UN Transcripts — https://transcripts.un.org/es/asset/k14/k14ej1ucqu
Open Source for AI and Emerging Technologies - UN Open Source Week 2026 (Part 1) — 23 June 2026
Language: en
Automatically generated transcript — may contain errors. Not an official United Nations record.

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UN · Host · Pepi Vananen [0:02]:

Good morning, everyone. Please take your seats. We are ready to begin. And please do come forward. There are some seats over here. We know that not every seat has a set of headphones, so do come closer if you can find an empty seat. Once again, good morning, everyone, and welcome to the Open Source for AI and Emerging Technologies Day. My name is Pepi Vananen. I work for the Office for Digital and Emerging Technologies. And I'm delighted to be the host for you today. So you're stuck with me all day, unfortunately. This day is really exciting because a lot of you have been coming to the open source activities in the past years, but this is the first time we're doing a fully dedicated day on AI. So it's very exciting to have such an extensive program, um, today. So if you were here yesterday, that was the Tech Over Day, and tomorrow if you're coming back, please do come back, it will be the DPI Day. And then on Thursday it's going to be Osmos, and on Friday community-led events. So a very exciting week, so we hope that you will be attending each day. This month, this week has been months in the making, so I know you come here for maybe 5 days, but as many of you have been organizing events, they take a lot of work. So please, let's give a round of applause Mendis Nene, Moritz, and Omar, and plenty of other people who've been working on this for months. Please, let's have the agenda. So we're going to start off with a keynote and a fireside chat, and then we will be doing a few setting the scene speeches. After that, there's going to be a high-level session on open source AI for development. And then a session on open robots. So especially you sitting on the right side of the room— sorry, left side of the room— you may have seen there's a robot there. So we will be talking about those later. Then we will have a 2-hour lunch break followed by breakout sessions, and there will be a lot more information on those breakout sessions before lunch. And then we will end the day with a session on open, open agents, and then there will be a closing panel. Before we begin this day's program, um, next slide please. There will be, um, a QR code on the slide here, so please do scan that. And this is really an exciting, uh, UN, um, initiative, which is the Digital Cooperation Portal. So many of you obviously work in the digital space, so this is your opportunity if you would like to, to add your projects on this, in this portal to to share your work, the work that you're doing, in a global platform. And this will be contributing to the follow-up of the Global Digital Compact. So a very exciting opportunity to showcase your work. Then to some very boring housekeeping, unfortunately. So, um, the microphones are set up in a way that you can't necessarily hear everything if you're listening without the headphones. So if you have a headphone or one of those earpieces on your desk, please feel free to use that. That is going to help hearing. Unfortunately, we don't have any at the back, so that's also why it's better to try to find a seat in the front. And please note that food and drinks are not permitted in this room. That's why we have a 2-hour lunch break so you can go and get something nice from outside. There are— there's no food served, but you can go to the 4th floor cafeteria. And if you want a quick coffee, there's coffee in the basement. So just a few floors down, there's another café there. And we have some amazing volunteers One over here wearing yellow shirts. They'll be showing you around this building, and I'm sure you all know it's a bit of a maze. So please do ask for help if you're trying to find the cafeteria or, or anything. All right. An important note from security. Lesson learned from yesterday. Please do not leave your belongings unattended. So if you're leaving this room, take them with you, um, so that they don't take them for you. So please do carry everything that you have with you at all times. Uh, last but not least, there's feedback forms. So if you're exiting the room, you'll see there's some QR codes by the doors. So please do scan those and leave us your feedback. It's so important so that we can continue to make this event engaging next year as well. With that, I will now hand it over to the Under-Secretary-General, Amandeep Gill, for the opening remarks and fireside chat session.


UN · USG · Amandeep Gill [4:54]:

Thank you so much, Pepi, and welcome to the Open Source Week. And please remember her suggestion, these earpieces are a special artifact. You will see them only in UN buildings, so use them. So let me welcome you to the second day of the Open Source Week, the first formal day in a sense. Yesterday was very exciting, the tech over with hackathon, maintenathon, and an editathon. On, and we are building quite a tradition there. And I want to give a shout out to all those who participated and all those who mentored. You know, they have taken out time for— from their jobs to fly over and help out with this. So thank you very much. So this opening is special. It's the first time ever that we are dedicating a full day to open source AI, and there are several reasons, and I'll get to that. I want to, of course, you know, start by thanking our co-hosts who come from all regions represented at the United Nations: the Dominican Republic, Estonia, Jamaica, Kazakhstan, Lesotho, Tanzania, and Luxembourg. And thank you also to all the partners who've made this week possible. This is a special week. You know, there are very few rooms, as the WAG said last year, in the UN where those in suits sit next to those in shorts and make things happen. And we deliberately take a back seat in terms of our policy agenda. So this week is not about, you know, the policy aspects of technology as much as it is about those who are actually building the technology. It's a conversation with those builders, those designers. Now, I mentioned this is the first time we are having a full day dedicated to open source AI, and this subject is not anymore on the margins of the conversation. Now it's a centerpiece of those discussing AI. Our expectation is that this will be an agenda-shaping day. We want to be able to leave New York this week with some guidance on how countries should decide which paths to take, what choices to make about openness that will shape who gets to participate in shaping our AI future. Openness matters, firstly because it lowers the barriers to entry, letting a researcher, a developer, a ministry, or a startup begin from an existing foundation rather than start everything from scratch. It also matters because it counters concentration. It pushes back against the buildup of points of failure, and it counters stagnation as well by bringing in fresh talent, fresh ideas into the innovation space. But openness alone is not enough. Access is not the same as capability. And for open source to deliver on its promise, especially for development, it has to be paired with investment in skills, in compute, and local innovation ecosystems. Capacity is actually the difference between consuming the technology and actually building it. So it's a bridge that connects the consumers to the architects. Takes them across the line. At the United Nations, this is a very special year for us. Pepe mentioned the Global Digital Compact 2024. So, the decisions of that moment have now been translated into practical mechanisms, functioning mechanisms. The International Independent Scientific Panel on AI— so, last week, Yoshua Bengio and Mariarissa were here, to talk about their inaugural report, the first global dialogue on AI governance, which gives every country a seat at the table in shaping the rules for artificial intelligence. That'll take place next month, early next month, 6th to 7th, in Geneva, and the Secretary-General would be hosting heads of state, heads of government, leaders from companies, civil society at that historic dialogue. So it'll be the first UN platform where everyone has a seat at the table to look at the opportunities, the risks, the direction that technology takes, and start the process of learning from each other. It's not a top-down process, it's a horizontal facilitation of international learning on the governance of AI. There's a third crucial aspect coming out of the Global Digital Compact, which doesn't yet have standing decisions, standing mechanisms behind it, and that's capacity building. There are ideas on the table, including for a global fund on AI to help the 90+ countries that are most at risk of getting left behind. There are some initiatives by independent centers, by member states, coming together in a network of centers for exchange and cooperation on AI capacity building, already more than two dozen, so very close to what the open source community is all about. But this is not enough. The needs are humongous, and we need to do more on this third pillar. Cutting through all these threads— science, policy, and capacity is open source as a foundational element, and we are going to be looking this week at everything that can make this connection the most powerful shaper of our AI future. So, ladies and gentlemen, now it's my profound honor to introduce a true titan of our field, a Turing Award laureate, One of the legendary godfathers of AI, whose convolutional neural networks literally taught machines how to see. And he was former chief AI scientist at Meta, and now he's the founder and executive chairman of AMI Labs, and where he's boldly betting on world models to take us beyond the current paradigm and the limits of today's AI. A pioneer, a visionary, and a contrarian. So please join me in welcoming the one and only Yann LeCun.


AMI Labs · Founder and Executive Chairman · Yann LeCun [12:33]:

Thank you very much for the introduction. Thank you for attending this session. I think this is a very timely event, and I'm truly honored to be here and to tell you about open source. So, first of all, I came to this building 18 months ago and argued for AI open source. At the UN Security Council. And the message I'm going to deliver today is very similar to the one I delivered 18 months ago, which didn't go very far back then. And I'm hoping since then many things have happened, and which I think motivate more countries around the world to have AI sovereignty, but also to subscribe and support an open source approach to it. So AI is becoming quickly a platform, or to some extent actually has already become a platform, a platform that a lot of us relies on. And when I'm talking about AI, I'm not necessarily talking about specialized application of AI, but the kind of AI that is built around large language models. And what is happening is that increasingly AI is mediating all of our interaction with the digital world and with information more generally. Now, if all of our information is being mediated by AI systems, and by the way, increasingly this, this is going to become more, more prevalent because In addition to our smartphones, we're going to be running around with smart devices like the smart glasses I'm wearing at the moment. I can take a picture of you. All right. I hope you smiled. So increasingly, you know, we're going to just ask questions to our AI assistant, which will be with us at all times. It will be personalized AI assistants that hear what we, what we hear. Eventually they will see what we see, and they'll become our best digital friend. They will really become kind of a staff member, if you want. All of us will be acting like a manager being constantly followed by a staff of helpers, but those helpers will be digital. Now, it's already happening to a large extent that whenever we have a question, we ask an AI assistant. We don't go to a primary source. We certainly don't go to libraries anymore, sadly. And it's a very drastic change in our behavior, which may lead to drastic changes in society. If the information, or information diet, is entirely mediated by AI systems, and those AI systems are proprietary systems produced by a handful of companies on the West Coast of the US and China, it's very dangerous for culture, linguistic diversity, Diversity in centers of interest, in value systems, in political opinions and biases, and for democracy, for human rights. We cannot afford that all of the information is funneled through systems that are absolutely necessarily biased. There is no such thing as an unbiased AI system. What should we do if we want sovereignty, if we want to preserve cultural diversity, linguistic diversity, if we want people to have access to a wide diversity of AI systems? In my mind, the only way to get to that point is open source AI platforms, or OpenAI platforms. I should say open AI platform, um, because most countries around the world, uh, cannot necessarily afford, or maybe don't have the resources or the talents to actually build their own, uh, LLM. A number of countries can do this. The LLMs they produce are good. They're not great, they're not at the top, but they're good. So the talents are there. Some countries have significant resources in compute, but there is a way that an open-source effort that would be collaborative around the world could actually surpass in performance the proprietary systems. Because in the end, people will just use the best system that's around. And here is how it would work. Each country, each region, each academic institution, whatever it is, countries would digitize their own cultural material and will contribute to training a global AI system that would constitute a kind of repository of all human knowledge. But they would not have to communicate the data. They could contribute to training a global model by exchanging parameter vectors, okay, which are you know, how AI systems can distill the data down to knowledge. I was trying to popularize this idea. I've been trying to popularize this idea for almost 3 years now, and I talked about it at the UN Security Council, trying to convince also the leadership at Meta to play an important role in there, but that didn't quite fly. So after I left Meta, I started with some colleagues at the AI Alliance, which is a nonprofit that is promoting open source AI, we decided to start a project called Project Tapestry. You can Google Project Tapestry, and it's a confederation of partners that can contribute to training a global AI model while preserving sovereignty on the data. And only exchanging parameter vectors as often as possible so that collaboratively each region in the world, each academic group, each private company possibly contributes to training a big model while retaining sovereignty on their data using their own computing infrastructure if they have some. And at the end, we'll get a system that speaks all the languages in the world, understands all the value systems. At least at the basic level, and all the cultural biases, political philosophies, et cetera, and centers of interest. Now, once we have such an open platform, anybody can take this open platform and fine-tune it for their own purpose, whether it's a commercial enterprise or a government or a academic group or nonprofit to serve a particular population. That way, people will have access to a wide diversity of AI assistants. And we need such a high diversity of AI assistants for the same reason we need a high diversity of the press. Okay, now there are issues with this. One issue is to get everybody to work together. The Tapestry Project is very much bottom-up. It's, you know, people who have expertise in training LLMs and other AI models who decide to collaborate. There's a GitHub repository. You can just sign up. There's no sort of heavy infrastructure or authorizations to get the means and the resources are brought about by the participants themselves, so that can be completely self-organized. But of course, there needs to be political support for it. If governments tell their academics, their companies, and give them an incentive to participate in this project, of course it will go faster. At present, Project Tapestry has participants. There was an inaugurating workshop that took place early May in Paris, and we had participants from several countries in the EU, Switzerland, UK, United Arab Emirates, India, Kazakhstan, Vietnam, Japan, Korea, various groups from academia and industry groups like IBM, NVIDIA, AMD, Intel, all the hardware suppliers, basically the main ones. So there's a groundswell of interest for this project, and we see new partners signing up every day. So I see the history of AI platforms as following the history of the platforms, software and hardware platforms, of the internet. In the late 1990s, if you wanted to start an internet service of some kind, a website, you would have to buy proprietary hardware from Sun Microsystems, Dell, Hewlett-Packard, and other companies, and then use their proprietary operating systems and software on top of it. All of this were completely wiped out in the early 2000s when people started using commodity hardware with open-source software stack. And the same thing is going to happen. A similar thing happened also for the software stack of the mobile communication network. Your cell phone very likely actually runs an open source operating system, unless you have an iPhone. And it talks to a cell phone tower that runs an open source software stack based on Linux. So there is a push by the market. It's not just government decision. The market wants open source platforms. Because it's cheaper, it's more secure, it's easier to port, to run locally if you need to, to preserve privacy and everything. There's tons and tons of advantages, which is why we're having this meeting today. I mean, this week, I should say. So, you know, I think that is the direction of history. It's inevitable. Government should embrace it and accelerate its progress. Okay, now there is another discourse around AI which is essentially opposed to this, and it's a discourse that essentially claims that AI technology is intrinsically dangerous and should be regulated. Its access should be regulated because bad people will do bad things to it, either with cybersecurity or getting a recipe for a bioweapon or something like this. I think those dangers are very, very widely overstated. I don't think those dangers are nearly as bad as some people have claimed, some people in the industry and academia have claimed. I think the alternative where if you believe AI is intrinsically dangerous and you should regulate its access and therefore open source AI should be banned, I think is extremely dangerous for democracy and human culture in general, as I pointed out earlier. But those arguments sometimes are justified by security arguments, which I think are contrary. This is a big debate. Where I disagree with some of my friends on this issue, but I think to some extent limiting access to AI technology because of security reasons is akin to in the 15th century limiting the use of the printing press because of course we can't control what information will be disseminated through printing. So I think this is akin to medieval obscurantism. And I think it's very, very dangerous to limit access to this technology, which essentially provides access to all of human knowledge to a wide population. So I'm really happy that this event this week is taking place. Okay, my last point is that there is a feeling in many countries that are neither the US nor China that they've lost the race towards AI, and that it's now in the hands of American industry and/or Chinese industry, and there is no way to catch up given the size of the investments that are necessary. I don't think that's the case. There is a race which perhaps many countries have lost or are losing, to build LLM-based AI systems. Those systems require enormous amounts of computing power to run them. They require enormous amounts of memory. And the reason they require a lot of memory and compute power is because they need to be very large, because they basically are accumulating declarative knowledge. Those systems are not very smart. They're very good at storing declarative knowledge and regurgitating at the right at the right time through questions. They're only smart in two areas which are very special, coding and mathematics. There they can invent new things a little bit, but those are very specific domains where the substrate of reasoning is actually linked to the language, first of all, and second of all, if a system sort of figures out like a new program or a new theorem, it can be automatically verified whether it's correct or not. And those are very specific domains. The self-improvement of AI systems does not apply to any other domain essentially, or in very limited ways. So what's going to happen is that we're going to have another revolution in AI. My new company is based on this hypothesis that we're going to have a new revolution, particularly AI systems that can deal with the real world. LLMs are really good at dealing with language and sequences of discrete symbols, not so good at handling the real world, which is why we have systems that can pass the bar exam, solve mathematical problems, and write code, but we don't have domestic robots. AI is still completely insufficient to deal with the real world. So that's gonna be the next revolution. It's up for the taking. And we're not going to have human-level intelligence over the next 2 or 3 years. This is going to take much longer. But there are opportunities for collaboration, international collaborations, both for LLM-based AI systems and for this next generation of AI systems. Thank you very much.


UN · Host · Pepi Vananen [28:29]:

Thank you very much for that opening keynote, and I hope we will all get a copy of that picture that you took of the, of the room. Uh, please, um, Jan de Coon and USG, you can take seats for the fireside chat.


UN · USG · Amandeep Gill [28:54]:

Thank you. Thank you, Jan, for setting the stage for this week. And you mentioned your most recent endeavor and the work on real-world models. So we are in this paradigm and we want to get to that paradigm. Can you give us an idea of the roadmap, what is required in terms of capabilities, research, to get us to that point?


AMI Labs · Founder and Executive Chairman · Yann LeCun [29:23]:

Sure. So current technology, LLM, as I said, deals very well with language and other types of data or modalities, as we call them, where the information can be formulated in terms of a sequence of discrete symbols. Okay, so human language, computer code, language, mathematics, these are sequences of discrete symbols. You can also include in this DNA, proteins, and other things. There are things that are very difficult to reduce to a sequence of discrete symbols. So for example, the state of this room, I cannot— we don't know how to reduce this to a string of— usefully at least— to a stream of discrete symbols, which is why current AI systems work really well with language, but not so well dealing with the real world. There are sort of things that we can bolt on top of an LLM that allow them to interpret the content of an image, not so much video yet, but it's very primitive in many ways. So how do we get AI systems to deal with the real world? The good news is that we have some ideas and systems that kind of are starting to work. The bad news is that we cannot use the type of generative architectures that have been so successful in the context of language. And I'll tell you very quickly, I don't want to be too technical, but I'll tell you why. If you give an AI system, a computer, if you give a system a sequence of symbols, let's say a bunch of words, right, a piece of text, and you train it to predict the next word that comes after, you cannot exactly predict which word will follow a sequence of words. But what you can train a machine learning system to do is predict basically what amounts to a probability distribution over all possible words in your dictionary. So every word in your dictionary now comes with a number that indicates with what probability every particular— every word will follow that sequence of words you just observed. Now what you can do is pick one word from this distribution, insert it into the input And now it becomes part of the input and you can predict the second word. Shift that into the input, predict the third word. This is called autoregressive prediction and that's what LLMs are based on. Works really well for discrete symbols. If you try to apply this to video, so basically show a chunk of video, a few frames to a system or a few seconds and ask it to predict what's gonna happen next, this model does not work. At all. It kind of works, but not well. And the reason is you can't train a system to predict every detail in a video, and you can't even train it to produce a probability distribution over all possible futures of a video because there is an infinite number of them, and it's a mathematically intractable problem. If I take a video of this room and I start from this side and I turn the camera slowly and I stop here, and ask the system, you know, predict what's going to happen next in the video. The system will probably predict that the camera is going to keep turning. There's no way it can predict what all of you look like. It's just— the information is just not there. It's impossible. When you train a system to make this kind of prediction, you basically kill it. It really doesn't work. So you have to use— but what you can tell at an abstract level is that there are people, you know, sitting in seats and I can't tell what they look like, but there's some diversity of origins and gender and everything. So what you can do is build a system that finds this abstract representation, description of the world. It's not in terms of language. So that you can make those predictions. This architecture is called JEPA, Joint Embedding Predictive Architecture. Would be a replacement for the GPT architecture, the GPT of ChatGPT, right? Which is generative. So those are non-generative architectures. I apologize if I was too technical, but basically it's a new breed of AI system completely. Now based on those architectures, you can also build world models. World models are quickly becoming a buzzword in the AI research, not so much in industry yet, but that will come. And what is a world model? It's a system that would allow an AI system to plan a sequence of actions to accomplish a task. So given the state of the world that you currently observe and given an action that you imagine taking, the world model will predict the state of the world that would result from taking this action. If an AI system has such a world model, it can imagine what the effect of a sequence of action would be on the world. And if it's asked to accomplish a particular task, it can search for a sequence of actions that will accomplish this task. I call this objective-driven AI. So this is the kind of stuff that we're building at my new company. And I think it's a new generation of AI systems. And we'll see in a few months, a few years whether this works out. Most of the applications are industry. You can use this technique to build phenomenological models of complex behavior of a complex system, like, say, a power plant, a chemical plant, a steel mill, a jet engine, or a human cell, or a patient. If you had a complete model, phenomenological model of a patient, you could plan a sequence of treatments that would bring the patient to a good outcome, for example. So I think there is tons of applications of this in industry, which we hope to develop over the next year. And, you know, this is a revolution that has not happened yet.


UN · USG · Amandeep Gill [35:20]:

Amazing. So we spoke a bit about the direction of potential travel for the tech. I think another issue you touched upon was the risks. And, you know, you've been very clear about existential risks, that these are often made up and, you know, there are false analogies being brought into the conversation. And so you've— that doesn't mean that you're careless, but your focus is more on the short-term and the medium-term risks. And that's something that concerns us at the United Nations as well. One of the risks you highlighted was this concentration of tech power and the need to kind of have a more open approach to building it out. So what would you counsel countries that they do not lose sight of the risks, but at the same time they don't constrain this open approach to innovation? So what's the sweet spot?


AMI Labs · Founder and Executive Chairman · Yann LeCun [36:20]:

Okay, so a number of different things that need to be considered. At the moment, AI systems in their current incarnation in the form of LLMs are not particularly dangerous because they're not particularly smart. They rarely, almost never, give you information that is not available from a textbook or a library. They just accelerate the access to it and widen the access, which is why people are imagining scenarios where, you know, badly-intentioned people will have access to recipes for bioweapons. Now, one thing that needs to be considered there is that having a recipe for bioweapon is relatively easy. Building a bioweapon is incredibly complicated, particularly if you don't want it to kill yourself. So in my opinion, this danger is hugely overestimated. There's cybersecurity. So people say, oh, look, you know, my new system is so powerful, we're not going to release it because, you know, it can break into lots of secure systems. Now, if you have a system like this that can detect weaknesses, you can use it to solidify your own cybersecurity system. So there it's a matter of, are there, you know, are the good guys faster, smarter, better, more numerous than the bad guys? And the answer is almost certainly yes. I mean, it's not almost certainly, it's certainly yes. And so, You know, having those systems, those systems have their own antidotes, if you want, in them. So we shouldn't be scared of this, right? This kind of first-order effect, oh my God, I can penetrate security systems. Okay, two questions. First of all, penetrating a system doesn't mean you're gonna bring down the electrical grid or anything. You know, you can do this at that scale. That's a good James Bond scenario, but it doesn't happen in reality. And then second, can I use this to protect myself? And the answer is yes, right? So just do that. I mean, every new technology opens the door to new nefarious effects by bad-intentioned people, but generally we have countermeasures that appear pretty quickly. And there's nothing absolutely disastrous that we can expect from those systems. So don't overestimate the dangers. Now, of course, a lot of people in counterintelligence are in the business of being professionally paranoid. So clearly, you have to think about those things, but don't overestimate and sort of kill your sovereignty because of this. That's one element. Second element is, as I was saying in my keynote, That is contrary to kind of a picture of open access to AI systems. And so those two things should be weighed against each other. That's where I disagree with my dear friend Yoshua Bengio, whom you mentioned earlier. He's much more worried about bad uses, in particular bad uses by even the companies producing those AI systems, because he thinks if it's motivated by profit, bad things will happen. I don't know if he's right about this. I don't think so. But again, the solution to this is open-source platforms. And then there is the issue, the sci-fi issue of, is AI going to take over the world? Are we going to lose control? Et cetera. So one point about LLMs, if you believe that we're going to scale up LLMs towards human-level AI, then you might be entitled to be worried because LLMs are to some extent intrinsically unsafe. They're not really controllable. Companies fine-tune them so that they don't give you a recipe of a bioweapon, but you can always jailbreak them. You can always give them a prompt that was not imagined by the designers that will get it to produce, to answer a question it should not answer. Again, as I said, they're not going to invent anything, so it's not going to be information you can't get in other sources. But it's a consideration. So those systems are intrinsically unsafe, but they're not that smart. And so they're not that dangerous. Now, the next generation AI systems may reach at some point human-level intelligence. It's not going to happen tomorrow. We have a number of years, perhaps decades, to think about it. But those systems, if they are based on the blueprint that I described, objective-driven AI, they are much more controllable because you give them an objective and they can do nothing but fulfill this objective, take actions that fulfill this objective, and you can make them subject to guardrails, safety guardrails that cannot be jailbroken. Because it's not fine-tuning, it's actually putting an objective function directly in the inference procedure that prevent those systems from producing unsafe behavior, assuming their world model is accurate. So I think there is a design for safe systems. Which I'm working on with several colleagues, and there I think we have a way to make those systems secure.


UN · USG · Amandeep Gill [41:58]:

Great, and let's move now to two quick points about something that matters to us a lot at the UN, apart from the risk versus opportunities discussion. One is the wider access to the enablers of AI. So there is this, you know, vectorized number string: 75%, 15%, 10%, and then 5%. So the, the compute that's available for training AI models in the US, China, Europe, and, you know, 5% is for 5 billion people around the world. So there is this immense concentration of talent as well in the Bay Area, in some other parts of the world. So how do countries get going in the light of this? And at the same time, we are getting some food for thought when you listen to the CEO of IBM say that, you know, all these trillions of dollars that are being bet on the scaling approach to, to AGI, You need nearly a trillion dollars of returns every year just to be able to repay the loans. What do they bet on, these countries that are in that 5% category?


AMI Labs · Founder and Executive Chairman · Yann LeCun [43:20]:

I agree with him that right now there's a big question as to whether the amount of revenue generated from AI services can justify the investments that are being done at the moment. And it's not clear at all. There's some number that was recently published. It may or may not be correct, but I'm going to repeat it anyway. You know, a typical service professional subscription to OpenAI or Anthropic is $200 a month. For the power users, the amount of the cost of serving one of those power users that pays $200 a month is about $15,000. This cannot go on for very long. Right now, the use of AI is being subsidized by the investors in those companies and in this infrastructure. At some point, the revenue are going to have to be commensurate with the cost, which means either the price has to go up or the cost of inference has to go down drastically, and probably both at the same time. And in the end, it's not clear that the service rendered by those AI systems would be cheaper than humans. It's already the case for program generation that if you pay the real cost of code generation, it's probably as expensive as a human. Some companies are kind of rethinking their strategy about this because of this. So there may be a day of reckoning over the next year, possibly. Which would be similar to the, you know, internet bubble crash in the early 2000s when companies were laying fiber optics everywhere, and then a lot of those fibers went dark, and then some companies like Google kind of snapped them up because, you know, they were cheap. There might be a similar phenomenon where we're going to have a lot of cold GPUs, and I'm sort of seek— I'm secretly crossing my fingers for this to happen because it will make the training of our model a lot cheaper. But I can't make predictions there. The people running those services, I think, know what they're doing to some extent at least. And you had a previous point which I forgot.


UN · USG · Amandeep Gill [45:37]:

Well, maybe we can bring that point up in the final question for our chat today, which is, you know, what's really useful today? Because apart from this concern about sovereignty, what do I invest in, uh, the question that comes up is what's useful for the development challenge in agriculture, in health, in education, small language models, narrow AI, in some areas perhaps LLM. So where do you see the most kind of like, you know, the low-hanging fruit, the most value coming out of that kind of low-hanging fruit?


AMI Labs · Founder and Executive Chairman · Yann LeCun [46:16]:

Certainly a lot of applications in things like agriculture and things like that are still based on LLMs. So basically, you know, give knowledge information to people, do not require the top-of-the-line super expensive models. You can do this with relatively simple small models that you can run locally in some cases. So I think there is a lot of opportunities here. I should say also on the hardware side, the kind of hardware you need for training LLMs or training AI systems and for running them is very different. And there's been some success in hardware companies that have been attempting to break the NVIDIA monopoly essentially on the side of inference. If you want to serve the entire population of India, the cost of inference— farmers in India, for example, they could be wearing smart glasses. The experiment was done actually by people at Meta. You give smart glasses to farmers in India and they can look at their crop and ask their AI assistant, is this the right time to harvest? What is this disease I'm seeing on my crop? What kind of weed is this? Should I get rid of it? Things like that. And this is extremely useful to them as long as those systems speak their language. But the price of the cost of inference must come down by a factor of 20 to 100 for this to become practical to a wide population. So a lot of work to be done in hardware, efficient software. There's a lot of incentive for existing companies to do this because most of their operational cost is actually power for the data centers. So they have a huge incentive to reduce costs as well. But that shouldn't stop countries, companies, researchers to participate in this.


UN · USG · Amandeep Gill [48:14]:

Great.

Please join me in thanking Jan Lecun for being with us today.


AMI Labs · Founder and Executive Chairman · Yann LeCun [48:19]:

Thank you. Thank you.


UN · USG · Amandeep Gill [48:31]:

Amazing.


UN · Host · Pepi Vananen [48:34]:

Thank you so much. Thank you so much. That was very exciting, and I'm sure that we're all now very much looking forward to the rest of the day. So without further ado, we're going to move on with some setting the stage, uh, this. So So the first one is going to be Sergio Gago, who is the Chief Technology Officer at Cloudera, where he leads the company's technology vision and innovation strategy, aligning cutting-edge data and AI capabilities with enterprise needs across hybrid and multi-cloud environments. So please, welcome to the stage.


Cloudera · Chief Technology Officer · Sergio Gago [49:14]:

Thank you very much. Well, it's a hard act to follow, so I'm going to try to first beat the drum a little bit and then set the stage for today on what is what we need in order to have whether it's LLMs or world models or robots, which are essentially AI systems with some servo engines on them, and what is what we need to make sure that that happens to all humanity in equal terms. Now, I was just starting to write these remarks last week, and something made it much easier to do it, because something happened with the fable and mythos models, right, that made us Europeans a little bit unable to use them. And then this made this speech a bit easier to write. So imagine a future where every individual, every company, every country can only hire employees from 4 companies. 5, maybe 3. But if you want to hire someone, it has to belong to one of these 4 companies. And these 4 companies happen to be in the same place in the world, happen to have the same kind of hive mind. They've been trained, educated in the same culture. And these companies can dictate how these individuals are, their skills, their salaries even, and even when they don't want to serve you anymore. Now, if you say, Sergio, that is crazy, That is not that different from the future or present where we are going. So we often speak as though AI begins with a model, right? Open models, weights, and so on. But it does not. It begins with data and infrastructure. And aside to that, institutions and people. That's what trains models of any kind. If those foundations are fully concentrated, opaque, and inaccessible, the AI built on top will reproduce all those biases. Only faster and a greater scale. In other words, let's not start with the weights. That's the tactical implementation. Let's wait on the infrastructure. So I'm going to try to make 3 points today relatively quickly. One is that interoperability is a condition for participation. Two is that sovereignty is a condition for continuity. And three is that private AI is a condition for having operational responsibility. So interoperability is usually treated as a technical detail, right? It's not a condition for freedom. A hospital should be able to use one storage system for their data, another analytics engine, a locally adapted AI model without having to copy sensitive data from patients from one place to another or hoarding into the same data platform. A government should be able to replace a technology provider without having to rebuild an essential service or public service. And a researcher anywhere should be able to use open formats and interfaces and adapt them to local languages, local data, and local needs as seamlessly as possible. Therefore, open source AI cannot be simply— cannot mean simply publishing the weights. It has to mean something else. An open model running on proprietary data formats, on proprietary orchestration, on proprietary cloud interfaces or proprietary governance, it's a locked system. It just has an open door painted into it. So we need openness across the full end-to-end spectrum. And private companies and institutions have to see this in very practical terms. I represent the practical— the private sector. Open table formats— Iceberg is one great example that the community brings. Open engines and catalogs like Polaris. As well. Open source compute engines and APIs. This is the convergence of many of the projects that Apache Foundation and others have been working on for a long time in the data side and ecosystem and converging that into AI. Having different teams and technologies to work with the same governed data without forcing unnecessary copies, moving lots of gigabytes, petabytes, or exabytes from one place to another and forcing everyone to put everything in the same ecosystem. That belongs to one single company. So interoperability allows an institution to replace a component without having to change everything, right? Or without forcing all your data to go into the same vendor. It prevents today's procurement decisions from becoming tomorrow's permanent dependency. So it's the fundamental element on the ecosystem for the future, or I should say for the present of today. Now, for years— this is my second point— Digital sovereignty was more like a policy preference, especially for enterprises and corporates. Something desirable, but too complicated or expensive for many players in the industry. And that position is no longer credible. AI is now a fundamental building block for administration, healthcare, education, defense, finance, you name it. So when this happens, control over the technology becomes a matter of continuity. The technology itself becomes the critical infrastructure and all the elements that are together with that. A private provider can change the price of a token. This happens every week. It can change the rate limit, retire a model, modify its license, the quality of the output, and it can alter the terms of service or decide a particular capability that will no longer available in a given market. And a government can impose, of course, an export restriction, a geopolitical dispute that can affect those accesses. So in essence, an institution or a company that believed it was buying technological capabilities suddenly realizes that it only rented permission to use it on a temporary basis. And we just saw an example of that last week. So imagine that dependency in a hospital, in a tax authority, in an electricity grid, your national AI strategy, or your corporate strategy cannot depend on someone else's terms of service. So AI sovereignty and private AI together need to have or need to answer the following question. One, where does your data really reside? Two, who can access it and how and under what conditions? Three, which models can use that data or family of models? Four, can we move the workloads the pipelines from one place to another seamlessly? 5, can we replace the models instantly one to another and the systems continue working? 6, can we audit and inspect the system? And 7, and most importantly, can we continue operating this if a provider changes its commercial or political position? So don't get me wrong, real sovereignty does not mean isolation or autarky for that matter. It is the ability to participate in a global ecosystem without surrendering control of the essential capabilities. And the solution for that, well, Mr. Lecun already mentioned that, but is open source, which is central to that answer. It converts dependence on a single supplier into participation in a shared economy. Now, open source alone does not create sovereignty. It removes the toll, the toll booth, but it's up to us to build the roads, up to every country. Private AI means making responsibility operational, making it happen. It doesn't mean private models. It means being open to the foundations but private in the operation, essentially being accountable for the outcomes. Institutions cannot simply sell all the sensitive data to external black boxes. They need to bring AI to the data rather than moving data to the AI. That comes with infrastructure and digital commons. They need to run— the ability to run the models in a public cloud. Those are good and helpful. A sovereign cloud, a lot of that going on in Europe, and a private data center or at the edge while applying consistent security and governance and other controls across that. So in a way, interoperability makes replacement possible, sovereignty makes the decision possible, and private AI makes control operations possible, being able to have the whole supply chain under wraps. Open models can be deployed privately, adapted to local knowledge, and used without— with clearly defined boundaries. An investment in true open source AI— and the key here is what is true open source AI— is a key element to realize that AI for humanity is something that we all use. And by AI here, I mean LLMs, but also world models, quantum algorithms, and anything that the future can bring us as well. Responsible deployment requires identity and access controls, data lineage evaluation, bias security testing, red teaming tools, continuous monitoring, transparent incident reporting, audit logs, human oversight, and appealing mechanisms with the ability to shut a system down when required. All that is public part of the digital public goods. So we need to move from innovation to deployment. Many of the AI use cases get stuck in the experimentation phase, never see production. This is especially true in the enterprises, in the corporate world, but also at the institutions. Our industry loves demonstrations, creating wow factor, right? But humanity does not benefit from those demonstrations. It benefits from systems that work on Monday and on Friday they are surviving an audit, systems that are HIPAA or GDPR compliant. That end-to-end is what we need to provide our citizens and our customers alike. Deployment discipline, clearly defined public benefit— why are we doing this? Legitimate and representative data, security testing, continuous monitoring, and a route for human appeal and readiness. Governments should protect rights and use procurement to require open standards like the ones I mentioned, interoperability, portability, auditability, and credible exit plans. Researchers, civil society, academia should test those claims, expose failures, localize systems, and ensure that affected communities, big or small, have a meaningful voice on creating them. And maybe here in this own— in this venue, the UN can help a lot with creating a common ground, coordinating standards, sharing safety resources, supporting capacity, and ensuring countries at the edge of AI can also become active participants on the future. So I'm going to be wrapping up. If we get this right, and again for all types of models in AI, open source will not merely be or make AI cheaper, maybe this inference becoming cheaper as Mr. LeCun was saying, it will make AI more contestable, adaptable, locally relevant, and worthy of trust. AI for humanity, for all of us, means that a teacher can adapt the system to local curriculum, that a public health agency can use sensitive information without surrendering control, that a small country can participate in the AI economy without renting its future to a company that's valued 10 times its gross domestic product, and that a citizen can know when AI is being used, for what, and understand the basis of important decisions that are being made. Don't get me wrong, we've done this with classical machine learning in the past. So we are not necessarily reinventing the wheel. Open source, not just the weights, but the content, the data, the training, the data platforms, and the data lineage gives us the possibility of building a commons. Interoperability keeps that commons fully connected. And private AI allows institutions to operate responsibly for themselves and for their citizens. Sovereignty ensures that humanity, and not a contract, a price and page, or a distant political decision remains in control. It is not technological nationalism, is not isolation, is the foundation of resilient, democratic, and human-centered AI, and it is no longer optional. Thank you very much.


UN · Host · Pepi Vananen [1:01:08]:

Thank you very much, Sergio, and we're going to hand it over to the next Setting the Stage speech, which will be given by Thomas Jarzombek, who's been having a very long political career in Germany. And since May 2025, he's been the Parliamentary State Secretary to the Federal Minister for Digital Transformation and Government Modernization. Over to you.


Germany · Parliamentary State Secretary · Thomas Jarzombek [1:01:37]:

So, ladies and gentlemen, esteemed Undersecretary Jill, So I'm very pleased to be here, and it's very important that UN is tackling the issue of open source. As the German government, we very much appreciate that, and we also want to contribute to that. That's very important, because when we speak about open source, that means freedom. It means freedom for you to choose solutions. It means for you freedom to understand solutions. That means freedom for you to create your own use cases on this solution, and it means freedom that you can operate it without any hazards and without any interference from third parties or third countries. So therefore, we very much appreciate all the initiatives here. And today is not only the fifth time that this Open Source Week of UN is happening, it's also the fifth year that the Sovereign Tech Agency that we founded as the German government, is working there. And so the basic idea is to contribute to the open source ecosystem without any political agenda. That's really important, without any political agenda. We want to prevent the freedom in this open source ecosystem, and therefore this sovereign tech agency is built independently and can make independent decision and is not under some kind of political control. And we want to scale that, we want to scale that in Europe. That's the reason why we are doing an organizational form that brings other countries, like our friends from France, for instance, and further countries on the place. And if there are further partners even outside the EU who want to cooperate and to scale that, governments in a free world with free democratic values without political agenda, want to scale here, you're highly welcome. So having said this, today the question is, what do we do on AI? And, uh, so the basic, the constitutional principle in Germany is that we say public money, public code. And beginning with the Sovereign Tech Agency, in all of the, uh, projects that are being made there, there is one project that's the Agentic maintainer support. I think that's pretty important as maintainers are challenged with all the AI content that's distributed and, uh, a challenge for their time. And so I think support here might help. The second thing is that we built, um, and even though it's not really AI but it's the base for that, we built a personal ID, a wallet. You can install this on your smartphone, and for us it's a Basic principle that the whole source code of this central identification system of the government is published as open source so that every citizen can see this, that we have full transparency. There is no tracking or any kind of things like that, and we make it transparent so that it's proven. Second thing that we do is that we build an AI platform for citizen service. So we have in Germany very much on the municipal level, we have thousands of municipalities with a lot of independency, but it's complicated if you want to do any proposals to find the right place in all of these municipal services and federal services and whatever. And we're building an AI platform with an app so that you have a centralized point for all the proposals to the government, even if it's local or on the federal level. This is also published as open source so that there is full transparency. You guess that there is no tracking or things like that behind. You can see that. The third thing, and that's really important, and the team's all also here today, is Spark. This is a platform for large infrastructures. If you want to build railroads or bridges or highways, so in a democracy You see, it's a pretty complex process and sometimes it takes years. And now we built an AI system with a huge budget on that. And that can, for instance, the first step is you need expert reports and you have a lot of documentations and it takes a pretty long time to check if all of this is complete and consistent. And the AI system can at first check is it complete, and if something is missing, it can immediately make an email or an announcement, say, okay, there is something missing, and it can check if everything is consistent. We save 6 months for a typical process here. And the next thing is we have objections sometimes, sometimes we have 100,000 objections, and all this from citizens, and all this can be handled by the system. So it's really accelerating all the processes for building large infrastructure. The next thing is, number 4, is our Agentic AI Hub. We want to foster startups. Ah, so stop. This Spark thing, you can completely— you guessed it— you can completely download it as open source. And the next thing is our Agentic AI Hub. We have all these solutions in municipalities and we want to bring them to the next level. There's a lot of legacy software and therefore we made a competition for startups. And right now we started with the first batch, 20 municipalities and 9 startups. Also a lot of solutions you can see there on open source. And to explain what they are doing, for instance, if you make a social security claim, you have to deliver 100 pages of documentation. What your earnings is and things like that. And so the system also can check if it's complete, it can check if it's consistent, and also it can make an executive summary for the public officers so that they don't have to go through 100 pages. They see immediately what are the core figures here in these claims. And the last thing I want to stress is our initiatives around OpenDesk and open code. When I just mentioned that all of this is published as open source, you can see it on our own open source-based platform. It's open code. You find it under opencode.de, opencode, and there you can download everything I speak here about. And the thing that's also been developed is OpenDesk. You may have heard about that, about that issue there at the International Court of Justice in Den Haag. And after they had, let's say, problems with Microsoft, we could help out. And so they're running on our platform. And we also would invite you and appreciate if we found further more users, not to replace anything, but to have choices, to have an alternative, to not be locked into a single vendor, with all the things that are upcoming. And that's not only question of freedom or access, also question of pricing these days. So in the end, I appreciate very much that today also there is this opportunity. I heard in the afternoon to talk about what is really open source AI and what is OpenWeights AI. A question also for us, pretty relevant. If you earn a trillion of numbers, do you really understand what's happening there? So that's open weights. No, we don't. And therefore we very much appreciate more open source AI. We are building this by ourselves these days. It's announced, a German initiative that's called SoFi, and they're building a full LLM. I suppose it's not this frontier LLM kind of thing, but in a hybrid solution. I think for a lot of workloads, This is attractive and this will be fully open source on this. And everything that we need, I think, except of the US, is a lack of compute. And therefore, the German government also is investing and encouraging for, and especially private businesses to invest more in compute because this is the beginning, not only for inference, but also for training and building models. So this is the strategy that we have in a nutshell. Thanks for listening. Thanks for the opportunity to talk about all this here. And if you want to join something like this, we are not only open source, we are open government in this case. So don't hesitate to come to us. Thanks.


UN · Host · Pepi Vananen [1:10:30]:

Thank you very much. And we are now done with setting the scene and the opening remarks, of course. So just want to check the room. Please raise your hand if you feel like the scene has been set, you're ready for the rest of the discussions. Pretty good. All right. We've also heard feedback that it's quite difficult to hear in the back. So to all the speakers that are coming up next, please speak as close to the microphone as possible. I'm practicing what I preach. And also, if you came a bit late, please note that there are earpieces. You can use those to hear better. And in the back there aren't any, but I believe that you can connect your own headphones to those, to those machines. So let's try with that and do let us know if you're still having difficulty hearing and we'll look into it. All right. Onto the next session. So let me introduce the moderator. I actually don't see where Verena is sitting, but please, Verena, konn'sher, konn'sher, Conchita, sorry, um, Chief Executive Officer of opendata.ch. Uh, please welcome to the stage to moderate the session on open source, on, um, open source for digital development. We are not seeing Verena. All right, I will, um, in the meanwhile hand it over to our colleagues, um, to, uh, start off the session. All right, we have a volunteer. Mädis nene.


Mädis Nene [1:12:25]:

I have to, I have to. Are we sitting there or sitting?


UN · Host · Pepi Vananen [1:12:28]:

Sitting over there.


Mädis Nene [1:12:29]:

Sitting over there. Do you have the list?


UN · Host · Pepi Vananen [1:12:39]:

I feel like this is what the tech takeover actually was preparing us yesterday.


Speaker 25 [1:12:53]:

So it's this one.

That one?

Yeah.


Moderator · Mädis Nene [1:12:57]:

Thank you. Thank you so much. So let me find a good chair and invite— oh, I have a lot of laptops and then screen and then iPad. Yeah, yeah, that's good. So let me invite our distinguished guests. Us today for the discussion about open source AI and development. We'll have a very high-level ministerial session today with distinguished Minister Salim Abaa, that I invite her to join the panel. I hope that she's in the room. Sierra Leone Minister of Digital— thank you so much. Please, yeah, yeah, I'll stand up so I give you space to go up on These are last-minute adjustments. Thank you so much. Thank you so much, Minister. Minister of the Responsibility for Efficiency, Innovation, and Deal from Jamaica, Her Excellency Audrey Marks, please join us. Minister Delegates to the Head of the Government for Deal Transition and Administration Reform from Morocco, Her Excellency Amal El Fallah Zaghrushny. Please join us on the panel. Oh, do we have—


Speaker 27 [1:14:22]:

okay, thank you.


Moderator · Mädis Nene [1:14:29]:

Please, uh, oh yeah, we will put names here for you. Sorry for last minute adjustment. This is very tight space, yeah? And it's hot today. Yeah, yeah, that's fine. I give it back to you.


Speaker 29 [1:14:47]:

Oh, very much welcome.


Moderator · Mädis Nene [1:14:48]:

How are you doing?


Speaker 31 [1:14:50]:

Yes, please.


Moderator · Mädis Nene [1:14:51]:

Very nice seeing you again.


Speaker 33 [1:14:52]:

Thank you.


Moderator · Mädis Nene [1:14:53]:

Welcome. We are very happy today to host you, to show firstly that the gender problem is not a real problem. We have top leaders in the world coming, leading their digital transformation, digital reform, AI strategy in 3 countries, talking today on reality, how they can really do better than men because we have failed. It was supposed to be a woman moderating the session. Please don't judge me on my physical aspects. So, and I'm happy to introduce the three ministers talking mostly about the open source AI and development and the vision that we need to build around that. From the top levels' vision, from a strategic vision, from policymakers' visions, how this could bring this advancement in the AI adoption into the digital reforms, into the digital transformation within government, within public services, to reach at the end— the goal is to reach the citizens, providing better services, providing lower cost in terms of of services, but are also ensuring that the countries are upscaling and are not leaving that gap getting bigger and bigger with all these AI advancements. So I start with all of you with the same questions, and I would like to get firstly, how is open source integrated into your national digital transformation or AI strategy? And then if you have any concrete case that have delivered measurable outcomes. We all know that we are talking about AI outcomes. It's very hard to measure the AI outcomes, so I want to hear from 3 ministers today how we do that. Your Excellency, Mr. Ahmed, please.


Morocco · Minister Delegate for Digital Transition and Administration Reform · Amal El Fallah Zaghrushny [1:17:10]:

Good morning, ladies and gentlemen. Very happy to join Thank you for this very important discussion on open source and in particular in the domain of artificial intelligence. We have been working on open source many, many decades before, but the rising of AI bring new, new challenges when it comes to open source. For example, For many decades, open source was like giving the, the source, the code of the, the software, and then it was— we, everybody was able to improve, to add, to remove, etc. But when it comes to artificial intelligence sources, the question is quite different because you don't need only the code, you need many, many other parameters and features that help you to fine-tune the models, to work on the model, to work on the data, to work on the tokenization. And you can open some aspects. If you close some others, you cannot really speak about open source. So in Morocco, as Minister of Digital Transition, Reform, Administration, I can say two things. The first one is, when it comes to digital pure digital, we have the same advancement as any places in the world. But when it comes to artificial intelligence, I think we started from— we came from far. We started a few years ago working really on artificial intelligence. We deployed many efforts to develop AI and What we can propose to our developers is two things: Data Factory. We have two concepts: the concept of Data Factory and the concept of Forge of Sources. This means that people can first learn how to deal with data to be, to be used in their codes, AI software. And the second is to mutualize somehow the development of algorithms within the country, but also we are open to the world. We use other GitHubs, etc., to bring and to take benefit from development in other countries. So today, when it comes to AI, we are at the We are at the very beginning of the process, to be honest. But we know exactly what we have to do and how we can do it because we have this experience of developing algorithms for labs, I would say, not to scale for the public administration or the other companies. But it's going very fast and we do a lot of activities around Open source in AI. And, uh, I have 2 minutes, I think. Yes, yes, I still have time.


Moderator · Mädis Nene [1:20:26]:

Yeah, please.


Morocco · Minister Delegate for Digital Transition and Administration Reform · Amal El Fallah Zaghrushny [1:20:27]:

Okay, so if I would like to, to summarize, uh, very quickly the most important, uh, uh, obstacles. If I— the first challenge, uh, for us is the fragmentation. For a while, many administrations were developing codes, algorithms, etc. And if you would like to have something coherent, in particular, when you tackle the question of interoperability between administrations, you should have more deep knowledge on the pieces that exist in different places. The second challenge is data readiness. For a while also, We had many papers everywhere in the world, I mean, not only in Morocco, but I think in Morocco and Africa it's still a huge challenge to move from papers to have real data, processed data, to move from papers to processed data. And maybe the third challenge is sustainability, because for most people Open source is seen as volatile sources. I mean, there is no maintenance, nobody can provide support when you use the open source, etc. So this make some— introduce some fairness in some fear in the process of development of AI and based open source AI. And we try to— this is why we put this Forge, because we need some safeguards to bring, to bring to the table this open source, to be able to make the added value on these sources, but also to provide some guarantees that it will work and somebody somewhere will take care of the advancement of this source. And of course, the last but not least challenge we face is the challenge of confidence, the trust. We, we think all the time about trustworthy AI. When it comes to open source, it's worse. People have to, to, to, to trust that the source is solid, the source is resilient, the source is not full of pitfalls, for example, in the development of the source. So we have also to work on the trust toward the use and the integration of open source in a huge development. Thank you.


Moderator · Mädis Nene [1:23:16]:

Thank you so much, Excellency. It's the trust, indeed, it's a big problem when it comes to the open source community. I remember that, meaning as a single developer, it was a big trust issue. Now, if we take it forward to the level of society, citizens, and public services, and then in particular with the rise of the AI, and I'm looking at you, Excellency, Honorable Ms. Audrey Marks, I'm asking about that trust issue that just Minister Amel was raising. We know that AI brings a lot of attention and spotlights on the open source in particular, and then that's why we are organizing even that day. How is the awareness, raise of the awareness, the uptake of the considerations of the open source within the government but also at the societal level is happening?


Jamaica · Minister with responsibility for Efficiency, Innovation and Digital Transformation · Audrey Marks [1:24:15]:

Thank you.

Let me first— it's on?


Speaker 40 [1:24:20]:

Okay.


Jamaica · Minister with responsibility for Efficiency, Innovation and Digital Transformation · Audrey Marks [1:24:22]:

Okay. Thank you. Let me first say good morning to Under-Secretary General Gill, Excellencies, ladies and gentlemen. Mr. Moderator, you have spoiled my opening line. It was supposed to be Madam Moderator, and I was Fascinated that we had an all-female ministerial panel, but so we will work with you. So for Jamaica, open source AI is fundamentally a capability issue, not just a technological one. As we accelerate our digital transformation, we have moved beyond viewing open open source merely as a cost-saving measure. AI has fundamentally shifted our perspective. We now recognize open source as the essential mechanism to inspect, adapt, secure, and govern our technology. Our vision is clear: Jamaica must not simply be a consumer of global technology; we must be active builders. This requires a concerted effort to cultivate data foundations, specialized skills, robust safeguards, and public sector capacity so that Jamaicans can fully participate in the global AI economy. To date, we have laid a strong groundwork with several major open source and open data government initiatives, alongside 9 significant AI policy programs since 2011. Our progress is accelerating rapidly. We have established the National AI Task Force, completed the UNESCO AI Readiness Assessment, and launched the National AI Lab and the, what we call, GAINS Program, Growing AI Innovation and National Skills. We are driving implementation through mandatory training of our public workers and AI initiatives in health and targeted AI pilots. Our existing foundation, including the Deakin-based Open Data Portal, Data for Development, Open EMIS, and DHIS2 Cancer Surveillance, demonstrates our commitment to open source. Moving forward, Jamaica's message is simple: open source AI must build enduring capability, not dependency, enabling us to serve our citizens better, protect trust, and create, and create lasting Jamaican-owned value. Thank you, Mr. Moderator.


Moderator · Mädis Nene [1:27:23]:

Thank you so much. That's very impressive. I know about the capacity building issues, the skills issues. I'm doing a lot and trying to do a lot of that. It's very good to hear how far have you gone into that. I'm looking to our last panelist, not least, Your Excellency Salim Abba, Minister from Sierra Leone. I've been there once, yeah, I've been there once with great colleagues working on AI strategy, helping and then trying to understand how we can support that at some point. But I'd like to get a better vision from you. You are the top leader there on the deal in AI. How is AI and open source going in Sierra Leone?


Sierra Leone · Minister · Salima Bah [1:28:13]:

Thank you. Thank you very much for that. I'm always excited to hear people have been to Sierra Leone. It means you've crossed the boat, so you have interesting stories to share, I'm sure. But really, again, want to join my colleagues in thanking the Under-Secretary for putting together this really critical conversation, and really everybody that's here. I really believe when it comes to advancement of digital transformation, really these are the conversations we need to be able to have a bit more critical. And I think specifically when we think about open source and open source AI now, as we're discussing, I believe the experience of Sierra Leone, or specifically our vision, or when we think about it, I think could be Illustration for a lot of countries to be able to also think through, and we're happy to work with other people to see how we can also further advance that. Because when we think about it, we think about the potential to change the economics of digital transformation. I think any country that sits down and thinks about your digital transformation agenda and journey, one of the hugest implications with thing that is the cost implications of all of that. If we think about African countries being on this journey, and forever long working with our partners to transform, as my colleague from Morocco mentioned, in Africa, a lot of our processes are still paper-based. We know everybody's working to be able to transition that because we want more accountability and transparency in terms of government service delivery and how we operate. Technology is critical towards that. But we look at what's been the biggest challenges towards that is some of it is the cost implications. We look at building or procuring proprietary-based systems when you're vendor-locked and not having those finances to keep up with those annual licensing-based subscriptions, and you realize it's almost like you're entering a trap and then you keep on going and going. It sometimes feels there's no way out of that. That's why we believe open source is a great opportunity to change that reality that we have. And then maybe also most fundamentally, when we look at it, especially across the region, we see different African countries building the same solutions, all using the potential limited resources that we have, building the same things at the same time. When we don't need to be doing that. So that's why Sierra Leone has always seen open source as one of the biggest game changers when we think about digital transformation, and specifically for developing countries such as ours. The idea that we don't have to build the same thing, walk the same road that somebody else has already done. We can take what is there and build on top of it, improve on top of it, contextualize it for what we need in Sierra Leone. That has always been critical. That's why Sierra Leone is a founding member of the DPG Alliance. We're a very proud founding member of that alliance, being able to have a seat at that table to be able to contribute to the global advancements of DPG, and we think it's critical. That's why we see— and Sierra Leone has always been a home to some of the— we see some of the biggest open-source solutions right now. G2 actually was piloted in Sierra Leone when it started. OpenG2P was piloted in Sierra Leone because we wanted to create that home where open source software was welcomed and it could be a place where this could be tested and scaled from, because we want to provide that opportunity. And, and we're further doubling down on this commitment to open source. I'm actually recently, um, supported by UNICEF and some of our other partners We've developed the first open source first policy, and really what this means is to say that in Sierra Leone, before we consider any proprietary-based solution, we will consider an open source as our first priority, as our first go-to. Only if we can't find an open source alternative that we can contextualize and work on, then we'll look at a proprietary solution. And this has been great for us because issues of fragmentation, as already everybody mentions, issues of these siloed systems, issues of sometimes these proprietary systems not really being contextualized for really our needs. Sometimes we find ourselves with these proprietary systems, we utilize them, but there's lots of difficulties within them because they weren't built for us. So getting them to work for us sometimes is really difficult. And also the issues of sustaining it, and, and issues of sovereignty, really. I think if we look at the digital landscape and, and where we are, so for us, open source has been critical. And I think how our commitment and our vision towards this can also then translate to when we talk about open source AI. And then I, I like to say it also provides, um, the opportunity to democratize intelligence, because with open source AI means that everybody can contribute and build towards this frontier in terms of where we're going through. So really, in a nutshell, I would say for Sierra Leone, open source has been critical. We're doubling down on our commitments towards it. And one of the areas in which we're doing this actually is we're building a DPG pipeline, because when we think about the advancements of open source sometimes within the region, we feel as if it could appear as if it's this thing being done to us and not being done with us. So we're looking to how do we create opportunity to have— how do we converge demand, talent, and resources when we think about open source and for the region. So again, supported in partnership with UNICEF, and we're developing this DPG pipeline platform that we believe can provide that convergence, not just for Sierra Leone, but we think it could be a tool that could really support the advancement of DPG within the region, because again, one of the things we want to be able to change is sometimes the perception of DPGs or open source, it's free. Free means cheap, cheap means bad, and we're trying to change that narrative. We believe we change that by getting more of our local talent contributing to towards this and building towards this. The platform of itself is open source as well, so invite others to further improve upon it and build upon it and contextualize it for their needs as well. So within that, we think the open source framework is building the foundation. We think within AI as well, the work we're doing— we just developed an AI readiness assessment because before moving to an AI strategy, we wanted to be able to understand Where— what role does Sierra Leone want to play in this whole AI revolution? What is our comparative advantage? Our comparative advantage might not be building the AI-based infrastructure. It might be looking at the datasets and how we're contributing towards that. We want to be able to define what our role is going to be and have a focus on how we achieve that. Because for a country such as Sierra Leone, that is important. We don't have unlimited resources. With the resources we do have, we want to be strategic in how we utilize it and how we contribute to the global advancement of AI. So we're working through that process. And as mentioned, we look to also provide regional leadership as the current chair of ECOWAS. Sierra Leone is the current chair of the ECOWAS, so I also chair the ECOWAS ICT Council. We just had an ECOWAS ICT Council meeting. Some of the conversations were around, as a regional perspective, how is West Africa— what comparative advantage does West Africa have and how are we able to come together? As mentioned, our biggest challenge is fragmentation. We believe within the region there's opportunities to leverage shared infrastructure rather than every country Building their own GPU, every country building their own data centers, which we might not have. There's an opportunity as a region to come together to see how, in the true sense of the word, make infrastructure also open in that sense, why every other country then would seek to see how their own comparative advantages could be. Lots of progress being made, but still lots more to do and looking forward to working with all partners to see how we further advance this.


Speaker 44 [1:37:15]:

Thank you.


Moderator · Mädis Nene [1:37:16]:

Thank you so much. That was very comprehensive, covering entirely the value chain of AI from the governance to the data centers at the end. Thank you so much. Just to raise a point related to the AI adoptions and open source, we have seen that many strategies are coming towards when we talk about sovereignty is about pushing more the private sector to take the lead on that, because that's a fundamental piece in the thinking about AI. But when it comes to the AI and open source, how to convince and bring startups building their autonomy, building also a partial sovereignty of the member states on AI, it's a bit more complex because it's a mix of things that are coming inside and things coming, getting outside. So with the rise of these big startups or private economies we've seen in China, we've seen in the US, we've seen in Europe, a lot of investment, state investment into these startups to build that ecosystem, to bring the agility, to bring the innovation capability, but also to bring the ecosystem inside because AI is good, getting benefits from AI is much better. I know there is a lot of countries running towards the Startups Act, building this and building the ecosystem. When it comes to the AI, I'm looking at you, Minister Amal Fella, how Morocco is doing, meaning I know it's very vibrant ecosystem, but how bringing the AI component inside of that ecosystem of startups?


Morocco · Minister Delegate for Digital Transition and Administration Reform · Amal El Fallah Zaghrushny [1:38:53]:

Yes, sir. Thank you very much for this valuable question. First, I would like to just I recall that in Morocco, our digital strategy 2030 has two main pillars. The first one is digital economy and the second one is the reform of administration, and both work together because when you work on digital economy, you need laws, you need procedures, you need many things that need that rely on the administrative procedures. So we need advances in administration to go ahead with digital economy, and also we need digital economy to advance the whole stuff in administrative area. So in Morocco, this digital economy can be articulated on 2 or 3 axes. The first one is offshoring, outsourcing if you want, and we attract many, many companies, more than 1,200 companies over the world, big companies that invest in Moroccan talents and develop AI within Morocco. Just to give you more comprehensive view, today we have 143,000 jobs created by outsourcing in Morocco. And of course, to do that, we need incentives and we need very vibrant startups, very vibrant development, etc. So when it comes to developing AI, I mean, I think the champions or the leaders of AI in Morocco are startups today. These startups are, of course, developing new technologies, new solutions, and try to connect these solutions to the big stakeholders, including the companies that provide this outsourcing. We try to have the whole chain from development to startup to big companies to export digital. One of the biggest incomes of Morocco in digital economy is digital export of this kind of solutions. Developed by startups and in the context also of offshoring. We have two big programs called— related to venture building and venture capital, more than 1.3 milliard of dirham, which means $1.3 billion dedicated to develop the startups' solutions. And very recently, last Friday, we got the opportunity to develop also this ecosystem by, I mean, the World Bank helping us by $200 million. $250 million, which includes $200 million for startups and digital ecosystem. This means that we have today proved that Moroccan startups can provide a very helpful solution to the rest of the world. This make World Bank trust in this development within Morocco. So we develop also, we have very, I think, something very new in the area of ecosystem called Institut Jaziri, which are physical spaces that put together researchers, startups, stakeholders to develop open source solutions. And these Jazari Institutes are spread on the country. We have 12 and they are specialized by region and by— they provide solution for regions. For example, in the south, we have problems with water, so we will specialize the Jazari in water. In other area, that will be energy. In other one is industry and so on. And we create this network of Jazari institute with more than 1 gigawatt capacity of compute. Today, we invest in data centers, we invest in compute, we invest in talents, and we invest in startups to build this ecosystem at large scale. I think what is very important for Morocco, and we will have some events during this week, in particular with the UNDP. Morocco is today chosen as a hub for digital and artificial intelligence, hub for AI and data for sustainable development within Africa and Arab states. So we try to provide a solution that can scale at the regional level and not only at Moroccan level. So our ecosystem is very vibrant. We have very brilliant minds in mathematics, in algorithmic, etc., and we are very confident when it comes to talents in Morocco. Thank you.


Moderator · Mädis Nene [1:45:05]:

Thank you so much. That's very impressive that you are that precise in the articulation of the impact in job creations. This is the first time I hear that very precise numbers. I was wondering how can we do that, the same thing for AI and say, oh, the AI is bringing that on GDP or on the job creation. That's another discussion. Your Excellency, Audrey, meaning it's the same question in different region. It's the startup, the ecosystem, the market, but also the open source, how this could contribute to foster and build a better startup and innovation ecosystem.


Jamaica · Minister with responsibility for Efficiency, Innovation and Digital Transformation · Audrey Marks [1:45:47]:

Thank you. Open technology is an important enabler of Jamaica's AI startups. And wider innovation ecosystem. They lower barriers to entry, speed up experimentation, and give developers access to advanced tools and knowledge that would otherwise be costly or difficult to obtain, especially for startups. So this allows entrepreneurs to build on existing innovations rather than starting from scratch. Improving time to market and competitiveness. However, the challenge that we, we have to note and recognize is that while open source reduces license costs, it introduces other considerations such as the need for local technical capability, ongoing maintenance, and in some cases, vendor or community support. These operational factors must be planned for to ensure sustainability. In Jamaica's context, the strongest approach is combining open standards, secure APIs, and shared public digital platforms to create a foundation for innovation. And I'm very pleased to share that back in April this year, we were able to launch Jamaica's data exchange platform. So, you know, it's very easy for governments, the different departments and agencies of governments to operate with their own data, but we are now cutting out the silos and ensuring that we have shared data, which will also provide a common area for entrepreneurs to access. Data. This enables developers across sectors such as agriculture, tourism, education, and government services to build solutions more efficiently while contributing to more connected and scalable national innovation ecosystem. Thank you, Mr. Molerito.


Moderator · Mädis Nene [1:48:02]:

Thank you so much, Your Excellency. Please, yeah, feel free. Probably the data exchange is the hardest part to achieve, in particular when it comes to how it's very siloed across different administrations. I don't know if you are pushing the— I'm asking because I'm really curious to know, are you making the data also available for startups?


Jamaica · Minister with responsibility for Efficiency, Innovation and Digital Transformation · Audrey Marks [1:48:30]:

Yes. It's secure data, so there will be a process. We are starting with government departments and agencies, and we have now put in a data protection system, and our companies, even startups, will have to register and go through a certain compliance to ensure that data is protected. Protected, and then there will be access. And especially, especially important for the banking system in Jamaica. At this point, it's still relatively complicated to get bank accounts. You have to go and get references and show a number of documents. With— we are doing two things. We are doing the data exchange platform, and we are also doing a national identification card. So every Jamaican will have a national identification card, which will be the One Card. Now, with the national identification card, which has already gone through the process of verifying your identity and the data exchange, we are combining both so that to make it easier for you to open a bank account and for startups to be able to identify who they are working with and be able to get going much quicker.


Moderator · Mädis Nene [1:49:50]:

That's setting the stage for tomorrow's discussion on digital public infrastructure. Thank you, thank you so much, Your Excellency. So I'm looking at, uh, Your Excellency Salim Abba on, on the startup, meaning I don't know much, to be honest, on, on startup ecosystem in Sierra Leone. I'm happy to hear a little bit more. And then again, how is open source could help building a stronger startup ecosystems in AI, at least.


Sierra Leone · Minister · Salima Bah [1:50:18]:

Thank you. Thank you very much. I really like conversations around the private sector's role, responsibility towards this, because I feel as if sometimes within this government, government, I think sometimes we have to centralize this where it always needs to be in the private sector role. I think specifically on startup ecosystem in Sierra Leone, I think as might be expected, it's a nascent stage, capacity is building, we're seeing the development of VC funds coming in, so you can— I think it's always a good indication whether your ecosystem is developing is when you see funding coming in, because that's always— will determine whether the investors see this as a viable ecosystem to invest in, so we're seeing a lot of that, so we're encouraged by that. But, you know, within this framework, and that's why I really liked the questions or responses around the impact of AI, and because when this conversation— we're not— AI is not for AI's sake, right? We're looking at how AI can improve healthcare, how AI improves finance, how it improves agriculture, how it improves mining, how it really transform and change aspects of our lives and for the better, right? So that's really the gain within all of this and why these conversations are happening. But then it's also critical then to discuss what's the role and responsibility of private sector, because if we think about the impact and the potential of AI, and I really would like to take it even beyond our local ecosystem where now the world is a global village, and with technology and these systems now, it's really a one, one pot where we're all in, especially when it comes to the impact. Social media. AI determines what you find out about Sierra Leone today. If you go on Facebook, the algorithm has decided the most interesting thing about Sierra Leone today is this, and all the way from the US, that's what you get to see. That is influencing your opinion about a specific country or region and has real-life impacts when it comes to financial. If you see a great story that portrays the security and advancement of Sierra Leone, as an investor, you might think, great place to go invest. If you see a story about instability, security risk, an investor may decide, that's not where I want to go invest my money. These are real-life impacts. Healthcare. AI now is determining global health outcomes and how it runs. There's real-life impacts, and within that, I believe the private sector has a critical role to play when we talk about lack of representation and within it. And if we're really going to see the opportunity for AI to actually reach its full opportunity and potential that it has, we really need to think about where does the obligation when it comes to ensuring representation lie? Is it just the sole responsibility of countries and governments to push and figure this out? Or is this a collective responsibility within that? Because can AI models that are not fully— that doesn't understand the full context of full humanity as a whole, can that actually be an intelligent system? Can we claim that to be fully intelligent if it doesn't understand understand and take into consideration every aspect of this. So I believe within this, when it comes to impacts and the private sector, I do think we should have more collective conversations around how do we, as a collective, contribute to this, because that's the only way AI of itself would really achieve its full potential of what it can do. For humanity and for the advancement of our shared agendas as well. I'll really end it by saying, specifically when we look at the Global Digital Compact that was signed with private sector partners, big tech private sector partners being parties to that, one of the critical things we talk about is digital cooperation. How do we really ensure everybody is well represented? I think within these conversations, I think I want to like to see more conversations with— I would have liked to see one or two private sector partners here, maybe as part of this panel, because I think those are the conversations we need to have for AI and open source and all of these that we're talking about, so for all of it to achieve the full impact and opportunity for humanity.


Speaker 53 [1:55:12]:

Thank you.


Moderator · Mädis Nene [1:55:14]:

Thank you so much. Thank you so much. So I'm offering you two options to continue questions or we take one question from the audience. It's up to you to decide and I think it's good to hear from the people who are participating. Is there any question before— If you don't have questions, that will be very hard. Oh, we have one. I can see it. Okay, we have a question there. Anyone could help with a microphone?


Speaker 55 [1:55:52]:

Yeah.

Moritz?


Moderator · Mädis Nene [1:56:00]:

And a short question, please.


Speaker 57 [1:56:02]:

And if you sit by a microphone, you can press the button and use it. So the gentleman back there.


Moritz [1:56:19]:

So it's not working. I'll come to you.


Moderator · Mädis Nene [1:56:34]:

Thank you so much.


Ehsan Shahyagan [1:56:35]:

Really appreciate the conversation.

My name is Ehsan Shahyagan. I work with a nonprofit community-centered data and research institute in Washington State. So the conversation of contextualization and also building a shared infrastructure came out in this discussion, which was very interesting and fascinating.

I have a question about, like, you know, how can—

when we talk about the contextualization of the models or the infrastructure. Can you talk or share some of the samples of the work or the project that you guys are doing? And also, how can we have a shared infrastructure when we are talking about contextualization? So this was basically the question.

Thank you.

I really appreciate it.


Speaker 61 [1:57:27]:

Thank you.


Moderator · Mädis Nene [1:57:30]:

We will start with the Kingdom of Morocco because we'll be talking about the contextualization. Please.


Morocco · Minister Delegate for Digital Transition and Administration Reform · Amal El Fallah Zaghrushny [1:57:35]:

Yes, this is a very interesting question. Thank you. When it comes to context, it means that we would like to deploy our algorithms in very specific manner that fit with the needs of the population or the city or the government. I can give you one example. It's about large language models. There are two kinds of contextualization. The first one is how to use large language models in some areas or with— under some infrastructure constraints. So when we, we think about, for example, languages, The existing models don't fit really in the needs of the population in Africa, for example. Very few models tackle some rare languages or some very specific languages. By rare, I mean the number of people using this language. For example, in Morocco, we have Arabic, We can speak French, English, but we have Amazigh, which is a language very specific to Morocco. And we need to contextualize the models, and sometimes we have to rebuild the models in order to take into account some very specific structures of the language, and also data, etc., etc. The second contextualization contextualization I see is that sometimes we have to move from large to small language models because of resources, because of logistics, because, because of power, computation, etc. So I think we should really be careful when we want to use existing models In particular, that's the advantage of open source, that you use existing development by others. So we have to be enough aware of the limits of reusing things and be able to adapt and to contextualize this software to your needs. Thank you.


Moderator · Mädis Nene [2:00:04]:

Thank you so much, Your Excellency. I hope that I was Comprehensive answer to your question. Please, Your Excellency, unfortunately, we have to end the session. That was not in the plan to end it that quickly, but I'm looking at you. Do you have any last message to the audience?


Jamaica · Minister with responsibility for Efficiency, Innovation and Digital Transformation · Audrey Marks [2:00:26]:

Sure. I'd want to say, in Jamaica, AI adoption is gaining momentum. But its use of, but its use of open source AI today is still at the foundation building stage, not yet at scale development deployment. Open source AI should therefore be understood not only as access to models or code, but as a complete ecosystem that includes documentation, transparency, security, local adaptation, standards and skills development. Jamaica has important enablers in place, including strong youth talent through our AI Innovation Lab at the University of Technology. And let me pause to say another great example, and I see the Intelibus CEO is here. Jamaica started to introduce hackathons, and at our last hackathon we had— when we just started, we had like 150. The last hackathon was 750 young people turning out to spend a day, 24 hours, in one location building solutions for, for this, for the problems that they see in the country. So we are pushing the youth policy, and along with emergency policy structures such as the National AI Task Force and a solid data protection framework alongside investments in skills and research. So current initiatives supported by international partners such as the World Bank and the IDB and local institutions are building, are helping to build, and the EU are helping to build this capacity through training, research collaboration, and blended financing models. The direction of policy is clear: strengthen domestic AI capability so that Jamaica can own— can not only use AI systems, but also understand, govern, and improve them over time. Ultimately, success will be measured by our ability to move from dependency to active participation and contribution within the global AI ecosystem. So Jamaica's message is simple: open source AI must build capability, not dependency.. It must help to serve citizens better, protect trust, and create lasting Jamaican-owned value. Thank you, Mr. Moderator.


Moderator · Mädis Nene [2:02:55]:

Thank you so much, Minister. It was very exciting having you with us today and very informative to learn about all the efforts you had, and I think this is how it should be done. Thank you so much for coming. Your Excellency Selina Gbah, please, any closing remarks?


Sierra Leone · Minister · Salima Bah [2:03:13]:

No, absolutely. I think I'd like to close by actually answering that question around shared infrastructure because I do believe if there was any maybe message to leave here with, and it's really on that, is the opportunity that because when we talk about AI, one of the biggest things, barriers towards that, obviously, talk about data, when you talk about the infrastructure elements, I think that is very real. For our region. So that's why shared infrastructure actually provides us with an amazing opportunity to be able to potentially leapfrog decades in terms of our development and our participation in the global AI advancement. I think it's critical. One of the things we're working towards that is to make this possible, because shared infrastructure, there's so many other factors that comes into it to consider and deal through. So, for example, at the recently concluded ECOWAS ICT Council meeting, one of the things Sierra Leone was able to bring to the table was for actually for us to develop a regional data embassy framework. This would ensure that, because we talk about data, one of the quickest things a lot of would point to is issues of data sovereignty, issues of sovereignty, where's my data stored, who has access to my data, who's utilizing my data, how can I utilize it? And I think we look at the global space now, I think more and more countries want their data a bit more closer to home. So, but with the understanding that for West Africa, every country in West Africa can't build their own infrastructure. But if we can develop the policy and framework that would allow us to take advantage of shared infrastructure, we believe it will put us way ahead of that, our development, goals. So this is really one of the things that we're pushing this Data Embassy framework to develop, because we believe it also would enable us to attract investments to develop infrastructure within the region that could be for the benefit and for all of us. So we believe, as of maybe a final piece towards it, it's also, by the way, fits within the open source framework for open source infrastructure. So maybe that should be Another part of the conversation is around open source infrastructure. Sierra Leone, we were able to launch one of the first 5G open access network in Sierra Leone. So we know it's possible even within private sector, the idea to make it open to within competition, but still it allows you the opportunity to be able to leverage that. So big push towards that. We're keen working with our partners and other regions. We're keen to learn lessons from what Morocco is doing, from what Jamaica is doing, to what other regions are doing to really see how we can take on board those lessons, but also look at how we can collaborate for the benefits really for all of us. I'm really looking forward to it.


Speaker 68 [2:06:11]:

Thank you.


Moderator · Mädis Nene [2:06:11]:

Thank you so much. I think that Minister needs to leave very soon. We want you to be on the panel picture. Meaning, we— this is not very often that we have that, so we want you to be the picture. So I, I just have— if you have 1 minute to— for giving us the keynotes so we can pick that picture.


Morocco · Minister Delegate for Digital Transition and Administration Reform · Amal El Fallah Zaghrushny [2:06:26]:

Really very short, 1 minute. So I would like to emphasize 2 things. The first one is, uh, something we didn't talk about is governance. Governance is crucial for AI, but also for open source AI. And, uh, sometimes in many, uh, domains open data and exchange of data may clash with the protection of private data and personal data. And this is why in Morocco we propose some law not yet adopted by the parliament but on the way to make possible exchange of data while preserving the protection of personal data for citizens. The second point I would like to emphasize is about this idea that using open source, we can maybe improve the explainability of AI algorithms. Open source can be audited and can allow us to open the black boxes of AI algorithms. And this is something very good, but very hard to do because it requires very qualified talents to be able to understand this open source, because open source is not enough. People should be able to really understand what's going on. And maybe the last thing I would like to to say is that in Morocco, we play a very huge attention to the talents, to the training of talents, and we also have a very big summer camp last week with 1,075 people building solutions on AI for 10 sectors: education, health, agriculture, governance, etc. And I was really very impressed by the quality of the solutions proposed by these young people between 20 and 25 mostly. And I think the challenge was to say what's going on after 70 years of AI. What is the new? What's new? What can we do with this AI coming onto the table? And the topic was to ask these young people to provide new vision of AI 70 years after the foundation of AI discipline. And it was really fantastic, and I will be very happy to share with you the outcomes of of this experience, one week in the desert of Morocco. Thank you.


Moderator · Mädis Nene [2:09:24]:

Thank you so much. Next time invite me, please. I'll be happy to visit the desert of Morocco. Your Excellencies, thank you so much for joining the panel. That was the first in kind, very nice. You show us the way how we should do it and hope that we see you next year giving us more insights of how you have successfully built this strategy. Big applause for all the ministers. Thank you so much.


Jamaica · Minister with responsibility for Efficiency, Innovation and Digital Transformation · Audrey Marks [2:09:51]:

Let me just say one thing before we leave. We have to acknowledge just the adaptability and readiness of our moderator.


Speaker 73 [2:10:04]:

Thank you so much.


Jamaica · Minister with responsibility for Efficiency, Innovation and Digital Transformation · Audrey Marks [2:10:06]:

Thank you so much.


Moderator · Mädis Nene [2:10:06]:

Thank you.


UN · Host · Pepi Vananen [2:10:14]:

Thank you very much. Whilst the picture is being taken, uh, we keep hearing that some of you cannot hear, and I feel like there's a joke in there, but, um, it doesn't come to my mind right now. So if you are having issues hearing, or if you just want to be closer to the stage, we have some empty seats around here. So especially the ones in the red seats, feel free to come closer and also use this as an opportunity to maybe get up Just stretch a little bit. This is the last session before lunch, and I feel like that's the infamous thing to say to lose people's attention. So apologies, but I came up with a joke to keep your attention here. So it's time to talk about the elephant in the room, which I'm sure you've all seen. It's the robot in the room. And we will be moving on to the session on open robots. So please, speakers, do come to the stage. It's a bit snug there, but you'll have to get close. And let me introduce you to the moderator of the next session.


Speaker 77 [2:11:31]:

Thank you very much.


UN · Host · Pepi Vananen [2:11:32]:

That was great.


Moderator · Mädis Nene [2:11:34]:

Thank you so much. Thank you so much.


Speaker 80 [2:11:35]:

Thank you so much.


UN · Host · Pepi Vananen [2:11:39]:

I see a lot of you moving down, so it seems to be working.


Speaker 82 [2:12:09]:

Yeah, the French company, Atiu.

Atiu, please.


UN · Host · Pepi Vananen [2:12:24]:

This session?


Speaker 84 [2:12:26]:

Oh, it's her.

Okay, so I see there's a different name here.

Do you want to—

should we just turn them off?


UN · Host · Pepi Vananen [2:12:41]:

Let's turn them off. Yeah, yeah, yeah, because this is— yeah, we'll turn them off.


Speaker 86 [2:12:46]:

Moritz?


UN · Host · Pepi Vananen [2:12:50]:

Moritz, Moritz, can you turn off the nameplates because they're not correct? Can we turn off the nameplates for the speakers because they're not correct? All right, we see. All right, we are seeing a lot of the people on stage and we see people moving closer. That's great. And it's lovely to see.


Speaker 88 [2:13:13]:

It's lovely to see. Hello?


UN · Host · Pepi Vananen [2:13:25]:

It looks like my microphone died. I think that's definitely a sign I've spoken too much. So let's hand it over to the panel. So let me introduce you to the moderator who will be moderating the session. So we have Lahari Chautauri from Amazon Web Services. Who will be introducing the session and introducing the panelists. Over to you.


AWS · Open Source TPM · Lahari Chowdhury [2:13:47]:

Good afternoon, everyone. Thank you all for being here today. My name is Lahari Chowdhury. I'm an open source TPM focusing on artificial intelligence and machine learning at Amazon Web Services. And today on the panel, we'll be talking about open robots. To get started, open source reshaped how we build software. It reshaped cloud infrastructure, and today it is reshaping how we build artificial intelligence. And we are— okay, we are asking whether the same can happen for robotics and what that actually requires, because the robotic ecosystem is still deeply fragmented with proprietary stacks closed hardware, incompatible middleware. Building a robotic system today still means rebuilding things that dozens of other teams have already solved behind closed doors, and that's very expensive, and it also limits who gets to participate globally. And this matters more now than it did 5 years ago. Physical AI is moving from research to reality very fast, and we are seeing how robots are entering manufacturing, healthcare, agriculture, and many other sectors. The field is scaling, and if the underlying infrastructure stays closed, and while that happens, the gap between who can build and who cannot will only further widen. We have 4 panelists with us here today who are building this future from very different vantage points. Over the next hour, we'll be going through the robotics value chain, and I'll let the panelists introduce themselves briefly.


Wondercraft · Co-Founder and Chief Executive Officer · Mathieu Maslin [2:15:29]:

Hi everyone, I'm Mathieu Maslin, co-founder and chief executive officer of Wondercraft. And Wondercraft is building in this space. So we started building exoskeletons, which are robots to make people walk again. And very recently, we repurposed that technology to build humanoid robots, kind of the ones that you see here, not this one. We do legged robots and have recently entered into the industrial space through a partnership with Renault Group and helping in manufacturing tasks.


AI Square Robotics · Christine [2:16:09]:

Hello everyone, my name is Christine from AI Square Robotics. So for those who don't know us, we are an AGI-native general-purpose robot company from Shenzhen, China. So we build productivity-oriented general-purpose robots. So from day one, we're building robots with a brain. So we're building and invading the technology and embodied foundation model. And we're also designing the most stable and reliable hardware platforms for real-world deployment. And we're finding use cases to have our robots get to work. So in fact, we are very happy to be able to bring our robots here. It's called the AlphaBots. So this is the generation of product that we have already achieved mass production and also real-world deployments in manufacturing services and public service. So if you have a chance to come visit China or come to Shenzhen, you will find our AlphaBots working at some of the major parks and shopping malls as a retail service, serving coffee and ice cream on average of 8 hours a day, making 100 cups of coffee and ice cream without eras. So my perspective today comes from bridging AI innovation to real-world deployments. As part of our commitment to making intelligent robots for every industry and community around the world, we have launched the AlphaBrain platform, which is an open-source community for embodied intelligence that helps researchers and developers to connect models, data, training and deployment all at once. So we see firsthand how hardware is rapidly advancing and intelligence is still the bottleneck. And our vision for open sourcing our AlphaBrain is very simple— empowerment. By providing a shared foundation, we want to lower barriers, to overcome ecosystem fragmentation, and to enable more developers to participate and innovate in embodied AI. So we're very excited to join this discussion and to talk about how we can build a more accessible robotic ecosystem together. Thanks.


Stanford University · Entrepreneur · Gary Bradski [2:18:16]:

Yeah, I'm Gary Bradski. I'm an entrepreneur and basically someone who just gets things going. So I did OpenCV, which is the open source computer vision library. It's currently getting about half a billion downloads a year. So it's one of the major pieces of open-source AI software. But I took part in winning the robotic race, the DARPA Grand Challenge. Stanley, that team became Waymo. Recently, I funded SpaceNG, which helped with the first and so far only successful commercial lunar landing that was Blue Ghost last year. I'm currently— I was with Bonsai Robotics, the AI. I've currently switched to advisor there. We produce agricultural robots, both automating large tree shaking robots and smaller ones for specialty crops and for mining. But now I'm back at Stanford University at their Robotic Research Center, kind of looking at the problem of home robotics and how to get robots for aging support and, and other things into the home, like really rethinking how that's going to happen and what it would look like, starting for security and entertainment points of view.


Shenzhen Robot Valley · Vice President and Head of Global Operations · Yiming [2:19:58]:

Hello everyone, my name is Yiming. I'm the Vice President and Head of Global Operations for Robot Tour and RobotX at the Shenzhen Robot Valley, where we are building a global collaborative and open-source community for AI hardware and robotics in China. Our goal is to integrate robust supply chain capabilities with open-source deployment standards, providing a platform for global teams to efficiently build and scale using China's manufacturing ecosystem. We warmly welcome everyone to visit our community in Shenzhen. Thank you.


AWS · Open Source TPM · Lahari Chowdhury [2:20:37]:

Okay, thank you.

Let's start with the stack then. Where is openness proving most effective today across the robotics stack, and where does adoption remain limited? Is open source in robotics primarily driven by software, or are we starting to see meaningful openness in hardware and manufacturing as well? Go ahead, Gary.


Speaker 96 [2:21:01]:

Yeah.

Okay.


Stanford University · Entrepreneur · Gary Bradski [2:21:04]:

So, you know, robotics, there's been a lot of advance with the AI models typically using VLMs, vision language models. And also, like Jan LeCun earlier, he's pushing the JEPA models. Those are having some impact. But I mean, the software is not quite there yet. But it does open up this possibility for, again, somewhat like on the flip side of what Jan said, like, well, AI is a universal mediator. So it should be done universally. Right now, hardware is done by people who have the money or the infrastructure for doing the manufacture. If we had truly capable open-source models that are, say, capable of getting a model and actuating anything, then hardware can start to be bespoke, or produced for certain situations. For instance, even to the level of your particular kitchen, there could be a robot that fits it best. And if the software is there that can actuate it, then we could have a different kind of industry where you get some— you get some motors or whatever that actuate and then 3D print parts, and you'd have a robot that's meant for that environment or for that— or more appropriate for that society. It also solves problems of how do you maintain these systems. Complex, like humanoid robots will need a lot of maintaining. And how do you fix things in the field? Well, if you can produce most of the parts near the source, then you can fix and maintain.


AI Square Robotics · Christine [2:22:45]:

Yeah, I agree with Gary. I think we already have a lot of open resources that we get access to, and it's genuinely getting— lowering the barrier to get started on robotics. But the challenge is really about— the problem is about fragmentation. We're building this type of technology with different data formats, different model architecture. Different stimulation environments, and different benchmark. So even if things were open, it's incredibly difficult to reproduce someone's results and compare the system fairly, and also take from a research paper and putting it— deploying into the real world. So that's why as our company, when we think about open source, our goal is to connect the dots to bring that data model, training, and evaluation, and deployment all in the one coherent framework. And we believe that open source should not just open up individual components, but it should give the whole community a shared foundation to build on.


Wondercraft · Co-Founder and Chief Executive Officer · Mathieu Maslin [2:23:55]:

In our view, the most important word in robotics is deployment. And you are talking about impact. The only way to have impact in the real world is to deploy actual robots. And if you look at where open source has been historically successful, you have software like OpenCV, which, as Gary said, is, is incredible. I think it's been widely used in academics as well. You have robot blueprints that can be built in every university, every college, and it's doing well. I think where it's not been able to crack anything yet is in real-world or industrial deployment. And when you think about why, when an industrial player is deploying a robot, they are looking for a couple of things: uptime, so how reliable the system is, throughput or cycle time, how effective it is to get the job done, and then of course ROI, price. And the thing is, I think from, from their perspective, they don't want software or hardware. They want a system that someone can stand behind in terms of responsibility. And because at the end of the day, if and when the robot fails, as was said, they are going to have a problem and they need to have someone that can be accountable for that. And I think that's where open source needs to progress or find its ways, maybe to think in terms of system or systems rather than a single brick. Otherwise, it's going to remain limited in its adoption in the real world.


AWS · Open Source TPM · Lahari Chowdhury [2:25:57]:

Okay.

Building on Mathieu's point, many robotics innovations succeed in labs, but they struggle in real-world environments. What are the main barriers to deployment at scale that we are seeing today?


Wondercraft · Co-Founder and Chief Executive Officer · Mathieu Maslin [2:26:09]:

Yeah, so I think reliability is one of the key points. Uptime, cycle time. And these machines are either very simple and then their deployment is contained to simple tasks, or they are very complex and complicated. And then we are not yet at the level where we can deploy them them in the general context. So for example, in our view, robots will not scale because they are general. They will become general because they scale. Meaning that we take the approach of deploying robots on well-defined use cases, like moving boxes or moving tires or things that are still very difficult to deploy reliably, but manageable with today's technology. And as we get more robots out there in the real world, we gather more data that can improve our models and extend the variability of the environments where they're able to perform.


AI Square Robotics · Christine [2:27:23]:

Yeah, building on Matthew's points, the barriers between labs and the real world. In labs, everything is controlled, but in the real world, everything is changing all the time. You have the lighting, you have the object, the workflow, people working around it. These are all the uncontrolled elements, and the bar for reliability, stability, and safety is much higher in real-world settings and also in productivity-demanding scenarios. So the robots can't just work once. It has to work every single time consistently. So we believe the key is to build a closed loop between real-world deployment and model iteration. Uh, simulation helps, but we do need real tasks, real environments, and real users. Um, and, uh, at AI Robotics, we treat commercial deployment as part of the learning process. So the robots generate operational data, The data helps improve our model, and the better model also helps with the next deployment to be more robust. So we believe that real world produce real intelligence.


Shenzhen Robot Valley · Vice President and Head of Global Operations · Yiming [2:28:36]:

We think there's a couple of reasons why there's a barrier. One is because the factory environment is flawless, but the real world is unpredictable. It's kind of like what Matthew was saying. Two is because the time and scale is very different. Three is because the economics of the value chain is different. We believe being the cluster hub of the supply chain is very important.


Stanford University · Entrepreneur · Gary Bradski [2:29:05]:

I just wanted to add the financial support, and I don't know how you would do this, but obviously I do OpenCV and it's probably We, you know, saved billions of dollars in software development, and yet, you know, we get approximately zero back for doing that, and yet have to run this organization. I wonder if, like, at some level of scale of an industry, so they're successful, they're making millions, that they have— they pay some kind of open source tax, and that's paid out on the radio songplay model. If open source— if a certain open source is used a lot, it gets a proportion of that money, and maybe some is also set aside for new innovation.


AWS · Open Source TPM · Lahari Chowdhury [2:29:59]:

Thank you. The deployment picture today assumes current robots, but AI is changing what they do. How is the integration of AI in terms of perception, reasoning, and autonomy? Changing what robots can do in real-world environments?


Wondercraft · Co-Founder and Chief Executive Officer · Mathieu Maslin [2:30:17]:

It's definitely changing a lot, especially in terms of flexibility and the number of tasks that you can consider. So now that you have vision perception and models to be able to analyze the environment, it's actually credible to deploy robots that are going to evolve with their environments, meaning that in manufacturing operations, you used to deploy fixed-arm robots. So like 6 DOF robots that could do a single task, and it would take 6 months to deploy those robots, and it would take a lot of engineers and PhDs to configure them. And once they were deployed, they would do one single thing again and again and again. Now with mobile robots, you can imagine a world where you deploy robots way faster because you don't need the same level of expert operator training. And whenever the manufacturing floor or the environment changes, you can repurpose or retarget or retask the robots to this new environment and these new constraints. So it could be the parts or the piece that the robot is interacting with that does change, like suddenly it's not a motor, it's a gear, or it's not that panel, it's this part. It used to be very complex to adapt robots to new requirements, and it's going to be increasingly easier. Now, we shouldn't sell dreams, I think. There are still a lot of progress to be made. And especially in terms of reliability. And this is why we have this idea that robots will become general because they scale, meaning that they will increasingly address larger or more complex environments.


AI Square Robotics · Christine [2:32:17]:

Yeah, so I'll say like a lot of traditional robotics, they are very sophisticated instruction followers. So you give them a very clear control environment with a fixed set of tasks. They would do it perfectly. But the moment when something changes, it fails to adapt. And that's what is changing with the rise of foundation model. Instead of programming, programming the robots with a long list of rules, you're giving it ability to perceive the physical world, reason, and act in a more flexible way. So take, for example, when the last time you bought like a new coffee machine, you're not going to go straight to the user manual and read through line to line. You're probably going to play around with it, look at it, recognize some familiar patterns, and figure it out along the way. So that's kind of like knowledge transfer. And the robots are starting to develop these capabilities. And our vision is to build robot intelligence that works similar to human brain does, not by adding more rules, but have developed deeper understanding. And that is what will move robots from pure automation to genuine adaptability. And that's also what make it possible to deploy these robots faster and across far more industry and use cases than ever before.


Stanford University · Entrepreneur · Gary Bradski [2:33:44]:

Uh, yeah, so there's obviously the, the world, or most places in the world, are already deeply into aging and, uh, not replacing the population. So there's an obvious need for, you know, the potential robot labor. At Stanford, one of the things I'm looking at is aging, and as One of the example robots of how to do this, how the robots, you know, help out an aging population, for example, is one area of deployment, is the Japanese toilet. It's a robot. It's kind of a medical device that's not very attractive. It's for people who've lost the ability to wipe themselves. But instead, it's sold as a high— high-luxury good that people aspire for, rather than this kind of sickly medical device. So I kind of use that as an inspiration, that how do you get robots in the home? Well, you want them as something— not as a sad robotic dog that pretends to, to be your friend, but, but for example, if, if we can get them in the house in a safe way, and I'm looking at ambient environments that, like, make the robot brain or world model the entire space to make it more safe. But in that, the robots would obviously have some utility, like a security— the security system function. But such robots could then enable embodied entertainment, like if you knew the movie— the show Westworld, there are those robots that have an intelligence and play a role. But that could be brought and generalized. Instead of some pretend companion, they are a character in a story that you're involved in when you're being entertained, but they're also that same thing when you want to be educated or cognitively helped— how to cook, how to whatever. So this is a way, a friendly way of, and a useful way of robots entering, you know, the home environment that starts to mediate the aging society. And of course, you know, this can be carried across to factories and whatever, but this is just the focus now. And they can start helping with healthcare. Like, there are very things that aren't scaling well in any society— education, because it's an old-school model of some dictatorial lecturer speaking at people, which doesn't really suit people. So, so like a home entertainment robot can do, you using AI, bespoke education for where you're at, finding what your gaps are and teaching you in an individualized way. It can also be for health monitoring, your questions, but also for exercise, you know, like exercise as a game rather than as a task.


Shenzhen Robot Valley · Vice President and Head of Global Operations · Yiming [2:36:45]:

I don't have too much to add, but I think in general it allows the AI to leave the safe lab environment and entering the wild world. At the same time, allow more people to be involved in the interaction with AI. So we're happy to see this happens.


Wondercraft · Co-Founder and Chief Executive Officer · Mathieu Maslin [2:37:03]:

Yeah, I think there's also something that's interesting too, because we all talk about LLMs and foundation models and how this can enhance robotics. And I just like everyone in the room to think— I'm sure everyone in the room does use LLMs like in any shape or form and interact with them on a daily basis. And if you think about your experience, think how often the answer that you got was completely missing the mark, like doing something a little bit crazy, saying something was the wrong color or completely wrong. And this happens very infrequently, like maybe every 1% of the time or even less. And it's okay because there is a human in the loop that's able to mediate the answer and to understand when to just disregard the answer. Now, in robotics, it may not be exactly the same because if there's a robot that falls 1 times every 100 times, this is not something that you can disregard. This can actually be catastrophic. Or if 1 times, 1 out of 100 times, it empties your fridge instead of replenishing it, that's going to be catastrophic as well for adoption. So we also have to think in terms of impact in the real world of physical AI versus LLMs and frontier models. And I think that's something that sometimes is overlooked.


AWS · Open Source TPM · Lahari Chowdhury [2:38:43]:

Thank you. That's really a great context to understand what the technology can do right now. But what does it take to build a competitive robotics ecosystem at the national or regional level today? Can open robotics meaningfully enable participation from emerging economies, or do structural barriers such as cost and infrastructure remain too high?


Stanford University · Entrepreneur · Gary Bradski [2:39:06]:

I, I, that it, it just reminds me like of, of one of the things for scaling robotics, and one of the missing parts is simulation. Now many people have simulators, and Cosmos is recent by NVIDIA. Um, they're still hard to use and resource intensive as far as the, the compute, but It is an enabler, and like for scaling things, for instance, the auto industry, there's now a lot of autonomous driving. They have a Tesla is driving, and many other cars spreading around the world. But one would hope, and this would take a government action, that any accident of any car that's autonomous should be reported into a national database where that becomes simulated and then that becomes the software requirements that before you deploy new autonomous software, it should pass all these accidents that have ever happened to anyone's car anywhere. And, you know, then we have a rising level of safety. But the same is true in factories and homes, that if we have these open general databases as a, as like a level that you must pass, then we get increasing capability and safety.


Shenzhen Robot Valley · Vice President and Head of Global Operations · Yiming [2:40:32]:

So our perspective regarding this question is there's two things very, very important. One is high-density supply chain is essential. Secondly is the collaboration with global talents is really important. So our core standpoint is from, is from our team's practice in Shenzhen Robot Valley. I want talk a little bit about Shenzhen Robot Valley. The unique value, value of Shenzhen Robot Valley is which it owns over 200 robotic enterprises and a 3-kilometer full closed-loop component supply chain. So this solves the universal global pain point of inefficient, high-cost hardware development for tech teams worldwide.


AI Square Robotics · Christine [2:41:17]:

Yeah, and I also wanted to add to answer that question. I think open source alone is not enough. You can release the most powerful model in the world, but if it requires a massive compute cluster and a team of specialized engineers, then in practice, it's still only accessible to a small number of well-resourced organization. So, for a lot of researchers, startups, university, especially in developing economies, the barrier is not talents. I think there we find brilliant people everywhere, and the barrier is access to access to compute, to infrastructures, to tools that let you actually do something with the technology. And it is not just about making the codes available, but it is genuinely lowering, lowering the bar so that a team anywhere in the world working on problems that matters to their own communities can actually get started.


Wondercraft · Co-Founder and Chief Executive Officer · Mathieu Maslin [2:42:27]:

I, I think to build an ecosystem, you first need clients. That's the first thing that's going to drive progress and innovation, people willing to get one of their problems solved, basically. And then the question is, what are these problems? So you need to identify use cases where robotics today, not in 10 years, but today can already bring value and you can deploy robots. And after that, of course, you need academics, you need great teams, you need talent and compute, and you need to have all of these together for ecosystems to meaningfully take shape. Just wanted to say that I love the idea of having a shared database of accidents and issues because it's true that once you have that, you have automatically safety levels that rise. Like, that's structural. I think it comes with a lot of complexities in terms of simulation because you may be able to simulate a lot of software issues and problems, but I think it's going to be way harder to recreate the hardware conditions that led to these possible accidents. So I'd love to understand how you think about that in terms of how to create something that's representative or helpful for all manufacturers.


Stanford University · Entrepreneur · Gary Bradski [2:43:56]:

Well, I mean, one model for this is something like CARLA. CARLA is an open-source driving simulator that was developed outside of NVIDIA, but now they support it. And one could, like, just report the accidents. And again, you need— it takes a model, the CAD model of whatever of the car, and then it simulates the physics to some level of fidelity, and that varies depending on the situation. But I mean, you could build this in all the areas that you want to do this in. If it's some kind of manufacturing robot, you provide a model and you provide whatever the sensing was and set up the situation that led to the accident or or whatever. This would also, you know, potentially be a repository of just data for other people to train and build their own systems or fine-tune them.


AWS · Open Source TPM · Lahari Chowdhury [2:44:59]:

One barrier that keeps surfacing is fragmentation, and that is that platforms do not talk to each other well. How much of that fragmentation across platforms and standards is slowing the robotics Dr. Day.


Stanford University · Entrepreneur · Gary Bradski [2:45:22]:

I mean, in one sense, you have fragmentation, which is sort of natural. Different companies have their proprietary systems. The one thing you do have, though, is data, and so If there is a way of collecting and sharing data, then other people— at least it theoretically prevents lockout, that someone owns everything. If there's enough data that someone can recreate and retrain a model to become competitive, then you never have that kind of lock-in.


Wondercraft · Co-Founder and Chief Executive Officer · Mathieu Maslin [2:46:05]:

Yeah, I don't know if fragmentation is such a big issue because in every industry you have a number of actors and they all compete, and, and in a way this drives innovation. Um, if you look at cars, all cars are different and, and proprietary, and, and it's never prevented anyone from like driving, number of cars. I think what cars have is a shared infrastructure. And when, when we think about humanoid robotics, we think of infrastructure because we believe it's going to be relevant to all kinds of industries and, and ultimately healthcare and then home. So I think the question is more how do you have a shared infrastructure that allows all these fragmented players to compete or be used rather than form big conglomerates or monopolies that have never been good for innovation.


Shenzhen Robot Valley · Vice President and Head of Global Operations · Yiming [2:47:10]:

I just want to add from the perspective of the operation cost, the idea of high custom integration tax without deployment standards is high. The cost is over the hardware itself, so we should be able to pay attention to this.


AWS · Open Source TPM · Lahari Chowdhury [2:47:30]:

OK. What role could open standards play in unlocking interoperability across systems and markets?


Wondercraft · Co-Founder and Chief Executive Officer · Mathieu Maslin [2:47:38]:

Yeah, I think that's an interesting point because when we look at robots, this is not the same thing as as LLMs again, and we've all seen what happened with the Anthropic issue a couple days ago, a week ago. And it's not such a big deal because in a way everyone was able to reconnect their systems to another model. And that's the beauty of API. It's not the same thing with physical systems because once you have a million robots deployed in all the infrastructure of a country or region, and suddenly someone has a button that they can push and that freezes all these robots, it's not as easy as just writing another line of code and saying, okay, now we are using another API. No, the robots are there and they cannot be changed that easily. Now with open source standards and interoperability, this may be a little bit easier, but the, the problem will— the problem— there will always remain a physical element that is going to be hard to overcome.


AI Square Robotics · Christine [2:48:54]:

Yeah, uh, today the real interoperability, the challenge is, uh, the, the AI layer. So, uh, talk about whether different models can work with the same data? And can you take stimulation environments built by one team and use it to test a model that is built by another? And can you actually compare results across different systems in a meaningful way? And right now, the answer to most of that question is no. So that's a serious problem. And there's no common standard at the level— the ecosystem The system is still fragmented, but we'd love to see how open standard can create a genuine shared foundation, so not just so things that can technically be connected, but also the whole community can move towards— forward together more efficiently and transparently.


Stanford University · Entrepreneur · Gary Bradski [2:49:57]:

I want to— probably abuse the common standards thing here to think about labor, and it's just an idea I want to push since we're in the UN. There's this idea of like, okay, what happens when robots are ubiquitous? Are they gonna, you know, cause the lack of work and whatever? And what's suggested for this is like, a standard universal basic income, which I think is the wrong thing to do because it turns people into beggars. And rather, I'd press the notion of universal ownership. So let's say government or governments tax a corporation on a share of their ownership. This is mixed into a mutual fund and distributed to the population. So this is an open standard for a better way of doing income that converts the population into owners, not beggars.


AWS · Open Source TPM · Lahari Chowdhury [2:51:11]:

Okay, coming to our last question. In which areas may open source approaches face limitations in robotics? And what role should governments and public sectors play in supporting safe and scalable open robotic systems?


Stanford University · Entrepreneur · Gary Bradski [2:51:32]:

Again, just to abuse things for fun, I'm more for replacing governments. I'm not very happy with any government on Earth. I think they're all kind of underperforming or severely underperforming. Performing. What if the AI and the robotics was also— you could swap out at least part of your taxes and whatever for the government of your choice. You would be under their rules giving money and they'd be delivering services, and now we'd have a huge competition for who's going to deliver the best government. But it's a different topic, sorry.


AI Square Robotics · Christine [2:52:10]:

Um, I think the most valuable role government can play is as an ecosystem builder. There are things that are genuinely hard for any single company to build on its own, say, for safety standards, data governance framework, shared testing environments, long-term fundamental research support. These are public good. And historically, the technological ecosystem has had the most lasting impact, where the ones are where governments step in and build that kind of shared foundation early. So the goal should be healthy, open ecosystem where innovation can happen broadly.


Speaker 128 [2:52:59]:

Yeah.


Wondercraft · Co-Founder and Chief Executive Officer · Mathieu Maslin [2:53:02]:

I think the number one thing that drives companies are their clients. Now, governments are one of the largest entities in the world, and so by all logic, they should be the one with the largest problems. Actually, that's what I hear on my right. So, so the number one thing that they can do is to become clients of, of robotics today so that that can shape where those companies are going and what kind of products they develop. Because at the end of the day, that's what companies do. They listen to their clients. Hopefully they should. And governments have the ability to become large clients of those companies.


Shenzhen Robot Valley · Vice President and Head of Global Operations · Yiming [2:53:53]:

I think there are two things governments should do. One is encouraging the regional manufacturing density. Two is invest in foundational open source infrastructure, both software and hardware standards. That's what we see in Shenzhen Global Valley. Thank you.


AWS · Open Source TPM · Lahari Chowdhury [2:54:12]:

Okay. Do we have any questions from the audience?

Okay.


ICANN · GAC Chair · Nico Cavaliero [2:54:32]:

Hello, maybe I can use this microphone. So my name is Nico Cavaliero, I'm the GAC chair at ICANN, and I have a question for the distinguished lady from China. You refer to fragmentation as a somehow negative thing. Don't you think My question is, wouldn't it be actually beneficial at the end of the day in terms of having different approaches to whatever problem there is on the table?


AI Square Robotics · Christine [2:55:04]:

Yes, when I talk about fragmentation, I was talking more about building some basic layers and tools for researchers and developers to start— get their hands started. In the same starting point, less than having— I think what you're referring to is having many different players in the market, different type of products, which I think is amazing. And but what we're seeing now with Embodied AI is that a lot of these building these Embodied AI foundation models is not about coming up with a clever algorithm, but It is about freeing the engineers from continuing to building the infrastructure, so providing all the baseline tools for them to get started with this technology and making it more accessible for them. So this is kind of what I was referring to with the fragmentation. But I think having different players and coming at robotics and embodied AI from different angles is very healthy for the market and for different use cases and application. And this is what we also wanted to build with this ecosystem is to empower different players to come in.


AWS · Open Source TPM · Lahari Chowdhury [2:56:22]:

Anybody else?


OSHWA · Yves Nazan [2:56:43]:

Hello, my name is Yves Nazan from the Open Source Hardware Association, and to the panel, I wonder, there was a lot of talk about scaling robots, using them in multiple situations such as in the home, in industry, What do you think the level of technical literacy is going to need to be such that we can actually deploy robots in not just research labs and things of that nature, but in actual everyday situations such as being in the home, you know, maybe working at a mechanic shop, something like that?

And how do you see open source, like the open sourceness of these robots, of their designs, things of that nature, either aiding or complicating that ability to reach a necessary level of technical literacy?


Stanford University · Entrepreneur · Gary Bradski [2:57:43]:

I guess there's the technical literacy for building and developing these systems and then for using it and As you see, most software is pretty bad at being usable. I can't remember— oh yeah, I got the Tesla and it has a vent icon and it's gray. Does that mean the vent's closed or is it open? When you press it, it turns white. Does that mean it's now open or did I just close the vent? I mean, that's just an example of stupid API design. With robots, I assume you're going to have the same kind of problems. And, you know, like if we have more open models, maybe more people can feed into just better design, more intuitive. Obviously, if they can demonstrate their use or one of the big advances, advantages of LLMs Lems is simply using natural language to explain things rather than this cumbersome interface. I, I could just say, "I want the vents open." You know, it, it's easier, and it— that's how we evolved to, to describe things. So maybe with the language model, that becomes the universal interface.


AWS · Open Source TPM · Lahari Chowdhury [2:59:10]:

We'll take one last question.


Sam Houston State University · PhD candidate · Chiamaka Femi Adeinka [2:59:17]:

Hi, my name is Chiamaka Femi Adeinka.

I'm here.

I'm a PhD candidate at Sam Houston State University in Texas. My research focuses on on AI-assisted crime scene investigations. And my question is, do you envision robots being deployed to assist law enforcement investigators to document crime scenes, to look at evidence, to make investigation easier for them? And also, what are the policy barriers or the technical barriers you see that make this a reality?

Thank you.


Wondercraft · Co-Founder and Chief Executive Officer · Mathieu Maslin [3:00:00]:

I think that's probably one of the last things that will happen because we would talk about robots that are enabled or authorized to exert force and violence on other human beings. Otherwise, they wouldn't be particularly helpful. So I think that's probably the last step if it happens at all. And I knew that there was a few years ago, there was an experiment by the NYPD with the Boston Dynamics dog. And I think they withdrew the experiment because there was a safe word for the dog or something that bad guys could just use to stop the dog because of course the robot needs a safe world. And so, so you run into all kinds of problems when you do that. So in our view, robots will first be deployed to help people rather than to exert violence on them. And there are a lot of topics and subjects where we all need help for physical help in healthcare or other kind of help. And that's, I think, where we should focus as the robotics community.


Stanford University · Entrepreneur · Gary Bradski [3:01:22]:

I just wanted to— I worked early at Willow Garage where we were developing the early robots. And I think robots are going to help the crime, not the law enforcement, because we once locked ourselves out of the building and we just teleopped the robot from the inside to open the door. Defeated the whole building security system. But I would imagine the first use would be in violent situations that a robot can go in, you know, and risk its life instead of a human's life in helping prevent things. But I imagine like anything else humans do, it's a two-edged sword and there'll be rich new areas for criminals to explore.


AWS · Open Source TPM · Lahari Chowdhury [3:02:14]:

Okay, we are all at time now and it's lunch break, so let me pull some key points together from the panel. So what we are all taking away from this conversation is that robotics value chain is still fragmented at different levels, but that, but that does not limit us because we also have open source. And we have heard the panelists speak about how open source can actually help us to build open robots. But there are some key points that were also highlighted by the panelists: how we require governments to build national databases that could record AI accidents, and then that could become a potential ecosystem for the builders and also be an ecosystem builder at the same time, and also support the regional manufacturers and support the open-source hardware and software ecosystems. The work that is being done by not only by the panelists and everybody in the room matters a lot right now because robotics will reshape labor, healthcare, mobility, and economic opportunity within next decade. Whether the transformation is broadly shared or narrowly captured depends totally in part on the choices we make about openness now. And thank you so much, and thank you to all our panelists for being Thank you.


UN · Host · Pepi Vananen [3:03:41]:

Thank you so much to the speakers, moderator, and all of the speakers and moderators from this morning. I know I'm your least favorite person in the room because it's only me between you and lunch, but we have a slide that we must show you, and please take a photo of it. It will be giving you information about the breakout sessions that are taking place after lunch. And since we give you 2 hours for lunch, we assume that this information will be lost in the meanwhile. So I see a lot of cameras out. That's great. So just for information, if you're looking for the conference rooms, those are in the basement. So you go to 1B.


Speaker 143 [3:04:20]:

Okay.


UN · Host · Pepi Vananen [3:04:21]:

And we have the volunteers who are around in the yellow shirts and they can guide you if you're lost. Um, I've been here for less than a year and I'm still lost every day, so you're not the only ones. And then there's the ECOSOC Chamber, and that's the room we are in right now. So if you're attending that session, you can come back here. And as I mentioned in the morning, please take your things with you. Otherwise, we are going to be in trouble with security. So take your bags, take your belongings, go for lunch. You have 2 hours, so you can obviously go outside, but we have a cafeteria on the 4th floor. And then there's also food in the basement in the cafeteria. So sorry, that's the cafe. Cafeteria, 4th floor. Cafe, 1st floor. All right, you're free to go. See you back here at 4:15.


Speaker 145 [3:05:06]:

Thank you.