CUDA
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Yeah just do CUDA. Easy

Non-Chinese companies are actually susceptible to US sanctions.
CUDA's absolutely the only game in town. Everything else is close sourced proprietary based on CUDA architecture. Or Google.
The question wasn't about others, it was about Huawei specifically. AKA China. What stops them from cloning it. The thing that stops them from cloning CUDA is the incredible complexity of chip manufacturing and design, specifically when it comes to lithography. You need to do all that before you can develop acceleration software around running certain mathematical calculations through the chip.

One cannot simply "copy CUDA". It's not some code you run on any GPU.
One has to design CUDA for their specific board. Huawei has spared no resources in attempting to copy TSMC/NVDA over the past several decades. They have hired top engineers from the companies. Poured billions of state dollars into the projects.
They have failed. Lithography is just one hell of a technology. It's insanely hard to play catchup, even if you're stealing.

Being absent from the blog post tells us nothing. Given you can convert CUDA code to other languages trivially, training data isn't a problem.

I asked chatgpt why Nvidia chips are superior to AMD chips for AI development and chatgpt itself says
"Nvidia has developed CUDA, a parallel computing platform and API, which allows developers to leverage the GPU's capabilities effectively. This, combined with a vast ecosystem of software libraries, frameworks, and tools, makes Nvidia chips a popular choice for AI development."
But you're saying its not important so we should all jump ships to AMD platform to develop AI more affordably! Its like half the price. Lol

Nvidia's moat comes from their CUDA software development platform. It could take developers up to a year tinkering with competing platforms just to get them work to a barely functioning level. By then, it would be already too late to launch your AI product or service as a competitor using Nvidia and CUDA would have launched theirs already.

I’m currently using a Lorentzian-based Approximate Nearest Neighbors (ANN) model-lightweight but highly responsive to volatility structure and displacement. XGBoost on CUDA sounds like a powerhouse setup, and yeah-knowing what to feed it really is the secret sauce. Feature engineering still beats model complexity 9 times out of 10. I’m still getting acquainted with Python myself, but since replatforming, I’ve started integrating it into my MultiCharts workflow. I’m a discretionary momentum trader by nature-usually managing 2–3 positions manually on low timeframes. I was able to replicate my full strategy logic in a non-ML model pretty easily, but I’m always hunting for edge-that’s what pulled me into ML. Not to fully automate intuition, but to scale my system and let the machine take more trades like me-faster and without fatigue. Right now, I’m building out TradingView automations and layering ML overlays that mimic my playbook, and the results are fire, even in a basic setup. The real edge isn’t just execution-it’s how ML sharpens scanner filters, trend gating, and SL/TP logic. Each position either hits its max loss and ends for the day, or churns out high win rates with strong 1:3+ R:R-until my risk filters cut it off. That guardrail system is what makes it sustainable.

On the first part, I don’t think they were seriously trying. AMD unlike NVIDIA, isn’t a dedicated GPU company. They put way more resources into their CPU department. You could argue that the leadership didn’t had good vision until it’s a bit too late, but good execution can catch up.
On the question, as a shareholder no (I’m pretty sure NVIDIA have no interest in opening up their CUDA library at this stage). But if the competitor is able to catch up, the company might be forced to.
UALink is a NVIDIA exclusive party at the moment, if the ecosystem is built, either NVIDIA opens up NVLINK so that UALINK borrows and eventually merge with it, or all the companies just go with UALINK and NVIDIA’s ecosystem collapse.

well zluda exists which was a translation layer for CUDA to work on AMD thats open source but it was shut down by nvidia. I dont think they would let it slide but a big company like huawei may not care

Consumer businesses are different from commercial. Consumer habits are tough to change, if they get used to iPhone and windows UI , it’s tough to change the. Behavior and buying patterns.
But commercial companies don’t want to get stuck with just one vendor. Like Linux, Java.. many.. Linux and are commercial hit just because it’s open ecosystem.. windows and .net are not commercial hit and. Not widely adopted..
AI is dominated by big corporations, they don’t get stuck with CUDA, they will adopt open standards.. just wait and see, once Rocm matures little bit, everyone start supporting ROcm..

Yeah, Nvidia enjoys a decent moat with CUDA, there's no doubt about it, but they charge insane prices for their products with insane margins. That opens the doors for competitors and other solutions will come to market and lower price points that can do similar jobs over time. Deepseek, AMD, Intel, ARM are not standing still. They will come under price pressure, margins will erode, market share will shift, and ultimately AI tools will become a commodity. It's not a question of if, but when.

Good messages for the most part.
I take issue with her #2 concern "Open Ecosystems." Lisa still doesn't get it that when technology is developing, there is no way "open standards" can take hold. Standards necessarily involve many parties, debate, agreement and approval processes. This slows everything down, you become a slave to the process rather than the result.
I completely get it that it would help AMD at this moment to have OneAPI take over or to open CUDA up to 3rd party devices. Seems like AMD would be the primary beneficiary.
Many attempts at "open" architectures were offered earlier in the AI ecosphere as solutions developed, but there is a very logical reason why economic-Darwinism didn't choose one of them: The were slow and laborious. Open Source only makes sense when markets are mature, which surely the AI market is not.

No indication of that anywhere on the blog post. RL post training requires data, which CUDA is much more plentiful of, and this system creates lock in which generates more CUDA code fueling the flywheel.

I don't know why you're getting so many responses that you don't know what you're talking about that don't actually lay it out for you.
And I'm not in a position to lay out out for you, because I may actually not know what I'm talking about, but it seems to me that CUDA is the relevant moat for Nvidia with western companies who have access to TSMC manufacturing, while access to TSMC manufacturing is their moat against Chinese competition.
In other words, Chinese companies can most definitely create their own binary or source compatible implementation of CUDA, but their hardware still puts them far behind. And western companies who can use the same manufacturing processes that Nvidia uses through TSMC (and to a lesser extent Samsung) can't legally steal CUDA.

That's not how AI works. It doesn't have existing LLM datasets to create something like CUDA. More accurately, it never will.

No, I think it's the Orange Man's rescinding Biden's chip act. They are still a lot more afraid of allowing China to use Nvidia + CUDA, but open-source ROCm is ok because any AI developments made in the open source ecosystem, the USA can just take and adopt.

Huawei making their own version of CUDA wouldn't be an IP violation, AMD tried to do it with ZLUDA but abandoned it.

Because NVDA designs the GPU's they know the ins and outs of everything it has and can do. They then create the software platform that helps the end user unlock all of these features.
The GPU and CUDA software are both proprietary - only NVDA knows how to produce it 100%. Sure you can try to reverse engineer but it won't be 100% and by that time NVDA already has improved upon it.
OpenCL can only take advantage of any publicly documented and "open" features. They need to wait until official announcement and releases to tune it. NVDA is already developing next generations hand in hand with the engineering teams.

I happen to be a programmer, one that has been working on enterprise AI solutions, and custom machine learning models. Although I don’t know much about hardware, I know that the software you install on your computer, CUDA PyTorch etc can be reverse engineered. You think China doesn’t have a few smart guys who couldn’t replicate it? Don’t be naive.

That is a VERY naive and simply incorrect statement to start off your post. Their MOAT is the whole stack, from chip, to interconnects and networking, to software/CUDA. Do you have the slightest idea how difficult it is to build efficient datacenter solutions? Bottlenecks need to be constantly improved and removed across all products. 
Also "they can just steal their API"? 
You obviously don't have a single clue what you're talking about. 
I should know this by now, but this sub still keeps surprising me with people's lack of knowledge and just pure ignorance.