U.S. AI Investment Is 23 Times Larger, but Performance Is Equal to China's

The Terrain of AI Hegemony Is Changing

China's catch-up, Meta's open-source retreat, and DeepSeek's break from CUDA, officially confirmed by the Stanford 2026 AI Index
April 14, 2026 | Attorney Kim Kyung-jin

A 400-page report came out of the Stanford campus. By length alone it looks like an academic document, but the numbers inside are more direct than any policy report in Washington. The 2026 AI Index. One sentence captures the core of this document, released on April 13 by Stanford's Institute for Human-Centered AI (HAI): "The performance gap between U.S. and Chinese AI models has effectively closed."

https://hai.stanford.edu/ai-index/2026-ai-index-report

Start with the numbers. In 2025, private AI investment in the United States was $285.9 billion. China was $12.4 billion. a 23.1-fold difference. But when you open the benchmark performance table, the scene changes. In February 2025, DeepSeek R1 stood alongside leading U.S. models, and as of March 2026, the lead held by Anthropic's top model is 2.7%only. Investment gap: 23x. Performance gap: 2.7%. Put these two numbers in the same sentence, and the assumption that America maintains AI hegemony with money begins to shake.

This is the starting point of today's story.

Rewriting the Conditions for an AI Power

Just two years ago, the grammar of AI competition was clear. The side with computing resources wins. The country that can buy more and better chips moves ahead. Under that logic, China should have remained second forever, because the United States blocked semiconductor exports. NVIDIA H100s could not go to China, and H20s were also blocked by additional restrictions.

DeepSeek first broke that grammar in January 2025. While the U.S. AI industry was pouring in trillions of won, R1, trained in Hangzhou at far lower cost, achieved GPT-4-level performance. Washington was shocked, and Silicon Valley looked awkward. Now, 14 months later, the Stanford report officially confirms that the shock was not a passing event but a structural shift.

There are certainly areas where the United States still leads. Of the world's AI data centers, 5,427 are in the United States, more than 10 times the number in the second-ranked country. The United States also leads in patent output and high-impact papers. The report also confirms that Korea leads the world in AI patents per capita. But on model performance, the most direct indicator, China has already arrived. The report records that U.S. and Chinese models have exchanged the lead several times since early 2025.

There is a more uncomfortable number: the AI Transparency Index. from 58 points in 2025 to 40 points in 2026, an 18-point drop. The reason is that OpenAI, Anthropic, and Google no longer disclose the size of training data or training duration for their latest models. Of 95 major models released, 80 did not disclose training code. The better performance gets, the more closed the inside becomes. This is the self-portrait of the AI industry in 2026.

The gap between expert optimism and public anxiety was also recorded this time. AI experts who answered that "AI will have a positive effect on their job" accounted for 73%. To the same question, only 23%of the general public agreed. A 50-point gap. The distance between the people building the technology and the people who must live inside it is compressed into this number.

16 Million Acts of Theft, and a Three-Company Alliance

Three Western AI companies have joined hands: OpenAI, Anthropic, and Google. They are competitors. Yet it became known in early April that the three companies would share information through the Frontier Model Forum and jointly respond to adversarial distillation by Chinese companies.

Adversarial distillation is a sophisticated technique. A specific Chinese company repeatedly calls large-scale APIs and collects millions of responses from high-performance models. It uses those responses as training data to create a smaller model with similar performance. This violates API terms of use and can be the subject of trade secret litigation. At Anthropic alone, three Chinese companies were confirmed to have attempted 16 million unauthorized exchanges.

This is the first time Western AI companies have mounted a joint defense operation. When they detected and responded individually, each company saw only a limited picture. Now, by sharing their detection results, they can read broader patterns. Chinese companies have not issued an official response.

This incident shows another face of AI competition. The race to develop technology is also an information war. Developing the best model and preventing that model from being copied now carry the same weight. There is also an irony in this defensive alliance. One cause of the trend toward lower transparency and less public information is the threat of copying, so closure and defense appear to reinforce each other.

We Will Go Without CUDA

DeepSeek founder Liang Wenfeng personally confirmed a late-April release. V4. 1 trillion parameters, 1 million-token context window. It is scheduled to be released under the Apache 2.0 license.

There is a fact more important than the numbers. V4 runs on Huawei Ascend 950PR chips. No NVIDIA. No AMD. No Western silicon. Over the past several months, DeepSeek has worked with Huawei and Cambricon Technologies to rewrite code that bypasses NVIDIA's CUDA ecosystem. It rebuilt all of V4 on a framework optimized for Huawei's CANN architecture.

NVIDIA was not even allowed early test access to V4. The door was opened only to Chinese chip companies. This decision immediately produced a market reaction. Alibaba, ByteDance, and Tencent preordered Huawei's next-generation AI chips in units of hundreds of thousands, and chip prices 20%rose. NVIDIA semiconductor shares saw temporary selling pressure.

Huawei Ascend 950PR's performance is assessed as sitting between NVIDIA H100 and H200. There is still a gap in raw performance per individual chip, but DeepSeek has been closing that gap through software optimization. DeepSeek has already proved twice the proposition that "smart software beats slower hardware."

The premise that U.S. semiconductor export controls can suppress Chinese AI is already shaking. In China's 2025 AI accelerator market, NVIDIA's share is 55%, still number one, but far below its former dominance above 90%. According to IDC data, Chinese domestic chipmakers now hold 41%of the local AI accelerator market. If V4 is confirmed to deliver competitive performance on Huawei chips, the proposition that "frontier AI is impossible without NVIDIA" collapses. Industry analysts assess that 2026 could become the first year of Chinese self-reliance in AI computing.

Another Front Inside the United States

The contest for AI dominance is not happening only between China and the United States. Inside the United States, the federal government and state governments are also clashing.

The Trump administration is maintaining a "light regulation" stance. Meanwhile, new AI laws have passed in 19 states. Representative examples include New York's algorithmic pricing disclosure law, Colorado's high-risk AI governance law, and California and Texas restrictions on medical AI. The federal Department of Justice has signaled lawsuits, arguing that state AI laws interfere with interstate commerce.

Each regulator has its own front as well. The FTC is targeting exaggerated advertising about AI capabilities under Section 5 of the FTC Act, the SEC is tracking "AI washing," and the DOJ is investigating federal healthcare billing fraud using AI. Without a single federal AI law, enforcement agencies are making scattered efforts to police AI through existing statutes. According to the Stanford report, 47 countries are currently engaged in AI legislative activity, but only 12 have enforcement mechanisms. An analysis found that compliance costs can differ by jurisdiction by up to eightfold.

The Stanford report captured another uncomfortable figure. The share of Americans who believe their government will regulate AI well is 31%. It is the lowest number among the countries surveyed. The country that regulates least is also the country whose regulation is trusted least.

The Tradeoff Between Speed and Opacity

The numbers in the Stanford report point in two directions at once. One is acceleration. The other is opacity.

On the software engineering benchmark SWE-bench Verified, AI performance rose from 60% to 100% in one year. In three years, generative AI was adopted by 53%of the world's population. Personal computers and the internet did not match this speed. Models are also improving quickly on Humanity's Last Exam.

Behind this speed sits black-boxing. The most powerful models no longer disclose the size of their training data, training duration, or training code. Companies cite regulatory risk and intellectual property protection. As a result, there is no longer any way for outsiders to know how AI is made or what it was trained on.

Environmental costs are being recorded too. The carbon dioxide emitted during training of Grok 4 alone was 72,816 metric tons of CO2 equivalent. There is also an estimate that GPT-4o inference over one year could consume more water than the drinking needs of 12 million people. The power capacity of AI data centers is 29.6GW. It matches peak demand in New York State. This is why investors identify power for AI data centers as a top priority.

While AI is writing the fastest adoption history of any technology, the traces it leaves on the planet are also record-setting.

Meta's Betrayal and Lies

Mark Zuckerberg spoke of open source as if it were a belief for a long time. From 2023 to early 2025, as he released the Llama series, his logic was consistent. "Open source is safer. Open source is democratic. Open source benefits Meta." Believing those words, developers on r/LocalLLaMA built businesses on top of Llama. Startups built products based on Llama. By early 2026, cumulative downloads in the Llama ecosystem reached 1.2 billion, with a daily average of 1 million.

On April 8, Meta released Muse Spark. It is a proprietary model. There is no download, no released weights, and no way for developers to build something on top of it. It was the first product from Meta Superintelligence Labs and a declaration of separation from the Llama lineage.

The journey to Muse Spark is interesting. In June 2025, Meta brought in Alexandr Wang of Scale AI through an investment worth $14.3 billion. The team led by Wang overhauled Meta's entire AI stack for nine months. When this project, codenamed Avocado, appeared in the world as Muse Spark, the report card met expectations. On the Artificial Analysis Intelligence Index, it scored 52 points. It ranks fourth in the world after Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro. On Humanity's Last Exam, it recorded 58%. It comes with multimodal reasoning, tool use, visual chain-of-thought, and multi-agent coordination by default.

"Meta used open source not as a belief, but merely as a competitive tool." - reaction from the r/LocalLLaMA community

That is not wrong. When Llama was released, Meta was behind Google and OpenAI in foundation models. The open-source strategy was a favorable choice while Meta was a chaser. Now Meta has calculated differently. With the goal of planting Muse Spark into Facebook, Instagram, and WhatsApp, used by 3 billion people, sharing model weights with competitors was no longer a strategically clever choice. Meta's expected AI-related capital expenditure for 2026 is $115 billion to $135 billion, about double the previous year.

Meta said it "plans to release future versions of Muse as open source." When that will happen is undecided. An ironic structure has emerged. Into the space Meta vacated in open source, Alibaba Qwen, ByteDance open-source models, and DeepSeek V4 (Apache 2.0), due later this month, are moving in. It means Chinese companies could become the new champions of open-source AI.

A Lawyer's View: The Link to Korea's AI Framework Act

How should Korea's AI Framework Act, in force since January 2026, be read on this terrain? While the United States shows confusion with 19 states legislating separately without a unified federal AI law, Korea has established a legal framework relatively early. But the core issues identified by the Stanford report, declining transparency, unclear responsibility, and the reality that model performance crosses borders, are problems that effectively exist outside the enforcement reach of Korea's AI Framework Act.

The fact that Anthropic's Claude Mythos 5 delivers expert-level performance in cybersecurity and coding, and that the AI Security Institute assessed it as solving expert-level security problems with a 73% success rate, signals that AI is also entering professional judgment in the legal field. From a lawyer's perspective, this is both a tool and a new responsibility problem. Who is responsible for the result of AI's judgment? No country has yet provided a clear answer to that question.

What Will Change at the End of This Month?

If DeepSeek V4 is released at the end of this month, it will formalize the fact that Chinese AI's independent survival around semiconductor export controls has entered the technical completion stage. The structure in which Chinese open source fills the place Meta abandoned will also harden.

One sentence in the Stanford report lingers: "This technology is evolving faster than society can understand, control, and trust." That sentence came from Stanford, one of the more optimistic institutions. Yet the report also recorded that 73% of AI experts expect a positive future.

Is that optimism grounded, or is it the confirmation bias peculiar to the people building the technology? Now that only 23% of the general public shares the same optimism, this question is becoming not a technology issue, but a trust issue.