IGF 2025 - Day 0 - Conference Hall - Event #251 Large models and small player - Leveraging AI in small states and startups

>> NATALIE BECKER-AAKERVIK: Hello. Everybody. Welcome back. We hope you had a chance to connect and meet with people you've met. I know the speakers have been in the networking or rather the lunch session, so if you wanted to chat with them, we hope you got the opportunity to do so.

Welcome back. I hope you're energized and ready for the next session. Good afternoon to our guests watching globally from online, welcoming you back as well to this session presented by IGF 2025 Host Country Norway.

You heard earlier on that Norway was the second country in the world to get connected to the Internet. That's an important fact. So we're looking at Large Models and Small Player - Leveraging AI in Small States

and Startups.

I'm Natalie Becker-AAkervik, I'll be your moderator for this session.

Now, AI has moved from the research lab to the boardroom to the factory floor, to the hospital, and increasingly to the centre of political and economic power. But here's the paradox. As AI becomes more accessible in some ways, it's also becoming harder to compete. So the biggest models demand enormous data.

Compute, investment, and resources, which are often concentrated in the hands of a few major players.

So what does this mean for the rest of us?

Well, for smaller states, or for startups and for those not operating at hyperscale, are we sidelined? Or are we, in fact, standing at a unique point of opportunity? That is the question.

Because here's what we do know, for example. Innovation doesn't always come from size. It comes from agility, it comes from trust, it comes from deep knowledge and from smart, sometimes surprising collaborations.

And today we're going to explore how small actors can play a big role in shaping the future of AI.

We'll talk about regulation that enables about start-ups that outmaneuvered giants and budgets are limited but creativity is not.

And most of all, we'll talk about partnerships. Very important word. Collaboration has come up strongly today, partnerships has come up strongly today, so we should take note and take that as an action takeaway, one of the money. And the kind that makes innovation inclusive, global, and sustainable.

In other words, how can we move from being small players to shapers of the digital world? First, we'll hear from Karianne Tung, Norway's minister of Ministry of Digitalisation and Public Governance. She will talk about how small states can shape AI policy in a way to protect fairness and transparency, but prioritizes countries like her as competitive innovation hubs in the global AI landscape.

A warm round of applause, please. Minister Tung, the floor is yours.

(Applause)

>> KARIANNE TUNG: Thank you. Good afternoon, everyone.

It is a pleasure being here and to start this very interesting discussion on leveraging artificial intelligence to increase business competitiveness and also to create better public services.

I think we all can agree that AI will transform industries and markets, as well as individual lives and our whole society. Because managed and prioritized correctly, it can be the tool we need to so most complex challenges that we are up against today.

And at the same time, quite understandably, I must say, many people feel uncertain and concerned. It's evident that the AI revolution raises many dilemmas and questions and concerns that we need to address. And as digitalization knows no borders, we need to work together to find the best solutions.

Artificial intelligence is no longer just a technological issue. It is a matter of geopolitics. AI must not become a playground for the powerful, it must serve the public good.

And small players are often well positioned to drive innovation with purpose.

Many groundbreaking and impactful AI innovations come from small labs, start-ups, and public agencies. Through some received support or investment from big tech, their activity and flexibility are essential forces behind AI's rapid progress.

As nations raised to harness the AI, the standards are emerging as a key strategic tool. By shaping the rules and norms that governs AI, we are not only ensuring safety and trust, but also asserting our values and influence in a rapidly evolving global landscape.

For many, the rise of large AI models feels like a race between giants. And indeed, the largest models today are backed by the largest companies drawing on massive data, infrastructure, and funding.

Being a representative for a small country, I know both the challenges, but also the advantages that comes with size. We do not have limitless resources.

But in fact, many small states are already global leaders in digitalization, cybersecurity, and tech regulations. These are not accidental achievement, these stem from long-term national strategies that prioritize innovation, citizen trust, and smart governance.

I would now like to take the opportunity to share with you some perspectives about how Norway is taking significant steps to harness AI in a responsible and relative way.

Our main goal towards 2030 as set out in our national digitalization strategy is for Norway to become the most and best digitalized country in the world. It is ambitious, but I believe it's not impossible.

We want the business sector to have favorable framework conditions for developing and using AI. And we want all our public sectors to utilize AI for greater efficiency and to create better services for our citizens already by 2025, but also for 2030 and the future.

To support these ambitious goals, we are now building a national infrastructure for artificial intelligence that can be used for research, for business development, and for a more modern public sector. Thus, placing Norway at the forefront of ethical and safe AI use.

We have allocated to the national library in cooperation with the state company Sigma 2 to train and to make available free of charge Norwegian and Sami language models.

We are developing our national infrastructure for high-performance computing and this will support both public sector and private entities in their effort to develop AI application and utilize AI within different sector of the economy, but also society.

And just last week, we switched on our newest super computer. It is called Olivia. It will have 17 times greater computational power than the infrastructure we used until now. And of course, we are also working on the implementation of the EU's Artificial Intelligence Act with a goal to make it applicable in Norway at the same time as the rest of EU.

The proposal for the necessary legislation will be sent out before this summer. To comply with the requirements that we can find in the EU act, we're establishing a supervisory authority and what we call AI Norway.

This will be an arena for collaboration, sharing of experience, and also -- and also experimenting with AI solutions within different sectors. AI Norway will also, among other things, manage our regulatory sandbox where Norwegian public sector organizations and companies, especially the SMEs, can experiment with and develop and train AI systems within safe, legal frameworks.

Also, a couple of weeks ago we allocated 1.3 billion Norwegian crowners to AI-related research. Six newly selected research centres will focus on various societal and technical aspect of developing and applying AI in different fields.

The centres will start their operation this summer. The Norwegian School of Economics has also recently published a report on Norwegian AI tool landscape.

The rankings in this report offers a unique perspective on the Norwegian AI company landscape, showcasing both established players and emerging companies.

Over 350 Norwegian AI tools and companies are described in this report. 30% of these have been founded in 2022 or later. 49 of the companies have ten or less than ten employees. As you see, Norway has a vibrant environment and many start-ups to contribute to this environment with their ideas and their knowledge.

We just need to create and sustain favorable conditions for these companies to thrive. Support for early-stage ventures including access to data, talent, and sandboxes is critical in that respect, as well as the public sector to utilize AI in developing better services and solving tasks more efficiently.

But we also need to focus on international cooperation, knowledge, and sharing and strategic partnerships. Our common goal should be a balanced and inclusive technological landscape that benefits everyone.

So to conclude, small players can be leaders in this technological shift, not despite the size, but because of their flexibility and innovation capacity, as well as a value-based approach to AI.

So let's don't miss out on this opportunity. Let's work together and build a future that is open, that is fair for the many, not for the few.

Thank you for your attention.

(Applause)

>> NATALIE BECKER-AAKERVIK: Thank you, Minister Tung. Thank you so much.

How small scales can promote innovation and regulation in AI, thank you for those insights.

Now we're joined by an entrepreneur and strategist and one of the leading voices in Norway start-up systems, all start-up ecosystems, you'll recognise him. John shows us that size doesn't limit inhibition. He's going to explain how small players can collaborate with large platforms and sometimes even out-innovate them. That's an idea. I'm talking about John Lervik who I will introduce in a second and he will be followed by Ole Christopher Granmo. And professor Granmo will give us a tour of the machine which is a light wait accuracy model ideal for small applicants and edge applications.

It's a reminder that you don't need to be a superpower to do powerful AI. You need smart, interpretable design.

Now a warm round of applause for Mr. John Lervik.

>> JOHN LERVIK: Thank you. I have a few slides, I think. So if you could put them up, it would be appreciated. Excellent.

So we were founded the beginning of 2017, and at that time we saw a fundamental need to improve the world's industries.

You know, we had -- we have a growing world population, a climate crisis, and also lately we have seen the geopolitics, which basically creates the demand for us to produce more but using less.

So produce more goods, but with less emission. This is really the problem that Cognite is about to solve. How can we make our computer more sustainable and efficient.

We are the first unicorn, but not only that, we're also a company that delivers data on AI technologies across the world, across industries.

So you can see both in energy sector, life sciences, pharma sector and many other areas. In many ways we have created a new product category for industrial data management and we are the leader in that, you could say, particular market.

So then how -- what do we do when it comes to AI? Yes, we use AI in our technology to be more efficient, to make our software more intelligent, and also to be able to access data in industrial in this case through new and better ways. The same way that you use ChatGPT for your personal lives, we, you know, basically Cognite provides software and AI to optimize how you operate industrial facilities.

But we're not happy with that. How can we take it to the next level? And of course we all know about the giants in California, open AI, for example, they built ChatGPT. There's many others that built Large Language Models which is basically large foundational AI models that use huge training text -- training sets from texts and grammar to build these large LLMs. Large language models that we all use every day.

Going forward you have companies like META or Facebook, they're building their own foundation both for textbooks and for images. They're investing $15 billion to create context for images. You can create large foundational models for images.

Of course there's no way Cognite or Norad can compete on that scale. It requires too much investments and resources to compete.

But if you're going to an industry, we see small Cognite have a lot more information than the big players. Very have more than Nvidia and all the other cloud provider. We can create foundational models for industrial data, because we have industrial data with context.

Then you can start to create another category of AI models beyond let's say the consumer models for text, images, videos, et cetera, to also do the same for -- for industrial data.

And with that, you can then also start to optimize industrial assets to make them more efficient, more sustainable in even better ways. Without using the conventional machine learning approaches and writing advanced software applications.

And also, of course, if you look at the graph to the right, it shows how quickly the cost efficiency of different technologies, the blue on this electricity, it took a number of decades. The second one was Internet, and the third one is basically, you know, generative AI, how quickly the price curve goes down.

So if you have access to unique data, the price curve for using that -- those unique data to build new models is very attractive and can enable us to create something very unique. But again, it's very hard for the conventional, you know, largest companies in the world to compete with.

So what does this mean for Norway or for another small country for that sake?

I think, you know, one key learning, you need to stay close to the problem. Of course, in our case, the problem is industry, asset-intensive industries. Which you could argue it's 30%, 40% of the world's GDP, but still it's a particular problem. And we have particular competence in Norway around industries, et cetera.

Again, as I mentioned, we have access to data. Not more text than open AI or more images than Facebook, but we have access to much more industrial data than anyone. That we can then use with our competence and in our context.

Then of course, so Number 1 and 2, I believe Norway and Cognite in particular have particular control over. The other, of course, we need access to compute. You need GPUs and ability to really train these models and continue to retrain them. So that's also, you know, one key area.

I heard the minister talking about buying into some logic computers, which is great, but we need a lot more. And to run these we need energy. And that's another area I would argue where Norway is in a unique position where we have 100% access to green energy and it's something we have to nurture. Particularly up north, it's very cheap, it's cold, so you don't need as much cooling.

So we have an opportunity in Norway by using our unique strengths, you could say, and advantages, to build technologies that make us -- it can be world champions even in some of the bigger and more important areas in the world.

Thank you.

(Applause)

(Video playing with captions).

(Applause)

>> OLE CHRISTOPHER GRANMO: So we are watching a revolution going non real time. It's revolution driven by machine learning. It's powerful algorithms that can learn to perform tasks from data.

In health, in legal, in public sector, everywhere. And the technology has become so powerful that you can solve almost any task with high accuracy.

It's very tempting to use this for all purposes.

Also, very recently we have seen Large Language Models. I have been skeptic for a very long time. I haven't found a use for it, but a few weeks ago when I used Deep Seek for my second machine, I knew the game had changed. It was scarily good. So if you can live with the hallucinations, it's truly a powerful tool.

I will almost say super intelligent in some cases. However, when you scale up, it breaks down completely. So it can't solve the bigger, complex task, but still very powerful technology.

So I know we are extremely exciting place in human history, but I have some concerns which I want to talk about today. And I call these concerns betrayals. And I'm going to talk about three betrayals.

The first one, betrayal one, is energy. Because one query with ChatGPT, it's extremely hungry. It is the same amount of energy as it takes to light one light bulb for 20 minutes.

Furthermore, every month ChatGPT produces more than 260,000 CO2, so that's equal to the emission of 60 flights from New York to London.

So this is immense. It's a huge environmental problem. And it raises concerns because we are running out of energy. And it's not good for the planet. So that's the first betrayal because we're endangering our future.

Second betrayal is transparency. Because for the first time in human history we are bringing in technology to use what we do not fully understand, will we fly a plane that the engineers didn't understand?

Deep learning models, models that are driving ChatGPT and other Large Language Models, they are so complex that we can't understand what's going on inside them. And we know that they're unpredictable and they are full of biases and discrimination and so on.

And still, it's taken into use. For instance, in U.S., algorithms are used to decide the length of sentences and the judges just don't understand them.

And studies show that these models are discriminating. For instance, the people are automatically flagged as high risk without any context.

Furthermore, another example from India, they use AI to decide who's going to get welfare. And thousands of legitimate receivers were removed by the AI because of faulty or weak algorithms.

So extremely powerful technology, but you have to be careful because we are endangering the freedom and the rights of people by using it.

And the last betrayal, betrayal 3, is power. Because certainly it's the big tech companies that are becoming extremely powerful because they have produced this technology, they're owning it, and they have to use it. So we are in the pockets of big tech, in my opinion.

And that affect everyone, because kids have to learn to adapt to the algorithms to get likes and be accepted. Governments adapt to technology they use for instance automatic policy which -- and then calling it objective by using the AI. Which we know is bias and we know have all these weaknesses.

So this is very gloomy, but I also have the solution. Because in Norway, you have a new kind of artificial intelligence based on a completely new principle. It goes back to a hidden gem in the history of science. It's from 1961. It's a very elegant, extremely efficient model of learning that was invented by a Soviet mathematician.

And it's kind of hidden and lost, but I saw immediately that this was what I was looking for in the Tsetlin machine. And it had some very interesting properties.

So I took that learning mechanism and then I combined it with propositional logic from philosophy, because logic is understandable. And I have the Tsetlin machine. It's an efficient way to do learning.

And right now we're doing deep learning in understanding [?] and in several domains. And this is just the start, deep learning got this decade, no, watch this space.

Thank you.

(Applause)

>> NATALIE BECKER-AAKERVIK: Thank you so much. Thank you also to John Lervik for the presentation and also thank you to Ole Christopher Granmo, director of CAIR.

Now I would like to introduce our next keynote speaker who will be delivering a presentation here. She's a fellow at the centre for technology of innovation at The Brookings Institution and one of the world's most recognized voices here today at the intersection of AI, equity, and global governance.

Chinasa has been recognized as one of the world's most influential people in AI by "Time." Please join me in giving a warm Oslo welcome to Dr. Chinasa Okolo.

(Applause)

The stage is yours.

>> CHINASA OKOLO: All right. So really happy to be here today and thank you again for the opportunity to speak.

And so I'll present briefly on how smaller countries, particularly those in the global majority, also known as Africa, Asia, Latin America, Caribbean and Oceana can really advance how they pursue AI and how to redefine it for their needs and communities?

First we know that the global AI divide shows disproportionate impacts in these regions. There's been much work published on this by the UN and other agencies. I was fortunate to write for a professor who is the most cited computer scientist alive.

We know that 50% of AI research is produced from the U.S. and China. This map is from digital science, and also the Stanford AI index indicates that 80% of all VC funding for AI companies is allocated to just these two countries.

Again, looking at this map and many others, we see that this also excludes many countries and regions like Latin America, Asia, and beyond.

We know despite these disparities in infrastructure, education capacity and talent concentration, this marginalization is actually breeding innovation. We see that small and emerging nations aren't relegating themselves to the sidelines in a global AI ecosystem. They're redefining and developing new models for how AI should work for them and their respective needs.

While Silicon Valley debates topics like AI alignment and risks, smaller nations liar Estonia, Rwanda and Singapore are reshaping a development, research, and governance on their own terms. For example, Estonia has built an AI-powered digital government. One of the most prominent in the world and it prioritizes citizens reducing bureaucracy, and also it advances public sector engagement.

Next, Rwanda has done a great job in increasing their public engagement and I attended their conference this year and it was a great event.

And finally Singapore has made great efforts to steer these efforts and have steered these scientific breakthroughs particularly when it comes to building LLMs and these critical approaches through red teaming and benchmarking.

This is only just a few.

To end this presentation, I'll present three pillars that can help global transformation particularly for smaller companies and those in the marginalized regions. This is not exhaustive and can be applied to larger companies, countries, and institutions as well.

So first, data sovereignty can be essential in helping small nations, organizations, et cetera control their digital resources and increase independence.

Estonia has done a great job particularly in redefining how they make contacts with large tech companies and encouraging regional and local talent to help provide services that their government needs.

Next, contextual innovation is really important and something that is promoted in approaches like human-centered computing and interaction more broadly.

AI context can leverage different methods, benchmarking evaluations and also small models which is something that lots of organizations and even companies are pivoting to because they notice that these models actually are more efficient and more accurate in many contexts.

And again, it's really important that we understand that these models and efforts should be integrated with values and knowledge.

And finally, peer-to-peer collaboration is essential for ensuring that we can develop regional networks that bypass these traditional power hierarchies, and combined resources to optimize AI development. And these resources can include computing infrastructure, and even educational institutions by distributing information so companies can ensure again that they're creating AI that meets their needs.

Thank you so much for listening and looking forward to the panel presentation later.

(Applause)

>> NATALIE BECKER-AAKERVIK: Thank you for a great presentation, Chinasa. Wonderful insights there as well and we're really looking forward to diving deeply into the panel discussion with the insights that our speakers have given us today.

But now we have a speaker who is going to be talking about or really focusing on AI playbook for small states and what are the main conclusions, looking at the playbook, how can Rwanda be in the forefront to shape the future for AI and how can data, sovereignty, innovation, and collaboration unlock opportunity.

And she is the Director General of innovation and emerging technologies and she Esther Kunda.

>> ESTHER KUNDA: Thank you for having me. To quickly start, when we did this AI for small states, we collaborated with Singapore to work on this particular framework and the idea is that small states in this playbook around AI have different key takeaways, but also we have different challenges.

So the AI playbook, the digital [?] are small states. Next slide. Thank you.

So the digital force is -- was established in 1992 by a platform of 108 small states to discuss common interests and the digital part of it was actually introduced in October 2022 to ensure that we also function to collaborate on that.

So when we started the playbook in 2023, one of the key areas we were looking at was this serves as a compilation of best practices and purposes on members for AI strategies and addressing challenges that we faced.

As small missions, I think one of the key areas that you'll all appreciate is that we don't have the same challenges that everyone has, and what we were looking at is to really understand how small states can navigate these opportunities and the risks that AI poses. But also provide actual guidance based on global governance, but also based on best practices from other Member States who were trying to see it become a [?] create these learning and collaboration and peer collaboration exchanges.

Lastly, I think one of the key elements that we wanted to also look at was [?] challenges on detail, compute resources funding, and as well as the fact that one of the key driver for small states around small domestic markets and in some [?] also being landlocked countries.

If you can go to the next, please.

In some of the -- in some of the key recommendations that we came up in the playbook in itself, what we were looking at was capability for countries to build foundational AI, capability for themselves. And this will look at human resource development. So how do we up-skill our workforce, especially in our workforce in public sector or in the existing workforce, because that's one of the key areas when we talk about AI is going to take [?] different demographics, this is one of the key areas that everyone talks about.

And then we also look at a [?] access to high performance computing and the quality of the data that is relatable to the sets and how do we innovate around that.

And lastly, I think a couple speakers have talked about energy, and this is also one of the key concerns that we looked at and looked at best practices towards that.

In terms of the second area, we looked at promoting AI development and use. We looked at different countries where communities and drive AI incorporation and transparent values and inclusivity.

Thirdly, we looked at how we fostered a trusted environment and we talked about the labour minister who talked about Norway creating its own sandbox. I think this is one of the areas that's very important. Open capability, knowledge exchange, and continuously promote these platforms for all of us.

And lastly, global partnership and cooperation in terms of -- in terms of how -- in terms of AI standards, AI systems and explainability and transparency of the AI systems that we do have.

So in Rwanda how we're doing that, if you can go to the next slide, in Rwanda what we are looking at today is we spent the last two years with a strategy that really looks at laying groundwork for what we want to do as a country.

So first of all, we put in place a strategy and policy that's really an innovation lab and continues for it to be an innovation lab.

Second, we're trying to do assessment and work around infrastructure and ecosystem readiness. So we've been working very hard and ensuring that we have detail that's available to deal when you also look at connectivity and availability of affordable detail, this is also something that we're working on.

And we're simply actually government has data sharing policy that will enable us to easily share data, but also [?] to the private sector so that AI [?] will be able to be trained on data that is [?]

Thirdly, as a country, continue to be a proof of concept. So we've positioned our self as a country that wants to allow innovators to test in an environment that's agile. Because for us, the technology's evolving very fast. So we have to, as policymakers, we have to work with how facility are evolving and make sure as we put a regulation in place we are aligning with how it is going.

And lastly, of course, we are ensuring that we have the talent and skills that is required. That's why we continue to partner with academia, connecting with the university, and our own universities to recreate the talent. That we need and ensuring that in the next few years you can find AI talent within Rwanda.

Lastly, if I go to the last slide, we're also working in ensuring that data is very -- is available, as I was mentioning. And then also pouring that our innovation ecosystem and start-ups and also continue to make partnerships with other countries, other institutions to ensure that AI is viable and useful to every citizen in Rwanda.

Thank you very much.

>> NATALIE BECKER-AAKERVIK: Thank you so much for those great insights and for your presentation.

Now we're introducing our next two speakers. We're pleased to welcome Jeff Bullwinkel, he's the deputy general counsel for Microsoft EMEA. Jeff will offer insights into how large platforms like Microsoft are working with small markets and governance -- governments to build innovation ecosystems. And how regulation and responsibility can go hand in hand.

After Jeff has done his remarks, we will hear from Kojo Boake VP or Vice President of public policy for Africa, the Middle East, and Turkiye in META. Kojo brings a valuable perspective on how small players and global platforms can co-create in tech futures especially in regions where connectivity and access and local innovation all intersect.

But please, first join me in giving a warm welcome and round of applause to Microsoft. Jeff, the stage is yours.

(Applause)

>> JEFF BULLWINKEL: Well, thank you very much, Natalie. It's great to be here in Oslo. Welcome to everybody here in the room. Good morning, good afternoon. Thank you for the opportunity to offer a couple of perspectives for what is a momentous moment in time. It's the era of AI as has been talked about already today.

As we think about that and reflect upon what's happening in this era of AI, it's worth reflecting on history of technology over the course of time. Think about the moment at which the printing press was perfected in the mid-15th century leading to innovation over the course of time that's changed the course of humanity in so many positive ways.

These innovations over the course of time, the steam engine, electricity of course as well, telephone, the combustion engine, that's where you get into the era of the PC, Internet, mobile telephony, the smartphone, and ultimately what is cloud and cloud in the era of AI.

These have been the building blocks that have defined what is today a modern civilization. But the focus really is at the moment not surprisingly on this era of artificial intelligence. And of course, we think about that, and must recognise that AI is really nothing new. We've talked about this at least for 75 years when they developed the towering test back in the '50s. But it's the last two-and-a-half years when AI entered the boardroom in the era of generative AI the conversation has changed.

That's why you can see the adoption incurred here change in this dramatic way. You see things here today that really are at this point taken for granted. The Internet, the mobile phone. Facebook has a platform. META has a platform. META is here today Arizona well. These technologies took up to many, many years to reach 100 million users, not so with ChatGPT.

Which really is one you see here at the end, it's a straight line, practically three months only to reach 100 million users for ChatGPT. When it was first launched in the world about two-and-a-half years ago. It's not perhaps surprising to think about that because it is, after all, a GPT. Not ChatGPT, but a general purpose technology, that's the technology that has the ability to reshape, reinvent, and improve in so many ways every aspect of the economy.

Technology is very good at one particular thing like a sewing machine, you know, GPTs like generative AI to have the ability again to reshape every field of human endeavor in dramatic and exciting ways.

We're finding, though, as you think about this moment that we're in that there is this additional technology stack that gets created. A stack that has three fundamental layers to it. One is infrastructure layer. Of course you need land, you need power. As has been talked about today. You need advanced chips and GPU. You need data infrastructure including what Microsoft and other companies like META are building across the Global North and Global South as well. That's the infrastructure layer.

But also you have of course the model layer, the foundation model layer, which includes of course data. The new lifeblood, as they say, the new oil of today's economy. You have a model's themselves, whether Large Language Models or smaller language models, and ultimately you have tooling as well.

So you have this model layer as well. Then beyond that, above that, you have the application layer. The various things that people can do with technology that animates in different ways so many aspects of life in really very exciting ways and end users.

You think about this, you realize there's opportunity for growth, for innovation, for progress in so many ways up and down every layer of this stack.

And I think it is very helpful to think about what Minister Tung said at the beginning in her remarks because she captured is so well in terms of the ability for a small country, medium-size country, large country, for individual entrepreneur, small company, large company, for a nonprofit, for a hospital, for a school all to benefit in remarkable ways from this technology which is really exciting to think about.

And of course because we are here in Norway, I'll just have up on the slide here various things that reflect the ways in which companies have involving logistics, financial services, health care, I.T., professional services all are doing very exciting things here in Norway with these new technologies.

So that really is remarkable for us to think about in terms, again, of every different aspect of human endeavor. At the same time it's worth reflecting on the fact that trust is key. And we all, after all, living in an era of geopolitical volatility. Trust has become an issue. Trust in technology, perhaps, has become an issue as well.

And that does mean that companies like Microsoft have to make sure they recognise responsibilities that come with a role we occupy.

This is a global audience in Oslo and online to be sure, but equally because we are here in Europe, I thought I'd spend a moment talking about how we thought about our responsibilities in the European context through the announcement quite recently about a month and a half ago of a new set of European digital commitment that have these five different elements, two of them you see on the slide.

The first really is a recognition of the fact that we have the opportunities, the responsibility to support a cloud AI ecosystem that is broad and diverse.

That definitely includes the infrastructure that Microsoft itself will build, is building as the company across the Global North and across the Global South as well. But equally it involves our work in supporting local European providers as well and local technology companies and other markets in this we operate around the world.

So we want a broad and diverse AI ecosystem for cloud infrastructure.

The second element of our digital commitments is focused on the need for us to be able to provide what I'll describe as digital resilience, even in an era of geopolitical volatility.

And this commitment has three different elements to it for us here in Europe where these concerns have become particularly pronounced over the last little while.

The first as a company we will oversee and manage our AI data infrastructure through boards and directors that are comprised exclusively of European nationals. That's our number one.

Second element of this commitment to resilience is making sure we are committing to our customers, partners, government stakeholders our preparedness to push back against any order from any government to either cease or suspend cloud services. This has become a fairly common point in conversations that we have.

Microsoft, what would you do in the event you are ordered to cease or suspend cloud services. Through this commitment, we essentially commit and we will do so contractually to national governments to resist, to fight back against any such order, including with litigation if that proves necessary.

The third element of this commitment, however, is focused on our need to do more than that. A customer might come to us and say, Microsoft, thank you for committing to resistant order and what if you lose, what then?

And so what we said here essentially is that we will have a mechanism by which we can provide business continuity in the very unlikely event of that happening.

And here we've talked about our plan to create a repository of software code sitting in Switzerland that would be overseen by three or four hundred providers that would be able to again provide continuity in the event of a very, very unlikely scenario such as the one people are now talking about.

A third commitment we have here really is building on what has been years of focusing on the need to protect the privacy, the security, the sovereignty of data in Europe and really data around the world.

In Europe we have already taken significant steps to make sure that our customers' data is being processed and stored within the European Union and other countries as well, including here in Norway. So that's been a longstanding investment we've made over the course of time.

Beyond that, though, we're doing additional things as well to make sure we're billing in sovereign controls to our own cloud services to address what really are very natural, understandable concerns and questions people have in this moment of geopolitical volatility.

And in fact, some of you may have seen that our CEO was in Amsterdam just last week on Monday and he gave a speech at that time when he announced a new set of sovereignty related controls that you can read about online in a blog written by Justin Altoff focused on our commitment to provide a cyber cloud, cyber sovereign cloud, and in some cases national partner cloud.

That's our third focus in terms of making sure we're focused on the need for sovereignty.

Cybersecurity of course is top of mind. It is for us and has been for some time, but it also is for everyone here in the room and online recognising the increasingly pernicious, malicious threats and attacks in cyberspace often from nation state actors. We see this frequently it as a company.

We have the ability at Microsoft to be aggregate data using AI looking at trillions of signals every day and look at how we can defend attacks before they become problems for the communities that we serve.

We also then following our initial announcement of these commitments that were made back in April announced a new European security programme focused on making sure we are doing even more to share threat intelligence and work with governments and other stakeholders in a way that will reduce the threat environment online.

And finally, what I would say here, that we're also very focused on is the need to make sure that we're committed to openness. And here we have a commitment to make sure we're also doing even more, it's for open source software development in the context of this era of AI.

We announced about a year and a half ago at the Congress in Barcelona, AI principles that really can be summarized in three words. One, again, is access. Here the conversation is very much about making sure that everyone can have access to infrastructure needed to benefit from AI and the way that everyone needs to benefit from AI.

So access is number one. Fairness is number two. Making sure once we're giving access to people to use our infrastructure, they're treated fairly and doing so in the context of the interoperable open standards as well.

Finally there's an element there of responsibility. Making sure again that we as a company are rising to the challenge, responsibility that comes with the role that we occupy, including a relation to developing our own set of principles when responsible and ethical AI, but ultimately making sure we're adhering and complying with laws that governments enact around the world.

So I'll pause and there look forward to the conversation in the panel. With that, invite Kojo to follow me.

(Applause)

>> KOJO BOAKE: Hi, everyone. As some people have mentioned, my name is Kojo Boake, I'm the Vice President of public policy for Africa, the Middle East, and Turkiye here with META. It's extremely hard to follow these speakers, Professor Chinasa's presentation was fantastic. So I'm going to try my very, very best. Be gentle with me.

I have to admit I was thrown by the question posed toward the panelists. What do they mean by small states, I thought.

In part, because I'm mindful that the region I look after, Africa, Middle East, Turkiye, is full of what some people might deem small states. But they punch well above their weight. The United Arab Emirates has the first AI minister friend in 2017. I'm told people laughed when they said they appointed an AI minister. People have seen what KSA are doing and from my unique vantage point, I've seen what's been going on in Nigeria and other small countries. So I'll probably be speaking more to small companies than small states.

How do we think about this era of AI as a small company? Our view is that we need to level the playing field that.

No one company or government can own the future and the promise of AI. And we attempt do that by open sourcing our models.

And since 2023, we've launched Llama models, now on Llama four, that have been downloaded 1 billion times. More than that now.

And we believe unique advantages and differences between closed models and open models shouldn't be seen as binary. We know some are more open than others. But the unique differences, most notably things like transparency and the fact that you have access to the weights and can fine tune as you wish create advantages that good for the world, for META, yes, but good for many of the small start-ups and small states that wish to use them and are using them.

The advantage of lower compute costs, the advantages of being able to fine tune as you would to meet your local purpose, national purpose, commercial purpose is amazing. The fact that you can actually see under the hood of how these models are created, and as we think about the risks of AI, the fact that we can learn from other people's attempts to get behind the back of it and use it in an even means. But to also share their learnings in respect to cybersecurity.

I said I would have a bias towards some of the small players that are using open AI in Llama to meet national goals, and increasingly continental ones.

In education and in public health, we've seen ourselves partner with the Africa union development agency to create Akili AI. We were told as a company across the Africa region that small to medium businesses that characterize the region didn't understand how they might scale or grow or work in other countries. How they might take advantage of the new Africa continental trade agreement and work in Ghana and Nigeria or Kenya and Botswana. I'm also mindful that we created an educational app allowing many go from primary school, junior school, to upper school proved incredibly responsible using Llama and the fact that we open sourced it has been a key driver in that development.

Digital green backed up by Llama AI is helping farmers across Kenya. And Jacaranda Health is a stunning example of how it's helping mothers across Kenya, and now with the government national health service and other countries as it grows is helping to create much, much safer outcomes in terms of maternal health.

A quick video. I always thought it's best if someone else speaks to some of the advances, rather than I. If it plays. I'm hoping tech might be able to help.

Is there someone from tech who can help with my amazing video? No? Okay.

Well, I'm sorry, you're going to have to miss that one. I'm telling you it's a blockbuster. That's me explaining how they use open AI, not only to meet the needs of students, as I mentioned students in junior schools, students who want to go to university and the fact that they've been able to scale. And they are scaling, it's super, super important as you can appreciate.

I'll take a bit more time to quickly say that much of this is done through our investments in in a holistic way. The investments in infrastructure and critical models is important. You'll hear that from the big players. But the grant which has enabled start-ups around the world receive thousands of applications in 2023, enabled start-ups around the world to get on Llama, use Llama and grow their businesses.

But also things on my team in the Africa region have developed, if you're quick enough, you can apply for the Llama impact accelerator programme which you'll see us have mentorship and skills development for organizations, small organizations that wish to use Llama and open-source AI to grow their businesses and accelerate their efforts to meet some of the national and local challenges that they face.

Of course, this is my 19th IGF. I'm surprised, none of you say -- you meant to say you don't look old enough to have done 19 IGFs, didn't hear that. But this is my 19th IGF. I know a lot is being decided this year on the IGF. I'm mindful that there are not just me, but a number from META here who are here to engage and to collaborate and to build partnership.

I'm also mindful that this era of AI, the promise that AI holds, as well as the risks that many of us are concerned about, will only come -- and negating those risks will only come back if we collaborate and if we build the multi-stakeholder partnerships.

I'm here till Thursday. As are many of the team from META. We look forward to engaging with you and I look forward to the panel. Thanks ever so much. Appreciate your time.

(Applause)

>> NATALIE BECKER-AAKERVIK: So we hope you enjoyed these really exciting presentations from our esteemed speakers who once again traveled from far and wide to be here with us today and to give their presentations and their insights.

Now we are again going to take a deeper dive into what they've touched upon in their exciting presentations and invite them on stage for a panel discussion.

I would like to invite back on stage Chinasa Okolo, John Lervik, Jeff Bullwinkel, Ole Christopher Granmo, and Kojo Boake.

(Applause)

(Video playing with captions).

>> NATALIE BECKER-AAKERVIK: What you have just watched while our panel was sitting down was a video on the automatic detection of humpback salmon in the rivers. And that was from Tech For All. So you see the role that technology does play in our everyday lives. Norway of course is known for its salmon, you would know that from all parts of the world and recognised for that globally. And of course technology and innovation plays a large part in making that a sustainable industry. We hope you enjoyed that video as well where technology and AI is helping to save the Atlantic salmon.

Now as we have our esteemed speakers and presenters here on stage, we're going to dive right into the panel discussion. And I would like to start with a question to you, Chinasa. What opportunities do you see for smaller nations and underrepresented regions to really lead in ethical and inclusive AI development?

>> CHINASA OKOLO: Great question and thank you. So many opportunities. I would say for me something that I mentioned in the presentation, you know, thinking about smaller models and I think again because of the benefits that they hold, particularly when it comes to domains or regions where there are deserts, I think that can help kind of solve the gap a little bit and some of the issues that we see currently in terms of current approaches to AI development.

And then also I think just in general many opportunities to really focus on these contextualized approaches. Not trying to build these general AI models again which I don't find or seem to see most beneficial for many contexts.

And then I would say finally again in terms of leveraging smaller models, taking advantage of model quantumization, which can provide many more opportunities not only for the rural areas or regions in these, you know, global majority communities, but also in the U.S. where I'm based, we do have rural communities and also more like marginalized contexts. I think these approaches pioneered by smaller countries with actually be more international across the Global North, quote unquote, and Global South for equally as well.

>> NATALIE BECKER-AAKERVIK: Thank you so much for your feedback on that question.

How can small tech companies complete or collaborate with hyperscalers to create unique value?

>> JOHN LERVIK: I think the obvious part of it is, of course, that you have to focus on something particular and be really good at it. That's the easier part.

And of course, again, as I talked about being Cognite have focused on industries.

But the other part of it is a little bit more particular and maybe you also need to focus on the problem that's sufficiently big that he cares. Because in most cases, they are not -- they just be just focused on their own things. So the problem we focus on needs to be sufficiently big so Microsoft or Google or Amazon or others or META for that sake also care about it. So you get some -- also some good competitive tension, which is I think exactly what we have in Microsoft, fantastic partnership.

But also little bit tension now and then where they see that we do things that they would like to do and vice-versa. I think that's the recipe for success.

>> NATALIE BECKER-AAKERVIK: Thank you so much for that answer, John.

And then Jeff, how can large companies, tech companies like Microsoft, as John alluded to you earlier on, support innovation ecosystems in small states while ensuring fair competition and responsible AI development?

>> JEFF BULLWINKEL: Well, I think a better point that John made so well which is that larger companies, despite that question of scale, in fact ultimately are platform companies. Microsoft is now and really always has been first and foremost a platform technology company. So we do have a lot of work to be done of course at the infrastructure layer with data-centered capacity to build across Europe, Africa, across the Americas, Asia as well.

And there's the infrastructure layer on top of which you have the model layer and the application layer. And we're just very excited about the amount of innovation we're seeing up and down that stack. So it certainly as a company that we're focusing on and trying to do is to make sure there's that broad access that we can provide and that we operate in a way that allows for open and operability across systems as well. So you have these small, exciting companies that are building on our stack and achieving great success. Whether in small states or large states. And indeed, you see this across Africa, which has been talked about a bit today and I've had the privilege of spending some time in Africa over the past year in Tanzania, Rwanda, I was two weeks ago in Nigeria. And the amount of excitement you see in these countries and innovation happening in these countries with how they localize applications or models is pretty exciting to see.

>> NATALIE BECKER-AAKERVIK: Thank you so much. Thank you so much for your response to that, Jeff.

Ole Christopher, massive AI models demand massive resources.

How can small states like Norway leverage energy-efficient AI like the Tsetlin model or the Tsetlin machine, rather, to compete with [?] big tech's infrastructure?

>> OLE CHRISTOPHER GRANMO: So my mission is to be able to complete the sovereign technology, and that involves building things from scratch. And we have two exciting projects with [?] port of Norway and the Parliament where we're going to build the Tsetlin machine stack. And it will solve the [?] problem in particular areas in society.

So by making flagship project that shows that it's possible, that's the main strategy, yes.

>> NATALIE BECKER-AAKERVIK: Thank you so much.

Now coming to you, Kojo, what are your thoughts on what opportunities do you see for smaller nations to lead in ethical and inclusive AI developments?

>> KOJO BOAKE: I wanted to say a quick shout because I think you asked a great question about small companies and what they seek to do with big players. I think the answer from my learned friend was -- to the right was to create things that big players are interested in and therefore, spark by competition.

I want to give a shoutout to those small players that aren't interested in that. They're actually interested in resolving, making viable businesses or resolving local issues and contextual issues that may never interest META, Microsoft, ChatGPT or whatever else at this point in time. But are extremely interesting to their locality or their nation as a business or as a solution provider.

So I just want to give a shout-out, it may be very different to Norwegian players than it might to be a player from Djibouti or Mauritania or others as well.

What we can do to stimulate, I think did I a reasonable job of outlining how META as I company and others use open source, Jeff spoke about the applications, how we're enabling small players and states by providing this openly. We're investing 65 billion this year in infrastructure. As I hope my slide made by good point of doing, the cost of compute, this often is the debilitating barrier, isn't there.

We're hoping people can create solutions using our models. We're enabling people to fine tune. But we're making telling investments as well. The Llama impacts grant which was launched by the company in 2023 saw thousands of applications. Two from our region I mentioned.

The team is continuing to invest in programmatic efforts to work with small companies and increasing the future governance. If you're in the room and interested in using Llama to solve your national problems, come and see us, they can make investments and do that as well.

I hope our approach, this idea that we can level the playing field by making massive investments on behalf of the company to provide open source AIs, is really what's key there.

>> NATALIE BECKER-AAKERVIK: Thank you so much for those insights, Kojo. And for, yeah, and for the clarity.

Then also in terms of, let's say, META -- we'll come back to that question. I wanted to ask you, Ole Christopher, again, Norway has renewables and AI, how to we turn this combo into a global blueprint, okay, for equitable AI growth?

>> OLE CHRISTOPHER GRANMO: That's a great question, and hardware is a key component there. And all the hardware today from META and others is rigged for deep learning, [?] that we have this work going on in the [?]

They will set the machine hardware. And like you said, extremely promising measurements. And to really build up green technology, we had to create an alternative to Nvidia technology, for instance, from the bottom up.

So if we manage to do that, that would be a big breakthrough in the energy area, yes.

>> NATALIE BECKER-AAKERVIK: Thank you so much for answering that question.

Chinasa, I want to pose a question to you as well.

How do you see these countries and regions potentially avoiding the challenges experienced by larger countries and companies in scaling AI development?

>> CHINASA OKOLO: Great question. I think it's a bit tough to say because we, again, do see the disproportionate impacts that occur in this country that are trying to have a foot in the race. I don't like to use that term, but more broadly. I would say it's really focusing on these contextualized models, and then also understanding how AI can benefit different sectors, you know, within their respective countries or regions.

Again, like AI doesn't need to be and adopted for every single thing. In many cases these basic general algorithms can work much better than AI-optimized ones or just straight AI models in general.

And also I would say, again, it's just understanding that there's the different impacts, whether you know, relates to labour which we see a need to support impacts in Latin America, East Africa, particularly in Kenya and also throughout Southeast Asia and trying to, you know, shift away, you know, from these extractive models to community-centered models. These value these Indigenous frameworks that understand community and, you know, I would say value building and et cetera, et cetera. And so, yeah.

>> NATALIE BECKER-AAKERVIK: Thank you so much for that. Do you want to add anything in terms of, let's say, ethical concerns between smaller nations and larger one as it pertains to AI research and development?

>> CHINASA OKOLO: Something I mention a lot, when we consider the Computer Science, I'm academic by training, we see that a lot of these issues are focused on western concepts. So when we consider things like race, which isn't relevant in many African countries, aside from South Africa and also throughout majority of countries, I think that this also provides a limited understanding of how AI models can exacerbate bias in these respective settings.

So there's a lot of interesting work emerging on CAS, particularly within the South Asian context which I think can really provide an interesting insights into how we can ensure that these models don't discriminate on this respective social identity aspect. Along with other things around gender, tribal affiliation, and the intersections which is something that's really important because these societies are so diverse.

This why I'm really in favour of these countries as you consider investing in AI development or forthright, you also have to bolster your respective academic ecosystems to support the sociotechnical research that can really understand how these dimensions of AI development.

>> NATALIE BECKER-AAKERVIK: Thank you, Chinasa. Now talking about policy and framework, Jeff, over to you.

How should smaller nations and underrepresented regions adapt governance frameworks to meet their local contexts?

>> JEFF BULLWINKEL: I think it's interesting how the global conversation about AI regulation is developing. I would say for starter as a company we certainly recognized before the AI act in Europe was even part of the conversation, a responsibility to make sure that we're developing and deploying solutions that are adherent to a set of clear principles that equates to responsible AI.

Things like fairness, transparency, accountability, safety, security, reliability, these sorts of things for us are paramount in what we do and how we do it.

Equally, we're just one company and one sector, and it's also up to the governments to tell us what the rules are. And so there has been a lot of discussion globally that seems to be leading towards something of a consensus in this area.

A group of seven countries a couple years ago in the so-called Hiroshima process during the Japanese presidency developed a really good ideas in this respect. That were built on during the Italian presidency and now Canada as well that's helping to drive also a bit of a global conversation. The OACD, the UN itself more broadly has been involved in a way that is I think very helpful and getting us closer to sort of a global cohesive approach to AI regulation that is based upon a risk framework that will create the right guardrails, but ultimately be pragmatic and allow for AI adoption.

This is something again as a benefit of meeting policymakers across Africa, for instance, you see what's strong interest and what's happening in Europe, is that the right model or not. Do we want to make sure it's a model that's going to create the right rules, again, the right safety frameworks, but not hinder adoption.

And a comment earlier was made I think by Chinasa as to what's happening in Singapore, where the government has taken a fairly light touch approach relative to some countries in Europe which might, indeed, become what you see happening elsewhere in the world too. Some people don't really hinder the fusion of AI which is so critical.

>> NATALIE BECKER-AAKERVIK: Thank you for that input. John, over to you. I may combine two questions and you can speak to the parts of them that you'd like to.

Opportunities, which of them to do you see to lead AI regions to lead an AI inclusive development. And then what constraints should these countries be aware of as they enter the AI ecosystem?

>> JEFF BULLWINKEL: I would say here in Europe they're starting with the cart before the horse. We started talking about ethical use and privacy and it's a fact that my friend to the left would never be here without privacy. Facebook is not a privacy company. They basically create a value.

I think about it like doing things right versus doing the right thing.

So we need to start understanding how do we create value from AI. This is also Microsoft did when they invented Azure and all those things, how did they create value, not how do you support privacy.

So I think this is super important, also you know, the comment about Singapore. We need to focus on the value, and for this of course we need the guardrails, but not opposite, because we'll never get to the value.

Secondly, referring to the comment from my friend here in META, there's also value to be had by small countries in Norway. The two of you. And we as a nation, we need to leverage that and improve the efficiency of the Norwegian government, companies, all those things. But I think also we need to have -- my last point is we need to aspire beyond that as well. We can't just be a country which leverages other people's IP.

So that's why I also, of course we are lucky, we export all the brown one, all the gas more or less to Europe, but we feed to take those unfair advantages and convert that into our own value, IP as well in the future where we can also export and not just sit on the shoulders of Microsoft and META. Which we're very happy to do. But we want to do more.

>> NATALIE BECKER-AAKERVIK: Thank you so much.

>> KOJO BOAKE: Just to make that super clear, I don't want to fly 82 or 74 back to Nigeria and say we only want to solve local problems. There are companies that want to go much broader and compete with us and we welcome that. I think it's missing from this particular panel that there are small companies as well.

Can I add a couple of things?

>> NATALIE BECKER-AAKERVIK: Please because you were next. How is META prioritizing local capacity building, research and development when building open source models with the global AI ecosystem. However, I see that you want to respond.

>> KOJO BOAKE: I think if I answered that question I'd be at a risk of repeating.

>> NATALIE BECKER-AAKERVIK: Okay. So please go ahead and response to --

>> KOJO BOAKE: Let me respond to Jeff and my friend to the right about how we ensure we don't have a cookie cutter approach. Jeff was extremely diplomatic about some of the problems that Europe has faced by what is called overregulation. Where would that be in respect to the GDPR and AI.

And we saw very recently the huge players, I suspect small ones had seen so much uncertainty by that former regulation that they held off launching some of the programs. For example, META delayed the launch the META AI on WhatsApp and Facebook so it had more clarity. I think when I travel around and speak to heads of states, whether it be Africa, and others, they're mindful they don't want a cookie cutter approach. That's really important.

The other piece, this is what the IGF lends itself to and why I'm eager to come here, is that to tackle this difficult problems and challenges and create the value that we think AI can have or believe it can have, we need multi-stakeholder conversations like this. They need to involve the big players, CSOs, CEOs and the academics and everybody else that needs to get involved as well.

I think that's really, really important. Want to stress that point.

>> NATALIE BECKER-AAKERVIK: Thank you so much. And with five minutes left, I'm going to give you each 30 to 60 seconds for last thoughts, parting words to leave the audience with if you'd like to. Where shall I start?

Any takers? Okay, so any -- does anybody want to touch on the -- on the opportunities also frameworks, I see that we've covered a lot of ground here actually. Everybody's been really good with time. 30 seconds, do you think?

>> JOHN LERVIK: I think my -- my perspective is that AI is changing everything. And we need to lean in, whether it's from global companies like Microsoft or nations like [?] or industrial companies. There's no time to lose.

You know, this is happening.

>> NATALIE BECKER-AAKERVIK: Thank you. Jeff.

>> JEFF BULLWINKEL: I would say I'm reading a book currently by a professor in Washington, D.C. called Technology in the Rise of Great Powers. His premise in the book fundamentally is it may not be so much about where a particular technology originated, where it was embedded for the very first time, but rather the degree to which a country is successful in adopting it, integrating it across every aspect of society and leading to this widespread diffusion. That's what you hear people wanting to do and talking about across the world, whether the Global North or the Global South. It's up to us as companies to provide for that. Up to governments to create clarity in relation to the regulatory environment and we hope a level of pragmatism for sure and everyone to work together in making sure you have the right level skills to make sure you can embrace the technologies in a way they want to.

>> NATALIE BECKER-AAKERVIK: Thank you, Jeff. Ole Christopher, anything you'd like to add?

>> OLE CHRISTOPHER GRANMO: I'd ask that's a good point and that's the sense. Today we don't fully understand the AI. If you don't understand the AI, the AI controls us. You have to turn it around. You have to fully understand AI so they become a tool for us so that we are in control. That's the essence here, yes.

>> NATALIE BECKER-AAKERVIK: Thank you for that. Chinasa.

>> CHINASA OKOLO: I didn't get to speak much on governance, which is my focus as a fellow. But I think there are also many opportunities not just to solely innovate in the actual development of AI, but really understanding how it can and should be governed, particularly for smaller nations. You know, just as -- just as we shouldn't rely on these big tech companies for -- to be the standard of AI development, we also should not rely on these bigger regional blocks or countries to also be the model for AI governance as well.

So I think there are many opportunities to innovate in this sector as well.

>> NATALIE BECKER-AAKERVIK: Thank you so much. Kojo, do you want the last word?

>> KOJO BOAKE: Not much to add, it's one of those panels where everybody's almost in complete agreement with the promises that AI hold and the fact that we need to create policy frameworks and commercial ones, opportunities and stuff than enable us to see those promises, even as I understand it, those that think those promises may come from a different technology. We're all in agreement on that piece.

I think what that means for me is ultimately what we just discussed that we don't want to get in the way of seizing those opportunities. And as my own bias I've been in policy and regulation for 23 years now, again, no one says you don't look old enough so it shows I do. We don't want that to get in the way. I think that's what's most important this point in time. That's why I'm thankful to have forums like this and sitting amongst learned people as I am. I hope to find solutions to whatever concerns, fears, challenges may get in the way of us seizing that promise.

>> NATALIE BECKER-AAKERVIK: Kojo, thank you so much. Thank you, Ole Christopher, thank you Jeff, John, Chinasa, we appreciate your input. Big round of applause for our wonderful panel, Ladies and Gentlemen.

(Applause)

Thank you so much for this great conversation. Before you leave the stage, we're going to ask you to please stand here for a group photo. Thank you so much. I'll make some announcements as to what is happening in the rest of the day, but thank you. I know our photographer is in the house, so we're going to have a group photo.

Okay.

We have a number of photographers.

Ladies and Gentlemen, we invite you back to our conference hall for the rest of the week's sessions presented by IGF Host Country Norway. Meet us right back here for the rest of the week and very engaging conversations as you have seen.

We also invite you to explore -- thank you so much -- a rich and diverse programme of sessions covering a wide spectrum of crucial topics from AI and sustainability. Don't forget to visit the open village just outside the hall.

And for everything else, the panels, workshops, networking opportunities, please check out the IGF 2025 app for the latest updates. On behalf of organizing team and our hosts here in Norway, we wish you a rewarding and inspiring and thought-provoking week of dialogue, insight, and collaboration, continuing to build digital governance together.

Thank you so much.