Executive Summary

NVIDIA recently announced two major moves: investing in Intel to co-develop custom x86 CPUs with NVLink, and partnering with OpenAI to build AI infrastructure at the scale of a million GPUs. These actions may seem independent, but they reveal the same trend: the bottleneck in AI is shifting from the number of GPUs to the efficiency of CPU–GPU integration.

In this transition, NVIDIA is reinforcing cross-platform standards through NVLink, Intel is focusing on CPUs to preserve its ground, and the narrative around custom chips from cloud providers is narrowing. Taiwan’s supply chain, meanwhile, is moving beyond contract manufacturing to become a direct driver of this structural shift, anchoring critical orders across both components and system integration.

For the industry, this is not simply a matter of technical choices. It is a structural rewrite that is redefining the global computing landscape.

Introduction: Two Headlines That Seem Unrelated

Within just two weeks, NVIDIA announced two market-shaking moves.

One was a partnership with Intel, including an investment to develop a custom x86 CPU with NVLink built in.

The other was a collaboration with OpenAI to build the “largest AI infrastructure in history,” scaling from thousands of GPUs to a million-GPU facility.

On the surface, these stories appear unrelated. One is about CPU collaboration, the other about demand-driven deployment. Yet viewed on the same narrative line, they reveal a hidden shift. The bottleneck in AI is moving from “who has the most GPUs” to “who can integrate CPUs and GPUs most effectively.”

Surface Impressions

At first glance, these announcements suggest a few straightforward points:

• NVIDIA’s move toward Intel seems aimed at filling a gap in its product line.
• Intel gains validation from NVIDIA, making the partnership appear mutually beneficial in the short term.
• OpenAI’s expansion once again confirms that AI models continue to grow in scale.

Yet if we ask why these developments are happening now, it becomes clear that they represent more than collaboration or investment. They signal a reorganization of power within the ecosystem.

Hidden Strategies

1. A Dual-Track CPU Strategy: Preserving Symmetry

NVIDIA’s Grace and Vera Arm CPUs, paired with NVLink C2C, can scale GPU performance up to NVL72, NVL144, or NVL576. This architecture is shaping the backbone of future AI supercomputers.

By contrast, x86-based HGX systems are limited by current designs, where NVLink can only connect up to eight GPUs. To scale beyond that, they must rely on InfiniBand or Ethernet, which add latency and reduce efficiency compared to NVLink C2C.

If this gap remains, the market will gradually divide into two distinct paths: high-performance Arm platforms and relatively constrained x86 platforms. Arm resembles a dedicated highway that allows GPUs to work together seamlessly, while x86 remains on city roads that become congested under heavy traffic. For enterprise customers long invested in the x86 ecosystem, this creates a major barrier to adoption. This is why NVIDIA brought Intel into the fold, ensuring symmetry between Arm and x86 so that market share would not be lost to architectural differences.

2. Intel’s Strategic Retreat

Intel has struggled repeatedly in the GPU arena. By allowing NVIDIA to participate in parts of its GPU and iGPU design, Intel appears to be stepping aside. In reality, it reduces the burden of GPU development and frees resources to focus on CPUs.

This is a deliberate sacrifice. Intel is limiting direct competition in GPUs in order to secure its share of the CPU market, a move that helps prevent deeper marginalization in the AI server segment. For Intel, it is more a survival strategy than a full exit.

3. NVLink’s Standardization Ambition

NVLink began as a way to connect GPUs, but has since extended into CPU–GPU communication through NVLink C2C. With Intel integrating this technology into its CPUs, NVIDIA has shifted from being a GPU vendor to becoming the de facto standard-setter for high-performance interconnects in AI supercomputing.

In the future, whether on Arm or x86, deploying large-scale AI systems will almost inevitably require passing through NVIDIA’s gate.

4. Extending CUDA to the PC

If Intel’s PC CPUs adopt NVIDIA’s iGPU technology, hundreds of millions of PCs will naturally become part of the CUDA ecosystem.

For NVIDIA, this comes at almost no additional cost, since its high-end Blackwell and entry-level RTX architectures already share the same software and drivers.

CUDA would then extend beyond data centers into personal computing, creating a broader base for development and applications.

The Push from OpenAI

OpenAI’s announcement provides a crucial timing signal. When a system expands from thousands of GPUs to a “factory” of millions, the real challenge is no longer just the number of GPUs but the efficiency of CPU–GPU interconnection.

If latency becomes too high or memory cannot be managed as a single pool, overall performance will drop significantly.

This is precisely where NVLink C2C proves its value:

• It allows CPU memory and GPU VRAM to function as a unified logical space.
• It enables CPU–GPU communication that approaches the latency and bandwidth of GPU–GPU links.

In other words, OpenAI’s demand at such unprecedented scale is pushing suppliers toward tighter CPU–GPU integration. This also explains why NVIDIA chose this moment to bring Intel into the picture, ensuring that x86 platforms follow the same set of rules as Arm.

Implications for the Industry and Market

1. The Marginalization of Competitors

By aligning with NVIDIA’s NVLink platform, Intel effectively pushes AMD out of the mainstream x86 + NVIDIA GPU configuration. Unless AMD can build a new narrative around Infinity Fabric and its own GPUs, its role in the AI server market will continue to weaken. Broadcom may benefit in the short term from large-scale shipments of switching ASICs, but it is gradually losing influence in setting standards for CPU–GPU and rack-level interconnects.

2. Declining Bargaining Power for Cloud Providers

In the past, CSPs such as Google, Meta, AWS, and Microsoft retained some flexibility in choosing CPU platforms and interconnects. Now, to deploy high-performance systems like NVL72, NVL144, or NVL576, they are largely limited to the Intel + NVIDIA bundle. This increases their dependence on NVIDIA and reduces their autonomy over pricing and system architecture.

3. Constraints on Custom ASICs

Broadcom’s retreat also narrows the strategic space for CSPs’ in-house ASIC projects. Solutions such as TPU and Trainium once relied on Broadcom technology to maintain a degree of independence at the network layer. Today, they face three major challenges:

• Difficulty matching NVLink’s performance in CPU–GPU integration.
• High costs when mixing custom ASICs with NVIDIA GPUs.
• A likely confinement of ASICs to narrow workloads, such as inference, rather than serving as broad substitutes.

This does not mean in-house chips will vanish, but their narrative and room for expansion are clearly shrinking.

4. The Upgrading of Taiwan’s Supply Chain

NVIDIA GPUs, Grace and Vera Arm CPUs, and Intel’s custom x86 CPUs are expected to be manufactured by TSMC, with ASE providing advanced packaging at the chip and module level. On the system side, companies such as Foxconn, Quanta, Wistron, and Inventec are directly engaged in rack-level assembly and delivery. Taiwan’s role now spans the entire value chain, from advanced semiconductor packaging to full system integration, making it one of the core recipients of orders emerging from this structural transformation.

Conclusion

NVIDIA’s investment in Intel and its expanded partnership with OpenAI may appear to be separate stories of capital and demand. Yet both point to the same underlying reality: the CPU–GPU ecosystem is being rewritten.

Through NVLink, NVIDIA ensures that whether the platform is Arm or x86, access to high-performance AI computing must pass through its gate. Intel has chosen to focus on CPUs, stepping back from the GPU battlefield in order to secure its position. AMD and Broadcom may still find short-term opportunities, but their roles are gradually moving from the core to the margins. CSPs retain some room for in-house chips, but the narrative around custom ASICs is clearly losing strength.

In this restructuring, Taiwan’s supply chain has seen its role expand. It is no longer only a manufacturer but a direct beneficiary of the structural realignment. This is a classic case of reflexivity: narratives guide the flow of capital, and capital in turn reshapes those narratives.

The open question is this: once NVLink becomes an indispensable standard and hundreds of millions of PCs fall under the CUDA ecosystem, how much computing will remain truly independent of NVIDIA? The answer may already be taking shape.

Note: AI tools were used both to refine clarity and flow in writing, and as part of the research methodology (semantic analysis). All interpretations and perspectives expressed are entirely my own.