Your Child's Data Profile Starts Before They're Born | Eamonn Maguire of Proton
Episode
55 min
Read time
2 min
Topics
Fundraising & VC, Marketing, Artificial Intelligence
AI-Generated Summary
Key Takeaways
- ✓Pre-birth data profiling: The moment a parent emails a gynecologist or fertility clinic using Gmail or Outlook, advertising platforms flag that household as expecting and begin building a child's profile before birth. Switching to end-to-end encrypted email like ProtonMail at the start of a pregnancy prevents this data from entering ad-targeting systems entirely.
- ✓AI training data opacity: Only 0.3% of GPT-2's training data came from the entire English-language Wikipedia. The remainder was scraped web pages, social media, and unattributed sources. Anthropic faced a $1.5 billion lawsuit for scanning thousands of purchased books then discarding them to eliminate copyright paper trails — a pattern users should factor into trust decisions.
- ✓Profile inference from minimal data: Three email sign-ups — Instagram, a political newsletter, and an AI publication — are sufficient for platforms to infer age, ideology, and interests, then expand the profile by serving targeted ads and measuring click behavior. Non-clicks on religious or political content are themselves used to fill profile gaps.
- ✓Open vs. open-washed AI models: Proton's Lumo assistant deploys genuinely open models — including GLM 5.1, Qwen 3.5, and NVIDIA's Nematron series — where training data, code, and architecture are all publicly verifiable. Models labeled open-source but with undisclosed training data, such as Meta's Llama, are described as "open-washing" and carry the same trust risks as proprietary systems.
- ✓Privacy-preserving AI within encrypted environments: Proton implements local indexing of Drive folders linked to Lumo projects, enabling retrieval-augmented generation without sending documents to external servers. Users can disable web search APIs entirely if their threat model requires it, and all chat history is end-to-end encrypted with user-held keys, making server-side data access structurally impossible.
What It Covers
Eamonn Maguire of Proton explains how data profiling begins before a child is born, how AI models are trained on scraped data without consent, and how Proton's ecosystem — including Lumo AI, encrypted email, and the Born Private initiative — offers a structural alternative to surveillance-based platforms.
Key Questions Answered
- •Pre-birth data profiling: The moment a parent emails a gynecologist or fertility clinic using Gmail or Outlook, advertising platforms flag that household as expecting and begin building a child's profile before birth. Switching to end-to-end encrypted email like ProtonMail at the start of a pregnancy prevents this data from entering ad-targeting systems entirely.
- •AI training data opacity: Only 0.3% of GPT-2's training data came from the entire English-language Wikipedia. The remainder was scraped web pages, social media, and unattributed sources. Anthropic faced a $1.5 billion lawsuit for scanning thousands of purchased books then discarding them to eliminate copyright paper trails — a pattern users should factor into trust decisions.
- •Profile inference from minimal data: Three email sign-ups — Instagram, a political newsletter, and an AI publication — are sufficient for platforms to infer age, ideology, and interests, then expand the profile by serving targeted ads and measuring click behavior. Non-clicks on religious or political content are themselves used to fill profile gaps.
- •Open vs. open-washed AI models: Proton's Lumo assistant deploys genuinely open models — including GLM 5.1, Qwen 3.5, and NVIDIA's Nematron series — where training data, code, and architecture are all publicly verifiable. Models labeled open-source but with undisclosed training data, such as Meta's Llama, are described as "open-washing" and carry the same trust risks as proprietary systems.
- •Privacy-preserving AI within encrypted environments: Proton implements local indexing of Drive folders linked to Lumo projects, enabling retrieval-augmented generation without sending documents to external servers. Users can disable web search APIs entirely if their threat model requires it, and all chat history is end-to-end encrypted with user-held keys, making server-side data access structurally impossible.
Notable Moment
Maguire describes how platforms actively probe unknown profile attributes — such as religion or political affiliation — by serving targeted ads and measuring non-clicks as data points. The absence of engagement is itself recorded, meaning passive scrolling still continuously fills gaps in a user's behavioral profile.
Episode Transcript
My wife is convinced that her iPhone listens to the conversations because we'll have a conversation at dinner and then she'll get served an app. The profiles of your child is being created even before they're even born. There's no real transparency over to say how exactly these profiles are are created in the first place. You think your child is safe in their bedroom, but all these companies are basically changing the behavior of your child. Do you think that they'll develop an ecosystem around maybe Proton will be the kernel around an ecosystem of privacy to Tom will be the kernel, around an ecosystem of privacy to counter the Googles and Metas of the world so that someone with the born private email address could then participate in social media or participate in search without having them having somebody build this elaborate detailed profile of them. We're gonna talk about Born Private, but, Proton has a bunch of privacy preserving products. I thought maybe you could start by introducing yourself. I know that you've been working in this space for a long time. I believe you have a PhD from Oxford and that you worked at CERN. Is that right? Mhmm. Am I wrong on that? Yeah. No. No. You're not wrong. So that's correct. So, yeah. Can you can you introduce yourself and and give give that background? My background is varied, I would say. I started most of my professional type of career was in bioinformatics. It was basically using computational techniques to analyze, like, genetic sequences or protein sequences and so on. And, that very much got me into the academic mindset of things. And I went and did a a master's in bioinformatics and then a PhD in in computer science, Largely focused on also, quite a bit of bioinformatics, but I also worked in security, applications, a lot of stuff in insider threat, for example. Worked in things of digital humanities, also like analyzing poetry, for the way things were are spoken and how it's, how you can visualize that type of information. So I sort of my my major interest for a lot of my life was working on visualization, data visualization. So the intersection of of computer graphics, mathematics, statistics, machine learning later, I would say, and then the computer science aspect of it. And, yeah, visualization opened up a lot of different avenues, in fact. So I worked in biology. I worked in security. I worked in digital humanities. I, yeah, I ended up doing my postdoc at CERN. I spent a couple of years there, then I went and worked in finance, for three years, taking a lot of what I learned from, I would say, the previous my previous career cycles, into finance also. So I did a lot of work, applying the techniques typically used in biology or bioinformatics, but doing it using those techniques in in financial time series analysis, for example, or looking at, different …
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by Proton
“Proton implements local indexing of Drive folders linked to Lumo projects, enabling retrieval-augmented generation without sending documents to external servers.”
by Proton
“Proton's ecosystem — including Lumo AI, encrypted email, and the Born Private initiative — offers a structural alternative to surveillance-based platforms.”
by Proton
“Switching to end-to-end encrypted email like ProtonMail at the start of a pregnancy prevents this data from entering ad-targeting systems entirely.”
other
by Proton
“Proton's ecosystem — including Lumo AI, encrypted email, and the Born Private initiative — offers a structural alternative to surveillance-based platforms.”
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