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

0:00If 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. 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. So if you don't understand how your AI works, is not you becoming more intelligent, is someone else becoming more intelligent with your data? 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.

0:51Is that possible that there'll be both? 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. Look, Tether was a company, is a company born in 2014. 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. 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.

1:58I call it the biggest financial inclusion success story in the history of humanity. Sometimes my blood boils because how is it 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 the dollar accessible. And you asked me before, well, why not just use the regular dollar? Well, the reality is that for four or five billion people, so more than half of the population in the world, they cannot have access to that regular dollar.

2:45They don't have access to basic financial services. The people that are unbanked, the number of people that are unbanked, the world is just enormous. And it's not like they are unbanked because they are bad people. They are unbanked because they are too poor for being of interest of the banking system. They live in countries where their national currency is devaluating so fast against the US dollar. Think about Argentina. The Argentinian 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.

3:30The Venezuelan Bolivar lost 99.8 % of its value against the US dollar. It could go on. No, I understand all that. And 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, but I have a side, and I want to get in an argument about that, but the... 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 is, the digital penetration is incredible.

4:24Even 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 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 talk about certain 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 it's the most exciting thing in the world for me.

5:10But the reality is that many of these populations have, you know, one of the common denominators of the emerging markets is that they have more youth. They have, so the youth has digital means or understands, you know, technology faster, of course, than the older generations. And they are more prone to change and also have access to, again, to smartphones. Like the average cost of a smartphone for Africa is$80. Of course, you cannot play the best games in the world, but they have browsing. They have like Opera, that is a public company, is one of the most known browsers in the world, has 300 million users in Africa alone, right?

5:56So we partner with them, by the way, as one of the main partners. But bottom line is what we learned with the USDT over the last 12 years is that we can truly disintermediate. So if finance didn't perform well in certain parts of the world, and 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.

6:45Actually, 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 rates of the dollars we keep in the bank account. So USDT for us and also for us internally was the demonstration that 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.

7:30We proved that we could be very successful, changing the equation and removing all the intermediaries and creating a free product, basically, like just a dollar, 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 the 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 feel Internet was born to be peer to peer, point to point.

8:17When you connect your computer to the Internet or when you connect your smartphone to the Internet, 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 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 the gravitational force so that being intermediaries of people's connections, i.e.

9:04people data, they could earn money, hosting, holding, and intermediating people's data. And that is a very huge problem in a growing society for many reasons. Of course, you have data privacy and all the things we know, but there are some issues that not many think about. If you have a person living in Rome, imagine you are a person living in Rome, and it's very likely that your family lives in Rome. and you send a message, you know, I send a message, let's say I live in 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.

9:53Is that good? Is that normal? 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, a peer-to-peer. So that thing is an unnecessary intermediation remediation that is only justified by the broken incentives of the internet. So what we realized at Tether is that we could, but there was already a solution also when it comes to data sharing, how we could build a protocol over internet that could scale to tens of millions of people.

10:54Because of course, the classic answer is, oh, but 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. It 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 in the perfect way. And so technically, we had already our answer. We could build, we could reuse structures similar to the BitTorium protocol 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.

11:43But this is basically the spirit that we have in us. We learn the concept of disintermediation from finance and what we did to the financial world. And by the way, now everyone talks about stablecoins. Even banks now are talking about stablecoins because they understand the power. And so now that everyone understands the power of stablecoins, 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 Hall Punch as a protocol and then QVAC as an AI, the same concept applied to AI. I mean, that's fascinating. And maybe I should have you on just to talk about stable coins.

12:25But what I really wanted to talk about is your Quantumverse automatic computer. 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 there is a path down which we can deliver the power of AI without having to go through massive data centers so yeah can you talk about that to explain what uh qvac for short what that is sure so 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 uh an interesting short story it's 14 pages i think it's the most beautiful short story ever written was written by Isaac Asimov, 1956, is called The Last Question.

13:30To me, it's beautiful because it's a story of science, AI, physics, religion, universe, philosophy, all come 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 And every single time humanity was asking one single question to this computer, to this AI, 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.

14:19And to me, that is very intriguing. It's very exciting. But bringing back that huge complex question to Earth means how we can stop society from dying, how we can stop Earth from dying, how we can make society stable. 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. Society was built for the last 5 ,000 years.

14:56society 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 interact 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 change it completely in the last 50 years. So we don't know how society will grow in which direction, how the decisions of intermediation that we made in the last 50 years, how they will affect society in long run. So when I go back to the sci-fi story, I think how we can truly learn from that.

15:46And 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. An AI that is so intelligent, that knows everything, that can scale 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 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 add another planet and another planet.

16:35Of course, we are years away from that or decades away from that. But still, I think we should design technology to scale with a scale of humanity and with our ambition in terms of expanding ourselves and 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 a couple of companies rich. And I became very bearish with data centers, also for multiple reasons. 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 ChachiBT and AlphaFold.

17:28It's a beautiful story. It's a great thing. But the reality is that 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 recipe and have at the end of the month some accounting. They want some translations. They want some education. They want things that a model, an AI model that runs on a smartphone or on a cheap laptop can already do today.

18:11So you have 95%, let's say the 90 % of the population that ask simple questions. They are not researchers. 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 taxes. But these are very, very simple things that simple models or models can run on a smartphone can do already. A few days ago, Tether launched, you know, there is a big race to medical health AI models. And one of the most popular ones is the flagship, which is considered state-of-the-art model, was MedGemma from Google, MedGemma 27 billion parameters. Our team was able to produce a 4 billion parameters model that was exceeding the performance and the accuracy of the 27 billion parameter model of MedGemma.

19:14So 4 billion model, I mean, 4 billion parameters model means that it 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 a 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 QVAC, we've created a platform, an open source platform, because open source is very important, so that people don't have to trust that they can verify the platform themselves. 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.

20:05In three years, I'm sure we can bring that percentage to 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, then why the hell we are building tens of gigawatts of data centers? when, you know, and the subscriptions to these AI models in centralized data centers cannot be justified because then the 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.

20:54One single smartphone will never be able to find the cure of the cancer of a dog. And that is extremely important. I'm not saying that this is not important. I think medicine will have huge breakthroughs thanks to AI, 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 day-to-day lives through their smartphones, maintain privacy or their own data.

21:36But 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 these GPUs can run these Lama models two times faster than the previous iteration. So again, that's why in five years, what we run on the smartphones will be so good that we will forget about paying a subscription to OpenAI. Yeah, and that's fascinating. And as I think I mentioned to you, I'm interested in the potential overcapacity of data centers that's being created. But while you're talking about consumer use of AI, the big transformational market is enterprise and government use.

22:42And those will likely run on data centers because the problems that they're solving, the speed at which they have to solve them, are going to require massive compute. so is is that possible that uh that uh there will be both i mean that that there will be enough demanded to take up the the data center capacity um you know to run governments to run uh the world economies and that personal use yeah will migrate to to the edge I mean, 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.

23:47AI started from CPUs, now is on GPUs, and last year, the first ASICs of 4AI 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 4AI were able to run LAMA 3.2 at 17 ,000 tokens per second. 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, spend$50 ,000,$100 ,000, but you can run an AI cluster that is extremely, extremely powerful through the ASICs. So the ASICs will bring efficiency or energy consumption down to 98%. So you can run much faster models with much less energy directly on site.

24:51And with the 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 someone else's data. Right. So I think over time, of course, now only centralized data center had the capacity to create very cool stuff. And over time, I think that many banks, they run their own data centers. 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 Italian by birth.

25:46But will all Italian banks run on a US-based infrastructure? Right? Probably not. And so will all the Italian public administration run on a US-based infrastructure? Probably not. Right? So, and the same thing as like old French, like, so there is, there is already a big push. If you, if you read one of the coolest new, um, uh, product lines for data centers is, um, or cloud providers is actually the, the sovereign cloud means that you go into a country, you install, you build a small data center directly for the country deployed there. your software. And so even for AI providers, anyway, they will need to install directly on premises certain capacity to run models there.

26:47So I don't think we are going to have to see this huge concentration for too much or a long time. Yeah. Well, we can talk about the overbuilding. I mean, but let's talk about the Quantumverse automatic computer. It's built around this BitNet LoRa 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 LoRa is and why that was a foundational breakthrough for what you're doing. In order to run on smartphones, we need, of course, to ensure we, in order to have a platform that is able and capable to scale and evolve on smartphones, you want to do two things.

27:43The 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 and design and learn how to respond in the best crags way. So you want to do that, you might want to do that, but 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, starting the emails in a certain way as I do, and so on, I needed to be able to do that running directly on my laptop and learning directly from me and maintaining 100 % of my privacy.

28:59Same with documents, same with how I start the company documents, my strategy company documents, everything that I do. 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 on 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.

29:50But with QVAC, we 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-bit model. So usually models have different levels of quantization. They use either 32 bits or 16 bits or 8 bits for representing the weights. Microsoft came up with this bitnet, so one bit weight model. The problem there is that it was not suitable to run on consumer devices.

30:46So technically it was what would have been the perfect solution to run on consumer devices, but was not suitable because it was too much relying 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 the Adreno GPUs or the Apple GPUs. Second, we also created in QVAC a common LORA fine-tuning 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.

31:41So you have your Lama 3.2 model, you have the Medjammer, you have a Qwenn, you have 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. How far along are you in this research? I must say that I'm very impressed by our team. Well, I read them, but they are doing incredibly well. I mean, and the thing is that we work, you know, the beauty of open source. I come from the huge appreciation for people like Linus Storvalds or Richard Stallman.

32:33They are the fathers of the open source. and 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, I think, is the most beautiful thing. I believe the crypto industry, the Bitcoin industry, came up with this motto. 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 would say not your AI, not your intelligence. So if you don't understand how your AI works, is not you becoming more intelligent?

33:21Is someone else becoming more intelligent with your data? And so we built, we are very far along now. QVAC supports OCR, so basically recognition of images and text in images, supports text-to-speech, speech-to-text, supports standard AI chats, like what you are used to with ChatGPT, 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, supports delegated inference. So you can, from your smartphone, you can use your laptop to run the heaviest of the tasks. It's becoming a very complete product.

34:10And 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. yeah i mean that's fascinating and i was looking at some of the models that you've trained to date uh you have this med sci is that right i think there's a four billion parameter version that can run entirely on smartphones so when when these models uh how are you going to distribute them or deploy them?