The U.S. data governance dispute with the EU over government data collection shows that the U.S. itself has not solved the data governance problem and faces disagreement with allies on these issues.

President Biden's recent comments calling Xi Jinping a dictator are counterproductive to trust-building and potentially derail recent diplomatic progress on issues like global warming, Ukraine, and Taiwan communication.

Susan Aronson argues that Oresman's analysis is 'utterly unconvincing' based on her review of China's actual data governance policies, contending that if any nation coherently thinks about data, it is the EU and South Korea rather than China.

Aronson argues that data cannot be compared to oil because it is simultaneously an economic good and a service; it is inexhaustible (unlike oil), and it can function as a commercial asset, global public good, and solution to collective problems like disease spread.

Aronson identifies three types of data: personal data, public data (government-controlled/collected), and proprietary data (trade secrets/algorithms), arguing that Chinese and Western strategies conflate these types and making coherent policy requires distinguishing them.

Aronson argues that the United States benefits from competition with China (specifically TikTok threatening Facebook) because it pushes U.S. companies to improve rather than become 'fat and lazy,' and that genuine competitive pressure is healthy for innovation.

Aronson notes that China's algorithms and AI systems are predominantly open-source, meaning China shares code with the global community for feedback and improvement, which differs from Western proprietary approaches but suggests collaborative intent.

Aronson references a study by Thomas Streitfeld examining China's influence on global data governance through Belt and Road partnerships, which found essentially no evidence that China is attempting to influence other countries' data and internet governance policies.

Aronson counters the asymmetry argument by noting that the presumption that larger data volumes lead to competitive advantage is not supported by evidence—data quality, completeness, and accuracy matter more than quantity.

The U.S. has tremendously benefited from an open approach to data and open immigration, open ideas, and open source models, and should not move toward restrictions that make us more like China.

There is a peculiar dichotomy in China where the government is willing to allow its people to study at Oxford or Stanford and to travel internationally, while simultaneously engaging in severe information control domestically, suggesting China is evolving on openness in some ways but not yet fully committed.

China's Belt and Road Initiative may include infrastructure development that serves as a mechanism for data collection, particularly through facial recognition technology and other surveillance systems being deployed in developing countries to build ethnic and demographic datasets.

The U.S. government and companies should not present evidence of why specific data risks exist before restricting Chinese data flows, as the burden of proof is inappropriately high and the precautionary principle should apply.

The public conversation about data's role in society has not occurred in the United States, except partially in Germany which is discussing digital public goods and developing a data strategy for the economy and polity.

The Trump administration's restrictions on international students made universities suffer, affecting not only Chinese students but students from Lebanon, El Salvador, Greece, and others, reducing valuable intellectual exchange.

Major multinational corporations like Citibank and MasterCard have collected personal data on a global scale for decades, raising questions about data collection volumes and storage practices that are not typically scrutinized compared to Chinese companies.

Aronson finds Pottinger's analysis thoughtful, well-researched, and well-sourced, but fundamentally disagrees with his conclusions and recommendations.

The US must be so much better off if China had its act together on data because China understands data as a national security asset, and understanding this allows China to see data's potential to solve community problems from real estate bubbles to finding jobs for underemployed college graduates.

Aronson notes that the U.S. government has not presented evidence (in legal proceedings) that TikTok poses a specific national security threat based on China's data laws, requiring more transparent specification of the threat rather than assertion.

Aronson argues that China is handicapped in AI development due to the Great Firewall: data scraped globally that feeds large language models cannot be included in Chinese models because of censorship, making Chinese generative AI weaker than Western equivalents.