Technology

The article wastes no time getting to the underlying point in the very first paragraph:

Top executives at leading Western AI companies are increasingly warning about the safety and national security risks posed by Chinese open-weight frontier models. What they tend not to mention is that these models are improving rapidly and, because they are freely available, pose a serious threat to Western labs’ business models.

I found the following two paragraphs interesting:

By mid-2026, however, open-weight frontier models from Chinese labs such as Alibaba, DeepSeek, and Moonshot AI had nearly matched the leading Western models in intelligence and performance. Many companies have already begun building their AI systems on top of these free models, avoiding the high cost of closed-model APIs.

Because businesses can host open-weight models in their own private clouds, they can also avoid sending proprietary data to systems controlled by outside providers. Developers can fine-tune the models for specific needs, build applications and tools on top of them, and optimize them for their preferred infrastructure.

The Western model was doomed to failure from the start. The only barrier to entry was being able to download thousands of TBs of internet archives/books and to have a lot of compute.

The math for these models isn't proprietary and most CS students are exposed to machine learning and neural networks while in school.

The only advantage western companies had was the ability to buy up the entire hardware market, pricing out domestic competition, and to use their politicians to manipulate trade policy in order to restrict sales of critical hardware to China.

Every US tech company has dumped billions investing in an unsustainable business model with the hope of buying a global monopoly by strangling competition.

China can destroy all of that by making their models open weight. The real money is in finding and implementing custom AI solutions... not in charging for access to the models. By having freely available models, they're making the barrier of entry as low as possible.

Not to mention that the insane amount of money being poured into hardware by US tech companies has created an environment where building fabs has a much shorter ROI, which also helps China's development in that sector.

US companies are playing Monopoly while China is playing Civilization.

I ran DeepSeek and Llama and Mistral at home on my consumer grade gaming PC.

With a little tweaking of the system prompts and configuring web search, I was running a local LLM that felt pretty darn close to the commercial LLMs.

With this technology out in the open internet where you can download the models in a few hours I don't see how the commercial AI companies are going to last. If selling "Artificial Intelligence" subscriptions is all your company does for revenue, you're screwed.

I downloaded and ran an LLM that I could have a conversation with and feed basic coding problems to for basically zero dollars and ran it on my puny gaming machine...puny compared to enterprise-class hardware. It would be trivial for a company with a very moderate budget to buy some servers and start running their own LLMs that they can use to feed all the PII and HIPPA data they want.

I just wish I could buy enough memory to run one of these models locally. Specially Kimi K3

The awkward part is that software has a habit of racing toward free once it becomes good enough. If an open weight model delivers 95 percent of the value without recurring API costs, plenty of companies will choose that and spend the savings on integration instead. History keeps rhyming, even if investors hate the tune.

Open models are still most cheaply hosted on a cloud, with batching and 24/7 use. API rates from developer lab are generally fair. Self hosting does have some significant tangible benefits though: Fine tuning for domain specific to organization, and not letting LLM provider train from your prompts/answers, followed by competing with your organization in the future as a result of "distilling your IP".

is their a meaningful difference between open weight and open source?

The localllama crowd has know this for years.

It happened faster than I expected, though; OpenAI/Anthropic hardly even got the chance to tighten the screws.

That's the reason they are trying to embed AI into everything.

I see a business model where "we're done, this one is (finally) good enough and now we'll stop bleeding cash on the training and turn up the screws on the customers we've hooked on loss leader pricing." Open weight models will never stop training for improvement, the costs for training seem to be inexorably falling, and any business model built on the idea that they can kick back and roll in the profits after their initial "hard work" is going to lose all their customers to better products.

This isn't some captive market like US automobile customers who have no choice but the limited selection of crap, crap and more crap that is put in front of them. At least not as long as the internet remains relatively open.

Good.

Yes, but only if either the required hardware becomes affordable (don't expect frontier models to run on 5090s, let alone on the 1050Ti you're keeping as a backup), or somehow the models become more efficient. Even if by reduced capability, which might be a good thing in the grand scheme of things. Some people use overengineered frontier models as synonym search engines.

We even got open weight models that's 27B + 1-bit (and it still has good performance)