This news should push users to reconsider an uncomfortable reality about today's artificial intelligence environment. These large language models – AI systems trained on massive amounts of text to produce human-like responses – are surveillance mechanisms we have not yet acknowledged as such, and as a result, we're handing them the most sensitive details of our lives. Therefore, users must be careful and thoughtful about the personal information they provide when using AI tools.

Anonymizing proxy services like VPNBook and Smartproxy may offer some practical alternatives to standard setups, removing certain identifying metadata and creating a privacy barrier between users and model providers. Although these services help reduce risks, they still rely on the same centralized infrastructure that contributes to privacy issues.

AI’s threat to our privacy is simply an evolution of an existing problem. The past two decades saw the giants of surveillance capitalism, including Google, Microsoft, and Meta, build billion-dollar businesses by converting human attention and behavior into algorithmic resources, showing us that "free" digital services always extract value through data harvesting. Search queries turned into behavioral records, social media activity became ad targeting material, and location data evolved into market insights. We became the product, and we accepted it—understandably.

Now, we’re repeating this transaction with emerging technologies, but the consequences are far more significant. The discussions we have with ChatGPT, Claude, or Gemini reflect our cognitive processes. We disclose creative ideas, business strategies, personal data, and half-developed thoughts, essentially allowing these platforms and their operators to witness the raw mechanics of human reasoning and decision-making. When it comes to using AI for financial planning, keep in mind that confidentiality cannot be guaranteed.

Privacy Meets AI Reality

This development marks a major shift when viewed alongside the evolution of digital rights over the last decade. The Snowden revelations of 2013 reignited interest in privacy-focused technology where end-to-end encryption—a method that encodes data so only the sender and receiver can decrypt it—became widely adopted, private messaging apps replaced SMS, and browser developers started blocking trackers by default.

However, the growth of conversational AI seems to have unsettled established privacy expectations. These systems feel so intuitive and engaging that we’ve overlooked the fundamental rule of digital privacy: If you don’t own your data, you don’t control it.

For users who want to regain some level of privacy while still utilizing these powerful tools, available options remain disappointingly limited. Running models locally offers full data control but demands technical knowledge and considerable hardware investment, often costing several thousand dollars for sufficient GPU computing power.

The most viable near-term solutions may come from cryptographic advancements that allow secure processing of sensitive data. Trusted execution environments—secure hardware compartments that isolate sensitive operations from external interference—can handle AI tasks while safeguarding data even during active processing, ensuring neither the infrastructure provider nor the app operator can access user inputs or outputs. At the moment, only technically skilled users can set this up themselves using desktop devices compatible with Intel SGX or AMD SEV. Perhaps in the future, if enough demand exists, companies may start offering accessible services for users who don't build their own computers.

Advanced cryptographic methods like zero-knowledge proofs—mathematical techniques that verify information without exposing the content itself—could let users prove valid queries without revealing what those queries are. Meanwhile, decentralized inference networks—distributed computing systems that spread AI processing across many nodes—might distribute computation in ways that prevent any single party from accessing complete interactions. For now, only advanced AI users can benefit from these privacy-enhancing tools by setting up custom computer systems.

Many types of hardware capable of handling intensive computations can support variations of the above-mentioned privacy approaches. Until commercial products become available for general users, people working with AI may want to avoid entering personal financial details into AI-powered chats.

Looking ahead, privacy-preserving technologies suggest that the conflict between AI capabilities and user privacy can eventually be resolved. While largely unfamiliar to the public, they ultimately offer the cryptographic foundation needed to make artificial intelligence truly trustworthy.

The Lesson For AI Users? Be Careful

Until these privacy solutions evolve further, users should interact with large language models with a stronger understanding of their data practices. These are not impartial intellectual tools. Instead, they are commercial offerings built to extract value from users. They are not your ally.

The court order requiring OpenAI to retain user conversations is just the beginning of how these platforms could jeopardize our private thoughts. Cognitive freedom is a right we will need to defend actively. Until then, ask yourself whether you’d be okay with your prompts appearing in a data leak or legal document. Never disclose names or addresses, switch between service providers regularly, and make use of features like ChatGPT’s temporary chat before discussing crypto trades.

Let us not return to old habits and give away our privacy easily. There is always a cost involved, and it should be greater than a stylized image.

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