Four independent forecasts, published between December 2025 and May 2026, converge on the same ramp shape: AI accelerator wafer demand is rising sharply, the broader semiconductor market is doubling, and memory pricing is the dominant volatility vector [S1][S2][S3][S4].
Next Move Strategy Consulting sizes the standalone AI chip market at USD 52.92 billion in 2024 and projects USD 295.56 billion by 2030, a 33.2% CAGR over 2025-2030, while CITIC Securities frames 2030 AI chip market potential as 5-9x the 2025 base and potentially above USD 1 trillion globally [S1][S4].
Wafer demand: an 11x climb into 2026, with the bottleneck at advanced nodes
TSMC stated on 13 May 2026 that global chip market revenue will reach USD 1.5 trillion by 2030, and disclosed that AI accelerator wafer demand is projected to increase 11-fold from 2022 to 2026 [S2].
The pressure point is geometry. Next Move Strategy Consulting segments the AI chip market by technology node into under 7 nm, 7-10 nm, and other categories, with under 7 nm expected to capture the majority share because training-grade GPUs, custom ASICs, and high-end NPUs all target the most advanced process available [S1]. The introduction of 2 nm chipsets is cited as a separate market opportunity rather than a substitution event, since node migration lifts wafer ASPs faster than it lifts unit volumes [S1].
For buyers of industrial compute platforms, this matters because AI accelerator boards sit alongside pressure transmitters, flow meters, and PLCs on the same procurement schedules, and the 11-fold wafer ramp compresses allocation windows for any fab-adjacent component.
HBM and memory: 12.1x by 2031, with a 2028 air pocket
Fuji Chimera Research Institute, in a 21 May 2026 release translated by IBTimes Japan, sizes the 15-device advanced semiconductor market at 224 trillion yen (USD 1.41 trillion) in 2031, 2.3x the 2025 level, and projects high-bandwidth memory (HBM) at 49.7 trillion yen in 2031, a 12.1x multiplier over 2025 [S3].
The same forecast flags a near-term volatility window: broader market growth is expected above 60% year-on-year in 2026, driven by sharp memory price increases tied to supply shortages, with memory price normalization projected around 2028 that may temporarily flatten growth [S3]. HBM is a stacked DRAM variant used on AI server accelerator boards, and Fuji Chimera's 71.52 trillion yen 2031 figure for AI accelerator chips implies unit prices keep rising as performance tiers migrate upward, not falling on a normalized curve [S3].
The 2028 air pocket is the call-out: any procurement plan that assumes a smooth 2025-2030 cost curve on accelerator memory is betting against Fuji Chimera's published trajectory [S3].
Compute-power demand: 10-39x incremental scale by 2030
CITIC Securities, writing in late 2025, framed the ramp from a demand angle rather than a supply angle: assuming a five-year chip lifespan, the incremental computing power scale by 2030 could rise to 10-39 times that of 2025, and after factoring in per-unit computing power cost reductions the AI chip market size by 2030 could grow to 5-9 times that of 2025, putting global AI chip market potential above USD 1 trillion [S4].
The 10-39x range reflects uncertainty in two variables that the chip-level forecasts do not disaggregate: model parameter count growth and inference-to-training ratio shift. Training workloads dominate today's accelerator mix, but inference at scale, particularly on edge NPUs and on-device AI, reshapes the unit mix without changing the wafer footprint proportionally [S1][S4]. Next Move Strategy Consulting segments workloads into training and inference, and deployment into cloud data center, edge, and on-device, and treats inference as the higher unit-volume, lower-ASP segment [S1].
Who this market is for, and who it is not
The forecasts are written for hyperscalers, AI-first cloud platforms, sovereign-AI infrastructure programs, and the OEM supply chain feeding them: foundry, HBM, advanced packaging, EDA, and high-end industrial valve and servo motor suppliers used in fab tool cooling and handling [S1][S2][S3].
It is not a market for commodity MCU buyers or for legacy automotive-grade microcontroller procurement: those segments are touched by AI indirectly (e.g., AI-assisted EDA, AI-driven yield analytics) but are not the volume or value driver in any of the four cited forecasts [S1][S4]. Gartner, cited by Next Move Strategy Consulting, projects semiconductor revenue used in AI rising from about USD 44 billion in 2022 to USD 120 billion by 2027, a 2.7x increase that already exceeds the broader semiconductor industry's normal growth band [S1].
Cross-source comparison: where the four forecasts agree, and where they diverge
All four forecasts agree on direction and rough magnitude: the AI chip market will roughly 5-9x between 2025 and 2030, with wafer demand and HBM demand growing on steeper curves than overall semiconductor revenue [S1][S2][S3][S4].
They diverge on three points worth flagging for spec work: (1) the endpoint year (2030 for Next Move Strategy Consulting and TSMC; 2031 for Fuji Chimera), (2) the base-currency framing (USD for Next Move Strategy Consulting, TSMC, and CITIC; JPY for Fuji Chimera, where 224 trillion yen in 2031 is the full advanced-semiconductor basket, not just AI chips), and (3) the volatility window (Fuji Chimera calls a 2028 memory normalization; TSMC and Next Move Strategy Consulting do not) [S1][S2][S3]. A 2025-2030 supply planner should treat 2028 as a known-storm window, not a known-storm event, and size HBM allocations with a cost-upside scenario, not a flat ASP line [S3].
The cross-source agreement on the 5-9x 2030 multiplier, versus a 33.2% CAGR endpoint, is the engineering takeaway: a straight-line extrapolation is too conservative, and a step-change in 2027-2028 is consistent with all four published views [S1][S4].
Signals to track: capacity, node migration, and the 2028 HBM inflection
Three trackable signals will determine whether the 2030 numbers hold, soften, or overshoot. First, the under-7 nm and 2 nm capacity additions disclosed in TSMC's Arizona footprint, which directly gate the 11x wafer multiplier reaching 2026 [S2]. Second, the 2028 memory price normalization called by Fuji Chimera, which is the single largest downside variable in any 2028-2030 AI capex model [S3]. Third, the inference-to-training workload split, which determines whether the 5-9x CITIC multiplier materializes at the high or low end of its range [S1][S4].
For a wider read on the parallel commodity ramps shaping 2026 industrial procurement, the Cutting Tools Market 2026 sizing piece and the Energy Storage Market 2026 capacity and battery-type split sit on the same fab-adjacent demand curve. The Measuring Instruments 2026 spec-anchored comparison is the natural pairing for any buyer reconciling accelerator-board instrumentation spend against broader plant-instrumentation budgets.