Open Above the Line: Nvidia's Bet on Open AI and the Crack Underneath It

Nvidia is championing open AI while keeping one of the most valuable parts of its own ecosystem proprietary. It releases open models, datasets and inference software, yet CUDA, the software platform underpinning much of its dominance, remains under its control. Nvidia is opening the layers that create demand while protecting the layer that helps it capture economic value.

There is nothing contradictory about this approach. Open models encourage experimentation, accelerate adoption and allow more organisations to build AI systems without depending entirely on a small number of frontier model providers. Every successful deployment creates demand for compute, and Nvidia is well positioned to supply it.

But there is another dimension to this strategy. Nvidia is not simply trying to grow the AI market. It also benefits from changing the structure of that market: who consumes compute, who purchases the infrastructure, and how much bargaining power those buyers possess. The question is whether the same openness that strengthens Nvidia's position today could eventually weaken one of its most important competitive advantages.

The Economics of Giving Things Away

In 2002, Joel Spolsky described a principle that continues to explain much of technology strategy: smart companies try to commoditise their complements.¹ When two products are used together, making one cheaper or more widely available can increase demand for the other. Microsoft benefited from an expanding market for inexpensive PC hardware because it created demand for Windows. Google later pursued a related strategy with Android, making the mobile operating system widely accessible while expanding the reach of its services.

Nvidia's relationship with open AI follows the same economic logic. Models, frameworks and inference software complement the accelerators required to run AI workloads. When developers can freely access capable models, the cost of building an AI product falls, and more organisations can experiment, fine-tune models, deploy applications and eventually operate production inference systems. Nvidia benefits from this activity even when it owns neither the application nor the model.

Its Nemotron family illustrates the approach.² Nvidia publishes model weights and supporting datasets, training tools and recipes, allowing developers to customise models for their own applications. These releases contribute to the open ecosystem while also establishing workloads that run efficiently on Nvidia infrastructure. The model expands the market for the infrastructure underneath it, but the deeper strategic benefit becomes clearer when we examine how that demand is distributed.

The Customer Concentration Problem

Much of the discussion about Nvidia's competitive position focuses on alternative chipmakers, particularly AMD and the emerging generation of specialised AI accelerators. But one of Nvidia's most consequential long-term risks comes from its largest customers.

Google has TPUs, Amazon has Trainium, and Meta has its MTIA family of accelerators. In June 2026, OpenAI and Broadcom unveiled Jalapeño, OpenAI's first custom inference accelerator, following their October 2025 announcement of a collaboration targeting 10 gigawatts of custom AI accelerator deployment.³⁴ These are not marginal buyers experimenting with alternative hardware. They operate some of the world's largest AI infrastructure programmes.

Their interest in custom silicon is economically rational. At sufficient scale, improvements in inference efficiency, memory utilisation and power consumption can justify substantial investment in specialised hardware. Meta provides a useful example. In March 2026, it described four generations of MTIA accelerators across a two-year roadmap, designed around an inference-first strategy.⁵ Rather than developing general-purpose hardware for every possible machine learning application, Meta can optimise its accelerators for workloads it understands and operates at enormous scale. Nvidia must serve a broad market with heterogeneous requirements, while hyperscalers can optimise hardware around narrower and more predictable workload distributions.

None of this means the hyperscalers are about to abandon Nvidia. Meta explicitly describes a commitment to a diverse silicon portfolio, combining internal and external technologies. Custom accelerators and Nvidia infrastructure can coexist within the same data centre strategy.

But complete replacement is not necessary to change the commercial relationship. A customer with a credible alternative has more negotiating power than one without it, and even partial workload migration reduces exposure to a single supplier. This is the risk of concentrated purchasing power: a supplier can dominate a market technologically and still become vulnerable when a small number of customers control a significant share of its demand.

The Structure of Demand

This is where Nvidia's commitment to open models becomes strategically important. When advanced AI capabilities are concentrated among a handful of proprietary frontier model providers, those organisations control a substantial share of the demand for serving their models. Open models create another route to production AI.

An enterprise can fine-tune a model for a specialised application. A government can deploy models within its own infrastructure. A regulated organisation can retain control over its data, deployment environment and inference stack. Startups can build domain-specific systems without paying a frontier model provider for every inference request.

These organisations have different requirements, budgets and technical capabilities, and most will never have the economic incentive or expertise to design their own accelerators. As adoption broadens, demand originates from a wider range of industries, customers and deployment environments.

That diversification benefits Nvidia, but there is an important distinction between who consumes compute and who purchases the underlying hardware. Many enterprises still obtain compute through AWS, Azure, Google Cloud or specialised inference providers, so thousands of independent AI applications do not necessarily translate into thousands of independent GPU buyers. Open models diversify the sources of demand without automatically eliminating concentration among Nvidia's direct infrastructure customers.

That cuts both ways. Nvidia still benefits from a broader market when cloud providers act as intermediaries, and the additional workloads expand its opportunities for direct enterprise, sovereign and on-premises deployments. But when open-model demand flows through AWS and Google Cloud, it flows through precisely the buyers with Trainium and TPUs to offer as alternatives. An open model served on a hyperscaler's own silicon grows the AI market without growing Nvidia's share of it. Open models can therefore expand the very market in which Nvidia's largest customers are building alternatives to its hardware.

There is also a subtler benefit. By releasing models alongside optimised inference stacks, Nvidia can influence how workloads are developed, benchmarked and deployed. Nemotron 3 Ultra, for example, has an NVFP4-quantised checkpoint designed to exploit Nvidia accelerator capabilities.⁶⁷ Its optimisation process uses mixed precision across different model components rather than applying a single four-bit format throughout, and the same checkpoint adapts its execution format to the hardware: on Hopper, which lacks native FP4 tensor cores, it runs as W4A16, while on Blackwell it uses native W4A4.

This is evidence of hardware-aware optimisation, not inherent hardware lock-in. The checkpoint is not conceptually inseparable from Nvidia silicon, but Nvidia provides an integrated route from model weights to quantisation, optimised kernels and deployment, and that integration lowers friction for developers choosing its hardware. The strategy is not simply to make AI open. It is to make AI more accessible while preserving advantages in the infrastructure market that accessibility creates.

The Crack in the Software Abstraction

The difficulty with this strategy is that open models do not inherently depend on Nvidia hardware. A model's architecture and numerical parameters are largely independent of the accelerator executing them. Their representations, quantisation formats and implementations may require adaptation, but the underlying computation does not belong to one chip vendor.

Much of the practical dependency comes from the execution stack: the compilers, kernels, runtimes and libraries that translate model operations into efficient hardware execution. This has historically been a major source of Nvidia's competitive advantage.

CUDA is more than a programming interface. Its ecosystem includes highly optimised libraries, profiling tools, compiler infrastructure, specialised kernels and years of developer investment. Production AI systems often depend on these capabilities indirectly through the frameworks and inference engines they use. Replicating that ecosystem is considerably harder than building a chip with competitive theoretical throughput.

But the software stack is changing. Most machine learning engineers work primarily with higher-level frameworks such as PyTorch, and compilers and runtimes increasingly translate those representations into device-specific execution without requiring application developers to write directly against proprietary hardware interfaces. Meta's MTIA software architecture demonstrates this shift.

In its March 2026 engineering blog post, Meta described a PyTorch-native software stack supporting vLLM and Triton.⁵ Its graph compiler builds on Torch FX and TorchInductor, while lower-level compilation uses technologies including Triton, MLIR and LLVM. Meta says its production models can run on both GPUs and MTIA without MTIA-specific model rewrites. That is Meta's own portability claim, not independent evidence of equivalent performance, and the same publication also describes substantial MTIA-specific compiler work, including custom optimisation passes, kernel fusion and extensions to Triton and MLIR.

That distinction matters. Portability does not eliminate the need for hardware-specific engineering. It changes where that engineering happens: instead of every application developer needing to understand a different accelerator architecture, more of the complexity moves into reusable compiler backends, runtime libraries and device integrations.

The resulting benefit is not automatic performance equivalence. It is the possibility of reducing the application-level engineering effort required to evaluate and adopt alternative hardware. As these abstractions mature, switching costs can decline, and declining switching costs change the economics of infrastructure competition.

Portability Is Not Parity

A model running on an alternative accelerator does not mean it runs equally fast, efficiently or reliably. Actual performance depends on memory bandwidth, communication overhead, operator coverage, numerical precision, scheduling efficiency, kernel quality and sustained utilisation under realistic workloads.

The distinction becomes particularly important in large-scale training, where distributed execution, collective communication and system-level integration determine whether theoretical accelerator performance translates into useful training throughput. Nvidia's position extends well beyond CUDA. Its GPUs, networking technologies, optimised libraries, developer ecosystem and rack-scale infrastructure provide advantages that cannot be reproduced simply by adding a compiler backend.

But the type of competitive advantage matters. Software lock-in can preserve market share even when an alternative has better hardware economics. A performance advantage is different: customers remain because the platform delivers superior results, not because leaving would require rebuilding their systems. As portability improves, the balance between those advantages changes, and customers gain more freedom to compare accelerators on effective tokens per second, latency under production load, energy consumption, memory efficiency, utilisation and total cost of ownership.

This is particularly relevant to inference. Interactive inference services face latency constraints, variable concurrency, memory-intensive decoding and continuous pressure to reduce cost per request. A specialised inference accelerator does not need to outperform Nvidia across every workload. It needs to deliver competitive or superior economics for the workloads its owner actually runs, which is a narrower engineering objective than building a universally superior accelerator, and a commercially meaningful one.

The DeepSeek Lesson

The market has already demonstrated how sensitive Nvidia's valuation is to assumptions about AI compute demand. On 27 January 2025, DeepSeek's emergence helped trigger a sharp sell-off in technology stocks. Nvidia shares fell nearly 17%, erasing approximately $593 billion in market capitalisation in a single trading session, as investors questioned whether more efficient AI models might weaken the expected demand for expensive computing infrastructure.⁸

But improving computational efficiency can have two opposing effects. It reduces the resources required for a particular workload while also making AI economically viable for applications that were previously too expensive. Lower inference costs may increase usage, enable new product categories and expand overall demand, an effect frequently associated with Jevons' paradox, in which greater efficiency increases total resource consumption rather than reducing it.

That outcome is possible, not inevitable. Total demand depends on price elasticity, product adoption, application economics and the extent to which lower costs create genuinely new usage.

There is also a distinction between aggregate demand for compute and demand for Nvidia hardware. Even if the AI compute market continues expanding, Nvidia's share of that market is not guaranteed. A larger inference market could benefit Nvidia while simultaneously making specialised silicon more economically attractive to hyperscalers and infrastructure providers. Growing the market and maintaining control over it are different problems.

What Would Weaken the Moat

I would watch three developments more closely than headline accelerator benchmarks. The first is the migration of production inference workloads. If hyperscalers progressively move high-volume inference to their own accelerators while retaining Nvidia systems for demanding training workloads, the infrastructure market may become more clearly segmented by workload. Nvidia could remain dominant in one segment while facing increasing cost competition in another.

The second is the maturity of portable compiler and runtime stacks. The meaningful question is not whether PyTorch executes on an alternative accelerator, but how closely that implementation approaches the efficiency of an optimised Nvidia stack without substantial application-specific engineering. The relevant measures are end-to-end latency, sustained throughput, cost per token, operational reliability and the engineering effort required to reach production readiness. Portability becomes economically significant when the cost of moving between platforms falls far enough to outweigh the performance penalty, migration effort and ongoing maintenance.

The third is where Nvidia continues to strengthen its own competitive advantage. Its investments in networking, integrated systems, inference platforms and developer software suggest that the company understands the importance of competing beyond individual accelerators. As execution software becomes more portable, differentiation can move further into memory architecture, networking, orchestration, system integration and operational efficiency.

If Nvidia continues delivering superior system-level economics, portability alone may not materially weaken its position. An interchangeable software interface does not make the underlying infrastructure economically interchangeable, and that remains the strongest counterargument to the idea that abstraction will inevitably commoditise AI hardware.

What It Means for Builders

For AI product companies, this debate is not simply about which semiconductor manufacturer will dominate the next decade. It is about architectural freedom. A production inference system that depends heavily on vendor-specific kernels, runtime assumptions or deployment tools may deliver excellent performance today, but it can also introduce exposure to future pricing changes, supply constraints and hardware availability.

The answer is not to abstract everything. Abstraction adds complexity, can limit optimisation opportunities and sometimes costs far more to maintain than the flexibility it provides. For small teams and latency-sensitive applications, optimising for a single mature hardware platform may be the correct engineering decision. The distinction that matters is between deliberate dependency and accidental dependency.

A sensible architecture keeps model definitions and application logic as hardware-independent as practical, isolates vendor-specific execution components and makes performance assumptions measurable. Hardware-specific optimisations remain appropriate when their benefits justify the resulting coupling. For example, a production inference service should be able to evaluate another execution backend against representative workloads without rewriting its application layer.

That evaluation must go beyond peak benchmark performance. It should measure latency distributions under realistic concurrency, sustained throughput, memory requirements, operational reliability and total serving cost. Just as importantly, it should account for migration and maintenance effort: a backend that is marginally cheaper to operate but requires continuous engineering intervention may provide no real economic advantage.

This creates options without requiring them to be exercised immediately. Portability becomes strategically valuable when the expected benefit of future flexibility exceeds the engineering cost of maintaining it, and that calculation looks very different for a startup operating a few GPUs and a hyperscaler deploying hundreds of thousands of accelerators. For engineering leaders, the objective should not be maximum abstraction. It should be maintaining enough architectural freedom to make infrastructure choices based on evidence rather than inherited constraints.

The Paradox at the Centre

Nvidia has helped create an enormous market by making AI compute accessible through an integrated, highly optimised hardware and software ecosystem. Its investment in open models and software can expand that market further, diversify the sources of demand and reduce the influence of individual model providers. Yet the same ecosystem is improving the software abstractions that allow workloads to move between different hardware platforms.

These forces are not necessarily in conflict today. Nvidia can benefit from broader AI adoption even as switching costs decline, and superior performance, software maturity, developer familiarity and system integration may continue to justify a premium. But the long-term balance of power could change.

When developers can move workloads between accelerators without prohibitive engineering costs, hardware vendors must compete more directly on the value they deliver. Performance, efficiency, availability and system economics become more important relative to the historical cost of leaving an established ecosystem.

The next phase of AI infrastructure competition will be shaped as much by software portability as by silicon performance. The decisive question is not simply who builds the fastest accelerator, but who controls the software layer that determines how easily workloads can move between them.

For those of us building AI systems, the practical lesson is to understand where that control resides in our own architectures. The more freedom we retain to choose where our workloads run, the less control any single infrastructure supplier has over the economics of the products we build.

References

Footnotes

1. Joel Spolsky, Strategy Letter V, 12 June 2002. ↩

2. NVIDIA, NVIDIA Nemotron AI Models, developer resources. ↩

3. OpenAI, OpenAI and Broadcom announce strategic collaboration to deploy 10 gigawatts of OpenAI-designed AI accelerators, 13 October 2025. ↩

4. OpenAI, OpenAI and Broadcom unveil LLM-optimized inference chip, 24 June 2026. ↩

5. Meta, Four MTIA Chips in Two Years: Scaling AI Experiences for Billions, 11 March 2026. ↩ ↩²

6. NVIDIA, Creating the NVIDIA Nemotron 3 Ultra NVFP4 Checkpoint with NVIDIA Model Optimizer, 26 June 2026. ↩

7. NVIDIA, NVIDIA Nemotron 3 Ultra Technical Report, June 2026. ↩

8. Reuters via Investing.com, DeepSeek sparks AI stock selloff; Nvidia posts record market-cap loss, 27 January 2025. ↩