Signal co-founder Moxie Marlinspike launched a new project in December called Confer, a privacy-conscious alternative to AI personal assistants like ChatGPT and Claude. Confer aims to provide a similar user experience to these popular chatbots while avoiding data collection and ensuring user privacy.

The core difference between Confer and other AI services lies in its backend architecture. According to Marlinspike, Confer is designed so that user conversations cannot be used to train the model or target advertisements. The host of the service never has access to the conversations, ensuring that personal data remains private. This approach contrasts sharply with the data collection practices of many AI companies, where user data is often retained and used for various purposes, including model training and targeted advertising.

Marlinspike emphasized the importance of privacy in AI interactions, stating that these interfaces "actively invite confession" and can learn more about users than any previous technology. He expressed concern about the potential for conflicts of interest when these intimate conversations are combined with advertising, comparing it to "someone paying your therapist to convince you to buy something."

The open-source nature of Confer, similar to Signal, is intended to foster trust and transparency. By making the code publicly available, users and developers can verify the privacy protections and contribute to the project's development. This approach aligns with a growing movement advocating for more ethical and privacy-focused AI development.

The rise of AI personal assistants has raised concerns about the potential for privacy violations. As these services become more integrated into daily life, they collect vast amounts of personal information, which can be vulnerable to misuse or unauthorized access. OpenAI, the company behind ChatGPT, is already testing advertising, raising concerns that the data collection practices common in the advertising industry could extend to chatbot conversations.

Confer represents an early attempt to address these concerns by offering a privacy-preserving alternative. While the project is still in its early stages, it highlights the growing demand for AI services that prioritize user privacy and data protection. The development of Confer could influence the future direction of AI development, encouraging other companies to adopt more privacy-conscious approaches. The project's success will likely depend on its ability to provide a compelling user experience while maintaining strong privacy guarantees.