America Is Using The Wrong Artificial Intelligence Scoreboard

 by Paul Meeks via RealClearMarkets,

Since OpenAI released ChatGPT in late 2022, America has kept score in the artificial intelligence race by asking which lab has the best model. Unfortunately, that scoreboard is dangerously incomplete. Frontier capability matters, but temporary benchmark leads do not by themselves create durable technological dominance by any single nation. The true national edge comes when its technology becomes the platform on which the world builds.

The distinction came into focus at the White House in August. Administration officials met with leading AI companies to discuss a voluntary framework for government testing of the most advanced models before release. However, the framework will not cover open-weight models, whose underlying parameters can be downloaded and adapted. That was the right decision. But declining to restrict open models is not the same as having a strategy to ensure that the world builds on American models.

The Trump administration already understands the stakes. Its AI Action Plan warns that open models could become global standards in business and research and therefore have "geostrategic value." It calls for leading open models founded on American values. The insight is correct. Implementation has not kept pace with the market.

In strict technical terms, open-weight is not the same as open-source. The former makes a model's learned parameters available; the latter also implies access to such elements as training code and data. Most models commonly called "open-source AI," including China's leading releases, are actually open-weight. Economically, however, the important point is that users can download, operate and adapt them without remaining dependent on a single provider.

American companies dominate the market for high-end closed models. Customers access them through websites or application programming interfaces, while the companies retain the model weights and charge for usage. China has pursued a different strategy. Its developers are releasing increasingly capable open-weight models cheaply or freely, inviting companies, researchers and governments to customize them and build products on top of them.

This is where China is converting diffusion into market power. Moonshot AI's Kimi K3 reached the top tier of global models while being released open-weight. Alibaba's Qwen family has spawned more than 100,000 derivative models on Hugging Face, more than any Western model family. The U.S.-China Economic and Security Review Commission's 2026 "Two Loops" analysis even cites one Andreessen Horowitz partner's rough estimate that 80% of American startups use Chinese base models to develop derivatives for their businesses.

Even if that figure is only directionally correct, the warning is extraordinary. A meaningful share of America's AI application layer may already rest on Chinese foundations because developers found capable, affordable and adaptable models when they needed them.

We are in a contest over far more than individual models. It is also a contest over the technology stack on which they will run. This will shape the skills developers learn, the products entrepreneurs build, the infrastructure customers buy, and ultimately own the standards that eventually become difficult to dislodge. America's objective should be clear: the world's AI economy should be built primarily on an American and allied technology stack.

China knows - and is building policy around the fact - that every adoption strengthens the ecosystem. Developers create compatible tools. Workers learn model-specific skills. Investors finance complementary applications. Businesses integrate the technology into workflows that become costly to change. The resulting feedback loop attracts more users and produces more improvements. In investor terms, diffusion creates the moat. In foreign policy terms, diffusion locks in global influence.

American closed-model companies are not behaving irrationally. Restricting access protects intellectual property and produces recurring revenue. But the business model that maximizes revenue for a few companies does not necessarily maximize American economic power. A closed model can sell many tokens while an open competitor becomes the technological language learned by the rest of the world. Washington should not confuse the commercial interests of dominant vendors with a national strategy.

Anthropic CEO Dario Amodei has raised the strongest objection towards open-source diffusion warning that once capable model weights are released, they cannot be withdrawn, and bad actors may use them without guardrails or monitoring. That concern deserves a serious response but it also does not justify American abstention. China will continue releasing capable open models regardless of what U.S. labs do. Unilateral restraint would not reduce the number of open models in circulation; it would determine which country supplies them.

America has the computing power, talent and capital to lead both the closed and open portions of the AI market. What it lacks is a sustained strategy to put those advantages into circulation - and to ensure that the developers, companies and governments adopting AI abroad can build on trusted American technology rather than becoming dependent on Chinese model ecosystems.

The AI race will not be decided by which company tops the next benchmark. It will be decided by whose technology stack becomes indispensable: whose models developers choose, whose tools they learn, whose infrastructure they deploy and whose standards organize the applications built above them. Implementing an American open-weight strategy is not a departure from AI dominance. It is how America ensures that the world builds on an American technology stack - and how technological leadership becomes durable.

Paul Meeks is a technology-sector investor with more than 30 years of experience in public and private markets. He is a Professor of Practice at The Citadel's Baker School of Business.

https://www.zerohedge.com/ai/america-using-wrong-artificial-intelligence-scoreboard