Meredith Whittaker on Who Controls Your Data in the Age of AI

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Welcome to the 386th 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 that's out 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 hair 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, 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, expecting AI to answer?

I'm pretty sure every ailment I have is because I'm in large prostate.

I'm convinced 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

like person or CEO 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 of sort of the clean well

that 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 looked in Europe.

I'm in Europe a lot.

I go between Paris and New York, we're small or we spread a lot of jurisdictions.

There you go.

So let's just write 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 who are especially concerned

about privacy, but on a practical level, how does signal actually work and what makes

it different from other messaging apps?

What 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 maybe before signal is obsessed

with maintaining the human right to communicate privately and we have builds, you know, an alternative

communications platform that does just that.

We end up rewriting core pieces of the proverbial stack to enable us to if 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 exposed 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.

I think in the most likable CEO and tech, which isn't saying a lot, but, um, yeah, well,

the bar, the bar is pretty low.

So the term, I meant that the term encrypted is, is a loaded term.

Can you talk about the biggest misconception about encryption and messaging apps?

I mean, I think it's, it's a little bit like, you know, the way skin care ingredients

are like, I don't know, gold or something gets invoked, right?

Um, we can say it's, you know, both of these have encryption in them, but one has 10%

encryption or encryption has only applied to 10% of the data, whereas another is fully

encrypted.

And so if you look at, say, what's up, and signal, what's up 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 contents of your messages.

So if I'm texting you like, you know, Hey, Scott, where are we going to meet it?

It's out by Southwest.

WhatsApp would not be able to see that.

WhatsApp does not encrypt intimate metadata and metadata is a fuzzy 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 a meta, which means that, you know, there is

not 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, you know, 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, you know, unalloyed, you know, 100% encrypted

to use that slightly metaphorically, but to, 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.

So I don't know if you've heard of AI, but it's in the news recently.

AI.

AI, right.

Infrastructures.

There you go.

What is that?

It's a movie by Steven Spielberg.

So the AI agent specifically, you've been pretty vocal about the dangers of the agentic

AI that the danger it poses 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 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, you know, the product updates in this case for Windows

and really recognizing that, you know, 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 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 giving 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, you know, pretty frightening set of data access points and ultimately

a security vulnerability because instead of, you know, having to break our gold standard

encryption algorithm, which has been, you know, 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, you know, are 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.

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What do you think the risks are?

If you're using a cloud or a chat GPT, what do you think realistically the risks are of

the next five or ten years that your data is compromised in some bad actor or the LLM

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 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, 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

I think, you know, with the introduction of advertising and, you know, increased targeting,

at least the plans to introduce advertising and 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,

and 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 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 sometimes

take into 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, you know, it was, it was created in, you know, 1956,

1957 by John McCarthy, who hosted the Dartmouth conference, those of us in, you know, in this

world will be familiar with that 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, 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 would look much more like decision

entries and was actually deeply skeptical of the neural approach, which predated, you

have the term by about 10 years and was, you know, McCullough and Pitts and neural networks

sort of stemmed 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, 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 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 intelligence, 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 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, the

Cotswolds and right 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 populist respond to it.

I think there are threats, particularly if we integrate these probabilistic, you know,

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, you know, 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 rushed 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, you know, 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,

that is captured by an industry invested in what I'd call this 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's, it requires huge amounts

of data. It requires huge amounts of infrastructure and I don't have to go into the wild catback spending,

the kind of, you know, Nvidia's picks and shovels, the monopoly on chips and the, you know,

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, 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 on to 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. Well, it's drilled 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, you know, we're going to see a massive

destruction and 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 this, I got to be careful here. This isn't really my lane, and I'm seeing a lot of

kind 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 demands 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 as with degradation of work and, you know,

degradation, meaning, you know, there are people who maybe used to have a job as a copyrighter 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, you know, it's the 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 a 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, you know, kind of 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 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, 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, of critical issues that are

caused by a kind of dependence without human oversight. We'll be right back.

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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've understand where you would land

on this or at least a bias or view on it. In London in 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 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

your signal, what we are talking about when we talk about it and 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 back door in there, you have a, you know, not actually random,

random number generator that means you could so it basically perturb the encryption, decrypt it.

That's not just a back door. That's just not not just a 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 an

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 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, 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, our 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 dissents 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, 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-host said something that really struck me. She said that

people have the right to have secrets and it really struck me in the kind of the smartest people I

know that also understand tech, all use signal. And I realized 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 the Trump administration, I'm like, wow, I spoke too soon. If you were to, and you have

advised the government, I know you were part of, you worked with Lena Kahn. What regulation, 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 and encryption or AI, you know,

kind of magic one 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 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 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 where you thought of

the Ring Super Bowl ad. My God. My God. I mean, I didn't expect it to become so flagrant so quickly,

I guess, and seeing that it was, 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? 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. 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 it's never stopped us from opining on it. I'll talk 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. So 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 obsolete.

When you see that happening, do you have any initial thoughts on the viability of these

somebody's software companies who are some of them lost 40, 60, 70 percent of their value?

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 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. I do anticipate that AI in some form or fashion will be integrated, will have impacts

here. Fundamentally, this is not a magic wand. There's a lot of legacy infrastructure, regulatory

burdens, and labor processes and modes of work that need to be accounted for. I don't see

the SAS software going away anytime soon, and I don't see AI doing anything to really

erase those other considerations. I think predictions of the demise are a bit self-interested

and far premature. 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. 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 basically can out 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 ship it a little bit. I think ultimately,

humans want to be loved, and they want to be included. 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

that anico chamber of my 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. I think we're in a world where 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. I got to get somewhere. I want to participate in life while I'm living

as do I think most people. 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. 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 our health data leads to our assurance

in ways that would harm us, but that's also a place we don't have that much control. In the meantime,

we 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, 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 in the place in the world. 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, 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 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? 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 to my

next participant technology, that vent overlap, they all use signal. It's almost like a badge of like,

I get it. 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. And I 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

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.

I'll do a rough 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 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, 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 train myself to just love that

term. It's the most important term in my life. And it just, it's more dope, but for me than anything,

it's 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 expect to rate 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 and consider it or even mean to me or are 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 Jenner. Cammy Reek is our

social producer Bianca Rosario Miras is our video editor and Drew Burrows is our technical director.

Thank you for listening to The Propji Pod from Propji Media.

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