Meredith Whitaker (4:02)

I'm in New York City.

Scott Galloway (4:03)

In New York. I thought you were. I thought you lived in Europe.

Meredith Whitaker (4:06)

I'm in Europe a lot. I go between Paris and New York. We're a small org, we spread a lot of jurisdictions.

Scott Galloway (4:13)

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?

Meredith Whitaker (4:34)

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 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, you know, 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.

Scott Galloway (6:22)

Well, I think you're the most likable CEO in tech, which isn't saying a lot, but.

Meredith Whitaker (6:25)

Yeah, well, the bar. The bar is where I'll take it.

Scott Galloway (6:30)

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?

Meredith Whitaker (6:41)

I mean, I think it's a little bit like the way skincare ingredients are like, I don't know, gold or something gets invoked. Right. We can say it's both of these have encryption in them, but one has 10% 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 signals 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 at? 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 bigbrother 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, 100% encrypted, to use that slightly metaphorically, but you get the gist. We really take that extremely seriously. We're not just sprinkling encryption dust on top of ultimately non private infrastructure.

Scott Galloway (9:26)

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. And AI infrastructures, there you go. 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, the danger it poses to our privacy and security. Can you elaborate on the risks here and what are most people not aware of?

Meredith Whitaker (9:54)

Yeah, the risks are the flip side 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 the users of the device can rely on. And our primary concern is that as agents get integrated into the operating systems by these AI companies, the people who maintain the operating system, and as they get leveraged beyond that in ways that are giving them very pervasive access to your life, it undermines our ability as Signal to guarantee the type of privacy that we guarantee at the application Layer. And I'll give, you know, that 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 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, you know, are just, from a security architecture perspective, very, very insecure. And I'll. 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, creating another security issue and potentially placing data in the hands of whatever company is running that agent. So 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 also pose this pretty significant risk that isn't getting the kind of attention I believe it should.

Scott Galloway (13:16)

We'll be right back after a quick break. Support for the show comes from Better Help. This international Women's Day. Better Help wants to remind all the mothers, grandmothers, aunts and sisters of the world that you deserve to take care of yourself as much as you take care of people around you. If you want help getting connected with a therapist, you, you could try BetterHelp. BetterHelp does the initial matching work so you can focus on your therapy goals. All you need to do is fill out a short questionnaire that helps identify your needs and preferences. And BetterHelp matches you with a licensed therapist operating under a strict code of conduct. After 12 years plus of experience, BetterHelp says they have an industry leading match fulfillment rate and if you aren't happy with your match, you can switch to a different therapist at any time from their tailored recommendations. With over 30,000 therapists, BetterHelp is the world's largest online therapy platform, Having served over 6 million people globally and out of over 1.7 million client reviews, BetterHelp's average rating is a 4.5 out of 5 for a live session. Your emotional well being matters. Find support and feel lighter in therapy. Sign up and get 10% off at betterhelp.com propg that's betterhelp.com propg Support for the show comes from LinkedIn It's a shame when the best B2B marketing gets wasted on the wrong audience. Like imagine running an ad for cataract surgery on Saturday morning cartoons. Or running a promo for this show on a video about Roblox or something. No offense to our Gen Alpha listeners, but that would be a waste of anyone's ad budget. So when you want to reach the right professionals, you can use LinkedIn ads. LinkedIn has grown to a network of over 1 billion professionals and 130 million decision makers according to their data. That's where it stands apart from other ad buys. You can target your buyers by job title, industry, company role, seniority, skills, company revenue, all so you can stop wasting budget on the wrong audience. That's why LinkedIn Ads boasts one of the highest B2B return on ad spend of all online ad networks. Seriously, all of them. Spend $250 on your first campaign on LinkedIn ads and get a free $250 credit for the next one. Just go to LinkedIn.com Scott that's LinkedIn.com Scott Terms and conditions apply. Support for the show comes from Square. Think about your favorite small business, that coffee shop on your block, or the salon you've been going to for years. Or that dog walker you always pass who seems to be having the time of her life. Square makes it simple to run a small business no matter what it is. Whether it's one brick and mortar, a pop up mobile service, or franchises, Square can help track sales, manage inventory and access reports in real time. Square even has built in tools like loyalty and marketing to help you connect with customers and reward them for showing up again. Square supports every major payment method, including tap to pay, and offers instant access to your earnings through Square Checking. A lot of the local businesses I go to seem to be using Square, which makes me actually makes me feel good about the brand. With Square, you Get all the tools to run your business with none of the contracts or complexity. And why wait? Right now you can get up to 200 off square hardware at square.com goprov gave that's sq U-A-R-E.com goprov g run your business smarter with Square. Get started today. What do you think the risks are if you're using Claude or ChatGPT? What do you think realistically the risks are over the next five or ten years that your data's compromised and some bad actor, 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 be cognizant of what they query, these LLMs?

Meredith Whitaker (17:17)

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. ChatGPT is sending that data to servers that are controlled by OpenAI Microsoft servers. They retain that 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 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 marking you as one or another type of person. Not to mention, I think, you know, with the introduction of advertising and, you know, increased targeting, at least a plan to introduce advertising. And in ChatGPT, 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, in the context of ChatGPT, often extremely intimate.

Scott Galloway (18:49)

You've actually referenced it AI as a marketing term. What did you mean by that?

Meredith Whitaker (18:52)

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 art. If you look back at the term AI, it was created in 1956, 1957, by John McCarthy, who hosted the Dartmouth Conference. Those of us in this world will be familiar with that. A kind of an iconic conference where a number of the 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 Wiener from the convening. They didn't get along. Norbert Wiener had, you know, 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, you know, a cool valence that would get some of that, you know, 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 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 McCullough and Pitts and neural networks sort of 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 because of the specific resources available and the recognition that out 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 are very particular approaches that have their own historical and political 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.

Scott Galloway (21:55)

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 Cotswolds and write 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.

Meredith Whitaker (22:21)

I think there are threats, particularly if we integrate these probabilistic, generative and decision making systems into high stakes domains, 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 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, you know, superior to human judgment, superior to human capabilities, which on some axis, measured in some ways, yeah, sure, they, 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 CAM via your platforms and then you train an AI model, sell it to advertisers, et cetera. And 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 Nvidia's picks and shovels, the monopoly on chips and the build out of data centers. And it requires huge distribution networks, which often gets left out of that calculus. But basically, if you're going to make money or you're going to integrate this, you need, you know, either a large social media or marketplace platform, or you need a cloud business model, or you need to latch onto one somehow. So all of that redounds to an industry that is highly concentrated in the hands of effectively the winners of the, you know, 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, all, 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.

Scott Galloway (25:45)

Well, let's, 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 it the west and Europe and the US over the short and the medium term. I've seen tiktoks of economists and AI executives saying, or AI thought leaders saying employment. We're going to see a massive destruction in the labor force. But the flip side is so far it hasn't really manifested. 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?

Meredith Whitaker (26:34)

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, some conversations that at least in part, AI has been a handy pretext for job cuts. You know, 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 we are seeing at least a sort of degradation of work and degradation, meaning there are people who maybe used to have a job as a copywriter or a translator, and 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 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 it's 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. The development work to agents. Yeah, 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 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 first wave of critical issues that are caused by a kind of dependence without human oversight.

Scott Galloway (30:11)

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 through their ingenuity and determination. And for generations, everyday Americans could be part of that journey through perhaps the greatest innovation of all, the US Stock market. It didn't matter whether you were a factory worker in Detroit or a farmer in Omaha, anyone could own a piece of the great American companies. But now that's changed. Today, our most innovative companies are staying private rather than going public. The result is that everyday Americans are excluded from investing and getting left further behind, while a select few reap all the benefits. Until now. Introducing vcx, a public ticker for private tech. VCX by fundrise gives everyone the opportunity to invest in in the next generation of innovation, including the companies leading the AI revolution, space exploration, defense tech and more. Visit getvcx.com for more info. That's getvcx.com carefully consider the investment material before investing, including objectives, discharges and expenses. This and other information can be found in the Fund's prospectus@getvcx.com this is a paid sponsorship.

Kara Swisher (31:26)

Hey, Kara Swisher here. I want to let you know that Vox Media is returning to south by Southwest in Austin for live tapings of your favorite podcast. Join us from March 13th through the 15th for live tapings of Today Explained, Teffy Talks, Prof. G Markets, and of course your two favorite podcasts, Pivot and On with Kara Swisher. The stage will also feature sessions from Brene Brown and Ashley, Adam Grant, Marques Brownlee, Keith Lee, Vivian Tu and Robin Arzon. It's all part of the Vox Media Podcast stage at south by Southwest, presented by Odoo. Visit voxmedia.comsxsw to pre register and get your special discount on your innovation badge. That's voxmedia.comsxsw to register. Really, you should register. We sell out and we hope to see you there.

Tuffy (32:22)

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 Tradwives Spooky and why the center of pop culture is in Utah. Now we do a deep dive on Chloe and Lamar. We talk Hilary Duff. You know what, Find us everywhere at Tuffy 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.

Scott Galloway (33:05)

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 by 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, in New York, they say you can't go more than 12 or 15ft 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 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 ever a reason for why people's privacy should be violated in the context of larger safety concerns?

Meredith Whitaker (34:18)

I want to back this up to the fundamentals of encryption. And when we're talking about signal, what we are talking about when we talk about end to end 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 not actually random, random number generator that means you could 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, you know, 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 a, all eggs in one basket. And that's, 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, that's 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 12ft 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 how the sands of that regime shift. I mean, maybe you vote or whatever it is, but that data is indelible. Those systems are pervasive. Meta is adding facial recognition to their Ray Ban glasses. Where is that data going? Which governments will access that. And so it is interesting to me that in an, you know, 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 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 finding the needle in the haystack of noise, in the haystack of data, not getting access to an encrypted channel. So my, my stance on that is, is very, very clear. But I also think the framing of the problem needs to be shifted a little bit.

Scott Galloway (37:31)

Yeah, my pivot co host said something that really struck me. She said that people have the right to have secrets. And it really struck me. And the kind of the smartest people I know that also understand tech all 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, pretty outspoken against the 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 Lina Khan. What regulation, if you were to 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. You know, kind of magic wand time. What do you think is most needed from our governments right now?

Meredith Whitaker (38:29)

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 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, that would wreck an 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 naturalized their right to sort us and order us and tell us our place in the 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.

Scott Galloway (39:51)

I'm just curious what you thought of the ring super bowl ad.

Meredith Whitaker (39:55)

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 tertiary market that was actually being addressed that wasn't eager doorbell owners.

Scott Galloway (40:34)

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 in the markets. There's been a meltdown around SaaS companies. From a valuation standpoint, you essentially work for or run a software company is the 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.

Meredith Whitaker (41:02)

I work for a software company.

Scott Galloway (41:03)

Yeah, there you go. So there's been an enormous destruction in value among SaaS companies believing that AI is going to come in and kick the crap out of these guys or make them obsolete. When you see that happening, do you have any initial thoughts on the viability of some of these software companies who are. Some of them lost 40, 60, 70% of their value?

Meredith Whitaker (41:26)

I think ultimately, when you're providing enterprise software, particularly to highly regulated industries, 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 cleaned 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 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.

Scott Galloway (42:57)

And last question, Meredith. It strikes me that 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 were to ever get hacked, a thin layer of AI on top could, 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? And that is consumers have just decided to trade off massive privacy for utility. And do you have a message for them?

Meredith Whitaker (43:41)

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 leaked 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 gotta get to work. We wanna see what our friend posted. We wanna be part of the popular people. And the ways of doing that have been slowly, we can say colonized or sort of 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 define ourselves and the place in the world. So I would say what we're seeing is a natural human inclination. 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 and I think becoming more acute and more 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.

Scott Galloway (46:40)

I'm not exaggerating and this is my final plug. The smartest people I know, and then the people who 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, 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 Alina Khan, 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 and I meant what I said. You're the, 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, buy all of these companies and put you in charge. Deal?

Meredith Whitaker (47:39)

It's a deal, Scott. It's a deal. Yeah. Looking forward to working with you and thank you for having me on.

Scott Galloway (48:07)

Algebra of Happiness, a hack for young dads. It is striking to me how 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 it's helped me a lot. Is that my favorite title? I've been a founder, you know, all these cool titles, Seal, whatever. My favorite title in the world is dad. And that is every time my kids call me or say, oh, hi dad, or dad, 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 dopa 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 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 vulnerability 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, dad. This episode was produced by Jennifer Sanchez and Laura Geniere. Cami Reek is our social producer, Bianca Rosario Ramirez is our video editor, and Drew Burrows is our technical director. Thank you for listening to the ProPG pod from ProPG Media.