In 5 Years, 90% of What You Use AI For Will Run on Your Smartphone | Paolo Ardoino, Tether

Hundreds of billions of dollars are flowing into AI data centers right now, and Paolo Ardoino, CEO of Tether - the company behind the world's most widely used stablecoin with 573 million users - thinks that investment is going to age very badly. In this episode, he joins Craig Smith to explain QVAC, Tether's open-source platform for running AI on smartphones, laptops, and edge devices, and to make a case that within five years, 90% of what ordinary people use AI for will run entirely on consumer hardware, without touching a data center. The evidence is already there: Tether's team built a 4-billion-parameter medical AI model that outperforms Google's 27-billion-parameter MedGemma, running on a good smartphone, and a 1.7-billion-parameter version that runs on the average $80 smartphone available in Africa. The deeper argument in this conversation is philosophical as much as technical. Ardoino applies the same disintermediation logic that made sending dollars to the world's unbanked free - zero transaction fees, revenue from treasury bill interest - to AI: "not your AI, not your intelligence." If you don't control how your AI runs and your data never leaves your device, the AI is genuinely yours. If it does, someone else is getting smarter with your information. He also makes a pointed economic argument: the AI companies currently charging $200 for subscriptions that cost $1,000 to $5,000 to deliver are subsidizing growth while private, and when they go public, retail investors will absorb the gap. His prescription isn't to stop building, it's to build differently, toward millions of small interacting models rather than trillion-parameter monoliths, toward devices that think locally rather than systems that route everything through Ireland and back. Subscribe to Eye on A. I. for weekly conversations with the people building and deploying the future of AI.

• 0:00Speaker 00

  If in 2026, people can already run local models on their smartphones to solve only 50% of the use cases of AI, in five years, 95% of the population are able to run 90% of the use cases of AI on a smartphone.

• 0:15Speaker 01

  They're building all of these data centers and it's not at all clear that the compute paradigm that we're currently operating under is going to continue, in which case, maybe you don't need all those data centers.

• 0:27Speaker 00

  So if you don't understand how your AI works, it's not you becoming more intelligent, it's someone else becoming more intelligent with your data.

• 0:35Speaker 01

  The big transformational market is enterprise and government use, and those will likely run on data centers because problems that they're solving, the speed at which they have to solve them, are going to require massive compute. Is that possible that there'll be both?

• 0:56Speaker 01

  Why don't we start by having you introduce yourself to listeners about how you came to be involved with Tether and what Tether is and why it's important, which a lot of people, including me, don't quite understand?

• 1:15Speaker 00

  Look, Tether was a company, is a company born in 2014.

• 1:22Speaker 00

  Simple idea, digital dollar. There are plenty of digital dollars, but only USD, our digital dollar, was able to achieve the holy grail of distribution and financial inclusion and impact in the world.

• 1:44Speaker 00

  Over the last 12 years, our company built the most-used digital dollar in the world, with 573 million users, growing by 30-plus million users per quarter. I call it the biggest financial inclusion success story in the history of humanity. Sometimes my blood boils because how is possible that a little company like Tether was able to achieve more for financial inclusion than all NGOs and charities and whatnot for the last 50 years? And we did it in a very simple way. We used new technologies like blockchain to make

• 2:27Speaker 00

  the dollar accessible. And you asked me before, well, you know, why not just use the regular dollar? Well, the reality is that for 4-5 billion people, so more than half of the population of the world, they cannot have access to that regular dollar. They don't have access to basic financial services. The people that are unbanked, the number of people that are unbanked in the world is just enormous. And these are not, it's not like they are unbanked because they are bad people. They're unbanked because they're too poor for being of interest of the banking system. They live in countries where their

• 3:08Speaker 00

  national currency is devaluing so fast against the US dollar. Think about Argentina. The Argentinean peso lost 94.5% of its value against the US dollar in the last five years. The Turkish lira lost 81% of its value against the US dollar in the last five years. The Venezuelan bolivar lost 99.8% of its value against the US dollar. I could go on.

• 3:37Speaker 01

  Yeah, no, no, I understand all that. I mean, you said poor people, but certainly the people who are trading and holding Tether are not, by and large, not poor people. I mean, you need a level of sophistication and infrastructure. You know, you need a stable internet connection. You need, you know, so, but, I have a, and I want to get in an argument about that, but, but, but the...

• 4:10Speaker 00

  We exactly can. I mean, there are people in Central South America, they have a stable connection. They, like, think about Argentina or Turkish. Turkey, Turkey is, the digital penetration is incredible. Even Nigeria. Nigeria has 300 million people. Or Philippines, huge countries, huge populations. They all have a smartphone. So they all have digital wallets. So the reality actually is that the digital penetration for these new technologies for, and these digital dollars, is much lower in Europe and the United States. Why? Because they don't need it. But actually, I, truly, of course, if you, if you talk about certain

• 4:55Speaker 00

  remote villages in Africa, well, we can talk a lot about it. We do some incredible things to bring connectivity and energy and access to the dollar there. We could spend hours and would be, it's the most exciting thing in the world for me. But the reality is that many of these populations have, you know, the, one of the common denominators of the emerging markets is that they have more youth. They have, so the youth has digital means or is, is, understands, you know, technology faster, of course, than the older generations, and they are more prone to change, and also have access to the, again, to smartphones.

• 5:36Speaker 00

  Like, the average cost of a smartphone for Africa is $80. Of course, you cannot play the big, the best games in the world, but they have browsing. They have, like Opera, that is a very public, is a public company, is one of the most known browsers in the world, has 300 million users in Africa alone, right? So we partner with them, by the way. So as one of the main partners. But bottom line is, what we learned with the, with the USDT over the last 12 years is that we can truly disintermediate. So if finance didn't perform

• 6:18Speaker 00

  well in certain parts of the world, and if, if, if so many hundreds of millions of people were left behind, it was for multiple reasons, because there were not enough incentives to solve that problem. And we proved with Tether that we could solve that problem, or help to solve that problem. We are still early, but still, we are doing a good job. And be very profitable doing so. And we are not taking money from anyone. Actually, well, we are taking money, we're earning on the interest rate of the United States. That's it. We are not charging any transaction fee. Sending dollars for the poorest people in the world costs zero in terms of transaction fees. We make the money, again, on the interest

• 7:00Speaker 00

  rates of the dollars we keep in the bank account. So we, we, USDT for us, and also for us internally, was the demonstration that the, in the world of finance, there are too many intermediaries. That they all need to make money, and they all need to make money on transaction fees or milking the last mile. And with USDT, we proved the opposite. We proved that we could be very successful, changing the equation and removing all them intermediaries, and creating a free product, basically, like just a dollar,

• 7:40Speaker 00

  simple as that. And we could find a way to be profitable that no one else in the world figured out before us, with the lowest risk possible, because, you know, again, we hold treasury bills in a bank account. So, simple as that. But the most important learning point of that is that not only finance is heavily intermediated, is overly intermediated, the entire technology is overly intermediated. The, you know, internet, I, I feel internet was born to be peer-to-peer, point-to-point. When you connect your, your computer to the internet, or when you computer, you, you connect your, your smartphone to the internet,

• 8:24Speaker 00

  you get an IP address. And that IP address is basically your home address, and is, you know, well, there are some nuances to it, but it is kind of unique to you, could be unique to you. That was the original promise of, of internet. Let's connect people. And suddenly, internet changed, and instead of let's connect people directly, became let's route their connections through data centers. Why? Because also in that case, the intermediaries created, created a gravitational force, so that being intermediaries of people's connections,

• 9:04Speaker 00

  i.e. people's data, they could earn money hosting, holding, and intermediating people's data.

• 9:13Speaker 00

  And that is a very huge problem in a growing society for many reasons. Of course, you have data privacy and like all the things we know. But there are some issues that not many think about. If you have a person living in Rome, like imagine like you are a person living in Rome. And you're, you know, it's very likely that your family lives in Rome. And you send a message, you know, I send a mess. Let's say I'm living Rome, my family is in Rome, my mom lives in Rome. I send a message on WhatsApp to my mom. That message goes from Rome to Frankfurt or Ireland and back to Rome. Is that, is that good? Is that normal?

• 9:57Speaker 00

  That adds enormous amount of latency. But even so, that is just a message. Every photo, every video, everything that I send goes to Ireland from Rome, Ireland, Rome. Imagine how much all the governments spent in internet infrastructure in the last 20 years to route packets, data that is completely unnecessary when internet was built to be point to point, peer to peer. So that thing was, it is an unnecessary intermediation that is only justified by the broken incentives of internet.

• 10:37Speaker 00

  So what we realized at Tether is that we could, but there was already a solution also when it comes to when it comes to data sharing, how we could build a protocol over internet that was, that could scale to tens of millions of people. Because of course, the classic answer is, oh, but how we can, how we can make internet usable if there are no data centers? Well, there is, you know, in early 2000, a new protocol was born, was called BitTorrent. It was the iteration of all the file sharing protocols. And was so great, it could scale to hundreds of millions of users and exabytes and exabytes and exabytes of data

• 11:23Speaker 00

  in the perfect way. And so technically, we had already our answer. We could build, we could reuse structures similar to the BitTorrent protocol. And, and to disintermediate servers and data centers in many, many applications. That's also part of the things that we are doing at Tether, happy to elaborate more. But this is basically the spirit that we have in us, like we learn the concept of disintermediation from finance and what we did to the financial world. And by the way, now everyone talks about stable coins. Everyone, every, even banks now are talking about stable coins because they understand the power. And so now that everyone understands the power of stable coins,

• 12:05Speaker 00

  we looked around and we thought, what else we can disintermediate? Because clearly we are on the right track. And that's why we built whole punch as a protocol and then Qvac as an AI. The same concept applied to AI.

• 12:18Speaker 01

  I mean, that's fascinating and, and maybe I should have you on again just to talk about stable coins. But what I really wanted to talk about is your quantum verse automatic computer.

• 12:32Speaker 01

  And so that's a research initiative, right? Can you talk about what that is? And you were alluding to it a minute ago, that

• 12:45Speaker 01

  that there is a path down which we can deliver the power of AI without having to go through massive data centers. So, yeah, can, can you talk about that? Explain what Qvac, for short, what that is?

• 13:08Speaker 00

  Sure. So

• 13:11Speaker 00

  the, I'm a big sci fi fan and just, you know, for the listeners, if they, for any reason, they would, they want to read tonight.

• 13:20Speaker 00

  An interesting short story is 14 pages. I think it's the most beautiful short story ever written. Was written by Azak Kazimov, 1956. It's called The Last Question. To me, it's beautiful because it's a story of science, AI, physics, religion, universe, philosophy, all coming together in 14 pages. And it's a story of humanity every hundreds of years and dozens of years and millions of years. Always perfecting this automatic computer, make it better, more powerful. And every single time humanity was asking one single question to this computer, to this AI.

• 14:02Speaker 00

  It was the last question, the most complex, it is the most complex question that you can ask and trying to get an answer for. That question was how entropy may be reversed? So how we can stop the universe from dying? And to me, that, that is, is very intriguing, it's very exciting. But, you know, bringing back that huge, complex question to Earth means how we can stop society from dying? How we can stop Earth to, from, from dying? How we can make society stable?

• 14:36Speaker 00

  And society can be made stable, going back to and connecting to my previous part is through technology and through connecting society directly without intermediaries. And having, you know, society was built for the last 5,000 years.

• 14:55Speaker 00

  In a, society was peer to peer, has always been peer to peer. People were meeting the streets, were talking. People were using cash or salt or coins to interrupt peer to peer. Only in the last 50 years, information and money became fully intermediated. So we built an entire society over the last 5,000 years that was built with a completely different construct. And we changed it completely in the last 50 years. So we, we, we don't know how, how society will grow in which direction? How the decisions of, of intermediation that we made in the last 50 years? How they will affect society in long run?

• 15:39Speaker 00

  So when I go back to the sci fi story, I think how we can truly learn from that? And if we want to achieve an AI that is even able to answer the most complex question of the universe? That should be an AI that is part of the fabric of the universe itself. It's almost like a new element of the periodic table.

• 16:05Speaker 00

  AI, an AI that is so, so intelligent, that knows everything, that is, that can scale to the, and be distributed to the four corners of the universe? Cannot sit in a data center on Earth? Cannot belong to one person? Even just an AI, how, how we can plan to have an AI on Mars? If the data center is on Earth? The time, the latency between Mars and Earth is too long. And then we had another planet and another planet. Of course, we are years away from that or decades away from that. But still, I think that we should design technology to scale with the scale of humanity

• 16:46Speaker 00

  and with our ambition in terms of expanding ourselves in other universes and so on. If we don't do that, we are just here for quick gains. We are not here to build a product for the safety and stability of humanity. We are just here to build a product that makes capital companies rich. And I became very bearish with, with data centers, also for, for multiple reasons.

• 17:13Speaker 00

  You have, you have, you know, there are some beautiful stories. There was this guy in Australia, this entrepreneur that was able to find the cure of the cancer of his dog using ChatGPT and an alpha fold. It's a beautiful story, it's a great thing. But the reality is that 99 or nine, between 95 and 99% of the people in the world, they will never do that. They use AI for basic things, for search or to take a photo of a grocery list or the grocery rec, receipt and, and, and have at the end of month, some accounting.

• 17:55Speaker 00

  They want some translations, they want some education, they want, they want things that a model, an AI model that runs on a smartphone or on a cheap laptop can already do today. So if, and so you have 95%, let's say even 90% of the population that, that ask simple questions, they are not researcher, they have other problems in their day to day lives. They need to go from point A to point B. They need to optimize their, their, their taxes. But these are very, very simple things that more, simple models or models can run on a smartphone can do already. Few days ago, Tether

• 18:40Speaker 00

  launched, you know, there is, there is a big race to medical health AI models. And one of the most popular ones is the flagship, is considered state of the art model was Mad Gemma from Google, Mad Gemma, 27 billion parameters. Our team was able to produce a 4 billion parameters model that was

• 19:06Speaker 00

  Exceeding the performance and the accuracy of the 27 billion parameter model of Mad Gemma. So four billion model, I mean, four billion parameters model means that can run on a smartphone, can run on a smartphone of a good smartphone. And we also have 1.7 billion parameters model that can run on smartphone that is an average smartphone for Africa. So, and this is today's 2026, is May 2026. So if today, I believe that today with the Q vac, we've created a platform, an open source platform, because open source is very important is, you know, so that people don't have to trust that or can, they can verify the platform themselves.

• 19:50Speaker 00

  If in 2026, people can already run local models on their smartphones to solve only 50% of the use cases of AI, of the use cases, the normal use cases of AI, the use cases of AI that everyone, all the normal people would run. In three years, I'm sure that we can bring that percentage to 70%, 70%. So in three years, 70% of the use cases of AI can run on laptops and smartphones. And in five years, it will go to 90%. So if in five years, 95% of the population are able to run 90% of the use cases of AI on a smartphone,

• 20:29Speaker 00

  then why the hell we are building tens of gigawatts of data centers? When, you know, and, and the subscriptions to these AI models in, in, in centralized data centers cannot be justified because then the subscription, subscription could cost millions, not hundreds of dollars. And maybe it's still great. I mean, a smartphone will never be able to find the cure. One single smartphone will never be able to find the cure, the cancer of your, of a dog. And that is extremely important. I'm not saying that this is not important. Those, I, I think medicine will, will have huge breakthroughs thanks to AI.

• 21:09Speaker 00

  But it will become a niche. What will run on centralized data centers on these behemoths will be a niche, will cost billions and billions of dollars and will be probably subsidized by governments because that is of fundamental importance. But all the normal people, the hundreds of millions of people, the billions of people will use AI as part of their today, today lives through their smartphones, maintain privacy or their own data. But look at Apple, every year they're releasing the M3, the M4, M5 GPU of their iPhone 16, 17, and now 18. And every year there, the, the, this GPUs can run these Lama models

• 21:53Speaker 00

  two times faster than the previous iteration. So again, why, that's why in the five, in five years, what we run on the smartphones will be so good that we will forget about paying a subscription to OpenAI.

• 22:09Speaker 01

  Yeah, that, and that's fascinating. And as I think I mentioned to you, I'm, I'm interested in, in the potential overcapacity of data centers that's, that's being created. But while you're talking about consumer use of AI, there, the, the, the big trends formational market is enterprise and government use. And those will likely run on data centers because the problems that they're solving, the speed at which they have to solve them

• 22:53Speaker 01

  are going to require massive compute. So is, is that possible that

• 23:04Speaker 01

  that

• 23:06Speaker 01

  there'll be both? I mean, that, that there will be enough demand to, to take up the, the data center capacity, you know, to run governments, to run the world economies and that personal use, yeah, will migrate to, to the edge.

• 23:28Speaker 00

  I, you know, coming from, this is a very interesting question, right? Coming from the Bitcoin world, you might know that Bitcoin was first, Bitcoin mining was first running on CPUs, then moved to GPUs and then moved to ASICs.

• 23:47Speaker 00

  AI started from CPUs, now is on GPUs and last year, the first ASICs of for AI were born. So that right now, if you have a good GPU, you can run Lama 3.2 at 150 tokens per second. The ASICs for AI were able to run Lama 3.2 at 17,000 tokens per second, right? So I think there is a world where even if you are a bank, let's say a big bank, you could buy in five years, 10, 15 powerful ASICs,

• 24:29Speaker 00

  spend $50,000, 100 dozen dollars, but you can run an AI cluster that is extremely, extremely powerful through the ASICs. So the ASICs will bring efficiency or energy consumption now down to 98%. So you can run much faster models with much less energy directly onsite. And with the benefit, other benefit of the fact that you can keep your secret sauce for you because you probably, you know, the more the time will pass, the more companies will realize that, you know, someone else is training data, their model on, on, on, on, on someone else's data, right? So I think over time, of course, now

• 25:11Speaker 00

  only centralized data center had the capacity to create very cool stuff. And over time, I think that, you know, many banks, they, they run their own data centers, right? So they, and the more the models will become good, the more, even on the enterprise, I think that they will have their own dedicated hardware. And so there will be, also because, think about it, like you don't want every Italian bank, they will, sorry to make all references to Italy, but I'm by, by, by, I'm Italian by, by birth. But will, will all Italian banks run on a US-based infrastructure, right?

• 25:54Speaker 00

  Probably not. And so will all the Italian public administration run on a US-based infrastructure? Probably not, right? So, and the same thing goes like all French, like, so there is, there is already a big push. If you, if you read, one of the coolest new

• 26:13Speaker 00

  product lines for data centers is, or cloud providers is actually the,

• 26:21Speaker 00

  the sovereign cloud means that you go into a country, you install, you build a small data center directly for the country, deploy there your software. And so even for AI providers, they, anyway, they will need to install and, and directly on, on premises, certain capacity to run models there. So I don't think we are going to have to see this huge concentration for too, too much a long time.

• 26:54Speaker 01

  Yeah. Well, we can talk about the, the overbuilding. I mean, but, but let's talk about the quantum versus automatic computer. It's built around this BitNet Laura framework. Can you talk about that? Describe what that is for listeners? A lot of listeners will not know what BitNet is. A lot of listeners will not know what a Laura is.

• 27:24Speaker 01

  And, and why that was a foundational breakthrough for, for what you're doing?

• 27:31Speaker 00

  In order to run our smartphones, we need, of course, to ensure we, in order to have a platform that is able to, incapable to scale and evolve on smartphones, you want to do two things. The first one is inference. The process of inference is, you know, you ask something to a model, you get a reply. And so that computation is called inference. The second part that is very important is how the model can learn from you. Imagine this, imagine if you could have a model on your laptop, on your smartphone that could read privately through all your emails

• 28:11Speaker 00

  and design and learn how to respond in the best Craig's way. So.

• 28:22Speaker 00

  You want to do that, you might want to do that, but you might, you also don't want to do that if it entails to send all your emails to someone else to train the model. So we wanted to make sure that if I want to have Paolo's assistant that responds in my own way, you know, making, you know, some, you know, starting the emails in a certain way as I do. And so like you, I, I, I needed to be able to do that. Running on directly on my laptop and learning directly from me and maintaining 1% of my privacy. Same with documents, same with, you know, how I start the company documents, my strategy, company documents, everything that I do.

• 29:07Speaker 00

  And I want something that is completely customizable and adaptable to me. That process is called fine tuning. And usually the best way, best technique to do fine tuning is LoRa, that is low rank adaptation. So basically you take an existing model and you adjust only a portion of the weights based and you fine tune them in order to be respectful and slightly adjust those weights. So that they could respect and customize based on your own behavior. And so we understood that, of course, inference was already a proven task to do on local devices, but with Qvac, we

• 29:53Speaker 00

  took the Lama CPP, that is probably the most known inference engine for open source AI. And we built on top of it and we optimized so much that now can scale to basically every single consumer GPU. And on top of that, the other breakthrough that was done by Microsoft was called BitNet. The problem is that BitNet is a one, a one bit model. So usually models have different levels of quantization. They have their, you know, they use either 32 bits or, or 16 bits or eight bits for, for representing the weights. Microsoft came up with this BitNet, so one bit

• 30:39Speaker 00

  weight model. The problem there is that it was not suitable to run on consumer devices. So technically was what would have been the perfect solution to run on consumer devices, but was not a suitable because was too much rely on Nvidia high end GPUs. And so we modified first BitNet to make it adaptable to run on any consumer device like in any consumer GPU, like the Snapdragon GPUs that you find on the Samsung phones or see, or the Adreno GPUs or the Apple GPUs. Second, we also created in Qvac a common fine tuning, LoRa fine tuning

• 31:24Speaker 00

  framework so that not only for the BitNet models, but any model that we support and we support hundreds of models. We give to the developer the same framework to fine tune any model on all the consumer GPUs. So you have your Lama CPP, Lama 3.2 model. You have the Mad Gemma, you have a Quinn, you have all these models, all these models that are supported by our Qvac platform. Now you can run fine tuning directly on your own device. So I can have my Paolo's assistant, you can have your Craig's assistant, and that will continue to maintain 100% of the privacy.

• 32:09Speaker 01

  How far along are you in this, in this research?

• 32:15Speaker 00

  I must say that I'm very impressed by our team. Well, we, I read them, but they are, they're doing incredibly well. I mean, and the thing is that we work, you know, the beauty of open source. I come from

• 32:29Speaker 00

  the huge appreciation for, for people like Lino Storvols or Richard Stallman. They are the fathers of the open source and

• 32:38Speaker 00

  the beauty of working with highest levels of transparency. So when we get out with a claim, it's for everyone up there in open source, everyone can check the code, everyone can challenge us. That's, I think, is the most beautiful thing. I believe, you know, the crypto industry, the Bitcoin industry came up with this motto.

• 33:02Speaker 00

  Not your keys, not your coins. So if you don't hold the private keys to your Bitcoin, those are not your, really you're not Bitcoin. Well, I, I would say not your AI, not your intelligence. So if you can don't understand how your AI works, it's not you becoming more intelligent, it's someone else becoming more intelligent with your data. And so the, we, we built, we are very far along now. Qvac supports OCR, so basically recognition of images and taxing images, supports text to, text to speech, speech to text, supports standard AI chats, like, you know, what you're used to with chat GPT,

• 33:46Speaker 00

  support medical models, support financial models, support so many different models. Plus has the ability to also interact with other devices at the same time. Supported for, supports delegated inference. So you can, from your smartphone, you can use your laptop to run the heaviest of the tasks. Is, is becoming a very complete product. And we support image generation, video generation. So there is so much that the team on a weekly basis is rolling out. So I'm very excited. On top of that, we are going to roll out very soon. Our AI assistant will be again fully open source for everyone to actually own their own intelligence.

• 34:31Speaker 01

  Yeah, I mean, that's fascinating. And I was looking at some of the models that you've trained to date. You have this MedSci, is that right? I think there's a four billion parameter

• 34:47Speaker 01

  version that can run entirely on smartphones. So when, when these models, how are you going to distribute them or deploy them?

• 34:59Speaker 00

  First of all, we want the Qvac platform is made so that is, is built as a software development kit. So that every developer, so I want to empower every developer to integrate these AI tools directly into their existing applications. So if you have, let's say that you are building, you know, a new version of Uber or you're building a mapping, mapping application, or you're building

• 35:27Speaker 00

  a financial application, a training application, or whatever you want. Or like a cooking application or whatever. You should be able to, with Cuba, you take, you, you integrate Cuba within your application. It gives you already all the primitives to support text based AI, like chat mode. You can, or like translation, transcription, voice, you know, voice recognition, voice modification, everything you want. So we give, so we, we created basically the Lego for AI. I like to describe it in that way. I, I'm a big fan of Lego products.

• 36:10Speaker 00

  But it's really the Lego blocks so that whatever application you're building, you can take the Lego block that is needed for you and integrate in your existing application. So that truly, back to the sci fi story that I like so much, AI can be part of the fabric of the universe. Then in order to showcase how exactly we work and how, you know, we wanted to build our own consumer application is called Qvac AI Assistant. Will be released in the next couple of months and will be open source as well. And we'll show how all these different tools and Lego blocks can come together.

• 36:49Speaker 00

  Almost like, you know, you build, I was making the wrong analogy with the Death Star because I just finished to build that on the Lego side. But so it's not, I don't want to build a Death Star, of course, or AI. But the, you know, you have all the schemas and from Lego from, and they tell you where to put their each block, right? So we want to do that to showcase exactly what in our opinion is the best outcome you can reach, but we give you all the different blocks. So if you want, you can take and build something else and it's all completed up to you.

• 37:23Speaker 01

  You said the Qvac Assistant will be direct to consumer when it reaches general availability. Is that right? And, and it'll what be an app that you can download onto your phone or laptop?

• 37:41Speaker 00

  Exactly that will be an app that will work on Linux, Windows, Mac, iOS, Android, name it. And on servers can work everywhere. And yeah, we, and again, will be fully open source.

• 37:54Speaker 01

  And so that, that clips the, the tether to the cloud. I mean, you're now independent of the cloud if you have that compute capability on your laptop. That's fascinating.

• 38:11Speaker 01

  And so what's your, so you're going to release that, what about the developer uptake? And, and has there been a lot of third party testing

• 38:28Speaker 01

  to see how effective the models are or how competitive they are?

• 38:34Speaker 00

  We have been collaborating with, and we are in direct collaborations with with many companies that are building on this technology already. Keep in mind that we open source it less than two months ago, and yet there are quite some companies that are building on it. And every day more are, are looking at it. But there are two particularly that are very interesting. You know, the other big part of, let's say, the tech revolution will come with robots. And that is where also things can become scary. I believe that

• 39:10Speaker 00

  for multiple reasons, of course, but I think robots. Imagine having like a robot that can only take a decision if they are connected to a cloud and to a centralized that are centered.

• 39:24Speaker 00

  Of course, there is a privacy issue, there is a control issue, many issues, but on top of that, there is a latency issue. If you have like your smart car and it will only break and stop only because, you know, the the image that is seen is analyzed by a data center. Well, that can go wrong in many different ways. So you want, and the same thing with a with a robot. Like if, let's say you have a robot that is helping, I don't know, a child, a kid. You want that robot to understand exactly and react in the microsecond or millisecond, cannot wait for packets to go to Ireland and back if you are in Italy, right?

• 40:07Speaker 00

  So the even robotics will move, will need to have a GPU or an ASIC in their brain to be able to analyze analyze information immediately locally. So I think it's it's becoming very obvious, at least to us, that is going to be is going to be the case. And so, um, yeah, we have these two robotics companies that are testing how QVAC is performing in the the brain of the robots they are building. So we are going hopefully to showcase something very soon. It's very exciting.

• 40:45Speaker 01

  So developers are now integrating this into applications. And that's the ambition really to to build a developer community to to uh to to disseminate this into uh into the personal device world or the edge device world. Um, and and just on the sort of larger question of what, because I've talked to other people that are working on smaller models that can operate at the edge.

• 41:22Speaker 01

  What do you think about the, I don't know, what is it this year? Six hundred billion, seven hundred billion in capital investment by AI companies

• 41:37Speaker 01

  to build data and primarily to build data centers? What what's going to happen with all that capacity?

• 41:45Speaker 00

  Uh, I think that, so I see a few problems there.

• 41:51Speaker 00

  It's quite interesting because if you think about it, the majority without naming names, but many, many of these data centers are built by third parties that are basically then sign up contracts for off takes. So it's not that the, so of course, the the big AI companies have their own data centers, but the majority of this expansion in the next years will happen where they go to a third party and they say, oh, I need this data center with this capacity. Can you build it for me? And I will, of course, sign a lease. But see, you know, someone else will foot the bill for that.

• 42:36Speaker 00

  So they are reinforcing themselves from the risk of building their own data centers. And also they are reinforcing themselves from the financial risk. If tomorrow they will find out, the world will find out that the data centers are not needed anymore. But on top of that, I believe the other issue is that

• 43:02Speaker 00

  right now, and this is a general, is an interesting moment in time, right? In two thousand twenty six or early two thousand twenty seven, many of these, yeah, big AI companies will go public. And it's clear that many of these big AI companies are subsidizing the cost of their subscriptions. So a two hundred dollar subscription will cost from one to five thousand dollars. That's the reality. Why they do it? Because they are private companies, so they can, they can do it, kind of hiding it. Second, because they can, they need to show growth.

• 43:42Speaker 00

  So if they have to subsidize, let's say that you are like a big AI company, that's again, not naming names. And your valuation is, let's say, one hundred billion dollars. And let's say that you can subsidize for five billion dollars the cost to bring on the next ten million users or like, I don't know, fifty million users. And if you do that, your valuation go to one hundred fifty billion dollars. So you spend five billion, but your valuation went to fifty billion. And you can do that or insert repeat. But then the company will become public, retail will buy the company. And you cannot, I mean, if you are like a public company, it's much harder to subsidize the cost

• 44:27Speaker 00

  because people will see through. And so someone else, you know, probably retail will foot the bill on that. So I'm slightly scared for for the financial engineering that is happening. Um, I think will, I don't know how it will happen. I I mean, as Tether, we just can just build an alternative.

• 44:51Speaker 01

  Yeah. Uh, yeah, so, um, and when you say you're worried about that, just the that there's going to be this big financial hole that eventually has to be backfilled by governments or or

• 45:13Speaker 00

  Could be.

• 45:14Speaker 01

  And there'll be all of this infrastructure that sits unused. I mean, there's one in Utah that the campus is projected to be twice the size of Manhattan. I'm sure you've heard that, uh, which is crazy.

• 45:32Speaker 00

  It's almost funny that, you know, the entire thing, so we are trying to create, we are trying to recreate the human brain. We did us, so our human brain consume the same electricity of a potato clock. And we are planning to spend ten kilowatt to recreate the human brain. Sure, you can say that, of course, there is more memory, there is much more, but

• 45:59Speaker 00

  we are doing something wrong, I think. I think our approach to intelligence is quite wrong. We will need, probably we need better research, we need better AI. So imagine, like, think about this question, right? So if you really, really want to solve the problem of AI long term. And if, let's say that you had one billion dollars, would you invest one billion dollars to hire two thousand AI researchers or would you spend the entire billion dollar in chips? In AI chips? I would hire researchers, the researchers will yield more over time, the chips will yield less over time.

• 46:39Speaker 00

  So our, our, the approach of AI is a financial game in this moment. And something has to go wrong to have a, I think, a sort of reset and start from, I believe, a better approach to to research, more down to earth. But something that understands that if the brain is so good, because our brain is so cool and good, probably if we are trying to recreate it with one, ten million times the energy, we are, we are wrong.

• 47:11Speaker 01

  Yeah, although, you know, it's a little bit like Big Pharma, you know, the actual drugs they produce

• 47:20Speaker 01

  don't cost that much, but the the the whole research pipeline behind developing those drugs costs a lot of money. And something like QVAC, I mean, these are in effect distilled models, right? From foundation models. But you need the foundation model first before you can distill it. The future may be in in these edge models, but you had to have the the massive investment uh to get there.

• 47:59Speaker 01

  Uh, I guess the question is going forward, whether you, you know, once you have the foundation, uh, whether then uh these smaller models will proliferate and make the foundation models obsolete at some point.

• 48:16Speaker 00

  You need more the foundational model, you need the knowledge. And so, but you have, the good thing, you have internet, and so internet is full of knowledge. So of course, I'm not saying that it is is zero cost to build a model, even an edge model. But the reality is, so I think the majority of the problems that, so I think that now there are companies working on trillion parameters models.

• 48:43Speaker 00

  That are basically becoming monolithic models. I believe monolithic models are not scalable and are not the right answer moving forward. I think that the right answer moving forward are very hundreds or tens of thousands or millions of very small models that are able to interact with each other with the highest efficiency. So that you you don't have to retrain the entire model. You can just return the, you know, if you have a small, let's say in a very simple way. If you have a model that is expert in physics and another one that is expert in chemics, you want, you can train a little bit more than one in chemics to become better, rather than having to go through the entire monolithic retraining of the model.

• 49:28Speaker 00

  And of course, you know, you could say, well, if you have the, in the ideal world, an entire, a monolithic model has the highest efficiency, has the highest accuracy and so on. But that is not sustainable and will eventually create, in my opinion, issues. And I think that the world will converge in having millions of small models rather than one single large model. So that's why the cost can also be reduced. And also you can fine tune, you know, task specific models with the, for, for local experiences. So over time, the ability and efficiency of GB, local GPUs will be so high that you don't need one huge model to solve all the problems of the world.

• 50:11Speaker 00

  You can have, you can just easily fine tune a model for what you need in that specific moment in time.

• 50:19Speaker 01

  Yeah, the, how is Tether organized? Maybe because this is a research initiative under Tether Data, is that right?

• 50:32Speaker 00

  So Tether has four silos. Oh, sorry. Please go ahead.

• 50:35Speaker 01

  No, no, that's exactly what I'm asking. Go ahead.

• 50:39Speaker 00

  So we have four silos or four verticals more than silos. We have Tether Finance, the stable coins easily put. Then we have Tether Energy. We are building some interesting energy avenues in Africa. Basically, in the most remote villages in Africa, we are building kiosks with solar panels on top and rechargeable batteries inside.

• 51:04Speaker 00

  We have already more than one dozen kiosks and more than one million users of these batteries. So it's, it's actually decentralized energy. I really like it. It's probably one of the companies I, one of the subsidiaries that I love the most that we're building. And then we have the other, the third vertical is telecommunications. So we build peer-to-peer communication protocols that can scale to billions of people or billions of machines and billions of AI agents without any data center. We took the idea from the BitTorrent protocol and we perfected it, we built it, we adjusted,

• 51:44Speaker 00

  we changed it to make it extremely efficient, but also not only good for file sharing, but for any type of communication. And fourth, we have this, as vertical, we have this AI platform. So these are, yeah, four verticals.

• 52:01Speaker 01

  Yeah, and, and which, I mean, this, this sounds promising, this, this QVAC, the Tether Data vertical, is where are you spending most of your time?

• 52:17Speaker 00

  I'm, I'm almost obsessed by everything we do. So I, I don't have, so I try, if I do something, I try to do it well. So I dedicate almost like even amount of that, probably energy is the part where I spend less. But AI, well, finance and AI are my top two priorities. Because I believe that if we want to have a stable society, we cannot allow four billion people that are the ones that don't have access to basic financial services, also to not have access to basic intelligence services. So I think society will have a hard time in the next year, sort of decade,

• 53:02Speaker 00

  if we increase the gap that is already existing on the financial side, adding intelligence gap.

• 53:08Speaker 01

  This is, this is really interesting. Well, let me ask you before we move on, although this is really about the, the QVAC initiative. But on the energy, that's a fascinating idea, decentralized energy. And this is all solar that you're pursuing?

• 53:34Speaker 00

  We serve the, so we installed these kiosks in the central, you know, central corridor of Africa. You know, that is, is very dark at night because people, that is where, you know, it's the poorest part of the world population. And so we, you know, there are two, sometimes you talk to someone that says, oh, we can solve Africa's energy problem with some nuclear plants and long distant distance distribution lines. That is crazy. It will never work. But what we wanted to do is to have an approach where we can build these kiosks. And so they're like sharks. There we put solar panels on top and a few thousand rechargeable batteries inside.

• 54:16Speaker 00

  And so for a couple of dollars per month, you can recharge the battery four times or you just bring back the battery to the, to the kiosk and you swap it four times. And so we wanted to do that. We wanted to start from the most difficult part of the world. And so with 1,000 kiosks, we have more than 1 million users, 1.3 million users now. And this, you know, because these people have smartphones, by the way. These people have like light bulbs, but they try, they have some weak batteries. They, they, they don't last long. They try to recharge them. Maybe they go to, you know, but it's, people like kids cannot study at night. They don't have, they don't have,

• 55:00Speaker 00

  if you don't have stable electricity, it's, it's very hard to be part of civilization, right? So we, we wanted to do that. And by the way, the company, I, this is not charity. It's a, is a very well-designed, very lean approach development that brings amazing utility to these populations and, and have a very reasonable cost. The average salary in those regions is $80 per month. So you can, you can spend $2, $3 to have electricity at home. These are, but these batteries are 145, 145 watt batteries, very powerful. So they can run like, of course, they cannot run a fridge yet, right? So it's, this is too, too expensive, but at least you have light during the night.

• 55:44Speaker 00

  You have like, you can recharge smartphones. And so that, that is the first approach. And we know that we can scale to 100,000 kiosks in the next five, six years. We'll bring energy to 30 million households. So, and, and that, in my opinion, will help a lot and is, is expensive, but not that expensive compared to many other things that are, that are going on in the world. And we believe that is going to be very important for Tether because it's a, that is our population, right? Servicing these people is, is, you know, it's, is our user base.

• 56:22Speaker 01

  Yeah, that's, and is, is this an open source design that someone can build? Or do you have kits or, and do you market them in the US? There's a lot of places in the US with, that are far from the grid.

• 56:39Speaker 00

  I mean, we, we never looked on as the US, at US to Europe. We started in, in Africa. The, I, I think we could, I think would be a good idea to open source the kits. I think is a great idea, actually. I should bring it back to the team. Because, you know, it's the, we, we design, we designed the batteries. They have, of course, in order to avoid any, any stealing of the batteries, we made a special firmware so that the batteries can only be recharged in the kiosk. I mean, the team managing that, that company is incredible. It's truly incredible. They, they, they are, I think, achieving something that no one else was able to do. So in, in, again, the poorest part of the world. But I like your idea of, or the suggestion to make it open source.

• 57:23Speaker 00

  I think is, is great for humanity.

• 57:25Speaker 01

  Okay, well, Paulo, this has really been fascinating.

• 57:31Speaker 01

  And, and I'm going to follow this QVAC more, more closely now. Yeah, and I'd like to have you on again. There's a lot of questions that we could pursue. Where are you based?

• 57:48Speaker 00

  I'm based all around the world, mostly in Salvador, because it's, it's an, I, I love the place. It's, it's a beautiful place. And also it's close to Central South America where there is a lot of, of our user base. But then I travel, I travel so much.

• 58:10Speaker 01

  Yeah, and I see outside the window behind you, it looks like a mountain. What's, what's up?

• 58:16Speaker 00

  No, now I, I came to, in Europe, in Switzerland, I, I needed to meet with a family. I still have family in, in, in Europe. So from time to time, I, I come, of course, still very attached to them.

• 58:27Speaker 01

  Okay, great, Paulo.