Главный ров Nvidia — не чипы, а CUDA: AI-агенты начали его переписывать

For twenty years, Nvidia's main competitive asset was not GPU speed but the CUDA software platform — the layer that turns the company's silicon into a tool for AI developers. According to The Next Web's analysis, it is precisely this moat that AI coding agents and new inference optimization tools have begun to erode.

Why CUDA matters more than any Nvidia chip

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

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

• CUDA has been running since 2006 — nearly 20 years of accumulated ecosystem
• PyTorch, TensorFlow, and JAX are natively optimized for CUDA
• AMD ROCm and Intel oneAPI — working alternatives with limited compatibility
• Historically, switching to another platform required months of GPU engineers' work
• It was the high exit cost, not chip superiority, that kept the industry on Nvidia

How AI agents started rewriting the moat

AI coding agents — GitHub Copilot, Cursor, Devin, and their equivalents — have begun taking on the mechanical part of CUDA migration: analyzing dependencies, rewriting kernels, adapting interfaces for ROCm or oneAPI. The cost that previously measured in months of low-level code specialist work is beginning to drop.

"Nvidia's true moat was never hardware.

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

According to the publication, Nvidia's moat held not due to CUDA's technical superiority per se, but due to the prohibitive cost of exiting it. Tools that reduce this cost fundamentally change the competitive equation in the AI hardware market.

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

What this means

The threat to CUDA does not mean Nvidia's collapse: the hardware performance advantage of the Blackwell line remains real, the GPU fleet replacement cycle in data centers takes years, and the ecosystem effect does not vanish in a single season. But the very fact that AI agents have begun lowering Nvidia's key non-technological barrier changes the long-term picture: AMD, Intel, and startups on custom chips have gained a real chance to attract developers who previously could not afford to leave the CUDA ecosystem.