The Prof G Pod with Scott Galloway - Meredith Whittaker on Who Controls Your Data in the Age of AI

Episode Date: March 5, 2026

Meredith Whittaker, president of the Signal Foundation and a leading voice on AI and privacy, joins Scott Galloway to examine the growing tension between artificial intelligence and personal freedom. They discuss how Signal actually works, why most messaging apps aren’t as private as they claim, and whether AI agents embedded in operating systems pose new security risks. Algebra of Happiness: a hack for dads. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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Episode 386 is the area code serving north-central parts of Florida. 1986, top gun hit theaters. True story. Tom Cruise is starring in romantic comedy about body positivity. He and his actress both came 300 pounds for the roles. The name of the film? Missionary Impossible. Just give it a second. Welcome to the 386 episode of the Prob G-Pod. What's happening? In today's episode, we speak with Meredith Whitaker, the president of the Signal Foundation and a leading voice on AI policy. I first came across Meredith at South by Southwest. She was on a panel. I never, I was bored, and I walked in. I almost never listened to panels. And I thought, who is this? Who is this

strange, dark-haired woman speaking all sorts of truth and logic about AI? And it ends up, she runs the app, and I had lunch with her, and she struck me as really intelligent. And I have been much more concerned about, for the first time, I don't know if I'm getting older, I'm much more concerned about my own privacy, worried that at some point all of my AI queries will be made public, right? Is that my prostate? Question mark, expecting AI to answer. I'm pretty sure every ailment I have is because of an enlarged prostate. I'm convinced there everything starts with the prostate.

Anyways, I don't know how I got here. Anyway, she's an incredibly insightful, intelligent person, and I would argue probably the most well-liked person or C-E. and tech right now, which isn't saying a lot. Very impressive, very intelligent, and sort of signals trying to or is, I think, carving itself out as sort of the clean, well-lit part of the Internet. And I'm fascinated with the trade-off between privacy and utility, and we'll speak more about that. Anyways, here's our conversation with Meredith Whitaker.

Meredith, where does this podcast find you? I'm in New York City. In New York. I thought you were, I thought you lived in Europe. I'm in Europe a lot. I go between Paris and New York. We're small org. We spread a lot of jurisdictions. There you go. So let's bust right into it. I want to start with the basics. Signal has been in the news a lot this year. And we'll get to that in a moment. We know it's widely used by journalists, public officials, and people are especially concerned about privacy. But on a practical level, how does Signal actually work and what makes it different from other messaging apps? On a practical level, Signal is the most widely used actually private communications platform. We go out of our way to collect as close to no data as possible.

And that's really what sets us apart because we exist in an ecosystem where, for better or for worse, in one way or another, most of the time you make money in tech by collecting and monetizing data. So you collect data about the users of your platform and then you sell access to different types of users based on that data to advertisers or you collect data and you train your AI model with it, et cetera, et cetera, et cetera. That's kind of the economic engine of tech since the 90s and, you know, maybe before. Signal is obsessed with maintaining the human right to communicate privately. And we have built an alternative communications platform that does just the that. We end up rewriting core pieces of the proverbial stack to enable us to do what is normal,

to provide a basic and easily usable messaging platform in a way that does not collect your data and thus does not put us in a position of being forced to turn it over if we get a subpoena of having a breach expose your most intimate information of violating the compacts that we make with the people who rely on us. So that's, in a nutshell. We're also open source. And open source matters here because that means you don't have to trust me. You don't have to like me. You can actually verify that, yeah, the thing that she or anyone says it does is what it does because we can scrutinize the code. We can prove it. Well, I think you're the most likable CEO in tech, which isn't saying a lot.

But, yeah, well, the bar, the bar is where it is. I'll take it. So the term, I meant that, the term encrypted is a loaded term. Can you talk about the biggest misconception about encryption and messaging apps? I mean, I think it's a little bit like, you know, the way skin care ingredients or like, I don't know, gold or something gets invoked, right? We can say it's, you know, both of these have encryption in them, but one has 10% encryption or encryption or encryption is. only applied to 10% of the data, whereas another is fully encrypted. And so if you look at, say, WhatsApp and Signal, WhatsApp uses Signal's encryption protocol. And this is the gold standard for encryption messaging was released in 2013, has stood the test of time, really advanced the field of

privacy preserving technology when it was introduced. That's licensed by WhatsApp, but WhatsApp only applies it to one layer of the WhatsApp layer cake, so to speak. They use it to encrypt the content of your messages. So if I'm texting you like, you know, hey Scott, where are we going to meet it, South by Southwest? WhatsApp would not be able to see that. But WhatsApp does not encrypt intimate metadata. And metadata is a fussy little term, but it's, you know, it's actually pretty revealing data. It's who you text. It's who's in your contact list. It's your profile photo. It's when you started texting someone, your therapist, your oncologist, your FBI cutout, whoever it is, that's very revealing data.

And then, of course, we're not owned by meta, which means that, you know, there isn't a bunch of Facebook and Instagram data. You could then join that intimate metadata with to make profiles, et cetera, et cetera, et cetera. So Signal is, you know, encrypted up and down the stack. We encrypt the contents of your messages, but we also encrypt your profile. photo, your contact list, who is texting whom, who is messaging with whom, who's in your groups. So you can look at our website, signal.org slash big brother, and we work to unseal any subpoena that we are forced to comply with. And what you see there is a long list of requests for data.

That's normal. That's what governments assume an average messenger is able to give up. and then you see what we're actually able to give up, which is very close to nothing. We can confirm that a phone number has an account. We can confirm a handful of other things. But we have gone out of our way to be unalloyed, you know, 100% encrypted to use that slightly metaphorically. But, you know, you get the gist. We really take that extremely seriously.

We're not just sprinkling encryption dust on top of a, you know, ultimately non-private infrastructure. So I wanted to talk about something that gets no news. I don't know if you've heard of AI, but it's in the news recently. AI. AI, right. There you go. What is that? What is that?

It's a movie by Steven Spielberg. So the AI agents specifically, you've been pretty vocal about the dangers of the agentic AI that the danger opposes to our privacy and security. Can you elaborate on the risks here and what are most people not aware of? Yeah. The risks are the flips. of the promises, really. And we actually started talking about this about a year ago when we were seeing things like Microsoft recall creep into the product updates, in this case, for Windows, and really recognizing that signal exists at the application layer, right, which means that we have

to trust the operating system. We build on top of iOS or Android or Windows, and we have to trust that the operating system will be a reliable set of tools that we as developers can leverage to ensure that signal works for the people who rely on us and that, you know, the users of the device can rely on. And our primary concern is that as agents get integrated into the operating systems by these, you know, AI companies, the people who maintain the operating system, and as they get leverage beyond that in ways that are giving them very pervasive access to your life, it undermines our ability a signal to guarantee the type of privacy that we guarantee at the application layer. And I'll give, you know, that may sound a little bit arcane to people who don't,

you know, live in these waters with me. But just a quick example, you know, if you have an agent running on your operating system or sort of given deep access to your file system and other, other sort of data on your device in order to do something like, you know, plan a work dinner. Well, the agent will need access to your calendar. It will need access to your browser, perhaps to look for a restaurant, maybe your credit card or your EA's credit card in order to book that work dinner. And in a scenario where you are, as you should be all using signal, it will also need access to your signal and your signal contacts to text them and coordinate dates and times. All of that becomes a pretty frightening set of data access points and

ultimately a security vulnerability because instead of having to break our gold standard encryption algorithm, which has been tested and so mathematically proven to be secure, you just have to leverage the type of access that these pervasive agents are being given into your applications, into your intimate data in ways that are, you know, are just from a security architecture perspective, very, very insecure. And I'll note that right now, almost every agent that we're seeing kind of in the mainstream is relying on large LLM models, models that are too big to run on your device, which means that, you know, ultimately most of this data would need to be sent off your device to a cloud server to be processed for inference, you know,

creating another security issue and potentially, you know, placing data in the hands of whatever company is running that agent. So that's, you know, our concern is really coming from a privacy integrity standpoint and from a concern for the people who rely on signal by the introduction of these tools, which can be useful for some things, but, you know, also pose this pretty significant risk that isn't getting the kind of attention, I believe it should. We'll be right back after a quick break. Support for the show comes from BetterHelp. This International Women's Day,

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Get started today. What do you think the risks are, if you're using Claude or Chat-G-P-T, what do you think, realistically, the risks are over the next five or ten years that your data's compromise in some bad actor or the LLMs themselves will have access to your private information and be able to link identity with, I mean, the John Oliver segment on finding people's data in the dark web, including their search history. Should people be really, should people be cognizant of what they query these LLMs? I mean, I think they absolutely should be cognizant. Any query to an LLM that isn't sort of a specialized private inference setup, you know, kind of what Moxie, who founded Signal is doing with confer or other similar setups. But any, you know, a general query chat GPT is sending that data to servers that are controlled by OpenAI, Microsoft servers. They retain that data.

data, they could leak that data. We know that when presented with a valid subpoena, they will turn that data over in a world in which norms and laws and definitions of criminality shifts from, you know, one year to the next perhaps. It's good to be cognizant of where that data could go and what it could do in terms of, you know, marking you as one or another type of person. Not to mention, And I think, you know, with the introduction of advertising and, you know, increased targeting, at least a plan to introduce advertising in chat GPT, I think there are also issues about what that can reveal about you, you know, in more mundane context as a consumer or as a job seeker and, you know, the kind of advantages or disadvantages that might accrue, given that the power to define you

based on data that is, you know, in the context of chat, GPT, often extremely intimate. You've actually referenced it AI as a marketing term. What did you mean by that? Yeah. I mean, I think it's, I'm being flatly literal, although I think that's sometimes taken to mean that I'm saying AI doesn't exist or it's not serious, which is, you know, marketing is, in fact, very serious. You know, what I'm talking about there is just sort of denaturalizing AI as a technical term of If you look back at the term AI, you know, it was created in, you know, 156, 1957 by John McCarthy, who hosted the Dartmouth conference. Those of us in, you know, in this world will be familiar with that at kind of an iconic

conference where a number of the quote unquote fathers of AI gathered to try to create intelligent life, you know, in the form of a machine over the course of a summer. and John McCarthy created the term in his own words in subsequent interviews because he wanted to exclude Norbert Weiner from the convening. They didn't get along. Norbert Weiner had created the term cybernetics in the field of cybernetics and McCarthy classically did not want to be a disciple. He wanted to be the father of his own thing, a very common academic urge. And he also wanted grant money. And he thought artificial intelligence was a kind of flashy term with a cool valence that would get some of, of that Cold War era, ARPA money flowing to his lab, which it did. It funded the conference. But over the history of the term, it's like over 80 years now, we've seen it applied to very disparate technical modalities. So McCarthy was invested in symbolic systems, which would look much more like decision trees and was actually deeply skeptical of the neural approach, which predated the term by about 10 years and was, you know, McCullough and Pitts.

neural networks stem from that. So what we see is a term that was invented primarily to describe an approach that's out of favor today has now been applied, you know, because of the specific resources available and the recognition that, you know, neural networks can do interesting things with data and compute and the type of business models we have. The term AI is now applied to an approach that was not actually under its umbrella when McCarthy invented it and why is any of this important beyond it just being very interesting if you're a nerd. I think it's important because it allows us to step back and actually

recognize that this is not a term of art and what we are describing our very particular approaches that have their own historical and political economic formulations and that we can actually sort of have a bit more agency to define what we mean by intelligence to choose the technologies that we are leveraging to produce intelligent seeming outputs and to be a bit more critical and actually regain a bit more of our own agency in relationship to mythologies that kind of naturalize these systems as just a sort of linear arc of technological and human progress. There's been a lot of, I don't know if it's warnings or catastrophizing from AI executives who said, I'm scared of what I've built and I need to retreat to the, you know, that, you know,

you know, the Cotswilds and Wright Poetry. I'm curious what you think the threat level is of AI. And if it's been overstated, understated, and where you see the biggest threats and how we as a populace respond to it. I think there are threats, particularly if we integrate these probabilistic, generative and decision-making systems

into high-stakes domains, you know, nuclear, defense, energy, and put them to tasks that they are ultimately not secured or suited for. So you can have reward hacking, you can have emergent behavior. All of those things are real. Those aren't things that are simply going to sort of spring out of nowhere or, you know, Athena from Zeus's head. And suddenly we have ephemeral technologies running around without our control or delegation in some sense, right? Those would need to be choices that are made by people and decision makers. And I do think, you know, in some sense, some of the fear has a bit of escape velocity from material reality and almost sounds a bit like a religious fervor rather than

kind of a, you know, technically grounded concern about the rush to integrate technologies that are not fit for purpose and could have collateral consequences, which is where I land on it. My primary fear, however, is the combination of the mythology of artificial intelligence, which is really framing these technologies as superior to human judgment, superior to human capabilities, which on some axis measured in some ways, yeah, sure, they do math much quicker. So does a calculator, they can, you know, produce things more efficiently, et cetera, et cetera. yes, but ultimately these are very centralized technologies that rely on huge amounts of data, data that is captured by an industry invested in what I'd call the surveillance business model, which is effectively, you know, collect all the data you can via your platforms and then, you know, train an AI model, sell it to advertisers, et cetera.

And, you know, so it requires huge amounts of data. It requires huge amounts of infrastructure. and I don't have to go into the wild CAPEX spending, the kind of, you know, NVIDIA's picks and shovels, the monopoly on chips and the, the build out of data centers. And it requires huge distribution networks,

which often get left out of that calculus. But basically, if you're going to make money or you're going to integrate this, you need either a large social media or marketplace platform, or you need a cloud business model, or you need to latch on one somehow. So all of that redounds to an industry that is highly concentrated in the hands of

effectively the winners of the last tech boom, the platforms who were able to establish, you know, data pipelines and massive amounts of data, large platforms, cloud infrastructures, global reach that were sort of cemented via network effects and economies of scale la, you know, classic communications, network monopolies. And so my concern, with all of that is that what we're looking at is a significant concentration of power over infrastructure and decision-making that is then rebranded as a kind of God's Head intelligence in ways that are making us less critical than we need to be about how that power is being leveraged. Well, let's drill down to specifics. What do you think, and nobody knows,

but what is your best guess with respect to AI and employment? And let's call at the West in Europe and the U.S. over the short and the medium term. I've seen TikToks of economists and AI executives saying or AI thought leaders saying employment, you know, we're going to see a massive destruction in the labor force. But the flip side is so far it hasn't really manifested. There's some, there's, you could potentially interpret that the job market is softening, but youth unemployment is about where it has been historically at average. AI and the labor force, what is your best guess? Yeah, and I got to be careful here.

This isn't really my lane, and I'm seeing a lot of competing headlines. It does seem clear to me from some conversations that at least in part, AI has been a handy pretext for job cuts. boards and media and shareholders will accept that, you know, hey, we cut X number of people because this is part of our AI strategy. That doesn't look like weakening demand. That looks like innovation. And so I do think there's some AI wrapping of downsizing that is happening. And I've heard that firsthand from some folks. I do think, you know, we are seeing at least a sort of degradation of work and, you know, degradation, meaning, you know, there are people who maybe used to have a job as a copywriter or a translator. And, you know, we've seen this with translation, who are now just kind of

editing AI output, right? And it's a less secure, maybe less fun, less rewarding job. But you, it's not removing the human. It's sort of removing the agency and power that a human would have in that job under different circumstances. I am really impressed with what I've seen or, you know, it's, it's the, the kind of new round of coding agents are very, very capable. And, you know, they're definitely, I'm seeing a lot of excitement across my industry there. It's, you know, you can't deny that these are very useful and produce output that is, you know, pretty commensurate with like a junior programmer. But again, you still need a senior programmer. You still need somebody.

who understands how it works to review the code and maintain it. And so even though you're seeing advances in capabilities, one thing that isn't being talked about enough is, you know, there are few things that many engineers I've worked with hate more than having to maintain someone else's shitty code. So you still need somebody who has an understanding of the systems level, who's bumped their head up against problems and understand them, you know, and can fix them, who understand how one,

you know, pull request or kind of tranche of code might interact with another. And that's the place where I'm not only concerned that the kind of rapid outsourcing of some of the development work to agents, you know, I think some of that could backfire in, you know, a kind of technical debt that is very difficult to pay down if what we're looking at is systems that are sort of, you know, built by agents or coding AI and not fully understood by the people who, you know, the kind of skeleton crew who are left to maintain them. So those are, you know, those, those are some reflections. I don't think I have a clear answer because I think this is not just a question of AI. It's also, you know, where is their market will? You know, how is AI going to be used as a pretext?

And then what happens when we do have the first significant. issue with the reliance on these AI systems. And I, you know, I say that as I recognize that, you know, Amazon went down apparently because of an error made by an AI agent that they integrated. So, you know, we have already seen a kind of, you know, first wave of critical issues that are caused by a kind of dependence without human oversight. We'll be right back. support for the show comes from VCX, the public ticker for private tech. For generations, American companies have moved the world forward to their ingenuity and determination.

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This is a paid sponsorship. Hey guys, it's me, Tuffy, the host of Tuffy Talks. On this week's episode, we're doing a state of the union, but more state of pop culture, 2026, from OZempic to Tadwives. Spooky. And why the center of pop culture is in Utah now? We do a deep dive on Chloe and Lamar. We talk Hillary Duff.

You know what, find us everywhere at Taffy Talks. Subscribe on YouTube and all the podcast platforms and Instagram and TikTok so you can share with your other work bestie and hopefully everyone you've ever met. We're back with more from Meredith Whitaker. So there's a tension between privacy and encryption and I think the potential weaponization of encryption and privacy by bad actors. And I would imagine my virtue of your position, I think I understand where you would land on this, or at least a bias or a view on it.

In London and New York, they say you can't go more than 12 or 15 feet outside without being on camera somewhere. And to a certain extent, I like that. I think I like it more in Britain because I'm less worried about it being weaponized by the administration here. But if you look at the decline in crime rates, I think some of it is because of technology and then court-ordered mandated. if you will, violations of privacy, if there's enough evidence that this person is a bad actor, and then we need to violate people's privacy to understand, you know, if something bad is about to happen. You must be given this question all the time. That tension, where do you land on that tension?

And is there, is there ever a reason for why people's privacy should be violated in the context of larger safety concerns? I want to back this up to the fundamentals of encryption. And when we're talking about, you know, signal, what we are talking about when we talk about it into an encryption and the way that it works is a technology that either works for everyone or it works for no one. If you undermine the math of encryption, if you put a backdoor in there, you have a, you know, not actually random, random number generator that means you could to basically perturb the encryption, decrypt it.

That's not just a backdoor. That's just not just an error that only the good guys can avail themselves of. That is effectively breaking encryption for everyone. So it really is a scenario where the people you hate the most have to be able to use it to exercise that right, so to speak. if the people you love the most are going to have access to it as well. It's all eggs in one basket, and that's at the level of math. I'm not answering the question, is it ever good or appropriate to undermine, you know, that is not actually what I'm talking about.

What I'm talking about is a world in which over the last 30 years, we are surveilled within an inch of our lives. You said every 12 feet were recorded great. And then you made the comment. you know, I'm more comfortable with that under one regime than I am under another regime. Well, that becomes the issue. You're not really in control of, you know, how the sands of that regime shift. I mean, maybe you vote or, you know, whatever it is. But that data is indelible. Those systems are pervasive. Meta is adding facial recognition to their rayban glasses, right? Where is that data going?

Which governments will access that? And so it is. interesting to me that in a golden age of surveillance, when unprecedented in human history, our actions, our preferences, our communities, you know, who we date, who we talk to, what we do for a living, how we spend our money, are surveilled and logged at a level of detail, unimaginable to the Stasi, that we are still pinpointing a tiny refuge where the fundamental right to private communication that is recognized as such that is necessary for a full and joyful and intellectually rigorous life that has intimacy and the ability to exercise our opinions and dissent and blow the whistle and do journalism and all of that, that one right is presented

as a problematic and as the barrier between stopping crime and allowing it to run rampant in a world where, you know, the issue is more often than not, you know, finding the needle in the haystack of noise and the haystack of data, not getting access to an encrypted channel. So my stance on that is very, very clear, but I also think the framing of the problem needs to be shifted a little bit. Yeah, my pivot co-ho said something that really struck me. She said that people have the right to have secrets, and it really struck me. And this, the kind of the smartest people I know that also understand tech, I'll use signal. And I realize how promiscuous and careless I've been with my own data. And I thought, what I do is just not that interesting. And most recently, when I hear

the Trump administration talking about assembling lists of people who are vocal, you know, pretty outspoken against a Trump administration, I'm like, wow, I spoke too soon. If you were to, if, and you have advised the government, I know you were part of, you worked with Lena Kahn, what regular If you were to advise the administration or the FTC, maybe it's under a different administration, on what would be the most thoughtful regulation as it relates to privacy, encryption, or AI, kind of magic wand time. What do you think is most needed from our governments right now? Yeah.

This is a bit of a tricky question for me because I've been not in the policy bubble for a while. I do think, you know, something as simple as a meaningful consent, and by which I do not mean just a bunch of click wrap and cookie banners around whether or not a given company or institution gets to create data about us at all, not what they do with our data, but whether they have the right to tell my story to know about me would go a long way. Of course, I would reckon in entire logic of the tech business model. But I do think the fundamental thing that needs to be done, however the regulatory paintbrush would paint this, is to question and then take back the authority to define who we are from a handful of companies that have kind of naturalized their right

to sort us and order us and tell us our place in the world. world. That's a bit of a philosophical answer, but I do think that's the core issue is the authority we've given tech companies who create data for advertisers to sort and order our world and tell our stories for us. I'm just curious what you thought of the ring Super Bowl at. My God. My God. I mean, I didn't expect it to become so flagrant so quickly, I guess. and seeing that it was, you know, I was like, do, who are they selling this to? Is it people who would install this, or is it the government contracts who recognize exactly what this is selling and want to sign up for data access?

Like I certainly wasn't the core demographic. It was aimed at, but it also felt like there was a, you know, a tertiary market that was actually being addressed that wasn't, you know, eager doorbell. owners. When you look at the landscape, well, I'm going to ask a market question. And my guess is you're going to tell me it's not your lane, but I just want to remind you that's never stopped us from opining on it on all manner of topics. We have no domain expertise. And in the markets, there's been a meltdown around SaaS companies from a valuation standpoint. You work, you essentially work for or run a software company as a way I would think of it. I don't know if you call it that,

but at the end of the day, I would imagine it's code. I work for a software company. There you go. So there's been an enormous destruction of value among SaaS companies believing that AI is going to come in and kick the crap out of these guys or make them, you know, obsolete. When you see that happening, do you have any initial thoughts on the viability of some of these software companies who are, you know, some of them lost 40, 60, 70 percent of their value? I think ultimately when you're providing enterprise software, particularly to highly regulated industry, it needs to be interoperable with legacy equipment. Even if you don't like that legacy equipment, there's a superstructure there, there's a foundation. It needs to work with the data that you have, even if that data isn't great,

that data needs to be clean and fungible. You need to be able to account for the different determinations that are made, depending on what kind of model you hook in there. That might not be a possible, particularly in financial services and other industries with high compliance burdens. You need to have often human oversight that is personally liable or accountable for different decisions.

So I do anticipate that AI in some form or fashion will be integrated, will have impacts here. But fundamentally, this is not a magic wand, right? And there's a lot of legacy infrastructure, regulatory burdens. and labor processes and modes of work that need to be accounted for. And I don't see SAS software going away anytime soon. And I don't see AI doing anything to really erase those other considerations, right? I think predictions of the demise are a bit self-interested and far premature. And last question, Meredith.

It strikes me the young people are absolutely, at least when I see their actual behavior, there's some consumer dissonance in that as people talk a big game about privacy, and then I see people basically telling the world where they are, what they are doing, and who they're doing it with. And it strikes me that even if you put a thin layer, if Uber would ever get hacked, a thin layer of AI on top could basically connote who's having affairs, terminating pregnancies, HIV status. It just wouldn't be that difficult to just know everything.

about someone with just their Uber data. Do you see the same dissonance I see in that as consumers have just decided to trade off massive privacy for utility? And do you have a message for them? I do see some of that. I would shift it a little bit. I think ultimately humans want to be loved and they want to be included. They don't, you know, even when we talk about signal and privacy, we're not talking about a vacuum. It's not Meredith by myself with none of my thoughts escaping, the anechoic chamber of my, you know, meaning making, I am using Signal to share what I think with other people because I am a human and communication maps to human relationships and the desire to be connected and to be included, et cetera, et cetera. So I think we're in a

world where, you know, ultimately we will opt as human beings. I use these services too because I want to go to the party. I want to see what people are doing. You know, I got to get somewhere. I want to participate in life while I'm living, as do I think most people, right? So the ways to do that are things we're going to do. And I don't think they represent actual choices about where we feel comfortable or uncomfortable with our data, whatever our data might be, right? We don't really have access to it. We know we don't want someone to share our mean DMs with our friend. We know we don't want, you know, our health data leaps to our insurance in ways that would harm us. But that's also a place we don't have that much control. And in the meantime, we've got to get to work. We want to see what our friend

posted. We want to be part of the popular people. And the ways of doing that have been slowly, you know, we can say colonized or sort of, you know, instrumented by these tech services that advertise convenience, advertise connection, advertise ease, and then below the surface have sort of hollowed out our privacy and our ability to, you know, define ourselves in the place in the world. So I would say what we're seeing is a natural human inclination. You know, we use what we can to be together to connect with each other to participate in life. Those services have themselves kind of, I think in some sense, betrayed us structurally. and that doesn't mean we don't care about privacy.

That means a meaningful choice around what it would take to care about privacy has not really been given to us. We do see the number of people using signal going up and up and up. We do see people's understanding of why privacy is so important, I think becoming more acute and more, you know, felt for people at a personal level when they see people's social media posts being used. at the border when they see these collateral consequences that are coming home, I think the issue then is, okay, what do we do about it? And you can't say, well, the choice is never to communicate with your friends, because that's simply unrealistic and anti-human. But you should use signal. I'm not exaggerating, and this is my final plug. The smartest people I know, and then the people understand technology, the most have domain expertise around technology. That vent overlap,

they all use Signal. It's almost like a badge of like, I get it. You know, these anyways, but my favorite quote from this is people or people want to be loved and included. Meredith Whitaker is the president of the Signal Foundation and a leading voice on AI policy. She co-founded the AI Now Institute at NYU, advised FTC Chair Lena Kahn, and was named one of times 100 most influential people in AI. She joins us from New York. Meredith, very much appreciate your time and your good work. meant what I said. You're the bright, well-lit, clean part of the AI technology bookstore. Let's put Meredith Whitaker in charge. Let's just, let's consolidate all of it. I'll go raise $11 trillion by all of these companies and put you in charge. Deal?

It's a deal, Scott. It's a deal. Yeah, looking forward to working with you. And thank you for having me on. Algebra of Happiness. A hack for young dads. It is striking to me. selfish kids can be. I mean, it's just a, I feel like I'm essentially a, essentially a credit card that occasionally gets to watch a football match with them sometimes. And let me just give you a hack. If you're a dad like me who thinks that you're going to have all these hallmark moments with your child, you'll have some of those. But for the most part, it's going to be mostly a one-way relationship. And I'm not saying it's not amazing, but the hack I have implemented and has helped me a lot is that my favorite title, I've been a founder, you know, all these cool titles, see, or whatever, my favorite title in the world is dad. And that is every time my kids call me or say, oh, hi, Dad, or they call out Dad, or, you know, I love you, dad.

every time I hear the word dad, I'm like one of those dogs that hears the word walk. And I've trained myself to just love that term. It's the most important term in my life. And it just, it's more dope of for me than anything is when these two things that kind of look, smell, and feel like me, called me dad. And what I've decided, and I started believing and training myself to believe five years ago, is that when my kids are awful, you know, they give me a little. hard time or they come home and expectorate their emotions or they're unreasonable or they slam their door. My kids, and what you'll find is generally speaking, your kids don't behave that way outside of the house. If you're like 90% of us, you're going to find that outside of the house, your kids are

pretty reasonable, pretty good citizens, pretty polite. And at home, they're fucking terrorists, assessing the household for vulnerabilities so they can strike when you're at your weakest. Now, why do they do that? Because they're processing, they're emoting, and they know what they can do with you because they know you are there unconditionally. They know you love them unconditionally. Why? Because you're their dad. And so what I have done, and it's been a real unlock for me, is that when my kids say something inconsiderate or even mean to me or aren't respectful or aren't kind. I'm not saying I let them roll right over me, but I assume they're saying one thing to me. They're saying, doubt. This episode was produced by Jennifer Sanchez and Laura Jenaire.

Cammy Rieke is our social producer, Bianca Rosario Ramirez, is our video editor. And Drew Burroughs is our technical director. Thank you for listening to the PropG pod from PropG Media.