Custom Silicon Is Testing Nvidia’s AI Moat … And the Pressure’s Rising
As hyperscalers roll out their own AI chips and open interconnects gain traction, Nvidia’s once-untouchable position faces new headwinds.
The AI compute map is being redrawn
For much of the past decade, Nvidia’s AI empire has looked unassailable. Its GPUs, NVLink interconnect, CUDA software stack and tight ecosystem created a self-reinforcing moat that delivered stratospheric market share and valuation. But tectonic shifts in how AI is being deployed and scaled are now threatening that dominance.
Today, large technology companies are designing their own chips. Open interconnect standards are winning early adoption. And the logic of vertical integration is pulling custom silicon into the mainstream. For institutional investors and semiconductor executives, this is not a hypothetical scenario. It is a call to study how the compute stack may fragment, and who could both gain and lose.
The rise of custom silicon: Now the norm, not the exception
Hyperscalers are deploying custom AI silicon in volume: AWS with Trainium and Inferentia, Google with its TPU, and Meta with its internally developed MTIA. Even Microsoft is reportedly working on silicon codenamed Maia, though external confirmation remains limited. These moves are far more than pilot projects or research bets. They reflect a hard-nosed economic calculus: designing chips internally allows these companies to optimize for performance, integration, and TCO (total cost of ownership), especially as their volumes are expected to increase, and without compromising core AI differentiation.
Evidence is mounting. AWS has announced that Trainium chips power a portion of its ML workloads and are available now in production environments. Google claims its TPUs deliver better throughput per watt than Nvidia GPUs on common AI benchmarks. Meta is integrating its MTIA chips across its recommendation and video systems, driving internal efficiency gains.
That transition from GPU to ASIC is already materializing in data centers. It means less GPU-hungry server orders, different power and cooling demands, and new pricing dynamics for Nvidia.
It also elevates the fortunes of companies like Broadcom and Marvell, which are now major enablers of this custom silicon wave. Broadcom, for example, supplies high-speed SerDes, switch silicon, and custom AI accelerators directly to hyperscalers. Marvell, meanwhile, has expanded its focus from 5G infrastructure into compute silicon and cloud-optimized chiplets. Both companies are helping shift value away from monolithic GPU platforms into vertically integrated, custom-built solutions, playing a key but often underappreciated role in this reshaping of the data center compute hierarchy.
Arm’s new act: From IP king to silicon challenger
Arm is no longer a passive IP licensing business. It is evolving into a strategic competitor in the custom silicon era. The company recently announced partnerships with Meta and OpenAI to co-develop chips built on its CPU and interconnect IP.
Arm’s parent company, SoftBank, also recently acquired Ampere, an AI compute silicon company. These efforts position Arm to enable next‑generation AI platforms alongside hyperscalers, in some cases even architecting the chips from the ground up.
That is a seismic shift. Arm’s involvement deepens the custom silicon trend, enabling entire compute architectures optimized for domain‑specific needs. It also gives investors and foundry partners a stake in future hardware trajectories beyond commoditized CPU cores. The 2nm node will likely showcase Arm‑designed custom accelerators built directly on GAA‑era silicon.
Interconnect is the new leverage Point: NVLink vs UEC/UAL and network enablers
Nvidia’s strategic advantage has long relied not just on its GPUs, but also on the NVLink/NVSwitch interconnect fabric, which tightly couples multiple GPUs into high-bandwidth, multi-GPU AI clusters. But an architecture-level shift is underway, one that could redefine what platform lock-in looks like.
On one front, UEC (Ultra Ethernet Consortium) and Ultra Accelerator Link (UALink) are emerging as open, AI-tailored interconnect standards backed by AMD, Intel, Meta, Microsoft, Broadcom, Cisco, Google and others. These standards aim for compatibility, scalability, and vendor-agnostic networking, posing a direct challenge to Nvidia’s proprietary fabric.
Simultaneously, NVLink Fusion has opened Nvidia’s interconnect to third-party integration. Cadence and Synopsys have joined the NVLink Fusion ecosystem as enablement partners, supporting design tools, validation flows, and system development environments for NVLink but without yet releasing standalone IP blocks.
Meanwhile, Synopsys and Cadence, along with Broadcom, have announced UALink and Ultra Ethernet IP stacks, including SerDes PHYs, controllers, and verification IP to support high-bandwidth fabrics connecting thousands of accelerators at up to 200 Gbps per lane.
Broadcom, in particular, has introduced the Tomahawk 6 switch, a 102.4 Tbps Ethernet solution with support for AI-scale fabrics and future UALink implementations. Marvell, similarly, is investing in co-packaged optics and silicon photonics as core enablers of AI fabric scalability. These companies now sit at the heart of the interconnect battle, equipping the industry with the building blocks for non-proprietary, hyperscaler-friendly networking.
The strategic implications are profound. If organizations adopt NVLink Fusion, Nvidia’s ecosystem remains central. But if UEC/UAL standards gain traction, custom silicon, and hyperscaler-built AI accelerators, could scale across open, vendor-neutral fabrics. That would undermine a key layer of Nvidia’s non-GPU moat, shifting real power in compute infrastructure to those controlling the interconnect rails.
CUDA’s edge: Still strong, but not future-proof
Nvidia’s software dominance through CUDA cannot be understated. It remains one of the strongest developer ecosystems in the AI world. But pressure is mounting.
The rise of PyTorch, ONNX, and compiler frameworks like MLIR are enabling workloads to run on non-Nvidia silicon without major refactoring. Cloud vendors, particularly AWS and Google, have invested in hardware abstraction layers that allow model portability across GPU, TPU, and custom ASIC backends.
While CUDA still gives Nvidia a meaningful advantage in time-to-first-inference and developer mindshare, it is no longer an insurmountable moat. Portability is becoming table stakes.
Strategic imperatives for executives and investors
Nvidia’s dominance remains real, but it is entering a more competitive, less vertically integrated phase. For institutional investors, the questions are less about short-term earnings beats and more about long-term platform share.
Key watchpoints:
Hyperscaler chip deployment velocity. How aggressively are Trainium, TPU and MTIA being integrated into production clusters?
Arm’s design wins. Do OpenAI and Meta continue to deepen engagement with Arm’s full-stack IP approach?
Toolchain enablement. Are Synopsys and Cadence IP packages for UALink gaining traction among cloud silicon teams?
Interconnect standardization. Does UEC become a requirement for new AI infrastructure designs, or remain a niche?
Each of these will shape where value accrues, not just among chip suppliers, but also across EDA, IP, and packaging ecosystems.
Bottom line: Nvidia is not losing. It is adapting. But the rules are changing.
Custom silicon, open interconnects, and portable software frameworks are re-architecting the AI compute stack. The next platform winners may not be those who build the fastest chip, but those who control the interfaces, orchestrate the layers, and support a modular, multi-vendor future.
The compute stack is fragmenting. But that fragmentation is not chaos. It is an opportunity, for investors who know where to look, and for technologists who build the next layer of abstraction.
This post only highlights some of the key points in a fast-evolving and highly nuanced space. Institutional investors tracking this shift would be wise to pay close attention to these developments, and to seek insight from those embedded in the technical and strategic layers of the industry. Many institutional investors consistently turn to SANIE Institute for precisely these insights.
Note: This post is for informational purposes and does not constitute investment advice.