Meredith Whitaker (4:26)

And on and on and on.

Meredith Whitaker (4:28)

We have ceded control of so much of our lives to a handful of companies in a way that I think is only becoming more apparent. And you know, that's, that's just the first part. I think we can also look at, you know, some of the decisions that are being made by these companies as they pursue what is ultimately their key objective, which is continued growth and continued increase in profits to please their board.

Meredith Whitaker (4:57)

To please their shareholders.

Meredith Whitaker (4:59)

And that that imperative is often at odds with what would be better for society, what would be better for the social good. And that collision is becoming more and more apparent. And I do think people across the board are waking up to it. And we haven't even mentioned the kind of tenuous AI bubble we're in. So no, I don't think that is happening immediately. I don't see a complete turnaround, but I do see those dynamics marching forward. And I see an increasing discomfort as we see an increasingly over leveraged market in AI that leads me to think I was onto something.

Steven Overle (5:43)

Well, I don't disagree with being onto something. I mean, the AWS outage example is a great one because literally yesterday when that outage happened, like it started in the morning where I couldn't order my bagel, which I now do online, and then I couldn't send a work invite because the software platform runs on aws. And at night I couldn't log into a portal for a class I teach because that also runs on aws. And it just sort of is illustrative of our dependence on technology. But as you said, how so many of our, the technologies we use are powered by few companies. AI though, as you're saying, and I'm be curious to get your thoughts on this idea of being in an AI bubble from the perspective here in Washington, it has created something of a renaissance for tech because when you talk to policymakers, whether it's about energy policy or national security or economic competitiveness, I mean, AI is inevitably part of that equation. Sometimes it's a big part of the equation. And I wonder what you make of that, that impact that AI is now having on this.

Meredith Whitaker (6:45)

Well, look, AI is many things and it certainly has uses, but I would dare you or anyone listening who has contacts with policymakers and politicians to just sit them down and say, what do you mean by AI? Let them answer that question in clear and precise terms. And I think what you'll get at that point is a lot of hype, a lot of fog, a lot of magical thinking where people who don't have a rigorous technical background, who don't understand the material realities of these systems, the energy, the infrastructure dependencies, the layers of open source software on which everything relies, the cost, the political economy of building these systems, give kind of hand wavy answers that sound more like they're talking about a magical genie than about actual technical systems. And that's a big problem because we are seeing a wave of hype washing over critical institutions, governments and key decision makers that are leading to decisions being made to outsource decision making to technology, to trust these technologies with, you know, key functions that those who understand the technical reality, the limitations, the fundamental, you know, threshold conditions for how these actually work, would never have advised. But, you know, and that does get us to discussing a bubble, because the reality is, however you slice it, yes, revenues are very, very high. There's a lot of money incoming to license AI models from the likes of OpenAI or, you know, Google or anthropic. People are paying to do that. But the cost of training AI, the cost of building out these data centers, the cost of inference, which is the term we use for kind of using AI, every time you send a prompt and it sends back a wall of text, you've done an inference that cost is still not being recouped. There is no break even happening in this industry. So you are seeing what I've referred to as kind of a flop sweat desperation to make AI happen not just as a cultural zeitgeist or a renaissance, as you said, but as a profit center. And that has not happened yet. And so that, you know, the bubble is getting more taught, there's more and more air going into the balloon. But as yet, that magical consumer market fit that can actually recoup these investments in, let's be real, highly depreciable infrastructure that will need to be purchased again and again and again as chips change. We're not seeing a profit there. And I think that has some explanatory power for just how phantasmagoric the rhetoric and the promises being made are, because, again, there's a bit of desperation behind this.

Steven Overle (9:41)

Well, what's the antidote to that then? Do you think we have policymakers who listen to the podcast? I mean, it is easy to get caught up in the hype around AI, all the big promises, especially of policy challenges it will fix, like healthcare or climate change or, you know, anything that the industry throws out as sort of the positives here. And you also always hear this argument that Washington or policymakers don't understand the technology well enough to regulate it or put guardrails on it. And I don't know that I've ever heard the solve for that necessarily.

Meredith Whitaker (10:14)

Yeah, that old trope that all you need is tech brains in Washington to move aside the dusty policymakers and get things on the rails of modernization has been around for a very long time. I remember this. In the mid 2000s, it was bring tech to Washington. Washington. Because they're too old and crusty to understand it. Yeah, okay.

Steven Overle (10:33)

Right.

Meredith Whitaker (10:34)

But I think, you know, they're not too old or too crusty to understand the domains in which they operate, be that education or healthcare or national security. And tech has a lot to learn on the fundamentals of those domains. So I think, you know, in some sense, there is a. It is very convenient for those building tech to say, move aside. We're the only ones who are both able to build this and to instruct how it should be applied. Right.

Steven Overle (11:00)

Yeah.

Meredith Whitaker (11:00)

Now, I'm going to say, like the antidote, there's no one weird trick here. But I do think, and this may seem a bit of a sideways answer that Just be brave enough to ask the dumb question, because there is a. There is a culture of, you know, what I'll call a culture of shame around technical knowledge. People are deeply afraid of being humiliated for being dumb about AI. AI is the future. AI is the renaissance. It's the revolution. It's not just the industrial revolution. It's also the invention of fire, to quote Sundar Pichai. It's that important. And yet people don't feel like they have a clear grasp on it. And I will hear NATO chiefs, I will hear CEOs of Fortune 100 corporations sort of repeating as received wisdom claims about AI that make absolutely no sense. And that in the context of any other technology or any other presentation to their board, they would be ripping apart because they understood that they have standing, they understood that they need to be a. They need to protect their company, they need to protect their interests, they need to acquit themselves honorably in their job. And yet we don't see that with AI. We see folks talking to the marketing arm or the one or another executive of a given AI company acting as if that is ground truth for technical knowledge and then repeating it as if it's an imperative in the context of shaping policy, shaping decision making, shaping how resources are distributed. So I think, you know, step back from that. These, you know, quote unquote stupid questions like how does this work? So do we have control over the data? So what are the privacy implications of managing a agentic AI orchestration layer that relies on a slurry of data that is non differentiated? And you know, are there vulnerabilities there? How vulnerable is an LLM to a data extraction attack? And on and on and on. These are just basic questions that should be the floor, frankly, before entrusting critical decision making to obscure systems that are, you know, often don't, in my opinion, meet that bar for safety use in critical domains.

Steven Overle (13:28)

Right. And it's interesting because I feel like we've seen in some ways that sort of, you know, climbing that learning curve around things like social media or things like privacy, where lawmakers have gotten much more sophisticated on it than where they were maybe 10 years ago. And you mentioned privacy, which I know is an issue you are primarily focused on care a lot about. And I've covered the debate in Congress over data privacy legislation for a long time. Congress has not passed a comprehensive data privacy bill. Now we're in this AI era where our data is being used in even more kind of opaque ways. I wonder what protections you feel are needed or what Washington might be able to do when it comes to privacy in the AI era.

Meredith Whitaker (14:15)

Yeah, I love privacy, obviously. And you know, without privacy we don't have the chance of a good life. Right.

Meredith Whitaker (14:25)

If.

Meredith Whitaker (14:25)

If those with power over us have insight into every movement, every utterance, every relationship, every decision, you know, they have the power to weaponize that, to oppress and manipulate and ultimately dominate. And that's not a theory. Right. That's borne out through history. So this is fundamentally important. And it's particularly important because we live in an age where we've ceded so much ground on that already. I don't think knowingly, but I think under as the Internet was being commercialized in the 90s, as key decisions were made not to put privacy restrictions in place for private companies that were commercializing network computation at that time, as the surveillance advertising business model was effectively inscribed as the economic engine of the Internet in the 90s, a choice made by the Clinton administration. What you created was kind of a, you know, a surveillance flywheel in which not only were private companies that were, you know, building kind of the Internet giants of the time that were commercializing the Internet in the 90s and 2000s allowed to collect all of the data, they were incentivized to do it because their business model was ultimately advertising. Know your customer, collect as much data as you can to create models of people that you can then sell advertisers access to. And frankly, that is still the business model of the Internet. It is still, you know, it is why OpenAI is looking at inserting ads. It is, you know, you become a massive platform and use that platform, you know, as a way to lure people in or conscript them to participate and then collect data about them and sell people access to that data, monetize that data. Whether it is training an AI model or creating advertising models that may or may not use AI, that remains the economic engine of the Internet. And I think a key example here is Amazon sort of casting aside privacy scruples around Alexa and saying, like, look, we're just sending all of your Alexa conversations back to Amazon for use because we're afraid we don't have enough data for AI. That dynamic is happening across the board and it is fundamentally threatening privacy. A threat that is now being supercharged by the introduction of so called AI agents which are presenting really, really potent privacy threats across our devices and across our digital lives.

Steven Overle (17:04)

I wanted to ask you about agency because there are sort of unique privacy risks you've talked about there. Tease that out for me a little bit.

Meredith Whitaker (17:11)

Look, agentic AI is this sort of brand term that is being applied to a lot of different systems, but it is effectively referring to AI systems that promise to complete complex tasks on your behalf. So the example that I've given is something like, you'll hear the marketing, one of the AI leaders on stage saying, our new AI agent will be able to book a vacation for you and your college best friends, find a hotel, find plane tickets, find a date that works for everyone, and then, you know, notify all your friends that this vacation is Booked, right. And that's kind of, that's roughly the vision, whatever use case they, they market it with, it's kind of, you know, you can lay back and put your brain in a jar, as I've said, and the agent will do it for you. We all have robot butlers running around attending to our every need. And that, you know, I guess that sounds fine, like, I don't know, kind of a slug. Life doesn't sound very pleasant to me. I sometimes like deciding on a hotel what's going to be fun for me and my friends, the social process of planning together, right? Like, you know, there's more to life than laying limp while robots do things for us. But that fundamental issue aside, the reality of what is required to make a system like that work again at the material level of like how TF does it actually do that is pretty chilling because what you're actually talking about, if you say, had an agent running on your operating system, on your mobile device and you say, hey agent, book that vacation for me, do all those things. Well, it's going to require extraordinary permissions, root access, you know, to use a Unix term for it, it's going to have to be able to do a lot of things with your device and it's going to have a, have to have a lot of access to your, to data. So just like, let's go through that scenario, right? Like book a vacation. Well, it's going to have to be able to open your web browser, it's going to need your credit card information. It's going to need permission to spend your money on your behalf. It's going to need permission to, you know, make decisions about your travel, your frequent flyer number, you know, your calendar access, well, you know, access to whatever else is in your calendar in addition. And you know, and now I'm speaking from signals perspective, it's going to need to have access to your signal, to your contact list, to message your friends, your college friends, in this case on your behalf and tell them, hey, this is booked, right? So all of that poses an existential privacy risk because what we just described in the context of signal and any other high security encrypted application running on that device is fundamentally a backdoor, that is access to data through a very insecure system that has root access on your device. That effectively nullifies the promise of our gold standard end to end encryption algorithm, which protects your signal messages, which means no one but you and the people you're talking to, including signal, can look at and access those. And now There is a sort of a hole punched in the hole of that, you know, steamship of protection, to use a little metaphor, that is allowing not only agents but anyone who wants to instrument that, you know, that backdoor, that vulnerability to access that data and the way these are being rolled out is extraordinarily insecure. You're talking about creating just a sort of undifferentiated data slurry in which it's your calendar data, your signal data, etc. This is an existential threat to our collective security and privacy and it is an existential threat to Signal. You know, if this vision, which hasn't yet been fully realized, but we hear in the marketing speaking speak of these companies, if this vision is realized, it's questionable whether Signal can exist at all, whether there's a point in us existing. We do not want a world where Signal can't exist. Militaries, journalists, governments, human rights workers, anyone with confidential information to share in a high stakes situation uses signal, it's core infrastructure for the fundamental right to private communication. And if that's gone, there's no amount of autonomous agents that are going to make up for that loss.

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Steven Overle (22:36)

You know, it seems like Signal to me is in kind of a unique position here to be a voice in this conversation. Because even before, you know, the Signal Gate scandal, if you will, back in March, I mean, you must know that sort of all of Washington's covert communication happens on your app. People here know what it is and rely on it every day.

Meredith Whitaker (22:59)

I just got to say we make it our business not to know. That's kind of our thing.

Steven Overle (23:03)

Fair point.

Meredith Whitaker (23:04)

But yeah, we've heard received wisdom is literally everyone uses Signal and you meet someone, not just in Washington, but any government, any high stakes job, and immediately they're like, let's connect on Signal. Right? So yeah, it's, you know, that's because it is the one thing we have to do that. And yeah, we are in a unique position where, you know, I'm really proud to be at Signal. I think it's, it's a great effort to be part of and I, I'M an extremely lucky person to be able to do work that I believe in so deeply. But you know, I think it's also, we should look around like, why is there only one Signal? Why is it so rare to be just a consistent and ethically aligned, you know, principled organization that does one thing well, that protects fundamental rights. Like, you know, why is it that Signal is such a core piece of, you know, let's say like military and governmental infrastructure? Right. Everyone uses it, but you know, militaries rely on it. And yet, you know, we're not able to even be a for profit company because if we were a for profit company in an industry where profit is made by collecting and monetizing data, then we would not be able to provide the level of rigorous privacy that we provide. So we have grifting mil tech companies that are basically white labeling Amazon API and reselling it with some janky user interface with billion dollar valuations. And Signal is raising money from good hearted donors every year in order to survive. There is something fundamentally wrong with the model in tech and I think Signal is also the litmus for that, is.

Steven Overle (24:55)

That, I guess this is maybe predicting the future type question. But is that always the case? Is it always going to be the case that profitability is going to be at odds with privacy and security and some of these sort of core tenants of technology that people say they want but yet we don't really have?

Meredith Whitaker (25:15)

No, no, no, no, no, no. These are, you know, one, there's nothing, I don't believe in inevitability. Right. This isn't just the natural order of things that, you know, it's we, we can always. Rules were created, they can be recreated. It's just a Meredith ethos. But you know, I do think we can kind of go back through the history and see key inflection points when I would say the wrong road was taken. And I refer to this obliquely at the beginning, but you know, in the mid-1990s when the rules of the road for commercializing the Internet were being decided, you know, by the Clinton administration, there were, you know, two key decisions that were made and I, I already mentioned them. You know, one was no privacy restrictions. The other was the business model of the Internet would be advertising. And that latter was pushed by the advertising industry because they didn't want to lose another platform. Right? They had magazines, print media, they had tv and they were like, well, we don't, we don't want to lose out on the Internet. So you know, let's push for that. To be the business model instead of something like a public broadcasting model or a kind of community network model or, you know, any of the other many, many proposals that were on the table at that time. So, no, these were clear choices that were made that led us to this place. And, you know, any choice can be unmade then.

Meredith Whitaker (26:38)

So this is not, you know, this.

Meredith Whitaker (26:39)

Is not a fundamental tension. And I think going into the future, like, you can always price in these things.

Meredith Whitaker (26:47)

The amount of money spent on cleaning.

Meredith Whitaker (26:50)

Up a data breach, the amount of money lost by IP theft, the amount of, you know, coercive control that your strategic negotiating points being leaked before you've made them enables. You know, you're kneecapped at the negotiating table at that point. Right. Like, privacy isn't just a nice little value that good people like. It's fucking fundamental. Sorry to swear, political audience, but sometimes you need to, you got to make the point.

Meredith Whitaker (27:21)

Yeah, I'm, you know, this is the.

Meredith Whitaker (27:23)

New Yorker in me just came out. So I think it's also, like getting a bit real about this and sort of expanding our scope. Like, quarterly returns may look good, but, you know, if in a year we've just, you know, foreclosed on the company because, you know, our customers are fleeing because their data isn't safe, we're not doing a very good job of, of leading the company.

Meredith Whitaker (27:45)

We're not actually doing, you know, what is best for our profits.

Meredith Whitaker (27:51)

But I, you know, I do think there needs to, you know, all of that aside, there needs to be a fundamental shift in the business model, in tech. You know, this surveillance business model, which continues to be how money is made in tech, is pernicious and has led to a huge number of problems, including the kind of agentic AI threat that Signal and others have been naming.

Steven Overle (28:15)

So with these AI agent risks that you've identified, what do you see as the solution? What do you want to see happen?

Meredith Whitaker (28:24)

Yeah, I think what I'm going to say now is, what I would say is this is the floor. This is the minimum to ensure that Signal and other applications providing privacy at the application layer can survive and that we maintain some modicum of security and privacy even as agents are being introduced. But this is certainly not everything we need. This is what we need right now as the tourniquet we apply to stop the bleeding out. So, you know, at first we need developer control. Application developers like Signal need to be able to say, no, we're going to mark our application as sensitive or whatever it is, and that means it's off limits. To agents. Second, we need what I would say is radical transparency. And right now we have almost no transparency. You know, there are vague assurances, there's marketing speak, but that's about what we know about what data these agents are accessing or what level they're being implemented at. It's often very, very confusing to piece this together. So we need clear and precise documentation about what data agents are accessing, how it's used, how it's stored, where is it processed, on device or off device, what security measures are in place. And really this should be a standardized rubric that every developer fills out as a matter of course, similar to a data sheet. And then we need, you know, we also need, I would say, privacy by default. So, you know, off should be the default setting for agentic access. And users should be able to opt in to where they're comfortable, giving these agents access, if at all. And then finally, we need much more hardened operating system designs. If we're going to proceed in any way close to this agentic rollout at the operating system level, then we need fundamental design changes to shield data from agents, to improve sandboxing, and to improve security guarantees which are simply not in place right now, given the rush to roll out. So again, that's the minimum necessary. That is certainly not the full extent of remediations, but I think we urgently need those. And we need policymakers, technologists, AI leaders, all of them to be pushing in the same direction to make sure we don't poison our technical infrastructure in the name of trying to make a return on investment in the middle of an AI bubble.

Steven Overle (31:01)

I want to ask you also about tech culture, because I do think there's a cultural component to how our technology is conceived and made that sometimes gets overlooked.

Meredith Whitaker (31:10)

Wait, tech has culture, right?

Steven Overle (31:13)

The tech has a culture, right? And it is sometimes, you know, sometimes it's kind of like an anthropologist, I'm like sort of studying and trying to understand this tech species. But you've, you know, I believe you're based in Paris now. I know you've been in Silicon Valley though, for a lot of your career. And culturally speaking, Silicon Valley has always felt like a world away from Washington. Nowadays, those two things, those two worlds do seem to be more intertwined than ever. And you know, a lot of headlines have been made about this idea of like the rise of the tech, right? And sort of this swing towards Trumpism among some Silicon Valley elite post Biden administration. I wonder, the conversations you still have in tech and what you observe. Does it feel like there has been a palpable shift to you, or was this something that was already there but just kind of in the shadows?

Meredith Whitaker (32:05)

Well, look, I'm, you know, all cracks aside, like, yeah, tech does have a culture. And when I joined in the mid-2000s, you know, there was, you know, it had issues, it was homogeneous, it was, you know, narrowly scoped in terms of a, you know, kind of expertise level. But there was a, you know, it was, it was warm and friendly and creative in a lot of ways. Some of the most intellectually generous people I met were people who were just deeply interested in math and computers and what you could do in the world with those. And, you know, then it become the money industry and all the kids who would have become doctors and lawyers in the 90s and, and you know, early 2000s, because they wanted to get a good paying job, you know, or finance, suddenly went into engineering. And that, you know, did change the nature of the industry as I experienced it. Right. You know, you were bringing in all the McKinsey people, you were bringing in the money people. And that sort of woolly quality of creativity and experimentation, you know, went the way it goes when that happens. And, you know, then the bottom line.

Meredith Whitaker (33:13)

Became increasingly prominent as the objective of these companies.

Meredith Whitaker (33:18)

Now it was always the objective, but I think it was, it was padded a little back in the day. And you know, what I will say to that is, you know, I think tech culture from then at least has, has kind of followed the political winds. I was working at Google during the Obama election. I was working there, you know, through the first Trump election. And at each sort of presidential election, what you would see is something, you know, really clear. The policy shop would, you know, basically get rid of the people who were yoked to the old guy and bring in the people who are close to the new guy and rearrange their positions. Get as close to power as possible. Move to Versailles to be close to Louis xiv, because you gotta be close to power. It's pretty old in terms of a rule book. And at that time, tech was extraordinarily close to the Obama administration. It was an osmotic layer is putting it a little strongly. It was almost no layer at all back and forth between Obama and, and Google and all of these companies. And that was celebrated because it was seen. You know, Google is virtuous and it's bringing virtuous tech to D.C. and you know, it's generally liberal, et cetera, et cetera. So I don't see what's happening now as necessarily different in terms of the structural dynamics, they're doing what they do, which is get as close to power as possible and then bend themselves to.

Meredith Whitaker (34:43)

Please power, to get what they want.

Meredith Whitaker (34:45)

What I do think this is showing is that that's a very dangerous archetype if what you're talking about is trusting an actor who's going to swing in the political winds from left to right, to center, to up to down, just to get close to power.

Meredith Whitaker (35:03)

And they have the most vulnerable and sensitive data on your life, they have.

Meredith Whitaker (35:09)

Control over decisions made by your core institutions, they are running your government's core infrastructure.

Meredith Whitaker (35:16)

And yet they're bending to the winds.

Meredith Whitaker (35:18)

Of political whim this way and that way.

Meredith Whitaker (35:20)

And I think part of the alarm is just recognizing like, oh, shit, that doesn't seem healthy or safe.

Meredith Whitaker (35:26)

And to which I'll say, yeah, it's not healthy and safe. It's actually incredibly perilous.

Meredith Whitaker (35:32)

And that is one more pressure that.

Meredith Whitaker (35:33)

Sort of leads to our kind of first discussion of are people becoming disaffected with big tech? And I think the answer is yes, increasingly.

Steven Overle (35:42)

You know, the other aspect of tech culture that honestly has always fascinated me and you, you know this better than most, is the kind of resistance culture, outspoken culture that for a long time existed at tech companies. And for those who don't know, you know, you worked at Google for 13 years. You left back in 2019 after leading a number of walkouts and protests around some of the company's policies on things like AI ethics and military contracts. And it wasn't just Google, but during the first Trump administration, I covered a lot of pushback in Silicon Valley to Trump's policies on things like immigration or climate and defense. I don't see any of that this time around. And I guess I wonder, you know, if Silicon Valley's kind of resistance culture is dead.

Meredith Whitaker (36:27)

I can only speak to my own experience, which was, you know, I joined Google in 2006, right out of college, and what I found was, frankly, one of the smartest environments I've ever been in, where there was just a tacit understanding that if you want really, really, really smart people working on your behalf, you gotta let them think, you gotta let them cook, you gotta let them talk. You gotta encourage a culture of sharing ideas. If you are at the table and you aren't raising key points and you aren't pushing back to make sure you understand a, an issue or a question, you aren't raising a problem that you see with that, then you're going to be kicked off the table. That was the culture that I joined, and it manifested in, you know, very rowdy mailing lists where people would debate any old topic. It manifested in a willingness to, you know, frankly question leadership at weekly meetings where Larry and Sergey and others would stand on stage and it was celebrated. And now, you know, obviously, that new power plays and dynamics and hierarchies and you sycophancy all plays a part in structures like that. But I would say that was, you know, it was much more like that than most environments I'd ever been in. And that was part of its success. And so, you know, in a sense, the, the sort of work that was pushing back on, you know, some of these business decisions was an extension of a culture that had existed for a very long time. And that had, you know, I would say, made Google dominant in many ways because it was selecting for people who were staunch about their analysis, who were demanded citations and demanded rigorous thinking. And that manifested also in demanding that from leadership and saying, what are you doing building drone targeting programs using AI that we know doesn't work? What are you doing yoking the fortunes of a massive surveillance company with so much intimate information to one nation's military in a way that historically, we know could be very, very dangerous for the people whose information you're stewarding. You know, questioning these decisions at a structural level. You know, again, that was a kind of core Google thing for a long time. But, you know, as you begin to hire the McKinsey types, as you begin to be more and more focused on that bottom line, as the, you know, horizon of trade offs, as I put it, grows nearer. Right. And you have to decide between trading, you know, leaving billions of dollars on the table or, you know, and, and sticking to your kind of moral compass or bending your moral compass. Increasingly the latter dominated. And I think that is just, you know, part of the cultural shift that I saw at Google. And, you know, again, that's, you know, I think that is one of the key problems with entrusting such serious, entrusting such serious functions, you know, decision making, you know, infrastructural control, the platforms that support our shared information ecosystem that are sort of eating up the media industry. All of this to companies that are ultimately primarily invested in ensuring that their bottom line grows, that revenues increase, that, you know, profits are made, that growth is persistent. And again, I think we're facing this head on. And I do think people are becoming more and more disaffected.

Steven Overle (39:54)

Listen, Meredith, fascinating conversation. Thank you so much for being here on Politico Tech.

Meredith Whitaker (39:58)

Thank you. This has been great.

Steven Overle (40:01)

That's all for this week's Politico Tech. If you like the show, be sure to subscribe. And for more tech news, subscribe to our newsletters, Digital Future Daily and Morning Tech. Our producer is Nirmal Maliko. Pran Bandy made our theme music. I'm Stephen Overleigh. See you back here next week.