Nvidia & DeepSeek: there is opportunity and risk for Nvidia

The buy vs sell bifurcation is overly simplistic. Reality is complex and non-linear

What follows are my very quick thoughts about how the rise of DeepSeek affects Nvidia.

The effects of DeepSeek on Nvidia and expensive training and inference are complex and non-linear. It’s not so much buy or sell though I know the facile and simplistic mind holds on to those binaries.

I think that DeepSeek, and models that use a similar strategy, will increase demand for both expensive AI and lower cost AI. And an increase in demand in expensive AI redounds to Nvidia’s benefit. But the AI market has become more complex and fluid than it was before. No longer is silicon cognition owned solely by Nvidia.

What I am referring to here is the nuance that many seem to miss when analyzing the AI hardware landscape in light of innovations like DeepSeek or similar strategies. The binary of buy or sell oversimplifies the various effects these developments have on both supply and demand within the AI market.

Dual demand creation: Expensive AI and lower-cost AI

• DeepSeek and its ilk expand the addressable market for AI applications by making AI inference and training more efficient for certain use cases, especially those with tight cost constraints. This creates increased demand for:

  • Expensive AI: High-performance applications that require cutting-edge GPUs and tightly optimized hardware (Nvidia thrives here). The bar is continuously raised as new models demand even more computational power for bleeding-edge tasks.

  • Lower-Cost AI: Scalable, low-power alternatives for less demanding or highly distributed tasks, creating room for ASICs, CPUs, and other accelerators that challenge Nvidia’s dominance in some segments.

Nvidia’s position: No longer the sole king

• Nvidia still dominates high-end AI workloads, but competitors like AMD (MI300 GPUs), startups (Cerebras, Graphcore), and the burgeoning RISC-V ecosystem are eroding Nvidia’s monopoly. DeepSeek-like strategies amplify these competitive forces because they give models a pathway to optimize usage of hardware rather than brute-forcing compute needs.

• Nvidia, however, continues to benefit from:

  • CUDA lock-in: A massive moat that entrenches Nvidia GPUs in enterprise workflows.

  • Software leadership: Nvidia’s AI software stack is often as critical as its hardware.

  • Demand elasticity: As the market grows, competitors increasing their slice of the pie doesn’t mean Nvidia’s absolute share shrinks, just its relative share.

Market complexity and fluidity

• The AI market will become more fragmented, rather than monolithic. Different layers of demand include:

  • High-performance training systems for foundational models (Nvidia’s bread and butter).

  • Efficient inference systems for real-time or edge applications.

  • Custom silicon for companies looking to optimize workloads at scale (e.g., Google’s TPUs, Amazon’s Inferentia, Tesla’s Dojo).

• This diversification means Nvidia is still vital, but DeepSeek-type strategies empower alternatives and diversify demand across the ecosystem.

Implications for Nvidia

• Nvidia still benefits from increased overall demand for compute, even if some of it shifts to competitors. DeepSeek-like strategies might increase the top of the funnel by enabling companies to experiment with AI more affordably, before eventually upgrading to Nvidia’s premium offerings for complex tasks.

• However, marginal erosion of monpoly rents is a risk. As the AI market becomes less centralized, Nvidia’s high margins may face pressure, especially as competitors and internal teams (e.g., Meta, Google) develop custom silicon.

Conclusion

DeepSeek and similar innovations signal a bifurcation of the AI market into high-cost, high-performance solutions and lower-cost, efficient alternatives. While Nvidia remains a net beneficiary of increasing AI demand, its market position is more precarious than before. The play is less about buy or sell and more about understanding Nvidia’s evolving role in an expanding ecosystem where innovation drives complexity, not binaries. This creates both upside opportunities and new risks.

If Nvidia is to maintain its dominance, it will need to lean even harder into software, ecosystem lock-in (CUDA, frameworks, etc.), and rapid iteration to stay ahead of both disruptive startups and hyperscalers developing bespoke solutions.