The grid(lock) slowing AI down

With AI moving from apps into the devices we use every day, Reid and Aria explore where the real value will be created. From Google Gemini powering hundreds of millions of devices to ChatGPT entering cars, Reid argues that distribution alone won’t decide winners but that depth of use, iteration, and personalization will. They also examine the $650B race to build AI infrastructure, the hidden bottlenecks and geopolitical risks behind it, and why U. S. capital still provides a key edge. Finally, they highlight the Trust in American Institutions Challenge and its winner as a case for how AI can help rebuild trust by making institutions more transparent and accountable.

• 0:00Speaker 00

  All right, Reid, we never do endorsements, but for those of you who are watching on YouTube, you may notice that I'm wearing a Patagonia and there's a picture of a mountain behind me, which can only mean one thing, that I'm at the Grand Canyon. So I'm just going to give a shout out to anyone. A lot of people have heard of the Grand Canyon, but it's really amazing and you should go and you should hike down it. And if anyone has kids, make their kids go on hikes. So that is my endorsement for the day before we get into lots of AI news.

• 0:28Speaker 01

  I'd say an endorsement of a national treasure is

• 0:32Speaker 01

  a good way to begin.

• 0:34Speaker 00

  People have heard of it. All right, so recently Samsung's co-CEO TM Ro announced at CES 2026 that the company wants Google's Gemini AI running on 800 million devices by the end of this year, which would be double the 400 million that it reached in 2025. Samsung is adding these features to TVs, home appliances. I feel like you do get some consumers that are a little annoyed by that, saying, we don't want smart TVs or we don't want smart fridges. So it'll be interesting to see what sort of the integration of AI into those smart appliances also means. But also, consumer awareness of Samsung's Galaxy AI brand has skyrocketed from 30 to 80% in just one year. And consumers are using AI a ton on their phones

• 1:18Speaker 00

  to search for generative AI photo editing, real-time translation. Actually, just yesterday, my husband gave me a photo of the Grand Canyon. I was like, that looks amazing. He's like, oh, yeah, AI edited out all the people. So people are using this in real time as it becomes accessible on their devices. Last week, OpenAI also became the first major AI company to launch a dedicated voice-based conversational app on Apple CarPlay Evermix. And they're rolling out ChatGPT as a hands-free voice assistant for drivers. So CarPlay 2.0 supports ChatGPT, Google Gemini and Claude. It is very clear that AI is coming to hardware. So my question for you is how important is this AI integration at the hardware layer?

• 2:02Speaker 00

  And does that mean that whoever owns the hardware actually ends up owning the majority of the value the AI creates as opposed to the software layer?

• 2:10Speaker 01

  Well, I'll start with the simple, which is I don't think The hardware ownership will dictate the greatest value in the AI layer. It doesn't mean that there isn't a significant impact from it because when people buy a piece of hardware, that hardware is their access for that, whether it's a car for the, you know, inside of the kind of car operating or AV, whether it's a phone, whether it's a, you know, a TV, you know, all of these things is, you know, that's the AI that they then get with that device and people, these tend to be big purchases and they've been

• 2:52Speaker 01

  to, you know, like the, the TV tends to be the central thing for the family, the car or two is the, is the transportation, et cetera. And so there is, there is a significant, you know, kind of exposure, value creation, value capture, generation moment there. And so I think it is important for that. On the other hand, when you think about, like, for example, part of what I think, you know, kind of AI should be looked at is, you know, what's the number of minutes, hours of AI being used to create value? And, you know, to some degree, when it's creating value for me, is that value kind of more substantive?

• 3:36Speaker 01

  And I think that's one of the reasons why, you know, value that's, you know, for example, comes through your chat GPT app on your phone or, you know, through your, you know, kind of copilot, you know, Claude, et cetera, app on your computer, those things, I think, hours and hours and hours of interaction and things that you're creating. And so there are on the more general platforms for this. And what's more, because the value of that, and that's one of the reasons why, of course, Samsung's using Gemini and why OpenAI is, you know, integrated, you know, along with, you know, substantive ones like Gemini and Claude into.

• 4:21Speaker 01

  The, you know, hands-free voice assistant, you know, for, you know, CarPlay. The iteration of these things into becoming more value comes out of the hours of interaction, you know, versus the driver of it's a commodity that I just happen to slot into our hardware. And so that's the reason why it's kind of like, it's not like, well, it's the hardware runaway story. Now, that being said, obviously, it's part of what is becoming a much more, you know, mainstream adoption of when it's just there. Now, you know, I think people are still a little bit slow to what are they doing when they're talking their TV? They're familiar with their remote, you know, et cetera.

• 5:03Speaker 01

  The AI is just, you know, okay, play Netflix, you know, uh, find Wednesday on Netflix. Like, okay, that's fine. And by the way, much better than the, the kind of remote experience and, you know, especially when you get to like Apple TV remote, which is like this, you know,

• 5:19Speaker 00

  the worst.

• 5:19Speaker 01

  Uh, simple and simply useless, you know, kind of, uh, interface point. But on the other hand, the thing that makes, you know, AI valuable is not its translation moments of, oh, I can now hear you say Netflix. And, you know, of course it's better than Siri and it's better than Alexa and all this. But it's like, eh, that's not the thing. It's actually kind of a much more substantive set of things that is in what you're creating and what you're doing. And the iterative cycle of that is within the frontier models themselves. And that will drive towards, um, you know, kind of upgradable, updatable, flexible hardware patterns, because there simply will be a huge amount of demand

• 6:03Speaker 01

  for, I want the one that really works here. And even if that demand is slow, because I don't realize that I can say, hey, um,

• 6:14Speaker 01

  You know Netflix, I like these 15, these seven shows recently. What are another five shows that you'd show me that would be interesting? And that's obviously when it begins to get, you know, kind of the beginning of much more interesting. And, you know, even when you're integrated into, you know, hundreds of millions of Samsung TVs, that's still a, something that we're building towards, where we're enabling the user adoption of, even if the functionality is all essentially there right now.

• 6:42Speaker 00

  Right, we're such at the beginning of this. And so as you think, like you've spent your career thinking around network effects, especially from a software perspective, but from a hardware perspective, does this mean that like the model that is on 800 million devices? Like, of course, your phone, you can use whatever app you want. But again, there is probably going to be some, you know, preferential models that people use. There's going to be deals that are struck between different companies. Does that mean that it's sort of game over for whatever model is on those 800 million devices? Because people will be locked in?

• 7:11Speaker 01

  You know, at this point, there's many more devices than there are people in the world, in a sense. And just because you have one device, like a Samsung device, doesn't mean you don't have other devices. And this gets, you know, there's, there's different ways of kind of understanding network effects. And you, just because you're on a network, doesn't mean you have a network effect. There's strong and weak network effects. Strong network effects are because I'm on this network, I'm not on other networks. Weak network effects are I'm on this network and I can adopt other networks, like instant messengers. Like, you know, people might be using Signal, but also WhatsApp, and also iMessage, and, you know, Telegram, et cetera, and using the whole set. And so those are weak network effects. It's like, I have a reason to stay on it when I do it,

• 7:55Speaker 01

  but my ability to adopt new networks is just the cost of that. And so, you know, so there's those. And then there's, like, you're on a network and being on a network doesn't necessarily matter anything. And that's part of the reason my answer to the earlier one was. Look, there's more subtle networks, like what is the reinforcement of how is the model getting better? Which gets drive driven through, you know, depth and engagement of use, which in the cases of TVs is likely to be low for a number of times, even if you're on hundreds of millions of TVs or devices, you know, in this case, now I think we'll be more on phones. And so the Samsung phones, which, you know, I have a couple of the, you know, the very nice Samsung phones.

• 8:38Speaker 01

  Those will create, let there's a form of network effect there in terms of the learning and adoption, like it's one of the things that being in the search engine business is knowing what the query stream is, is the way of doing it, the same thing in terms of like, how do you? How do you make an AI thing more magical? Also, how is it learning? I mean, this is part of how, you know, the kind of question of figuring out good sets of queries and good sets of answers is part of how AIs are trained, it's part of how search engines are, are, are improved, you know, et cetera. And so I think that engagement pattern really matters. Now, that being said, it's a, none of this is to underplay that it's a distinct advantage to be on a number of things.

• 9:24Speaker 01

  Especially if like, it's kind of the equivalent of, hey, I'm using this thing and I see how great it is. And that's part of like, why, like everyone who actually, you know, uses other models other than Grok realizes how bad Grok is. Because Grok trained to the benchmarks and, you know, but isn't not, is actually just not as useful on almost any vector. Other than, you know, maybe creation of questionable pornography.

• 9:56Speaker 01

  You know, than, than any of the, the primary models. And, and so you get exposure to that. So say, for example, you're, you're getting exposure to chat GPT through CarPlay. And you go, oh, this is really good. And that's useful to have that, that exposition and, and, and, you know, then, then, you know, if someone is trying to say, hey, I use this other model. Instead, and it's like a lot of quiet. It was like, ah, I want to stay with this. Um, plus I'll get familiar with it a little bit in various ways. And then, of course, the subtle thing that might begin to get, it's not a network effect, but a sticky effect is like, well, it starts having memory and it remembers you. So like I've been driving for two years with CarPlay and it knows,

• 10:39Speaker 01

  you know, what kinds of things I like, and it knows that when I say play the police, it isn't lookout for the police around me. It's, it's, you know, take this band that many young people don't know what it is and, and, and, and kind of.

• 10:51Speaker 00

  Totally. It knows your favorite smoothie store. It knows the songs you like. It knows that in the evening you want to pick me up. And so, yeah, that's super valuable.

• 10:58Speaker 01

  Yeah. So those things I think kind of contribute, but they're kind of not exactly network effects.

• 11:03Speaker 00

  Moving to the question that is on a lot of people's minds, everyone is talking about data centers. Alphabet, Amazon, Meta and Microsoft are expected to spend more than $650 billion in 2026, just this year alone, to expand AI capacity. And analysts estimate, though, that somewhere between 30 and 50% of these AI data centers that are planned for deployment in the US will be delayed or canceled. And the reason is electrical components. That is the bottleneck. Batteries, transformers and circuit breakers, which make up less than 10% of the cost to build a data center, but without which it's impossible to build one at all. And so lead times for high-power transformers used to be around 24 to 30 months before 2020.

• 11:47Speaker 00

  But now that timeline has stretched out, in some cases to five years. So these construction projects, even if we get them on the ground right now, they won't be able to help us for years to come. And so across 140 construction projects, data centers representing at least 16 gigawatts of capacity, they're slated to come online, but only around five gigawatts are currently under construction. And at the same token, US utilities imported more than 8,000 high-power transformers from China in 2025, and that's up from fewer than 1,500 in 2022. So needless to say, we are importing some of the most critical components of our data center capacity from China.

• 12:31Speaker 00

  And obviously, last week, we talked about the geopolitics of it all. Like, what does it mean for China to be supplying some of the most important things for our AI? So if we're spending $650 billion to win the future of AI, but it is fundamentally dependent on a geopolitical rival, what does that say for what we're doing? And is this a problem as we try to stay on top of AI in a geopolitical sense?

• 12:56Speaker 01

  There's various ways in which we have dependencies in the AI value chain, which is one of the reasons why I think, you know, kind of call it a national policy of being less terrifable or other kinds of, you know, kind of puns on terrible. And it's not just the transformers. It's it's kind of chip supply, which obviously is hugely TSMC Taiwan dependent. It's adoption, which has other dependencies. Like if you go all the way to the, you know, kind of construction of the components of which, you know, like the transformers are a kind of surprising to kind of chips.

• 13:40Speaker 01

  And then people frequently under rate the networking infrastructure. You know, then you get kind of like data centers, the composition of data centers. Then you've got the, you know, the kind of build out of the computer infrastructure. Then you've got the the models and the train or engagement. So you've got this whole thing all the way to to people actually using it. So I think there's a lot of different dependencies and a dependency on a geopolitical rival is certainly worth paying attention to. But, you know, it's a little bit of the reason why, like, for example, Nvidia, you know, kind of wants to have its cake and eat it too, e.g. sell a huge amount of chips at very high margins, but also be the builder and provider of, you know, AI models and so forth. And so it's kind of doing both.

• 14:24Speaker 01

  But their challenge is, is, you know, since they've got massive demand for the chips, all the ones they hold on to to do any kind of internal project, then, you know, hit their bottom line in terms of of undercutting current sales, you know, book kind of booked in margin. Well, what that means, and it's one of the reasons why, like, Nvidia has been in a strong position, because everyone says, well, look, the thing that most matters is that I can continue to build out AI in strong ways. So if you get China, you go, well, I suspect the price of high powered transformers are going to go up, but then they're going to be selling them broadly and probably to whoever meets the price, which it can include. The US, you know, kind of as a way of doing this.

• 15:08Speaker 01

  And this is actually one of the orientations by which the investment of capital is one of the other things that keeps the US in a substantive lead. Because if you think about, you know, you go to the $650 billion of investment in a set of things which have partial demonstrated revenue, but a whole bunch of uncertainties. You go, well, which countries in the world can do that? And the answer is one, right? The US. None of the other countries, including China. I mean, the government has that potential capability, but the companies don't operate that way. They have much, they have much lower revenue streams. They have much lower, you know, kind of ability to kind of invest in this is one of the reasons why a lot of the AI innovations that are coming out of China relative to software

• 15:50Speaker 01

  tend to be efficiency and tend to be using distillation of various models as a way of doing it. Because it's like, actually, in fact, we have, we have a mass amount of talent, we have a mass amount of data, and we have some compute, but we also have a lot less pure capital to just burn with an uncertain turn into revenue. So, so I think that the, you know, I would say I'm, it's worth paying attention to, it could turn into a sudden, you know, terrible vulnerability. It's one of the things that is of the many kind of nuttinesses around, you know, piss off our friends and allies as much as we possibly can, you know, is kind of the, the strangeness of this.

• 16:32Speaker 01

  I think it's one factor among many, not a, you know, five alarm fire.

• 16:39Speaker 00

  No, fair enough. Something certainly that we need to watch. And I think another thing, when it comes to AI, people are talking about trust. Like AI has come along at a time when trust in government, in institutions, in companies seems to be at an all time low. And so we've been talking about AI at the national level and let's, I want to take a moment to talk about our government institutions. Longtime listeners will know that you launched a challenge last year with Lever for Change called the Trust in American Institutions Challenge. It was a $10 million open call and we were asking organizations to submit and tell us what they were doing to rebuild trust in institutions in the United States, whether

• 17:22Speaker 00

  this is the criminal justice system, the education system, our national media, our local media, all of these things are critically important and we're not going to have a functioning society if our citizenry doesn't trust these institutions, but also these institutions aren't responsible back to citizens. And so, months ago, we announced the five finalists for the Trust in American Institutions Challenge and it was a $10 million, again, open call for these bold ideas to rebuild and scale public trust. And the five finalists were the American Journalism Project, Cal Matters, Recidiviz, Results for America, and Transcend. The great news is that yesterday Lever for Change announced a winner, Cal Matters.

• 18:06Speaker 00

  Cal Matters is a nonprofit, nonpartisan news organization and it's focused on transparency in government. And right now, they're focused on California politics and public policy with an eye towards expansion all around the United States. So I loved hearing about what Cal Matters did and especially what they are planning to do with the integration of AI. I think when we look at government right now, AI is actually super, AI is something that can really help it analyze the enormous troves of data that we have. We have building codes with 10,000 pages of things that people need to do. We have congressional votes over years and years and years.

• 18:50Speaker 00

  Everything we have around government, around data, AI can be enormous force multiplier in terms of understanding what's really going on and actually providing solutions for our citizens. And so, Reid, I would ask you, you were a part of this process, you were really excited about all of the organizations that submitted and the five finalists. What excites you about Cal Matters and as well as the role of this challenge in helping rebuild and scale public trust, especially with AI?

• 19:18Speaker 01

  Start from the very top. One of the things that's interesting with this is I've been helping the Lever for Change from its very beginning and spin out of MacArthur because They have a really interesting model of using networks to create, you know, highly validated and leveraged philanthropic dollars. And so, you know, having, you know, their hundred and change, which is the thing they launched, and then creating a new platform and spinning out, and Cecilia Conrad doing an amazing job of this, and, you know, having some folks doing that. And so, as you know, we've been talking to them for years about what kinds of projects to do. And the reason why we started with, you know, kind of trust in institutions is because,

• 20:00Speaker 01

  you know, the thing that probably is most scary and disheartening about our current moment in many Western democracies and maybe other institutions, places, is a tendency to say, burn all the institutions down, like, they're not working for me, so burn them down. And when you look at history, the burn them down leads to just terrible outcomes, whether it's the, you know, kind of French Revolution, you know, whether it's, you know, the Cultural Revolution in China, you know, like, each of these things, and there's just, you know, dozens and dozens of them, lead to enormous suffering, setbacks in society, et cetera. Because the intelligent thing is say, look, we really depend on institutions functioning to have society function.

• 20:43Speaker 01

  And by the way, we need informational institutions to function, to function as a democracy. And, you know, they get, they've been, tend, tended to be highly politicized and say, well, everything is political. Now, my personal point of view is something like The Economist that says, hey, this is, this is our, we, we have an informed point of view, here are some of our principles, and let us tell you what, on the thing on the informed point of view. You know, as opposed to, you know, slogans of fair and balanced, which means, you know, slanderous and unbalanced, you know, kind of equivalent. And so, it's like, you know, that, that, that, that trust in these kind of information things really matters. And so, that's why we Said, "This is what we will do in terms of trust and information."

• 21:28Speaker 01

  And to be clear, we actually wasn't focused on only journalism, like it was like libraries and a bunch of other things, because rebuilding institutions is the thing that we most need in society. Now, what works really well in the lever for change is that they go out and get a whole bunch of different institutions, nonprofits and organizations, aware of the challenges, even inventive individuals. People submit widely divergent proposals, something that's far beyond the vast majority of philanthropist capabilities, including my own. And then they bring in networks of experts and networks of people in order to evaluate and say, "What's the probability of this?"

• 22:12Speaker 01

  Now, one of my delights at watching at arm's length from all this, because part of it is to have it as an independently driven organization and doing all that, was that all of the finalists were amazing. And Cal Matters happens to be one of the finalists that I'd actually already been a donor to over time. And so when I came back with, "Oh, this is what we think is the top pick", was like, "Well, that was kind of cool", because I had paid attention to them, because I always tend to have this point of view of having some responsibility to the communities that have enabled me, the communities that I've participated in.

• 22:58Speaker 01

  So, for example, not just Silicon Valley with Second Harvest Food Bank, but also California. Cal Matters was part of that, because one of my big frustrations is people go build important things in California, and they go, "Wow, I've got this frustration in California." And by the way, you might have a very legitimate frustration in California. There's all kinds of nuttiness with a prop tax, you know, proposition, wealth tax, and everything else that's kind of going on. And it's genuine, but like, this is also the place that enabled you to do these amazingly scale magical things. And so you have, you should also have some sense of participation, give back, loyalty,

• 23:38Speaker 01

  reinvesting, the kind of the seed corn that allowed the flourishing of the own crops that you made and so forth. And so the fact that Cal Matters was part of this was, was awesome. And of course, part of the thing that's really important to having a functioning democracy is to have access to, to good information, like good information about how well is the legislator working? What are the policies that are working? What are the things that really matter for citizens? Are they, is the budget stuff actually working out? Is this, is this lying or, or truth? You know, has there been, you know, good, the results that are claimed? Is the, is it working or not for the people the right way?

• 24:18Speaker 01

  And Cal Matters basically says, we're gonna do it as kind of the equivalent of a, like, our only real point of view is understanding, you know, what are the things that actually are working and not working programs? What are, you know, kind of like, you know, when, when various politicians are making claims about things, which of those things are accurate? When propositions are making claims of things, which of those things are accurate? And to facilitate so that people say, okay, this is a sort of thing where you're just trying to make sure I have the information as a California citizen, as a California resident, to inform what I'm doing.

• 24:59Speaker 01

  And of course, try to create that as a basis to, to be an incentive system for politicians to operate the right way, for journalists to be able to, to understand truth and write stories that help with that the right way, that, that, that then, you know, kind of citizens can go, okay, that's a perspective that is trustworthy, not because there aren't just as anything in life sometimes, because they did a lot of real work to try to make it accurate to, to what they're representing. And so, um, it's, it's a delight that they're, they selected honoree and, um, you know, I couldn't be more happy for them and all the finalists were amazing.

• 25:41Speaker 00

  Awesome. Reid, thank you so much. I will give one final pitch to our listeners. If you are looking for a not-for-profit to get involved in, to donate to, these organizations have been vetted, as Reid said, by, we brought in hundreds of experts to look at all these organizations. So, once again, if you are excited to give back, if you care about trust in American institutions, The American Journalism Project, Cal Matters, Recidiviz, Results for America, and Transcend are all incredible, amazing organizations that are doing great work. Reid, thank you so much for being here.

• 26:10Speaker 01

  A pleasure. Possible is produced by Palette Media. It's hosted by Ari Finger and me, Reid Hoffman. Our showrunner is Sean Young. Possible is produced by Tanasi Delos, Katie Sanders, Spencer Strasmore, Imo Zoo, Trent Barboza, and Tafadzwa Nirmurundwe.

• 26:27Speaker 00

  Special thanks to Surya, Yala Manchili, Sayida Sapieva, Ian Alice, Greg Beato, Parth Patil, and Ben Ralas.