The Irony of Banning Open Source AI: The DeepSeek Controversy

In the rapidly evolving landscape of artificial intelligence, several countries including the United States, Australia, Italy, South Korea, Taiwan, and even New York State have restricted or banned DeepSeek—an open source AI model developed in China. These bans highlight a troubling contradiction in how we approach AI regulation and security on the global stage.

The DeepSeek Phenomenon

DeepSeek, founded in 2023 by Liang Wenfeng in Hangzhou, China, disrupted the AI sector with its open-source large language models. Its flagship model, DeepSeek R1, released in January 2025, rivals top-tier models like OpenAI's GPT-4o and Meta's Llama 3.1, but reportedly cost just $5.6 million to train—a fraction of the hundreds of millions required for its competitors.

The company's AI Assistant app surged to the top of Apple's US App Store, even outranking ChatGPT and causing market ripples including a reported $600 billion drop in Nvidia's value on January 27, 2025. This rapid rise has clearly caught the attention of regulators worldwide.

The Bans and Their Rationale

The restrictions on DeepSeek have been swift and widespread. The United States proposed the "No DeepSeek on Government Devices Act" citing national security concerns. Australia banned the app on federal government devices due to unspecified national security risks. Italy removed DeepSeek from app stores citing GDPR non-compliance and data handling issues. Both South Korea and Taiwan restricted its use on government devices, while New York State barred the app from state computers.

Officials cite fears that user data stored in China could be accessed by the Chinese government under local laws that mandate data sharing with intelligence officials. Some reports even suggest the app contains hidden code potentially sending data to China Mobile, an entity banned in the US. These concerns have led to a cascade of restrictions that effectively limit DeepSeek's reach despite its technical merits.

The Open Source Paradox

Here lies the fundamental irony: DeepSeek's models are open-source, released under the MIT License, allowing anyone to inspect the code for malicious components. Unlike closed-source alternatives, the transparency of open source should theoretically address spying concerns.

This contradiction becomes even more apparent when we consider the nature of open source software. Anyone can download and run DeepSeek R1 locally, completely avoiding the app's servers. The code can be scrutinized by security researchers worldwide for backdoors or malicious features. Data can remain entirely within an organization's network through self-hosting. If the concern is truly about data security and not geopolitics, the open source nature of DeepSeek represents a solution, not a problem.

A Tale of Two Models: The Privacy Divide

The differences between open-source models like DeepSeek and closed-source alternatives like ChatGPT, Claude, and Grok reveal a striking disparity in how we approach privacy and security. With DeepSeek's open-source foundation, the code is fully visible and modifiable by anyone with the technical expertise to review it. Users can host the model locally, keeping their data within their own infrastructure rather than sending it to external servers. This level of transparency and control stands in stark contrast to closed-source models.

Proprietary AI systems like ChatGPT, Claude, and Grok operate behind closed doors. Their code remains hidden from public scrutiny, with data typically processed on company servers based primarily in the United States or other Western countries. Users have little control over how their information flows through these systems, yet these models face significantly less regulatory scrutiny despite similar or even greater privacy implications.

ChatGPT, for instance, has faced its own privacy controversies. Research has demonstrated that it can extract contact information and other personal data from conversations, yet it remains widely accessible without similar government restrictions. The New York Times and other publications have documented substantial privacy concerns with closed models, yet these haven't triggered comparable regulatory actions. This disparity suggests that geopolitical considerations may be taking precedence over genuine security evaluations.

Impact on the Open Source Movement

The bans appear to favor closed-source models like ChatGPT, Claude, and Grok, which also collect user data but face less scrutiny. This double standard could significantly impact the open source AI movement in profound ways. Developers may become hesitant to contribute to open source AI projects, particularly if their nationality might trigger restrictions regardless of their code's quality or security. International collaboration—the lifeblood of open source development—could suffer as contributors become wary of geopolitical implications.

The current regulatory approach risks reinforcing the market dominance of proprietary systems, which would be a significant setback for democratizing access to AI technology. Perhaps most concerning is the chilling effect on transparency in AI development. When governments restrict open source technologies primarily based on their country of origin rather than technical merit, they send a troubling message that transparency is less valued than geopolitical alignment.

The Practical Reality Gap

While the theoretical benefits of open source are clear, there's a disconnect between potential and practice. Most users lack the technical expertise and infrastructure to set up and maintain AI servers. The app, being user-friendly, serves as the primary access point for the general public. Banning the app effectively limits DeepSeek's accessibility despite its open source foundation.

This reality highlights a common challenge in the open source world—the gap between theoretical freedom and practical accessibility. While experts and organizations with technical resources can benefit from DeepSeek's open nature, average users rely on the convenience of the app. By targeting the app rather than addressing specific security concerns in ways that preserve accessibility, regulators are effectively cutting off access to innovative technology for most people.

A Path Forward: Local Hosting as a Solution

An innovative approach suggested by some officials, including India's Union Minister Ashwini Vaishnaw, involves supporting local hosting of DeepSeek's open source model. This would address privacy concerns while leveraging its benefits. Such an approach would require development of local infrastructure and technical expertise, along with government investment in secure implementation standards. Countries could establish guidelines for organizations to properly secure self-hosted instances and create certification programs for verified deployments.

This balanced approach would allow countries to benefit from innovative AI technology while maintaining control over sensitive data. Rather than an outright ban, it represents a more nuanced response that recognizes both the legitimate security concerns and the value of open source innovation. By fostering local capacity to deploy and secure open source AI, governments could protect national interests while still allowing citizens and organizations to benefit from technological advances.

Moving Beyond Country-of-Origin Regulation

Instead of blanket restrictions based on nationality, a more effective approach would leverage the inherent transparency of open source projects. Governments and organizations concerned about security could fund independent security audits of open source AI models, providing objective assessments rather than assumptions based on country of origin. They might contribute to developing better security standards for AI deployment, ensuring that all models—regardless of source—meet rigorous requirements.

Establishing universal regulations for data handling that apply equally to all AI systems would create a level playing field, removing the current double standard between Western and non-Western technologies. Certification programs for verified AI implementations could give users confidence while allowing innovation to flourish across geopolitical boundaries. These approaches would focus on the technical reality of the software rather than making assumptions based on where it was developed.

Conclusion

The tensions surrounding DeepSeek reflect broader challenges of balancing innovation, security, and geopolitical concerns in the AI era. However, by embracing rather than restricting the transparency that open source provides, we can address legitimate security concerns while continuing to benefit from global collaboration.

The current approach of banning apps based primarily on country of origin rather than technical merit threatens to fragment the AI landscape and hinder the open source movement that has been fundamental to technological progress. As we navigate these complex issues, technology leaders and policymakers must advocate for policies that recognize the unique security advantages of open source development.

Open source AI represents one of our best opportunities to democratize access to artificial intelligence while ensuring security through transparency. Let's not sacrifice those benefits due to misplaced fears that could be addressed through the very openness these models provide. By focusing on technical merit rather than geopolitics, we can build a more secure, innovative, and equitable AI ecosystem for everyone.