As chip manufacturers like NVIDIA release new hardware, inference providers like Base10 absorb the complexity and engineering effort required to optimize AI models for the new chips. This service is a key value proposition, saving customers from the challenging process of re-optimizing workloads for new hardware.

While known for its GPUs, Nvidia's real competitive advantage comes from years of hands-on work integrating its entire stack with companies across many industries. This deep partnership model makes it incredibly difficult for customers to switch to competitors.

While known for its GPUs, NVIDIA's true competitive moat is CUDA, a free software platform that made its hardware accessible for diverse applications like research and AI. This created a powerful network effect and stickiness that competitors struggled to replicate, making NVIDIA more of a software company than observers realize.

While NVIDIA's CUDA software provides a powerful lock-in for AI training, its advantage is much weaker in the rapidly growing inference market. New platforms are demonstrating that developers can and will adopt alternative software stacks for deployment, challenging the notion of an insurmountable software moat.

Hardware vendors like NVIDIA (CUDA) and AMD create fragmented, proprietary software stacks that lock developers in. Modular builds a replacement layer that enables AI models to run consistently across different hardware, giving enterprises choice and flexibility without rewriting code.

In SaaS, value was delivered through visible UI. With AI, this is inverted. The most critical, differentiating work happens in the invisible infrastructure—complex RAG systems and custom models. The UI becomes the smaller, easier part of the product, flipping the traditional value proposition.

Large tech companies are actively diversifying their AI chip supply to avoid lock-in with NVIDIA. However, the true challenge isn't just hardware performance. NVIDIA's powerful moat is its extensive software and developer ecosystem, which competitors must also build to truly break free from its market dominance.

As AI commoditizes software, hardware is re-emerging as a key defensibility layer for startups. A decade ago, VCs avoided hardware, but now a physical device tied to a software subscription creates powerful stickiness and justifies high valuations, representing a major shift in investment strategy.

Nvidia will likely only revive its ambitions to compete with AWS if its massive hardware profit margins are threatened by competitors like AMD or hyperscalers building their own chips. Only then would Nvidia move up the stack to capture value through an "inference as a service" business model, moving beyond hardware sales.