The Multi-Billion-Dollar Question: Why Isn’t Anybody Catching Up With NVIDIA?

The Critical Software Moat No One Talks About

To Smart Investors,

By the close of 2024, NVIDIA’s share price had skyrocketed nearly 170%, catapulting the company’s market capitalization beyond $3.6 trillion—an astronomical figure that puts it on par with the largest corporate titans of any era. The press loves discussing “AI mania” and generative AI products. Still, those discussions rarely capture the full story of why Nvidia’s lead in the AI hardware arena is so difficult for rivals to overcome.

Given my computer science and economics background, I’m uniquely positioned to assess NVIDIA’s hold on the AI ecosystem.

This article will peel back the layers of Nvidia’s “moat,” the deep, structural competitive advantages that make it extremely hard for upstarts (or even established industry leaders) to dislodge it.

In the next few sections, I’ll unpack exactly why the next two to three years are Nvidia’s to lose, no matter how many billions of dollars flow into rival GPU or AI chip projects. Spoiler: it’s not just about the hardware or money.

1. From a GPU Maker to a Market Giant

In the late 1990s, Nvidia’s bread-and-butter was discrete graphics cards for consumer PCs—video games, 3D rendering, and the occasional workstation job. But as deep learning started to explode in academia around the early 2010s, Nvidia’s GPUs turned out to be shockingly good at training neural networks. Their parallel nature, initially designed for pixel shading, mapped well to matrix multiplications and vector operations that underlie modern machine learning.

A Quick Step on the Gas

• Early Moves: Nvidia started courting academics and labs in the mid-2000s to experiment with using GPUs for computations beyond gaming.

• CUDA Launched (2007): Seeing the potential, Nvidia released CUDA (Compute Unified Device Architecture) to make GPU programming more accessible outside of pure graphics.

• Supercharged AI Hype: By the mid-2010s, the deep learning revolution was in full swing, and Nvidia had all the right puzzle pieces to become the go-to AI hardware provider.

Now, they are far more than a “graphics card company.” They sell the backbone hardware for AI supercomputers, provide specialized libraries and frameworks, and even build entire networked systems (like DGX servers) that major enterprises use for AI workloads. AI soared in 2023–2024, lifting Nvidia’s valuation through the roof.

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2. What Exactly Is CUDA?

You can’t talk about Nvidia’s commanding lead without talking about CUDA—arguably the biggest reason they’re so far ahead. If you’ve written advanced GPU code, you know that typical graphics APIs (like OpenGL or DirectX) are not exactly intuitive for machine learning tasks. CUDA changed that by giving developers direct hooks into the parallel computing capabilities of Nvidia’s GPUs.

• Parallel Computing Platform: CUDA is effectively a collection of APIs and libraries that let you write code to run on Nvidia’s GPUs for tasks like matrix multiplication, image processing, or complex simulation.

• Integration: Major machine learning frameworks—TensorFlow, PyTorch, scikit-learn—integrate seamlessly with CUDA.

• GPU Access: The reason GPUs can accelerate AI so well is that they handle thousands of floating-point operations in parallel, and CUDA is the gateway to harnessing that parallelism.

Since launching in 2007, CUDA has evolved into a behemoth: an entire ecosystem of compilers, performance libraries, debugging tools, and developer documentation. It’s not just an API; it’s an entrenched software ecosystem sitting at the center of the AI universe.

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3. How CUDA Became Nvidia’s Moat

In business terms, a “moat” is a long-term competitive advantage that protects a company from rivals. CUDA is the central plank of Nvidia’s moat for several reasons:

1. Hardware-Software Synergy
   Nvidia’s GPUs are literally built with CUDA’s needs in mind. Chip designers optimize transistor layouts, memory pathways, and specialized tensor cores with the intention that CUDA—and the libraries built on top—will harness them. This synergy means that if you’re writing CUDA code, you can usually count on maximum hardware efficiency.

2. Complexity and Closed Source
   CUDA is proprietary. Unlike open frameworks that any competitor can fork or replicate, Nvidia locks down its software stack. Even if a competitor tries to reverse engineer, they immediately slam into legal walls and enormous complexity.

3. Developer Lock-In
   Writing highly performant GPU code isn’t trivial. Skilled engineers spend years learning the intricacies of CUDA. The friction to switch—rewriting code, learning new toolchains, or adopting a different GPU architecture—can be a show-stopper.

4. Massive Ecosystem
   CUDA’s ecosystem includes:

   • Libraries (cuDNN, cuBLAS, cuOpt, cuQuantum, etc.)

   • Framework Integrations (native support in PyTorch, TensorFlow)

   • Educational Resources (hundreds of official tutorials, entire courses on Udemy or Coursera)
     When a technology becomes the standard, momentum feeds on itself, making it even less likely that developers will leave.

5. Continual Innovation
   Nvidia hasn’t rested on early success. They constantly roll out updates, features like tensor cores or new parallelization strategies. This keeps the ecosystem on the cutting edge and others perpetually trying to catch up.

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4. The Open-Source Challenger: AMD’s ROCm

If there’s one name that comes up often as a potential “CUDA killer,” it’s AMD’s ROCm (Radeon Open Compute). AMD’s strategy differs from Nvidia’s in that ROCm is open source. That means anyone can examine the code, contribute, or modify it. Why does this matter?

1. Community Growth
   An open platform can theoretically grow faster due to community contributions. This is how Linux eventually dominated certain segments of computing. AMD hopes the same pattern will unfold with ROCm.

2. HIP to CUDA
   ROCm offers a compatibility layer called HIP (Heterogeneous-Computing Interface for Portability) that aims to convert CUDA code into something AMD GPUs can run. This is a big step forward, but it’s still nowhere near 100% seamless.

3. Late to the Game
   CUDA has been around since 2007, whereas ROCm launched in 2016. Plus, until early 2023, AMD’s ROCm only officially supported Linux—leaving out Windows developers, a significant chunk of data scientists. That gap matters.

4. Performance and Ecosystem
   Many HPC/AI veterans report that ROCm performance and stability lag behind CUDA in many workloads, especially large-scale deep learning. Documentation is also less robust, and the community, while growing, is much smaller than CUDA’s.

Bottom line? ROCm is an earnest attempt and has seen real progress—AMD has even acquired compiler companies like Nod.AI to close the software gap. However, it still isn’t at feature parity with CUDA, and the market momentum remains squarely on Nvidia’s side.

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5. Other (Partial) Threats: Custom Silicon, Triton, PyTorch 2.0

a) Custom Hyperscaler Chips

Companies like Amazon, Google, and Meta are developing in-house accelerators: AWS Trainium, Google TPUs, Meta’s upcoming designs. The logic is straightforward: these cloud giants consume so many GPUs (and pay Nvidia billions) that building their own chip might be cheaper if they can reach or beat Nvidia’s performance at scale.

• Economies of Scale? Possibly, but building a robust ecosystem plus a developer base to match CUDA is non-trivial. Google’s TensorFlow faced an exodus to PyTorch. AWS Trainium and Inferentia remain niche for specific workloads.

• Performance is often good for certain tasks, but the general-purpose flexibility of Nvidia’s GPUs usually wins out.

b) OpenAI’s Triton

OpenAI released Triton, an open-source compiler framework that simplifies writing custom GPU kernels in Python-like syntax. Triton can bypass some of Nvidia’s proprietary libraries (like cuBLAS) by generating its own optimized code. Initially focused on Nvidia GPUs, Triton aims to expand support to AMD, Intel, and beyond.

• Why This Matters: Triton lowers the barrier to writing high-performance GPU kernels for AI, reducing developer reliance on closed-source CUDA or Nvidia’s libraries.

• Still a Work in Progress: Full multi-vendor hardware support is on the roadmap but not yet realized. Meanwhile, Nvidia is also improving its own toolchains.

c) PyTorch 2.0

PyTorch 2.0 introduces compile-based optimizations that can reduce the overhead of “eager mode.” More code is fused at runtime, which drastically cuts memory reads/writes—one of the biggest bottlenecks in GPU-bound deep learning. The big news is that PyTorch 2.0 is designed to be more backend-agnostic, using “PrimTorch” (reduces ~2,000 operators down to ~250) and “TorchInductor” to generate better code for multiple types of hardware. This could open up the field to AMD, Intel, and specialized accelerators.

• Key Insight: The impetus behind these changes includes dissatisfaction with Nvidia’s closed approach. However, Nvidia still benefits from first-in-line optimizations within PyTorch because they’ve dedicated entire teams to collaborating with Meta’s devs.

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6. Nvidia’s “Second Moat”: Ecosystem Depth and Developer Lock-In

When people say, “Nvidia is unstoppable,” they often focus on hardware advantages (e.g., high memory bandwidth, advanced HBM packaging, advanced cooling solutions). But the real moat—especially from an economics perspective—is the ecosystem.

1. 15,000 AI Startups, 40,000 Large Enterprises
   At major conferences like Computex, Nvidia touts that they have millions of registered CUDA developers, tens of thousands of enterprise deployments, and thousands of applications built around CUDA. This is a self-reinforcing feedback loop.

2. Educational Pipeline
   University courses, boot camps, and online tutorials overwhelmingly default to Nvidia GPUs for teaching. Students graduate knowing CUDA as the default, which cements the next generation of AI workforce into the Nvidia ecosystem.

3. Turnkey Solutions
   Nvidia sells not just chips but entire solutions: DGX and HGX servers, software-defined networking with Mellanox (acquired in 2020), specialized operating system stacks, etc. By providing an end-to-end package that “just works,” they remain ahead of the piecemeal competition.

4. Network Effects
   The broader HPC (High-Performance Computing) and AI community regularly shares best practices and code snippets that assume an Nvidia-based system. If you’re off that train, you’re often blazing your own trail.

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7. Why Money Alone Won’t Dethrone Nvidia

You might think, “Surely, if a competitor invests enough billions, they can catch up, right?” But it’s not that simple:

• R&D Compound Effects: Nvidia has spent nearly two decades refining GPU computing for HPC and AI, building a layered stack of code, performance libraries, and hardware synergy. Throwing money at an AI chip doesn’t replicate those two decades overnight.

• Developer Retraining Costs: Even if AMD or a startup gave away “free superchips,” developers would still need time and expertise to convert large codebases. Risk-averse enterprises often won’t gamble on unproven platforms unless the performance gains are enormous.

• Minimal Slack in HPC: AI shops and HPC data centers can’t afford major downtime or under-optimized code. This is mission-critical infrastructure for big tech, finance, pharma, automotive, and more. They want proven reliability and 24/7 vendor support—areas where Nvidia is battle-tested.

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8. The Future: 2–3 Years of Runway

Despite the emergence of AMD’s ROCm, Google TPUs, custom cloud silicon, OpenAI Triton, and PyTorch 2.0’s more open approach, Nvidia’s runway as the dominant AI acceleration platform likely extends another two to three years at least. Here’s why:

1. Technical Debt and Software Maturity
   Even if a new competitor launched an amazing GPU tomorrow, the major frameworks and big enterprise codebases would need many quarters (if not years) to reach production readiness.

2. Continuous Innovation
   Nvidia is not complacent. They’re integrating cutting-edge networking (Mellanox), rolling out new versions of CUDA with advanced features like distributed memory, releasing new GPU generations (Hopper, Grace Hopper “superchips”), and exploring advanced interconnects. They are also bundling optimized AI models, vector databases, and entire HPC frameworks to lock in customers.

3. Ecosystem Gravity
   The “best short-term strategy” for many HPC shops is to wait for Nvidia’s next GPU release. Upgrades are typically simpler than switching to a completely different platform. The ecosystem’s inertia creates a gravitational pull that is tough to escape.

4. Deep Partnerships
   Nvidia is aligned with every major cloud provider—AWS, Azure, Google Cloud, Oracle Cloud, Alibaba Cloud—ensuring their GPUs are available globally on demand. Partnerships with system integrators and OEMs (Dell, HPE, Lenovo) also drive near-automatic adoption in enterprise HPC.

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9. Conclusion

When you see Nvidia’s trillion-dollar-plus valuation, remember that its leadership goes beyond raw GPU specs. At the heart of it all is CUDA, a parallel-computing juggernaut that seamlessly integrates hardware and software. Decades of R&D, an extensive library ecosystem, countless trained developers, and a near-ubiquitous presence in AI frameworks form the real moat.

Yes, serious challenges are emerging from AMD, from open-source projects like ROCm, from custom silicon in Big Tech, and from compiler frameworks like OpenAI’s Triton or PyTorch 2.0’s Inductor. They will undoubtedly chip away at Nvidia’s stronghold, especially if these alternatives quickly reach parity for the most popular AI workloads. But these are incremental steps in an environment where incumbency matters—a lot.

A key economic principle applies here: network effects plus high switching costs equal a durable monopoly (or quasi-monopoly). Nvidia exemplifies that. They have both the largest developer network and the highest friction for anyone trying to leave. Combine that with the reality that HPC and AI systems are crucial for the biggest, most profitable companies on Earth, and you see why “just spending more money” rarely topples an entrenched juggernaut.

For the next two to three years, Nvidia’s AI hardware and software leadership looks nearly unassailable. Even as new chips and open-source frameworks appear, the company’s first-to-market advantage, massive developer lock-in, and ecosystem synergy ensure that Nvidia is not just coasting—it’s continuously expanding its empire. Competitors need more than deep pockets—they need an entire revolution in architecture plus a bulletproof software stack that big players trust. That’s easier said than done.

So if you’re looking at Nvidia’s dominance and wondering if it’s sustainable, the short answer is: absolutely—at least for now.

May the LORD Bless You and Your Loved Ones,

Jack Roshi, MIT PhD