If NVIDIA’s silicon and interconnect layers form the foundation of AI, the platform layer is where its dominance faces its greatest existential test. At this layer, control over ecosystems, frameworks, and adoption patterns determines not just near-term revenues but the durability of NVIDIA’s moat. CUDA remains the beating heart of this advantage, yet both hyperscalers and open-source collectives are working relentlessly to undermine it.
Hyperscalers—cloud service providers (CSPs)—represent ~50% of NVIDIA’s revenue. On paper, this looks like strength: guaranteed demand at massive scale. But concentration cuts both ways.
NVIDIA’s top two customers account for 39% of revenue (Customer A: 23%, Customer B: 16%).
This dependence means hyperscalers wield outsized bargaining power.
Every dollar NVIDIA makes at 72.4% margins is a dollar hyperscalers want to reclaim.
The result? Hyperscalers are building their own chips.Google with TPU, Amazon with Trainium, Microsoft with Athena, and Meta with MTIA are not optional experiments—they are long-term hedge strategies against CUDA’s lock-in.
Strategic Reality: Hyperscalers consume GPUs today, but their strategic objective is to reduce reliance on NVIDIA tomorrow.
The AI-First Lock-In
If hyperscalers threaten through substitution, AI-first companies such as OpenAI and Anthropic secure NVIDIA’s present. Their hunger for compute has created future purchase commitments of $45.8B.
It reinforces NVIDIA as the default infrastructure provider.
But it also creates a paradox: AI-first labs scale faster than anyone else and will inevitably seek ways to reduce dependence—just as hyperscalers are doing.
The very customers locking in NVIDIA’s near-term growth may also be the ones accelerating the search for alternatives.
The Open Source Threat
The largest existential threat, however, doesn’t come from hyperscalers or AI-first labs—it comes from open-source models.
Projects like DeepSeek and Qwen are proof of concept that CUDA’s moat is not unassailable. Open source thrives on three dynamics that erode NVIDIA’s platform control:
Democratization of access → lowers barriers to entry.
Rapid iteration by distributed contributors → faster improvement cycles than centralized teams.
Ecosystem momentum → once open models gain adoption, switching costs flip against proprietary lock-in.
In this environment, CUDA’s once-unbreakable lock on developers may gradually weaken as open ecosystems attract talent, experimentation, and adoption.
Platform Vulnerability Assessment
The current balance of power can be summarized as follows:
CUDA Moat: ~80% strong, still the default for AI development.
Open Source Threat Level: ~60% and rising, increasingly credible.
Market Control: ~90% dominance, but fragile if developers begin defecting to open alternatives.
NVIDIA’s biggest risk is not losing customers overnight but losing narrative control. If CUDA is perceived as “yesterday’s platform” while open models represent the future, market dominance can unravel faster than the revenue backlog suggests.
The Critical Tension
The platform wars ultimately ask one defining question:
Will AI’s future be defined by proprietary platforms or efficient open models?
If proprietary platforms win, NVIDIA consolidates control, continuing to extract high margins across hardware and software.
If open models win, value migrates away from proprietary CUDA lock-in toward distributed ecosystems where compute is commoditized and NVIDIA’s margins compress.
This is not a purely technological fight—it is a strategic and cultural one. Proprietary platforms depend on defending moats; open models thrive on tearing them down.
The Broader Implications
The outcome of the platform wars will ripple across the AI stack:
For hyperscalers: proprietary NVIDIA control threatens long-term margins, making open source attractive as a bargaining chip.
For AI-first companies: open ecosystems may provide escape velocity from costly infrastructure dependence.
For enterprises: open-source lowers adoption costs, accelerating diffusion.
For regulators: open models may become the preferred pathway for reducing systemic concentration risks.
NVIDIA’s challenge is to sustain CUDA as the indispensable layer while managing its dependencies on customers who have every incentive to erode that indispensability.
Final Takeaway
At Layer 3, NVIDIA faces its sharpest paradox: it is both indispensable and vulnerable.
Indispensable because CUDA remains the universal standard.
Vulnerable because hyperscalers and open-source collectives are eroding the very foundation of that standard.
The existential threat is not immediate collapse—it is erosion by substitution and diffusion. If CUDA becomes less central to innovation, NVIDIA shifts from platform overlord to commodity supplier, with margins collapsing accordingly.
The platform wars are the make-or-break battlefield of the AI stack. Silicon and interconnect guarantee revenue, but only platform control guarantees enduring power.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
