NVIDIA’s stranglehold on the AI chip market is no accident — it was built on superior silicon, a vast software moat and a perfect timing of demand — but cracks are appearing in the foundations as hyperscalers, geopolitics and emerging regional champions all push back against a single-vendor world.
The accelerated-computing supply chain powering generative AI stretches from fabless chip designers to foundries, then to hyperscalers, system integrators and finally software vendors and end customers. At the lowest level sit the silicon designers and intellectual-property owners: NVIDIA, AMD, Intel, specialist startups and the emerging Chinese houses (Huawei, Alibaba, Baidu). Foundries such as TSMC translate designs into wafers, while ASML supplies the critical extreme‑ultraviolet (EUV) lithography equipment that makes the most advanced nodes possible. Above silicon are the hyperscalers — Amazon Web Services (AWS), Microsoft Azure and Google Cloud — which both consume and increasingly design their own processors. This multi-layered stack is the modern backbone of enterprise AI deployment.
The narrative of “NVIDIA as the engine of AI” is rooted in two facts: the company’s GPUs consistently delivered the best raw performance for training and inference at scale, and NVIDIA invested years building CUDA, cuDNN, NCCL and other libraries that created a massive developer ecosystem. That combination produces a high switching cost for enterprises and researchers who have tuned tooling, models and operational pipelines around NVIDIA’s stack.
But an industry that rewards winners handsomely also invites concerted efforts to dethrone them. The industry is now coping with three accelerating challenges to NVIDIA’s dominance: (1) hyperscalers building their own accelerators and CPUs, (2) geopolitical measures that limit market access and create gray-market distortions, and (3) rapid progress by Chinese chipmakers and system integrators that aim to keep domestic workloads on native silicon. These trends are already visible in market data and regulatory actions.
That means two things:
However, caution is required where the piece moves from empirical claims to sweeping predictions or stylistic flourishes (for example, personality or political analogies about CEOs). Those are opinion and should be distinguished from measurable market facts. Also, some operational claims (for example, precise timelines for Maia production or the commercial parity of specific Chinese chips) are fluid and have been subject to revision; Microsoft’s Maia program experienced design and production timing changes reported in the press, illustrating the uncertain nature of custom‑silicon timelines. Treat individual roadmap dates as provisional until confirmed by vendor releases and manufacturing partners.
Short term: NVIDIA still supplies the engines for the world’s most demanding models and captures outsized margins. Medium term: expect incremental share migration in specific workloads to hyperscaler and regional silicon where cost, scale or policy advantage dictates. Long term: the market will almost certainly be more heterogeneous than it is today — not because one incumbent failed spectacularly, but because the incentives for vertical integration, national security and cost optimization are powerful and persistent.
The prudent posture for IT leaders and investors is not to assume permanence but to plan for resilience: preserve multi‑vendor options, run migration pilots, and follow the real signals of hardware performance, software portability and geopolitics. The AI chip race is an arms race that rewards engineering excellence and political savvy alike — and in that contest, the leader today must keep earning its position tomorrow.
Source: Times of Malta Could NVIDIA’s reign in AI chips start to crumble?
Background
The accelerated-computing supply chain powering generative AI stretches from fabless chip designers to foundries, then to hyperscalers, system integrators and finally software vendors and end customers. At the lowest level sit the silicon designers and intellectual-property owners: NVIDIA, AMD, Intel, specialist startups and the emerging Chinese houses (Huawei, Alibaba, Baidu). Foundries such as TSMC translate designs into wafers, while ASML supplies the critical extreme‑ultraviolet (EUV) lithography equipment that makes the most advanced nodes possible. Above silicon are the hyperscalers — Amazon Web Services (AWS), Microsoft Azure and Google Cloud — which both consume and increasingly design their own processors. This multi-layered stack is the modern backbone of enterprise AI deployment.The narrative of “NVIDIA as the engine of AI” is rooted in two facts: the company’s GPUs consistently delivered the best raw performance for training and inference at scale, and NVIDIA invested years building CUDA, cuDNN, NCCL and other libraries that created a massive developer ecosystem. That combination produces a high switching cost for enterprises and researchers who have tuned tooling, models and operational pipelines around NVIDIA’s stack.
But an industry that rewards winners handsomely also invites concerted efforts to dethrone them. The industry is now coping with three accelerating challenges to NVIDIA’s dominance: (1) hyperscalers building their own accelerators and CPUs, (2) geopolitical measures that limit market access and create gray-market distortions, and (3) rapid progress by Chinese chipmakers and system integrators that aim to keep domestic workloads on native silicon. These trends are already visible in market data and regulatory actions.
How NVIDIA built — and sustained — the moat
Hardware + software = stickiness
NVIDIA’s library stack (CUDA, cuDNN, TensorRT, NCCL), plus system-level products (DGX systems, NVLink, NVSwitch and the NVL72 networking appliances), created an end-to-end environment where models run well with a known performance envelope. That reduces engineering risk for enterprise adopters and hyperscalers. The company’s ability to deliver both the fastest chips and the developer ergonomics to exploit them is rare in silicon — and it drives durable demand. Recent quarterly reports show extremely high gross margins for NVIDIA’s data-center business, consistent with a premium position in the value chain.The numbers that matter
- Discrete GPU shipments and AIB (add‑in‑board) metrics show extreme concentration: analyst snapshots for Q2 2025 reported NVIDIA commanding roughly 94% of discrete AIB shipments, a dramatic consolidation that underscores both performance leadership and a temporary inventory race.
- NVIDIA’s data‑center revenue and gross margins have reached unprecedented levels (gross margin in the 70% range in recent quarters), reflecting the premium for high-end accelerators and the economics of selling to hyperscalers.
Hyperscalers: the disintermediation threat
Building silicon to own the stack
Hyperscalers are not passive consumers. They can be customers, partners and, increasingly, competitors to chip vendors.- Microsoft launched its in‑house AI accelerator family (Maia) and the Cobalt Arm CPU to reduce dependence on one supplier and optimize costs for Microsoft’s Copilot and Azure AI services. Maia was publicly detailed at Microsoft Ignite and has evolved under testing and iteration; subsequent reporting indicates production and ramp timelines that shifted as Microsoft refined the roadmap.
- AWS has developed the Graviton CPU line (widely used across general workloads) and purpose-built AI accelerators Trainium (training) and Inferentia (inference). Strategic partnerships — notably Amazon’s multi‑billion dollar investments in Anthropic and commitments to train Anthropic’s models on Trainium — give AWS commercial and technical channels to scale its silicon inside its own cloud.
- Google has iterated its TPU family; its 2025 Ironwood TPU announcement explicitly targets inference economics at hyperscale and shows how a vertically integrated cloud provider can optimize chip design for the models it runs.
How big a threat is this to NVIDIA?
In the near term, the risk is muted: the largest, most demanding training workloads still rely on NVIDIA for absolute top-end throughput and multi‑GPU networking at scale. But the long view is different. Hyperscalers can:- Route a portion of workloads to their silicon where performance/cost tradeoffs make sense (inference at massive scale; many enterprise training jobs that don’t need the absolute top tier of floating‑point performance).
- Use pricing, bundling and integration to nudge customers toward non‑NVIDIA accelerators where acceptable.
- Co‑develop software stacks that reduce some of NVIDIA's lock‑in for specific classes of models.
Geopolitics and China: the wildcard
Regulatory interference and market access
Global politics is reshaping where and how AI chips can be sold. The U.S. export‑control regime has already limited the shipment of top-tier accelerators to China in recent years. Beijing’s own policy actions — notably, the Cyberspace Administration of China (CAC) instructing some large domestic firms to halt testing and orders of certain NVIDIA China‑tailored products (for example, the RTX Pro 6000D) — are a clear signal that market access is both commercial and political. The CAC’s guidance in September 2025 forced large local players to pause purchases and amplified domestic momentum behind Chinese silicon alternatives. Those actions materially change NVIDIA’s addressable market for the affected SKUs and shorten the runway for continued unabated growth.China’s domestic champions
Chinese firms are closing gaps rapidly:- Huawei has publicly laid out an aggressive Ascend roadmap (Ascend 950/960/970) and is shipping scaled systems like Atlas SuperPoD platforms. Huawei positions Ascend chips and system software to serve domestic hyperscalers and government customers. Independent reports and vendor roadmaps show aggressive claims for performance scaling and capacity plans.
- Alibaba Cloud has made system‑level efficiency improvements (pooling and scheduling) that claim to reduce GPU usage dramatically for inference and model serving, demonstrating that software and system design can erode per‑token compute demand and reduce dependence on premium accelerators.
- Startups and foundry ecosystems in China are rapidly iterating NPU designs and system interconnects, aided by abundant domestic demand and government support. Where U.S. export controls restrict supply, that demand is creating incentives to substitute and scale alternative architectures.
Software lock‑in: the long tail of migration cost
CUDA’s dominance is more than APIs; it is millions of lines of optimized kernels, compiler tricks, third‑party libraries and years of research papers assuming NVIDIA primitives. Migration toolchains (SYCL, oneAPI, AMD’s HIP, SYCLomatic converters, and other porting tools) are making headway, but for production‑grade, latency‑sensitive, multi‑GPU training stacks the conversion task remains substantial.That means two things:
- Software portability projects and conversion tools will shorten the time horizon for effective multi‑vendor deployments, but they do not erase the short‑ and medium‑term advantages of NVIDIA’s ecosystem.
- If hyperscalers and alternative chip vendors can offer a total cost of ownership advantage, customers may accept migration work for long‑term savings. That dynamic is precisely what hyperscalers and alternative silicon builders are banking on: reduce compute cost per token and the incentive to stay with premium GPUs wanes.
Strengths and fragilities of the current landscape
Notable strengths (why NVIDIA still matters)
- Performance leadership at the high end remains real and relevant for top-tier training runs.
- Software ecosystem (CUDA and related tooling) continues to be the primary path of least resistance for enterprises and research labs.
- Commercial relationships with hyperscalers, OEMs and software vendors keep NVIDIA close to large customers’ roadmaps and procurement cycles.
Structural risks and fault lines
- Geopolitical fragmentation: losing access to large markets (such as the CAC’s effective pause in China) can meaningfully reduce addressable revenue and complicate supply‑chain planning.
- Hyperscaler substitution: cloud providers can and are building alternatives that chip away at GPU hours where the economics favor verticalized silicon. AWS and Google’s investments are explicit hedges; Microsoft’s Maia program is the latest example of in‑house risk mitigation.
- Software portability: improving compiler and ecosystem tooling will lower migration costs over time, reducing the time‑dependent value of NVIDIA’s software moat.
- Algorithmic efficiency: research that reduces the compute required for a given model quality (quantization, sparse models, better system scheduling) can make lower‑cost accelerators more attractive for many workloads. Alibaba’s pooling work and papers on efficiency demonstrate that innovation on the software and systems level can alter infrastructure economics.
Where other vendors stand — competition and cooperation
- AMD: With ROCm, MI300X and recent deals (notably reported collaborations with some model labs), AMD is positioning its Instinct line as an enterprise alternative. Platform and library maturity remain barriers, but revenue diversions at scale are possible.
- Intel: Investing heavily in software portability (oneAPI, SYCL) and foundry capacity (Intel Foundry Services). Intel’s recent reported talks with Microsoft over Maia manufacturing show the complex supply relationships in play.
- Chinese vendors (Huawei, Alibaba, Baidu): Aggressive product roadmaps, a large captive market and vertical integration with domestic cloud services give them realistic options to displace NVIDIA in China and in segments where regulatory or cost constraints push customers to local suppliers.
Practical implications for IT teams, Windows users and investors
For enterprise IT and procurement
- Inventory your CUDA dependencies. Catalog which models, pipelines and SDKs are CUDA‑native and which could be ported with lower effort.
- Run migration pilots. Use HIP, SYCLomatic, and containerized experiments on alternative accelerators for non‑mission‑critical models to gather empirical performance and cost data.
- Negotiate cloud contracts with explicit terms about accelerator availability, migration credits and transparent pricing for GPU vs. provider silicon. This preserves optionality as hyperscalers expand their custom offerings.
- Plan for supply shocks. Include scenarios where specific SKUs are regionally restricted or delayed. Maintain multi‑quarter procurement lead times where sensitivity is high.
For Windows users and on‑device AI
- Expect continued deep integration of AI features in Windows and Microsoft 365, but recognize that many consumer AI features will be served by cloud stacks. On‑device inference will grow for privacy and latency use cases, but training-scale workloads will remain cloud‑bound for most organizations for the foreseeable future.
For investors
- Diversify exposure across the stack. Betting solely on GPU vendors captures part of the upside but misses cloud providers, foundries and software tooling companies that will benefit if the market fragments.
- Watch key signals:
- Hyperscaler silicon ramp schedules and public performance publications.
- China regulatory moves and domestic chip shipments.
- Software portability advances and large model evaluations on alternative hardware.
- Beware headline narratives. Some claims (for instance, political or personality comparisons about CEOs) are color rather than predictive evidence. Where a claim is opinion or unverifiable, treat it as market sentiment rather than a technical fact.
Two concrete scenarios and what to watch
- Stabilized multi‑vendor world (most likely; medium‑to‑high impact): NVIDIA stays the high‑end winner but cedes share in volume segments to hyperscalers and regional incumbents. Prices and margins compress modestly; ecosystem fragmentation increases.
- Rapid fragmentation with regional champions (plausible; higher impact): Regulatory closures or domestic substitution in China plus maturation of alternative stacks push large parts of enterprise and national-scale workloads off NVIDIA, materially reducing its growth trajectory and re‑rating multiples.
- Public performance benchmarks of Maia, Trainium, Ironwood and Ascend on representative LLM training and inference workloads.
- Migration case studies from major enterprises or open benchmark suites showing parity or acceptable deltas on alternative hardware.
- Quarterly gross‑margin and data‑center revenue trends for NVIDIA and foundries’ capacity utilization (TSMC/ASML order books).
Strengths, risks and an objective read on the Times of Malta thesis
The Times of Malta piece (the interview excerpted earlier) frames the debate well: NVIDIA’s position is powerful but not permanent; hyperscalers and China represent credible threats. That framing is supported by shipment data showing remarkable recent concentration and by public hyperscaler investments in custom silicon. The article’s broad strokes — that consulting firms face disintermediation, hyperscalers will keep building silicon, and Chinese firms are racing to substitute domestic components — align with the technical and commercial signals visible across industry reports.However, caution is required where the piece moves from empirical claims to sweeping predictions or stylistic flourishes (for example, personality or political analogies about CEOs). Those are opinion and should be distinguished from measurable market facts. Also, some operational claims (for example, precise timelines for Maia production or the commercial parity of specific Chinese chips) are fluid and have been subject to revision; Microsoft’s Maia program experienced design and production timing changes reported in the press, illustrating the uncertain nature of custom‑silicon timelines. Treat individual roadmap dates as provisional until confirmed by vendor releases and manufacturing partners.
Checklist for CIOs and investors (actionable)
- Map model dependencies on CUDA and classify workload criticality (training vs. inference).
- Run at least two benchmark migrations in the next 6–12 months using HIP/SYCL on representative workloads.
- Insert contract clauses with cloud partners that allow trial access to provider silicon and transparent cost comparisons.
- Monitor geopolitical regulatory alerts and escalate procurement flexibility if a target region shows signs of policy volatility.
- Allocate a portion of capital to foundry and tooling plays (TSMC, ASML‑adjacent suppliers, ecosystem software) as defensive hedges.
Conclusion
NVIDIA’s dominance in AI chips is the product of superior performance, a deep software ecosystem and a virtuous commercial flywheel. Yet dominance does not equal permanence. Hyperscalers are removing tail risk by designing silicon tuned to their economics; China’s strategic moves — both regulatory and industrial — aim to build parallel domestic supply chains; and system‑level software advances can materially shrink per‑token infrastructure needs.Short term: NVIDIA still supplies the engines for the world’s most demanding models and captures outsized margins. Medium term: expect incremental share migration in specific workloads to hyperscaler and regional silicon where cost, scale or policy advantage dictates. Long term: the market will almost certainly be more heterogeneous than it is today — not because one incumbent failed spectacularly, but because the incentives for vertical integration, national security and cost optimization are powerful and persistent.
The prudent posture for IT leaders and investors is not to assume permanence but to plan for resilience: preserve multi‑vendor options, run migration pilots, and follow the real signals of hardware performance, software portability and geopolitics. The AI chip race is an arms race that rewards engineering excellence and political savvy alike — and in that contest, the leader today must keep earning its position tomorrow.
Source: Times of Malta Could NVIDIA’s reign in AI chips start to crumble?
