMs could not fully match the speed and efficiency of large single dies.
Compute Chips
The rapid rise of AI has created unprecedented demand for high‑end accelerators, from data‑center GPUs to custom AI chips and complete inference boards. As supply challenges, long lead times, and aggressive deployment plans collide, a secondary market for AI chips has emerged and grown dramatically.
