Market Heated Debate Over NVIDIA CUDA's Moat Facing Threats

About the deepest moat of Nvidia, doubts are fermenting on Wall Street and Silicon Valley with an unprecedented density.

The core proposition of this discussion is: CUDA (Unified Computing Architecture), this software moat that supports Nvidia's trillion-dollar valuation, how deep is it really?

The catalyst for this discussion is partly due to the recent high-density, direct questioning of Nvidia founder Huang Renxun by podcast host Dwarkesh Patel: Anthropic's shift to TPU, the shrinking of the Chinese market, cloud vendors developing their own chips, top laboratories bypassing cuBLAS. The market noticed a detail: when pressed about the CUDA moat, his argument shifted subtly from "irreplaceable software ecosystem" to "we locked in supply chain capacity".

The super buyers are diversifying their bets

The strongest part of the CUDA moat has always been customer stickiness. Over more than a decade, the global AI research community has accumulated a vast code base, toolchains, and engineering experience on CUDA, and the migration cost is considered unbearably high.

But this judgment is being challenged by the actual actions of super-scale cloud vendors.

Google has long migrated Anthropic—one of Nvidia's most important strategic customers—to its own TPU. Huang Renxun confessed in the interview that the mistake was "investing in Anthropic too late." The subtext of this statement is: when the computing power requirements of top AI laboratories are deeply bound to specific hardware, migration is not only possible, but has already occurred.

Amazon's Trainium and Google's TPU are actively being promoted as partial replacements for Nvidia's GPUs. These super buyers are both Nvidia's largest customers and its potential biggest competitors—they have enough resources to write custom operators, bypass cuBLAS, and also have enough motivation to reduce dependence on a single supplier.

In the Chinese market, the situation is even more severe. Export controls have effectively forced Nvidia out of this important battlefield, and Huawei's Ascend is filling the void. Huang Renxun stated that if DeepSeek is ultimately first launched on Huawei's chips, it will be "a terrible result for the United States"—this statement precisely illustrates that the feasibility of breaking away from the CUDA ecosystem is being verified by the reality of the Chinese market.

Triton and TileLang are breaking up the monopoly of cuBLAS

The second line of defense for the CUDA moat is its underlying mathematical computation libraries—cuBLAS and cuDNN. These two libraries, after more than a decade of optimization, have long been unparalleled in performance for core operators such as matrix multiplication and convolution operations, and are considered the most difficult part to replicate in the CUDA ecosystem.

This line of defense is experiencing the most direct impact. Triton, developed under the leadership of OpenAI, has allowed developers to write GPU kernels with a syntax closer to Python and achieve compute kernels close to handwritten performance without directly calling CUDA primitives—this set of toolchains naturally has the potential for cross-platform migration.

More noteworthy is TileLang. This domain-specific language specifically for GPU kernel development has achieved attention calculations 30% faster than the original FlashAttention with less than 100 lines of code. DeepSeek has officially adopted TileLang, and Huawei Ascend has also announced support at the first opportunity. One set of code, multiple hardware platforms—this is precisely the situation that the CUDA ecosystem would least like to see.

Nvidia is clearly aware of the threat. In CUDA 13.1, Nvidia introduced its own cuTile programming model, claiming to be twice as fast as Triton in scenarios like FlexAttention. But this move itself acknowledges a fact: Triton has already constituted an alternative that can be compared with cuBLAS, otherwise there would be no need for a specific response.

AI is dismantling the myth of "irreplaceable engineers"

The most hidden layer of the CUDA moat is the barrier of people—the engineers who can write high-performance GPU kernels are extremely rare, and the training period is as long as several years. This scarcity gives the CUDA ecosystem a natural self-reinforcing ability: the harder it is to replace, the higher the migration cost.

But AI programming tools are directly eroding this layer.

In 2025, a study by Stanford University found that CUDA kernels generated by AI unexpectedly performed at **179.9%** of native PyTorch in two-dimensional convolution tasks and **101.3%** in matrix multiplication— not "close to human level