I started building Confer because I saw how amazing LLMs are, and as a result, how much of our data is flowing through them. Already, AI chat apps have become some of the largest centralized data lakes in history, containing more sensitive data than anything ever before. We are using LLMs for the kind of unfiltered thinking that we might do in a private journal – except this journal is an API endpoint to a data pipeline specifically designed for extracting meaning and context.
Confessions to a data lake
I’ve been building Confer: end-to-end encryption for AI chats. With Confer, your conversations are encrypted so that nobody else can see them. Confer can’t read them, train on them, or hand them over – because only you have access to them.
Confer — Private AI. Speak freely.
A private AI assistant where you can learn about the world — without data brokers and future training runs learning about you instead.
User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies
Hundreds of millions of people now regularly interact with large language models via chatbots. Model developers are eager to acquire new sources of high-quality training data as they race to improve model capabilities and win market share. This paper analyzes the privacy policies of six U.S. frontier AI developers to understand how they use their users' chats to train models. Drawing primarily on the California Consumer Privacy Act, we develop a novel qualitative coding schema that we apply to each developer's relevant privacy policies to compare data collection and use practices across the six companies. We find that all six developers appear to employ their users' chat data to train and improve their models by default, and that some retain this data indefinitely. Developers may collect and train on personal information disclosed in chats, including sensitive information such as biometric and health data, as well as files uploaded by users. Four of the six companies we examined appear to include children's chat data for model training, as well as customer data from other products. On the whole, developers' privacy policies often lack essential information about their practices, highlighting the need for greater transparency and accountability. We address the implications of users' lack of consent for the use of their chat data for model training, data security issues arising from indefinite chat data retention, and training on children's chat data. We conclude by providing recommendations to policymakers and developers to address the data privacy challenges posed by LLM-powered chatbots.
User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies
Hundreds of millions of people now regularly interact with large language models via chatbots. Model developers are eager to acquire new sources of high-quality training data as they race to improve model capabilities and win market share. This paper analyzes the privacy policies of six U.S. frontier AI developers to understand how they use their users' chats to train models. Drawing primarily on the California Consumer Privacy Act, we develop a novel qualitative coding schema that we apply to each developer's relevant privacy policies to compare data collection and use practices across the six companies. We find that all six developers appear to employ their users' chat data to train and improve their models by default, and that some retain this data indefinitely. Developers may collect and train on personal information disclosed in chats, including sensitive information such as biometric and health data, as well as files uploaded by users. Four of the six companies we examined appear to include children's chat data for model training, as well as customer data from other products. On the whole, developers' privacy policies often lack essential information about their practices, highlighting the need for greater transparency and accountability. We address the implications of users' lack of consent for the use of their chat data for model training, data security issues arising from indefinite chat data retention, and training on children's chat data. We conclude by providing recommendations to policymakers and developers to address the data privacy challenges posed by LLM-powered chatbots.
When Private AI Chats Become Public: Lessons from Grok’s Privacy Spill
Understand the implications of the Grok breach where hundreds of thousands of private chats were leaked to the public.
LLM Agents Are the Antidote to Walled Gardens
While the Internet's core infrastructure was designed to be open and universal, today's application layer is dominated by closed, proprietary platforms. Open and interoperable APIs require significant investment, and market leaders have little incentive to enable data exchange that could erode their user lock-in. We argue that LLM-based agents fundamentally disrupt this status quo. Agents can automatically translate between data formats and interact with interfaces designed for humans: this makes interoperability dramatically cheaper and effectively unavoidable. We name this shift universal interoperability: the ability for any two digital services to exchange data seamlessly using AI-mediated adapters. Universal interoperability undermines monopolistic behaviours and promotes data portability. However, it can also lead to new security risks and technical debt. Our position is that the ML community should embrace this development while building the appropriate frameworks to mitigate the downsides. By acting now, we can harness AI to restore user freedom and competitive markets without sacrificing security.
We Can Just Build Things
Build the tools your community needs — production-grade, privacy-respecting freedom tech, made with an AI agent and grounded in a verified, values-aligned catalog (Nostr, AT Protocol, and beyond).
AI Privacy Concerns Explained: What Chatbots Do With Data - Brightside AI | Protect your team from AI threats & deepfakes
Discover AI privacy risks when using ChatGPT and other chatbots. Learn what happens to conversations and how to use private AI alternatives.
Data Streaming for AI: From Extractive Training to Sovereign Infrastructure DWeb Camp 2026
AI systems are consuming the world's content without compensating its creators. This session explores data streaming as a new paradigm — where content flows to AI in real time, with built-in rights management, usage tracking, and fair compensation — and asks what it would take to make this infrastructure decentralized, sovereign, and governed by the communities it serves.
AI Fiction in the Wild
This website hosts anonymized ChatGPT-user conversations where users requested some form of fiction generation—including stories, novels, scripts, roleplay, hypothetical scenarios, erotic imaginings, and more. The data is drawn from WildChat and was collected voluntarily and with users’ consent between 2023 and 2024. The models were powered by GPT-3.5 and GPT-4.
AI Fiction in the Wild
This website hosts anonymized ChatGPT-user conversations where users requested some form of fiction generation—including stories, novels, scripts, roleplay, hypothetical scenarios, erotic imaginings, and more. The data is drawn from WildChat and was collected voluntarily and with users’ consent between 2023 and 2024. The models were powered by GPT-3.5 and GPT-4.
The key questions: how much power will apps be given to determine and enforce what data is permissioned? What incentives will apps have to make any data public? So far the protocol has been a forcing function for (adversarial) interop. We need to be intentional about not recreating app silos.
daniel holmgren 🫠
hey we're really working on permissioned data! read the first in a series of posts i'll be doing about our design decisions along the way. this one is about our decision to not do an e2ee system
ICYMI: We have a new "AI Preferences" setting This allows you to announce to the entire ATmosphere network what you consent to regarding AI use of your public data. Other apps can see this but ~may not~ respect the settings We built this to give you a place to set these and will 100% respect them
Introducing Lumo, a privacy-first AI built by Proton, where every conversation is confidential ✅ Zero-access encryption ✅ No-logs policy ✅ Open-source and auditable Try @asklumo.proton.me for free, no sign-up required: lumo.proton.me
