Your Child's Data Profile Starts Before They're Born | Eamonn Maguire of Proton

Your child's data profile doesn't start when they get their first phone. It starts before they're born, the moment a parent emails a gynecologist or visits a fertility clinic website. That's the core argument behind Born Private, Proton's new initiative that lets parents reserve an email address for their child at birth, anchoring their digital identity in a privacy-preserving ecosystem before the profiling machine gets started. Craig Smith sits down with Eamonn Maguire, Engineering Director, Machine Learning & AI at Proton, who has spent his career at the intersection of data, security, and visualization to explore what's really happening to our data and what, if anything, we can do about it. The conversation covers the mechanics of how just three email sign-ups can allow Google to infer your age, politics, and religion; why OpenAI and Anthropic have shown "not much regard for the law" when it comes to training data and copyright; and why social media platforms are operating like unregulated gambling companies - engineering addiction with no structural incentive to stop. It's one of the most grounded, specific, and genuinely alarming conversations about digital privacy you'll hear, and it ends with a simple, actionable proposition: privacy should be a decision you make at birth, not a problem you try to solve after the damage is done. Subscribe to Eye on A. I. for weekly conversations with the people building and deploying the future of AI.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Craig S. Smith?

  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.

• 0:08Speaker 01

  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 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.

• 0:23Craig S. Smith?

  Do you think that they'll develop an ecosystem around, maybe Proton 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.

• 0:51Craig S. Smith?

  We're going to 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?

• 1:13Speaker 01

  Uh huh.

• 1:14Craig S. Smith?

  Am I wrong on that? Yeah.

• 1:16Speaker 01

  No, no, you're not wrong. So it's correct.

• 1:18Craig S. Smith?

  Uh, so, uh, yeah, can you, can you introduce yourself and, and give, give that background?

• 1:25Speaker 01

  My background is varied, I would say. Um, I started, most of my professional type of career was in bioinformatics. Um, it was basically using computational techniques to analyze, um, like genetic sequences or protein sequences and so on. And, um, that very much got me into the academic mindset of things. Um, and I went and did a master's in bioinformatics.

• 1:52Speaker 01

  And then a PhD in in computer science, largely focused on also a quite a bit of bioinformatics, but I also worked in security applications, a lot of stuff in insider threat, for example. Uh, worked in things with digital humanities, also like analyzing poetry uh, for the way things were are spoken and how it's uh, 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. Um, so the intersection of of computer graphics, um, mathematics, statistics, um, machine learning later, I would say, and then the computer science aspect of it. And

• 2:35Speaker 01

  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 end up doing my postdoc at CERN.

• 2:48Speaker 01

  I spent a couple of years there, then I went and worked in finance uh, for three years. Um, taking a lot of what I learned from, I would say the previous, my previous career cycles um, into finance also. So I did a lot of work uh, applying techniques typically used in biology or bioinformatics, but doing it using those techniques in in financial time series analysis, for example, or looking at uh, different ways of of correlating and different signals together. And um, after that, I ended up working at uh, Facebook, where I worked a lot and with a, it was more machine learning and data science for all external and internal threats. So everyone trying to get into Facebook's network, preventing that happening.

• 3:34Speaker 01

  And then anyone trying to exfiltrate data out of Facebook from within Facebook, um, also stopping that as well. So we're detection engineering.

• 3:43Speaker 01

  And then after that, I came to Proton. So I've been at Proton for six years.

• 3:48Craig S. Smith?

  Okay, I gotta ask, the digital humanities is fascinating. You're creating visualizations of language patterns in poetry. Is that essentially what you're doing?

• 4:03Speaker 01

  So one project, in fact, was looking at how the tongue moves. There's basically different ways. There's a there's a representation about how you create sounds, like from the back of the throat, from the front, from the tongue, and so on. And basically, we had a grid representing that. And then we're able to visualize the changes of this over time. So you could see how how different poems were supposed to be read in a more smooth way versus those which are more harsh tongues, where you have big dynamic changes in in positions, which manifested themselves then as being very large changes in in tonality, for example. So it was kind of cool.

• 4:47Speaker 01

  I I like everything which is interesting. And yeah, we worked on, you know, lots of stuff looking at, which is relevant also today for for computing side of things, like looking how how documents change over time. You know, like, if you look at things like the the Bible, for example,

• 5:07Speaker 01

  it's it's many manifestations of copies over many, many hundreds or thousands of years. That, you know, the things were added, things were removed. It was different languages, so you've got different translations.

• 5:22Speaker 01

  And looking at how these are done also in in other types of texts as well. So like the additions and deletions, which then, if you think in in computing terms also, it's the same, like looking how files change over time is a is a complex thing, or how code changes over time. And using that in security is also interesting. So we ended up using a lot of those things in in security visualization problems as well.

• 5:46Craig S. Smith?

  Wow, that's fascinating. And and how did Proton get started? And what was its original mission?

• 5:55Speaker 01

  Well, the story goes that Proton got started in the CERN cafeteria. And Andy was doing his PhD at CERN.

• 6:05Speaker 01

  I think many of the founders were also still at the time doing their PhD at CERN in particle physics. And it was the time of the Snowden revelations.

• 6:17Speaker 01

  And, you know, Andy is, of course, he's Taiwanese-American descent. So for him, what was happening in Taiwan, for example, the threat of China, for example. But then also in America, where it's a country where it was purporting itself to be the free democratic sort of flag bearer in some way. And then to understand like just how deep the surveillance operations were going. I think it sort of scared a lot of people at the time. And I think that Andy decided at that point in time to create something for meal. Because, you know, in the day to day, especially in science, I would say.

• 7:01Speaker 01

  It doesn't seem very scientific, but a lot of the day to day operations go around sending emails. And, you know, all of this IP that is being sent around is suddenly globally available to players like Google or Yahoo, for example, or Microsoft. And, you know, a lot of your personal life is attached to these emails. So starting at email seemed to be a logical step. And that's when he started in 2014. And it was crowdfunded. So there was a Kickstarter project that raised, I think, around half a million dollars. And that went a long way to setting up the company, bootstrapping everything to make it, you know, have the servers and have the capacity to handle the users.

• 7:47Speaker 01

  And then since then, it's funded by our subscribers. We don't have investors or we don't have venture capital. It's basically just funded by the fact that we have paying users. And they really believe in the product. And they believe in what we're trying to do. And they, by paying for the subscriptions, they're able to also help bring privacy to other users, which are not, who are not paying. Because of course, Proton is also available for free to users as well, so across all of our products.

• 8:20Craig S. Smith?

  I didn't realize that. So the business model is there's a a freemium offering and then you pay for premium services or something?

• 8:32Speaker 01

  Yeah, for instance, for for VPN, um, if you're a free user, you get access to certain countries. You don't have uh very much choice. But if you're a paying user, you've got access to hundreds of countries, I think, something it's a a lot of different um uh gateways you can connect to. And for for mail, for example, you'll have more storage, you have the ability to have custom addresses or custom domains. Um for Lumo, for the product I control or I run, um, basically you've got increased limits, better models and so on um by paying. So we believe in the the need to be able to make privacy available to all.

• 9:14Speaker 01

  But we also need to run a business as well, which is actually paying for all the people who are working in these and these things and all the infrastructure it requires to run it. And, you know, AI infrastructure in particular is quite expensive, so we um it's difficult to run that on a a free model. Unless, of course, you're getting into things like advertising and so on, which is the way that most players um try to monetize.

• 9:39Craig S. Smith?

  Yeah, and uh we're gonna talk about Born Private, this this new product uh or project. Uh and that fascinates me, which is uh that that parents can register uh an email address for their children at birth and that follows them throughout their lives, right? But but before we get to that, uh, wha- what are the risks, particularly in using AI of? Uh, I mean, before we got on, we were talking about proprietary models.

• 10:17Craig S. Smith?

  And I'm gonna start paying more attention, but my understanding was that proprietary models, most of them say that they don't use your data for training. I can't, I know I've seen that, that notice on some of them. What happens with with your models and can you talk about, is it Lumo? Is that the name of the of your model, which is open source, but how do you protect

• 10:51Craig S. Smith?

  privacy? What makes that different from from other models? And if you don't have persistent memory, doesn't that limit the the capability of the model? Can you talk about that?

• 11:08Speaker 01

  Sure. So first, like, most models, so if you go to, as a consumer, let's say, if you go on to many of the the big platforms like ChatGPT or Claude and so on, you'll, by default, typically, you you give them permission to train on your data. That's that's their, I mean, if you're a free user, basically, they need to make something from your free users, right? Otherwise, why give you access to the platform unless you're not paying for it? If you're a paid user, then it's a bit different. If you're enterprise user, typically, they'll have it in the document and the contract saying, we do not use your data to train models. But then it's also a trust problem,

• 11:52Speaker 01

  right? Because they do have access to all of your data. And not not only all of your, the the conversations that are happening back and forth between the user and the model, but also all the context that is provided as well, right? And the business practice of these, at least at this point in time, for all these providers, the the real valuable thing is data that hasn't seen before, a text that has not seen before. Right? Because this is what makes the model better, right? It's not, you know, you can have all these fancy derivations of algorithms or mixture of expert types of architectures and and small changes to to how things are being processed or attention is being given.

• 12:36Speaker 01

  But at the end, the only thing that really matters is compute. So how much you're willing to throw at it, but also how much data you have. And for them, getting access to data is the most important thing. So that's why you're opening up to enterprise and the education platforms is particularly of interest to these platforms because they want the data that hasn't seen. And you know, within a corporate infrastructure, you know, inside Proton, for example, we have probably hundreds of thousands of documents sitting around. That I've never been exposed to the the public web. And education is the same. So if you have a partnership between, for example, ChatGPT and Oxford University.

• 13:18Speaker 01

  What do do ChatGPT get in return, right? Because they give favorable return favorable terms to to Oxford. And Oxford are presumably also giving back some data to them. But even if they didn't do it directly, all the students are still uploading all of their files into Open OpenAI servers that they have access to. And then you have to trust that they're not going to use your data. And I think that's a difficult thing to trust whenever you've seen that they basically threw away all sorts of regard for copyright law. When they decided just to start scanning lots of books. And you see that with Claude Anthropic as well, where there is one point five billion dollar lawsuit against them.

• 14:02Speaker 01

  Because they basically went along and bought lots, thousands and thousands of books. Scanned each of the pages and and threw away all the books. Right? So that there be no paper trail because there were no digital trace. But at that time, they were going to buy all these books just in the shop. I mean, at least they bought the books, I would say. But then what they did afterwards is is quite bad. And then you also have the same thing with with Meta, for example, where they were found to have used this big archive of of pirated books as well. In order to to train their models. So I think, you know, trusting ChatGPT or OpenAI are anthropic with copyright.

• 14:48Speaker 01

  They haven't really shown themselves to have much regard for for the law, I would say, at this point. Not to say that others are much different, but there are open. There are providers of models such as the Allen Institute, for example, so AI two. Developing the open models which are called OML. There's the Swiss AI Initiative also, which has developed the Paratus, which is all based on open data too. Nvidia are also creating their Nematron models, which are all based on on open data as well. So there are people who are doing creating open models. You know, but truly open, not just open weights, but also you can see where the data comes from, you can see the code.

• 15:33Speaker 01

  You can you can see the process behind it. And I think, yeah, I think open models has kind of been. What do you call it? Like, the term has been degraded and somewhat.

• 15:48Speaker 01

  You know, it's open washing where people, you know, they say something is open source or whatever, something is open. But actually, it it doesn't really satisfy the requirements of being open because just one small facet of it is.

• 16:00Craig S. Smith?

  Yeah, that's like llama, not Islam, is that right?

• 16:05Speaker 01

  Open weights, yeah. Open weights, but you don't know where the data comes from, yeah.

• 16:10Craig S. Smith?

  That's right.

• 16:10Speaker 01

  I mean, in the end, no one wants people to know that because we know, for example, for for GPT two. Which is a long time ago now. There is like some crazy stat on the Wired article from around that time. Which stated that the total data, so zero point three percent of the training data used for for GPT two. Can be accounted for the entirety, the entire English language section of Wikipedia. So only zero point three percent of of the training data is basically everything on Wikipedia, an English language. The rest, script, webpages, social media profiles, whatever else before everything got locked down by by Reddit and X and so on.

• 16:56Craig S. Smith?

  You know, I remember hearing and repeating in the early days of of these.

• 17:03Craig S. Smith?

  Uh pre-trained transformer models that they had read the entire English language internet. And I I I never knew whether that was really true. It sounded unlikely. Uh but but what's your view? Is is that possible even?

• 17:25Speaker 01

  Yeah, I think so. I mean, with the databases that you have, you've basically got OpenCrawl, for example, um that you can go and use, you can figure out which which pages are interesting. And you have to filter out all the garbage, of course, because there's a lot of garbage there too, and scam websites and so on. But I mean, knowing which pages are in this language is pretty easy from mostly from the domain name, I would say.

• 17:51Craig S. Smith?

  Yeah.

• 17:51Speaker 01

  And then going off and and having giant crawlers, getting all that content is also not a big deal. You know, the the most impressive thing at the time for companies like OpenAI, for example, where, you know, the the whole transformer model thing was around for quite some time, but no one really thought it would actually give decent outputs. And the fact that they went off and generated GPT two with, and there's a lot of investment behind it. There was still like hundreds of millions of investment. So spending the money to go off and build cheap crawlers to go and get the the content from the internet was not a big deal. So like, if you know that the data is the is the king and this data is the the oil for your machine,

• 18:36Speaker 01

  you make sure that you go and secure the oil. And that's what all these companies did. And all the companies that do the best in terms of having the best models are the ones that manage to acquire the most data.

• 18:48Craig S. Smith?

  Yeah, um it's so fascinating. I I want to get get back to Born Privy, but let me ask one more question. There's so much, uh, you know, I I do a lot of research and there's so much uh that's not on the end of this uh that's in uh on paper, that's not uh digitized. Is, do you think at some point all paper archives all over the world will be digitized and then available for training?

• 19:21Speaker 01

  I think probably yes. I think one one project, which is kind of surprising and and kind of mocked at one point, was OpenAI's pen. You know, they're they had this pen thing, so you could, when you write, it takes out your notes and so on. This is an interesting concept because in some way it's seen as being backwards. It's like, why would you create a pen? And the other side, a lot of people still write a lot of their notes on paper. Um, I'm the same, like even to do lists and stuff like this. I still, there's something to be said about writing something to formulate your ideas better than typing it in the computer. And sometimes I still do it by via pen and pen

• 20:03Speaker 01

  or paper and pencil. And I think that's, that's interesting. Then you have a, I think a lot of these collections, for example, the Bodleian Library in Oxford, it's one of the biggest archives of of research material. I've not confirmed it, but I have no doubt that that OpenAI are looking at trying to get access to that because it's a, it is like literally a treasure trove of information and books and knowledge that many people haven't seen. Which in that case is kind of a shame as well, right? The fact that it is locked away is it's kind of a shame. Um, you know, Google Books, when it came out, Google Books was amazing is a, you know, on one side is Google, so I have, maybe I have to say it's a not great, but on

• 20:46Speaker 01

  the other side, uh, what they did was incredible because, you know, there is this, you know, having all this knowledge, but in a book that you had to go to your library, and that was nice in itself. But to find the information was really laborious and you had to go, takes a long time to be able to find the right book and the right information, then laborious to go to each shelf and take off each book. And now just having this tool where you could go and search across all this knowledge and and find the references that you're looking for. I remember at the time, uh, when I was at Oxford, I, you know, in the colleges, you sit beside,

• 21:25Speaker 01

  like everyone, um, at the table, like different profiles and so on. And there was, uh, one girl who was, um, doing her PhD on, it was English literature.

• 21:38Speaker 01

  But looking at how electricity, the advent of electricity uh became evident in in literature over time. And she was spending a lot of time in the library and it was it was kind of Google Books was around, but it wasn't super well known. And I told her, have you looked at Google Books to to see, you know, type in electricity and see when the the trends are, but when it first exists, or first is mentioned and so on. And she said, no, I never heard of it. And then I I showed her it, I showed it to her. She said, wow, this is amazing. And okay, it doesn't have everything inside it, but it it opens up, you know, the ability to type a word and then see how that word was trending over time.

• 22:23Speaker 01

  It would have taken her years maybe to go through the same uh collective knowledge to be able to have something similar.

• 22:30Craig S. Smith?

  Yeah, and and one one last question, is there are there machines that have been produced that can turn pages and scan? And there must be some robotic uh, yeah.

• 22:46Speaker 01

  Yeah, they they have, I mean, I know that some of them are quite quick. So you've got uh ones which are basically like a magic wand. You pass over the page.

• 22:55Craig S. Smith?

  Yeah.

• 22:55Speaker 01

  Um, so you pass over the page, you pass over the next page and so on. And then I think there's a way then that they can control like to understand the context with which page it was.

• 23:05Craig S. Smith?

  Yeah.

• 23:06Speaker 01

  And but but if you read it properly, of course, you've had the page number as well.

• 23:11Craig S. Smith?

  Yeah.

• 23:11Speaker 01

  But you know, the this is something that's been done, you know, automated automated by by Google already for Google Books and then existing systems like I I don't know exactly what they were doing at Anthropic. When they they did the same thing. What I understood was that they they basically tore out the pages. But that seems a little bit brutalist for my liking. I would have thought that, you know. This tech company would have come up with something uh more interesting, but maybe Occam's razor came to effect here and the simple solution wins.

• 23:45Craig S. Smith?

  That's right, literally a razor, right?

• 23:48Speaker 01

  Yeah.

• 23:50Craig S. Smith?

  Yeah, uh so um yeah, so let's talk about what what before we get again to uh born private. Uh the the model that you that you're using, that that you've built, where the proton is built. Uh if there's no persistent memory, doesn't that make it very uh limited in in its use?

• 24:15Speaker 01

  Uh so just to to clarify, we don't use our own model. We don't uh build our own models. The reason is primarily from financial. Um it would cost us much money to do that. Uh instead, we use open models or different flavors of open, I would say. Um so like Almo was one that we're using at the start, Almo two.

• 24:38Craig S. Smith?

  Right.

• 24:38Speaker 01

  We're not using it anymore um because it's it's not, you know, since July last year where we launched Lumo. The expectations about what these models can do has changed somewhat, I think, over over that time period even. So we've we're basically keeping up with the frontier models by deploying the best current open models that we can deploy. So that includes things like GLM five point one, KIMI uh uh two two point six, which is released just today. Um you know, Qwen three point five, we were using GPT OS as one twenty billion.

• 25:18Speaker 01

  Um but basically, any model that comes along, which looks uh to be performant. Like the next loud frontier in performance, we will use that. Nematron is another example, um Nematron by Nvidia. They they have released um two of the three flavors. So the first flavor was uh, I think it's can't remember exactly if it's twenty billion parameter model, uh thirty billion parameter model. Um they have a hundred and twenty billion parameter model, which they just released uh recently, and they will have a five hundred billion parameter model as well. They're using fully open code, open data.

• 26:00Speaker 01

  It's it's aligns very much with what we want to do as well. If that model is close enough in terms of performance, we'll use that too.

• 26:09Speaker 01

  Then there's a Perthus, which is the Swiss AI one. The version one of that was not probably not at the level where we could deploy to users yet.

• 26:21Speaker 01

  It wasn't it wasn't created for that purpose, I should say also. It wasn't created as a general purpose chat GPT type of competitor. It was created as a base model that was going to be used for different applications. So in sciences and, you know, in in legal contexts and financial contexts and so on, that could be fine tuned towards those different application cases, but not as a edge of the box chat GPT competitor. But their upcoming releases or plans will also improve that as well. So if if it's close enough to something that we can deploy to production that users like, we will be able to run that as well.

• 27:02Speaker 01

  And for the context side of things,

• 27:06Speaker 01

  it's it's more complicated, let's say. So we we don't, just because we're end to end encrypted for the chat history, for example, doesn't mean that we cannot use the information that's already there, right? So there is no real persistent context. There's different flavors of that. So for example, memory, memory itself is, it can be implemented in many ways. It can be like a global database that we are pushing to all the time. It can be something which is local to the user that you can just look up as you're as you're making calls. It can be something which is hybrid. We keep it locally and then we sync it every so often, which is basically what we do. So when we think we keep it locally, it's encrypted. When we sync it, it's encrypted with the user's key, so we can't read anything on our servers.

• 27:53Speaker 01

  For Drive, for example, for in projects, so Lumo has this projects feature. You can link your project with a drive folder. And then that drive folder can have all the information that you need to be able to answer questions in that domain. So if you're an enterprise and you've got one team which is focused on some financial stuff, you can have all your financial documents loaded there. What Lumo does then is it goes and looks that folder is now synced. It downloads all those files, it transforms them into text. We index them all locally. So then we know for, we can basically do two types of search. One is just basic keyword type of search.

• 28:38Speaker 01

  But the second is that you can, you know, you can type in full prompts and it finds the most relevant documents based on the most relevant extracted terms. And then we inject those documents automatically into the context for the prompt. And then send those back to the GPU. Get the result back and then the user has their, their context from their businesses within the response because we provided it at, at runtime. So we do, even if, I think doing things properly in end-to-end encrypted environments is, is, is hard. But just because it's hard doesn't mean we can't do it.

• 29:16Craig S. Smith?

  Sure.

• 29:17Speaker 01

  We've already, we've been doing it for, for mail. I think search is, is particularly hard because you need the data on the client. And not all clients are at the same level and the ability to be able to take all that information. With projects, we sort of, we made the decision that if someone links their drive folder, they would link something which is more around the topic that they're looking at, rather than say, link all my drive files, which is basically could be everything. It would be a lot to be able to sync onto your machine. Whereas linking a folder which is very narrow or much more narrow, it, it gives us the ability that we can do that quite well without overloading the user's machine.

• 30:01Speaker 01

  So we do have ways and then you've got, you know, the whole search thing. Our search is pretty good. So we have web search, we have financial searches with APIs in the background. Users can enable or disable those as they see fit depending on their, their threat model. So if they think that they, you know, I don't want the risk of anyone seeing anything I'm typing in any query going to some third party, you turn it off, right? And if you do want it on, you can actually see what it searched for and, and what results it got back into one as well.

• 30:36Craig S. Smith?

  Yeah.

• 30:38Craig S. Smith?

  So a born private, describe born private to me, to listeners.

• 30:44Speaker 01

  It's basically, I mean, the basic premise is is more that parents can go and reserve their email address for their kids. I would say, you know, the the process is that you choose your email address, you donate one dollar or whatever you want to the proton foundation to support the privacy mission. It's more of a symbolic donation. You get a a secure voucher back to your, basically a link. And if you want to unlock your account for your child immediately, you can. If you want to wait for fifteen years, you can as well. That's it. It's basically, I would say, born

• 31:24Speaker 01

  private more than being that particular feature, which, I mean, maybe it sounds underwhelming at this point, is more about the act of of thinking about your child's privacy. Where does the privacy journey start? Or where does the data collection journey start? Depending on your point of view, you know, so some of the things that we like to to talk about is that, you know, once a parent starts having emails with their gynecologist, for example, or perhaps for a fertility clinic or for something else, suddenly these systems outside are able to know, okay, these people now want to be parents, we should already start sending them advertising on on

• 32:08Speaker 01

  maybe some hospitals for, you know, that are good for for birth, or some advertisements for some gynecologist or whatever else, or pediatricians and so on. And as soon as people start thinking

• 32:25Speaker 01

  more about how their profiles are being created, and how the profiles of your child is being created even before you they're even born, I think that part is the most powerful part of of the initiative, really. It's more about, you know, thinking about your child's privacy, thinking about the privacy of not just your yourself, but also the people around you. And and also highlighting some, I think, important things about what tech companies and what companies in general are doing, um, which is not the benefit of you at all, but only at the benefit of them and advertising.

• 33:02Craig S. Smith?

  Yeah, uh, uh, well, uh, maybe you can explain how that data is collected and merged, because we've all had the experience, you know, 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 ad. But I, my argument is no, your, your profile is, is very complicated. You interact with digital systems all the time and it's all uh, companies, data brokers, collect all of that and, and create a profile, but how, uh,

• 33:43Craig S. Smith?

  I mean, it surprises me that, that, uh, Google, who of course, if you have Gmail, has access to your emails, uh, uses that data, uh, the contents of the emails. I, I know they use the metadata, but can you talk about, uh, that, that ecosystem and, and where all of that data gets collected and where all of it goes through email, I mean.

• 34:14Speaker 01

  Uh, through email, specifically through email, I mean, from.

• 34:19Craig S. Smith?

  Or, or generally, I mean, that experience that we've all had of uh, talking to your spouse and then suddenly getting ads served related to that.

• 34:31Speaker 01

  Well, Google is, is a, is um, a complex machine because not only do you have your Gmail, you have um, your Google Home system perhaps that's listening uh, for keywords that are going to come up. They're like Amazon Alexa has plenty of examples of them listening also to conversations of people um, as well. I think there's a lot of conspiracy theories about what's done and what not, what's not done and that, those conspiracy theories happen because there's a lot of, um. Uh gap in knowledge, but which is also unintentional, right? There's no real transparency over to say how exactly these profiles are are created in the first place.

• 35:14Speaker 01

  But let's say from from a very simplistic nature and only focusing on email. Um you create an email address and the first thing you do is go off and and sign up for Instagram, right? Um, that tells me already something about your age, probably.

• 35:30Craig S. Smith?

  Yeah.

• 35:30Speaker 01

  Let's say, okay. Um, then you go in and uh sign up for uh some newsletter, some political newsletter, right? So now I know basically your age and probably also your your um political leanings. Um, then you sign up for uh some uh book or newsletter on AI, right? So at this point, now I've got three data points and then from that I can infer a whole bunch of stuff, right? Just by connecting those things like which people are interested in AI and also have right wing ideology, for example. And then you find a whole bunch of stuff, uh which people are are using Instagram.

• 36:13Speaker 01

  Or the age of Instagram users, let's say, also interested in AI and also um are interested in in uh this uh right wing commentator or left wing commentator, whatever else. And then you have a whole, you start off with those three data points and then you end up with a whole bunch of suggested data points. And then if you're a place like Google, you can basically say, oh, let's send a an advert for this thing. That's tangential, right? We think you might be interested in this. And then the user clicks on it, right? I said, okay, the user was interested in that. Now you've got a new data point that helps you build a different profile. Or you might create a, send another advert and they don't click on that uh for things which are not super clear.

• 36:54Speaker 01

  For example, um, maybe you don't have any information about their political leanings or say religion, let's say. And you you send an advert for some Catholic uh church thing, whatever. Maybe you're not going to click on it anyway, I don't know. But maybe you don't click on it, but then you send another one for a Protestant one. And they say, are they clicking the Protestant one? Okay, now we know their religion too. So Google.

• 37:19Speaker 01

  And other providers like this, which it's not just about creating the profile with what data you give them directly, but also how they change, they're able to sort of interrogate certain questions that they might be having, their own model gaps by basically proposing content and seeing what you click on. Or say putting a link in a Google search result higher up or lower down and seeing if you click on it or don't click on it. So changing all these visual cues and changing everything around the version of the internet, which is being tailored for for you, also can have an impact on how you perceive the world, right? So let's say, let's say you didn't have politically right leaning ideologies.

• 38:05Speaker 01

  But then there was a, an advertisement came up, which was super interesting and something you agree with. And all of a sudden, you're pushed down a rabbit hole of believing in this ideology, but you didn't have that inclination to start off with. And that's even a more interesting thing because you're starting to change people's behavior. And change, not only in changing who those people are, based on what you serve them and so on. And I think there is an interesting film, I met the director, in fact, last month it was. The director is called Mark Silver and the film is called Molly versus the Machines. I don't know if you heard the story about Molly Russell. He was 14 years old, she killed herself.

• 38:48Speaker 01

  Things, severe depression caused by, basically she had suicidal thoughts. And the Instagram feed kept propagating more and more adverts and more and more content, let's say, on suicide. And at the end, she killed herself. And the whole thing is about, you know, you think your child is safe in their bedroom. But all these companies are basically changing the behavior of your, of your child, even if you, you're not in the room with them and they're not physically in the room. But you know, there's no real safeguards to stop people having a negative impact on on your on your child's psychology.

• 39:29Craig S. Smith?

  Yeah, that's that's fascinating. And the way you describe, which hadn't occurred to me, this this profile that different companies are creating of you is is kind of a cloud that's constantly morphing through time, right? It was reminds me a little of your visualization work. So how does how does

• 39:55Craig S. Smith?

  born and private work?

• 39:57Speaker 01

  Well, born and private starts by saying, you know, a lot of people's their digital identities are anchored in the email address, right? So where you're getting information from at the start. So if the parents already are using Proton

• 40:12Speaker 01

  for all the communications with their their their gynecologist and hospitals and whatever else. Basically, none of that information is ever going to be used for any of to create any of those profiles. Now, that doesn't stop people from, you know, if you use Proton, but then, for example, use Google Chrome. And you use Google Search. Well, you've you've basically negated a lot of the the benefits, right, from using something like Proton? Because on one side of things, yes, you don't have all these things that are coming into your your inbox that are tracking you. Maybe making sure that you're seeing what advertisements you click on. We're not profiling at all, right? So, you know,

• 40:56Speaker 01

  Google already knows that you're going to see your gynecologist, therefore, or the

• 41:02Speaker 01

  and maybe you've got some appointment for for some fertility clinic. Then it already knows that you're planning to do those things, so it can start creating advertising profiles. If you start going and doing that also on Google or on Google Chrome and so on, then they will know that information too. So, you know, Born Private is more about, yeah, this is one step in the process, but it's more to to highlight that we should be trying to to increase the sphere, the privacy sphere for individual users, but also within that individual user, a lot of people make decisions which are also going to impact their entire family tree, right? So, you know, a classic example is like if you go to

• 41:48Speaker 01

  twenty three and me a few years ago and got a genetic test, not only have you basically made a privacy decision on your behalf to give away your data which can be used for something in the future, but also for all your your your kids, but also your family, your brothers and sisters and aunts and uncles, you've basically given away part of their privacy as well. So, understanding, basically if Born Private does anything, it's about trying to increase the understanding of what privacy means, where it starts. Reserving an email address is one part of that, but it gets the conversation going in people's

• 42:26Speaker 01

  heads, like how you want to to deal with your your your kids' privacy online. So, a guy have a daughter, there are no pictures of her online. You know, every time the school asks to, the school does ask at least, do you want the photographs to appear and or even internally? I say no. My family tried to, you know, my sister, for example, will try and take photographs and put them on Instagram or whatever. I say no. I say you can remove her from the picture. We might appear in the pictures because I think our our day is gone probably, but for for her,

• 43:06Speaker 01

  we've made the explicit decision that we don't want anyone that we don't know seeing pictures of her daughter. And if you see what happens, you know, like later on with like defects or the potential futures for how all that information can be misused, I think people would take a second look at their decisions and maybe not do that.

• 43:32Craig S. Smith?

  Yeah, do you think that is awareness of privacy and it's the negative impacts of lack of privacy spread through society, that there'll develop an ecosystem around, maybe Proton will be the kernel around the, an ecosystem of privacy to counter the Googles and Metas of the world so that, you know, you're, you're, someone with a born private email address could then participate in social media or participate in search without having

• 44:17Craig S. Smith?

  them, you know, having somebody build this elaborate, detailed profile of them.

• 44:24Speaker 01

  Hmm. So, I mean, at, at, at the minute, I would say, maybe there are private social media systems, but I'm not sure if they're any good, or if anyone's using them. You know, platforms like Facebook and Instagram and these types of tools, they're basically, TikTok, whatever, they're basically the dominant force. And, and many people have made the decision that either they don't recognize the risk and they've, their decision making has been basically zero. They just fell into the crowd of peer pressure. No, your friend

• 45:09Speaker 01

  does it, therefore I have to do it too. If you don't do it, then you're left out. I think a lot of people are in that boat. I think maybe social media as it stands today will not be the social media that stands in a year from now or or five years from now, especially with all the pressures that are coming on regulation for how how people are treating people's data. And the example I give for Molly Russell, for example, is a is a big, a big part of that type of drive, right? But people typically gloss over danger for convenience. But that's, that's not something which is is unique to privacy space. It's probably for almost everything, right? You know, if something is easy, people are probably just going to do it.

• 45:54Speaker 01

  If something is a bit harder, then they're going to, it's going to be about no resistance to doing it. You know, it's it's easy to follow the crowd, it's harder to reject.

• 46:03Speaker 01

  I think with Proton, you know, before Proton mail came along, it was kind of difficult to do, to use BGP. I mean, for creating secure email, it wasn't that easy at all. And Proton came along and then made that whole process easy and transparent. The idea is to get to a point where privacy is, is, is not just the default, but it's also easy for people to do. And sometimes, you know, it's a long process because there will be, there are unique cases, unfortunately, which show what can really go wrong. You know, it's the same what you get from data breaches, right?

• 46:44Speaker 01

  You know, it's easy to set up a website or to vibe code a project these days or whatever else. It's still hard to do security, to do security well.

• 46:56Speaker 01

  And so people just ignore security as the first, the first stage. Then they're breached and then suddenly all the records for all their customers or everyone that's been on their platform is now leaked online. And they say, oh, that was a mistake, I should have invested more time on the, on the security side. People, there's drip, drip.

• 47:17Speaker 01

  Drift bred, I would say, uh, data leaks in the general population. And people are are more wary of it, you know, for identity theft in the in the U. S. For example, is super easy. Um, if you have just a small piece of information, um, less easy in in Europe. Um, because you can't just create, um, bank accounts so easily in Europe with someone else's name. But, you know, all these things are, if you make it easy for for people to have a private ecosystem where they don't feel like they're they're left out because they don't use a certain tool. Um, they're overall safer for it. They have no manipulation.

• 48:00Speaker 01

  You don't have cases where companies are trying to modify people's psychological profiles in real time. Um, if we can get people to think more like this, that would be a success. And ideally, they use, uh, they'll come to proton and use our products where possible. I don't see a day where we create a a social media, uh, platform. Email is kind of like social media, let's say.

• 48:26Craig S. Smith?

  Yeah.

• 48:27Speaker 01

  You know, you send messages, you've got connections and groups and so on. Um, but I also don't think maybe social media is not going to be, um, super popular in in a decade from now.

• 48:43Craig S. Smith?

  Yeah, because of the all of the risks. But but on the other hand, um, there there is a positive side to this profiling because you it does make you aware of things. You are served ads of things that you are interested in that you wouldn't have found before. Is is there a way of of managing that so that it's not abused?

• 49:12Speaker 01

  Well, transparency is kind of is the important part of it. So, I mean, they do something on this, like you say, why do why did I see this ad? Um, type of thing does happen on on protector profile or platforms. But, you know, the the regulation call.

• 49:29Speaker 01

  As it stands, is happening because we've long treated uh social media as some sort of utility function rather than being a platform that was being that was basically abusing the users, right? Um keeping them hooked the same ways you have for uh cigarettes or alcohol or gambling. I think gambling is a good, is the best example. And I brought this up a while back and I it's I think now I've read more that people are using that example also in public discourse. But you know, gambling systems have regulation in place because otherwise what's to stop the gambling company from just taking everyone's money?

• 50:11Speaker 01

  Right? And if you don't have these regulations that people abide by, then you know, the population would be miserable. You'd have a bunch of people just taken for granted. They would probably be higher divorces and mental health problems and so on. But if you look at the effects of social media, it's it's largely the same, right? You've people who are addicted to it, they scroll all day because of the things that have been you know, engineered to make people stay on the platform. The whole thing is about increasing active users on the platform. So increase engagement. The more engagement you have. The more time they spend on your platform, the more opportunity there is for shareholders. And they will invest more, so stocks will go up.

• 50:56Speaker 01

  Basically, the reward system is broken. Um, there is no real incentive for social media companies to try and limit usage because it would damage themselves. So I think when these sorts of things happen where the user um health and the user uh focus is diverging from the the platform goals, then you need regulation to bring them back together. Um, and I think that's it's fair that people are talking about it now because for a long time, people just said, yeah, just let them self-regulate and so on, but clearly that hasn't worked.

• 51:33Craig S. Smith?

  Yeah, and also the the our digital lives have overtaken so much of our.

• 51:42Craig S. Smith?

  Realize that in the beginning, you know, you you didn't spend that much time in the digital world. But now we're unfortunately, we're all doing what we're doing right now. Speaking through a digital medium. The proton, so proton is kind of a proton or born private is kind of a a starting point, at least you know if a proton email address that the content of your email emails are not contributing to this profiling.

• 52:22Speaker 01

  Exactly.

• 52:24Craig S. Smith?

  Yeah. Uh, how does somebody get a in in born private is a great concept. But presumably anybody could get a proton email address.

• 52:38Speaker 01

  Yeah, just go to protonmail.com. Is it is it protonmail.com? I'm going to check just to make sure.

• 52:46Speaker 01

  Yeah, protonmail.com. Um, it's a it's a long time since I actually had to go to the website. Um, yeah, protonmail.com and create a free account or, you know, if pay also paying is good. It keeps us uh, gives us jobs and so on. Um, but yeah, protonmail.com for create your free account. And that gives you then the access into the rest of the ecosystem. You've got access and to to VPN, to proton drive for your your document storage or also photo storage.

• 53:18Speaker 01

  We have proton pass for your password manager. We have docs and sheets. We have Lumo um for the AI assistant. Um, Meet, which we launched, I think, two weeks ago. We are two weeks ago, three weeks ago, um, which is our video compressing solution. So you can keep everything private there too.

• 53:40Speaker 01

  And more, so yeah, it's pretty easy to set up.

• 53:45Craig S. Smith?

  Yeah, that's fascinating. You can build.

• 53:49Craig S. Smith?

  I mean, as you were saying, you're not going to create a social media platform, but you can build a digital ecosystem through Proton. That at least within that ecosystem, your privacy is protected.

• 54:01Speaker 01

  Yeah, and we just released a Proton workspace, which is also our plan. And I'm more for B2B focus. But the idea is that, you know, basically, companies can go along and they will have everything that they need

• 54:17Speaker 01

  to be able to function as a company by using Proton products. So all of your IP stays with you. You don't have to worry about, you know, confidential data getting lost somewhere. If you want to use AI assistance, you can use it within Proton Mail. We also have Scrape, which is the first product we, first AI integration we did,

• 54:40Speaker 01

  which is similar to what you have with Gemini now for Gmail. And you can use Loom for everything else and connect it to your documents. You can ask questions about it and use it for your productivity gains, hopefully. And that's the, the whole idea is to create this ecosystem or workspace

• 55:00Speaker 01

  where you don't have to compromise on functionalities anymore to be able to use something which is fully private.

• 55:07Craig S. Smith?

  And for Born Private, that's also at, parents can find that on the Proton website, is that right?

• 55:16Speaker 01

  Yeah, if you take, you select a search for Born Private in your favorite privacy-friendly browser.

• 55:26Speaker 01

  Then you'll be able to find it, yeah. But basically proton.me/mail/bornprivate.

• 55:32Craig S. Smith?

  Okay, well, why don't we leave it there? That's all fascinating.