## The Dilemma of Cost Efficiency in AI The pursuit of low-cost, high-performance AI models is tempting, but the implications for data sovereignty require careful evaluation. Recent revelations about DeepSeek, a Chinese AI lab, have raised concerns regarding data sharing with state intelligence services. Bill Conner, former advisor to Interpol and GCHQ, and current CEO of Jitterbit, emphasizes that the initial interest in DeepSeek was linked to its ability to offer high-performance large language models (LLMs) without the prohibitive costs typical of Silicio Valley. However, efficiency cannot be separated from data security. ## Risks to Data Sovereignty LLM integration involves connecting to proprietary data, customer information, and intellectual property. If the AI model has a "back door" or shares data with a foreign intelligence apparatus, sovereignty is compromised. Conner warns that DeepSeek's involvement with military procurement networks and alleged export control evasion tactics should alarm corporate leaders. ## Governance and Accountability The decision to adopt or ban an AI model is a matter of corporate responsibility. Shareholders and customers expect their data to remain secure and used only for legitimate business purposes. Conner states that for Western leaders, it's not just about performance or cost, but about governance, accountability, and fiduciary duties. Companies cannot justify integrating a system where data residency, usage intent, and state influence are opaque. The DeepSeek case calls for an audit of AI supply chains to ensure full visibility into where model inference occurs and who holds the keys to the data. ## Beyond Cost Efficiency As the generative AI market matures, trust, transparency, and data sovereignty will outweigh the appeal of pure cost efficiency. Transparency in data management becomes a crucial competitive factor.