Nvidia's Real Moat Isn't Its Chips — It's CUDA, and AI Agents Have Started Rewriting It

For twenty years, Nvidia's main competitive asset has not been GPU speed but the CUDA software platform, the layer that turns the company's silicon into a tool for AI developers. According to an analysis by The Next Web, AI coding agents and new inference-optimization tools have started to erode that moat.

Why CUDA matters more than any Nvidia chip

CUDA launched in 2006 and over nearly two decades became the foundation of AI development: PyTorch, TensorFlow, JAX and most leading deep learning frameworks build on it. Thousands of highly optimized kernels, enterprise ML pipelines and university curricula were all built around CUDA abstractions over the years.

Migrating from CUDA to an alternative meant not swapping out a few functions but rebuilding a stack that had been debugged for years. AMD offers ROCm and Intel offers oneAPI, but their compatibility with the existing CUDA ecosystem has remained limited, and porting critical kernels required deep knowledge of GPU microarchitectures. The result: even companies with an economic incentive to change hardware vendors usually stayed with Nvidia, because the cost of leaving the software ecosystem was too high.

• CUDA has been running since 2006 — almost 20 years of accumulated ecosystem
• PyTorch, TensorFlow and JAX were originally optimized for CUDA
• AMD ROCm and Intel oneAPI are working alternatives with limited compatibility
• Switching platforms has historically required months of work by GPU engineers
• It was the high cost of leaving, not the superiority of the chips, that kept the industry on Nvidia

How AI agents started rewriting the moat

AI coding agents — GitHub Copilot, Cursor, Devin and similar tools — have begun to take over the mechanical part of migrating off CUDA: they untangle dependencies, rewrite kernels and adapt interfaces for ROCm or oneAPI. A cost that used to be measured in months of work by low-level code specialists is starting to fall.

"Nvidia's real moat was never hardware.

AI has started rewriting it," — from The Next Web's analysis.

The publication argues that Nvidia's moat rested not on CUDA's technical superiority as such but on the prohibitive cost of leaving it. Tools that lower that cost fundamentally change the competitive equation in the AI hardware market.

In parallel, hardware-agnostic inference optimization is advancing. OpenAI's Triton framework, MLIR compilers and Modular's Mojo language make it possible to write GPU kernels without being tied to the CUDA API. Together with AI agents, they form a toolkit that systematically lowers the barrier to changing hardware platforms and opens the way to real competition.

What this means

The threat to CUDA does not mean Nvidia is headed for collapse: the performance advantage of the Blackwell line remains real, data centers take years to cycle through their GPU fleets, and the ecosystem effect does not vanish in a single season. But the very fact that AI agents have begun to lower Nvidia's key non-technological barrier changes the long-term picture: AMD, Intel and startups building custom chips now have a real chance to win over developers who previously could not afford to leave the CUDA ecosystem.